Jason Jabbari, Yung Chun, Wenrui Huang, Stephen Roll
October 2023
Researchers found that program acceptance was significantly associated with increased earnings and probabilities of working in a science, technology, engineering, and math (STEM) profession.
Robert R. Martinez, Jr., James M. Ellis
September 2023
Researchers found that STEM-CR involves four related yet distinct dimensions of Think, Know, Act, and Go. Results also demonstrated soundness of these STEM-CR dimensions by race and gender (key learning skills and techniques/Act).
Rosemary J. Perez, Rudisang Motshubi, Sarah L. Rodriguez
April 2023
Researchers found that because participants did not attend to how racism and White supremacy fostered negative climate, their strategies (e.g., increased recruitment, committees, workshops) left systemic racism intact and (un)intentionally amplified labor for racially minoritized graduate students and faculty champions who often led change efforts with little support.
Kathleen Lynch, Lily An, Zid Mancenido
, July 2022
Researchers found an average weighted impact estimate of +0.10 standard deviations on mathematics achievement outcomes.
Luis A. Leyva, R. Taylor McNeill, B R. Balmer, Brittany L. Marshall, V. Elizabeth King, Zander D. Alley
, May 2022
Researchers address this research gap by exploring four Black queer students’ experiences of oppression and agency in navigating invisibility as STEM majors.
Angela Starrett, Matthew J. Irvin, Christine Lotter, Jan A. Yow
, May 2022
Researchers found that the more place-based workforce development adolescents reported, the higher their expectancy beliefs, STEM career interest, and rural community aspirations.
Matthew H. Rafalow, Cassidy Puckett
May 2022
Researchers found that educational resources, like digital technologies, are also sorted by schools.
Pamela Burnard, Laura Colucci-Gray, Carolyn Cooke
April 2022
This article makes a case for repositioning STEAM education as democratized enactments of transdisciplinary education, where arts and sciences are not separate or even separable endeavors.
Salome Wörner, Jochen Kuhn, Katharina Scheiter
, April 2022
Researchers conclude that for combining real and virtual experiments, apart from the individual affordances and the learning objectives of the different experiment types, especially their specific function for the learning task must be considered.
Seung-hyun Han, Eunjung Grace Oh, Sun “Pil” Kang
April 2022
Researchers found that the knowledge sharing mechanism and student learning outcomes can be explained in terms of their social capital within social networks.
Barbara Schneider, Joseph Krajcik, Jari Lavonen, Katariina Salmela-Aro, Christopher Klager, Lydia Bradford, I-Chien Chen, Quinton Baker, Israel Touitou, Deborah Peek-Brown, Rachel Marias Dezendorf, Sarah Maestrales, Kayla Bartz
March 2022
Researchers found that improving secondary school science learning is achievable with a coherent system comprising teacher and student learning experiences, professional learning, and formative unit assessments that support students in “doing” science.
Paulo Tan, Alexis Padilla, Rachel Lambert
, March 2022
Researchers found that studies continue to avoid meaningful intersectional considerations of race and disability.
Ta-yang Hsieh, Sandra D. Simpkins
March 2022
Researchers found patterns with overall high/low beliefs, patterns with varying levels of motivational beliefs, and patterns characterized by domain differentiation.
Jonté A. Myers, Bradley S. Witzel, Sarah R. Powell, Hongli Li, Terri D. Pigott, Yan Ping Xin, Elizabeth M. Hughes
, February 2022
Findings of meta-regression analyses showed several moderators, such as sample composition, group size, intervention dosage, group assignment approach, interventionist, year of publication, and dependent measure type, significantly explained heterogeneity in effects across studies.
Grace A. Chen, Ilana S. Horn
, January 2022
The findings from this review highlight the interconnectedness of structures and individual lives, of the material and ideological elements of marginalization, of intersectionality and within-group heterogeneity, and of histories and institutions.
Victor R. Lee, Michelle Hoda Wilkerson, Kathryn Lanouette
December 2021
Researchers offer an interdisciplinary framework based on literature from multiple bodies of educational research to inform design, teaching and research for more effective, responsible, and inclusive student learning experiences with and about data.
Ido Davidesco, Camillia Matuk, Dana Bevilacqua, David Poeppel, Suzanne Dikker
December 2021
This essay critically evaluates the value added by portable brain technologies in education research and outlines a proposed research agenda, centered around questions related to student engagement, cognitive load, and self-regulation.
Guan K. Saw, Charlotte A. Agger
December 2021
Researchers found that during high school rural and small-town students shifted away from STEM fields and that geographic disparities in postsecondary STEM participation were largely explained by students’ demographics and precollege STEM career aspirations and academic preparation.
Kyle M. Whitcomb, Sonja Cwik, Chandralekha Singh
November 2021
Researchers found that on average across all years of study, underrepresented minority (URM) students experience a larger penalty to their mean overall and STEM GPA than even the most disadvantaged non-URM students.
Lana M. Minshew, Amanda A. Olsen, Jacqueline E. McLaughlin
, October 2021
Researchers found that the CA framework is a useful and effective model for supporting faculty in cultivating rich learning opportunities for STEM graduate students.
Xin Lin, Sarah R. Powell
, October 2021
Findings suggested fluency in both mathematics and reading, as well as working memory, yielded greater impacts on subsequent mathematics performance.
Christine L. Bae, Daphne C. Mills, Fa Zhang, Martinique Sealy, Lauren Cabrera, Marquita Sea
, September 2021
This systematic literature review is guided by a complex systems framework to organize and synthesize empirical studies of science talk in urban classrooms across individual (student or teacher), collective (interpersonal), and contextual (sociocultural, historical) planes.
Toya Jones Frank, Marvin G. Powell, Jenice L. View, Christina Lee, Jay A. Bradley, Asia Williams
August/September 2021
Researchers found that teachers’ experiences of microaggressions accounted for most of the variance in our modeling of teachers’ thoughts of leaving the profession.
Ebony McGee, Yuan Fang, Yibin (Amanda) Ni, Thema Monroe-White
August 2021
Researchers found that 40.7% of the respondents reported that their career plans have been affected by Trump’s antiscience policies, 54.5% by the COVID-19 pandemic.
Martha Cecilia Bottia, Roslyn Arlin Mickelson, Cayce Jamil, Kyleigh Moniz, Leanne Barry
, May 2021
Consistent with cumulative disadvantage and critical race theories, findings reveal that the disproportionality of racially minoritized students in STEM is related to their inferior secondary school preparation; the presence of racialized lower quality educational contexts; reduced levels of psychosocial factors associated with STEM success; less exposure to inclusive and appealing curricula and instruction; lower levels of family social, cultural, and financial capital that foster academic outcomes; and fewer prospects for supplemental STEM learning opportunities. Policy implications of findings are discussed.
Iris Daruwala, Shani Bretas, Douglas D. Ready
April 2021
Researchers describe how teachers, school leaders, and program staff navigated institutional pressures to improve state grade-level standardized test scores while implementing tasks and technologies designed to personalize student learning.
Michael A. Gottfried, Jay Plasman, Jennifer A. Freeman, Shaun Dougherty
March 2021
Researchers found that students with learning disabilities were more likely to earn more units in CTE courses compared with students without disabilities.
Ebony Omotola McGee
December 2020
This manuscript also discusses how universities institutionalize diversity mentoring programs designed mostly to fix (read “assimilate”) underrepresented students of color while ignoring or minimizing the role of the STEM departments in creating racially hostile work and educational spaces.
Miray Tekkumru-Kisa, Mary Kay Stein, Walter Doyle
November 2020
The purpose of this article is to revisit theory and research on tasks, a construct introduced by Walter Doyle nearly 40 years ago.
Elizabeth S. Park, Federick Ngo
November 2020
Researchers found that lower math placement may have supported women, and to a lesser extent URM students, in completing transferable STEM credits.
Karisma Morton, Catherine Riegle-Crumb
August/September 2020
Results of regression analyses reveal that, net of school, teacher, and student characteristics, the time that teachers report spending on algebra and more advanced content in eighth grade algebra classes is significantly lower in schools that are predominantly Black compared to those that are not predominantly minority. Implications for future research are discussed.
Qi Zhang, Jessaca Spybrook, Fatih Unlu
, July 2020
Researchers consider strategies to maximize the efficiency of the study design when both student and teacher effects are of primary interest.
Jennifer Lin Russell, Richard Correnti, Mary Kay Stein, Ally Thomas, Victoria Bill, Laurie Speranzo
, July 20, 2020
Analysis of videotaped coaching conversations and teaching events suggests that model-trained coaches improved their capacity to use a high-leverage coaching practice—deep and specific prelesson planning conversations—and that growth in this practice predicted teaching improvement, specifically increased opportunities for students to engage in conceptual thinking.
Maithreyi Gopalan, Kelly Rosinger, Jee Bin Ahn
, April 21, 2020
The overarching purpose of this chapter is to explore and document the growth, applicability, promise, and limitations of quasi-experimental research designs in education research.
Thomas M. Philip, Ayush Gupta
, April 21, 2020
By bringing this collection of articles together, this chapter provides collective epistemic and empirical weight to claims of power and learning as co-constituted and co-constructed through interactional, microgenetic, and structural dynamics.
Steve Graham, Sharlene A. Kiuhara, Meade MacKay
, March 19, 2020
This meta-analysis examined if students writing about content material in science, social studies, and mathematics facilitated learning.
Janina Roloff, Uta Klusmann, Oliver Lüdtke, Ulrich Trautwein
, January 2020
Multilevel regression analyses revealed that agreeableness, high school GPA, and the second state examination grade predicted teachers’ instructional quality.
: Contemporary Views on STEM Subjects and Language With English Learners
Okhee Lee, Amy Stephens
, 2020
With the release of the consensus report , the authors highlight foundational constructs and perspectives associated with STEM subjects and language with English learners that frame the report.
Angela Calabrese Barton and Edna Tan
, 2020
This essay presents a rightful presence framework to guide the study of teaching and learning in justice-oriented ways.
Day Greenberg, Angela Calabrese Barton, Carmen Turner, Kelly Hardy, Akeya Roper, Candace Williams, Leslie Rupert Herrenkohl, Elizabeth A. Davis, Tammy Tasker
, 2020
Researchers report on how one community builds capacity for disrupting injustice and supporting each other during the COVID-19 crisis.
Tatiana Melguizo, Federick Ngo
, 2020
This study explores the extent to which “college-ready” students, by high school standards, are assigned to remedial courses in college.
Karisma Morton and Catherine Riegle-Crumb
, 2020
Results of regression analyses reveal that, net of school, teacher, and student characteristics, the time that teachers report spending on algebra and more advanced content in eighth grade algebra classes is significantly lower in schools that are predominantly Black compared to those that are not predominantly minority. Implications for future research are discussed.
Jonathan D. Schweig, Julia H. Kaufman, and V. Darleen Opfer
, 2020
Researchers found that there are both substantial fluctuations in students’ engagement in these practices and reported cognitive demand from day to day, as well as large differences across teachers.
David Blazar and Casey Archer
, 2020
Researchers found that exposure to “ambitious” mathematics practices is more strongly associated with test score gains of English language learners compared to those of their peers in general education classrooms.
Megan Hopkins, Hayley Weddle, Maxie Gluckman, Leslie Gautsch
, December 2019
Researchers show how both researchers and practitioners facilitated research use.
Adrianna Kezar, Samantha Bernstein-Sierra
, October 2019
Findings suggest that Association of American Universities’ influence was a powerful motivator for institutions to alter deeply ingrained perceptions and behaviors.
Denis Dumas, Daniel McNeish, Julie Sarama, Douglas Clements
, October 2019
While students who receive a short-term intervention in preschool may not differ from a control group in terms of their long-term mathematics outcomes at the end of elementary school, they do exhibit significantly steeper growth curves as they approach their eventual skill level.
Jessica Thompson, Jennifer Richards, Soo-Yean Shim, Karin Lohwasser, Kerry Soo Von Esch, Christine Chew, Bethany Sjoberg, Ann Morris
, September 2019
Researchers used data from professional learning communities to analyze pathways into improvement work and reflective data to understand practitioners’ perspectives.
Ross E. O’Hara, Betsy Sparrow
, September 2019
Results indicate that interventions that target psychosocial barriers experienced by community college STEM students can increase retention and should be considered alongside broader reforms.
Ran Liu, Andrea Alvarado-Urbina, Emily Hannum
, September 2019
Findings reveal disparate national patterns in gender gaps across the performance distribution.
Adam Kirk Edgerton
, September 2019
Through an analysis of 52 interviews with state, regional, and district officials in California, Texas, Ohio, Pennsylvania, and Massachusetts, the author investigates the decline in the popularity of K–12 standards-based reform.
Amy Noelle Parks
, September 2019
The study suggests that more research needs to represent mathematics lessons from the perspectives of children and youth, particularly those students who engage with teachers infrequently or in atypical ways.
Rajeev Darolia, Cory Koedel, Joyce B. Main, J. Felix Ndashimye, Junpeng Yan
, September 30, 2019
Researchers found that differential access to high school courses does not affect postsecondary STEM enrollment or degree attainment.
Laura A. Davis, Gregory C. Wolniak, Casey E. George, Glen R. Nelson
, August 2019
The findings point to variation in informational quality across dimensions ranging from clarity of language use and terminology, to consistency and coherence of visual displays, which accompany navigational challenges stemming from information fragmentation and discontinuity across pages.
Juan E. Saavedra, Emma Näslund-Hadley, Mariana Alfonso
, August 12, 2019
Researchers present results from the first randomized experiment of a remedial inquiry-based science education program for low-performing elementary students in a developing country.
F. Chris Curran, James Kitchin
, July 2019
Researchers found suggestive evidence in some models (student fixed effects and regression with observable controls) that time on science instruction is related to science achievement but little evidence that the number of science topics/skills covered are related to greater science achievement.
Kathleen Lynch, Heather C. Hill, Kathryn E. Gonzalez, Cynthia Pollard
, June 2019
Programs saw stronger outcomes when they helped teachers learn to use curriculum materials; focused on improving teachers’ content knowledge, pedagogical content knowledge, and/or understanding of how students learn; incorporated summer workshops; and included teacher meetings to troubleshoot and discuss classroom implementation. We discuss implications for policy and practice.
Elizabeth Stearns, Martha Cecilia Bottia, Jason Giersch, Roslyn Arlin Mickelson, Stephanie Moller, Nandan Jha, Melissa Dancy
, June 2019
Researchers found that relative advantages in college academic performance in STEM versus non-STEM subjects do not contribute to the gender gap in STEM major declaration.
Nicole Shechtman, Jeremy Roschelle, Mingyu Feng, Corinne Singleton
, May 2019
As educational leaders throughout the United States adopt digital mathematics curricula and adaptive, blended approaches, the findings provide a relevant caution.
Colleen M. Ganley, Robert C. Schoen, Mark LaVenia, Amanda M. Tazaz
, March 2019
Factor analyses support a distinction between components of general math anxiety and anxiety about teaching math.
Felicia Moore Mensah
, February 2019
The implications for practice in both teacher education and science education show that educational and emotional support for teachers of color throughout their educational and professional journey is imperative to increasing and sustaining Black teachers.
Herbert W. Marsh, Brooke Van Zanden, Philip D. Parker, Jiesi Guo, James Conigrave, Marjorie Seaton
, February 2019
Researchers evaluated STEM coursework selection by women and men in senior high school and university, controlling achievement and expectancy-value variables.
Yasemin Copur-Gencturk, Debra Plowman, Haiyan Bai
, January 2019
The results showed that a focus on curricular content knowledge and examining students’ work were significantly related to teachers’ learning.
Rebecca Colina Neri, Maritza Lozano, Louis M. Gomez
, 2019
Researchers found that teacher resistance to CRE as a multilevel learning problem stems from (a) limited understanding and belief in the efficacy of CRE and (b) a lack of know-how needed to execute it.
Russell T. Warne, Gerhard Sonnert, and Philip M. Sadler
, 2019
Researchers investigated the relationship between participation in AP mathematics courses (AP Calculus and AP Statistics) and student career interest in STEM.
Catherine Riegle-Crumb, Barbara King, and Yasmiyn Irizarry
, 2019
Results reveal evidence of persistent racial/ethnic inequality in STEM degree attainment not found in other fields.
Eben B. Witherspoon, Paulette Vincent-Ruz, and Christian D. Schunn
, 2019
Researchers found that high-performing women often graduate with lower paying, lower status degrees.
Bruce Fuller, Yoonjeon Kim, Claudia Galindo, Shruti Bathia, Margaret Bridges, Greg J. Duncan, and Isabel García Valdivia
, 2019
This article details the growing share of Latino children from low-income families populating schools, 1998 to 2010.
Rebekka Darner
, 2019
Drawing from motivated reasoning and self-determination theories, this essay builds a theoretical model of how negative emotions, thwarting of basic psychological needs, and the backfire effect interact to undermine critical evaluation of evidence, leading to science denial.
Okhee Lee
, 2019
As the fast-growing population of English learners (ELs) is expected to meet college- and career-ready content standards, the purpose of this article is to highlight key issues in aligning ELP standards with content standards.
Mark C. Long, Dylan Conger, and Raymond McGhee, Jr.
, 2019
The authors offer the first model of the components inherent in a well-implemented AP science course and the first evaluation of AP implementation with a focus on public schools newly offering the inquiry-based version of AP Biology and Chemistry courses.
Yasemin Copur-Gencturk, Joseph R. Cimpian, Sarah Theule Lubienski, and Ian Thacker
, 2019
Results indicate that teachers are not free of bias, and that teachers from marginalized groups may be susceptible to bias that favors stereotype-advantaged groups.
Geoffrey B. Saxe and Joshua Sussman
, 2019
Multilevel analysis of longitudinal data on a specialized integers and fractions assessment, as well as a California state mathematics assessment, revealed that the ELs in LMR classrooms showed greater gains than comparison ELs and gained at similar rates to their EP peers in LMR classrooms.
Jordan Rickles, Jessica B. Heppen, Elaine Allensworth, Nicholas Sorensen, and Kirk Walters
, 2019
The authors discuss whether it would have been appropriate to test for nominally equivalent outcomes, given that the study was initially conceived and designed to test for significant differences, and that the conclusion of no difference was not solely based on a null hypothesis test.
Soobin Kim, Gregory Wallsworth, Ran Xu, Barbara Schneider, Kenneth Frank, Brian Jacob, Susan Dynarski
, 2019
Using detailed Michigan high school transcript data, this article examines the effect of the MMC on various students’ course-taking and achievement outcomes.
Dario Sansone
, December 2018
Researchers found that students were less likely to believe that men were better than women in math or science when assigned to female teachers or to teachers who valued and listened to ideas from their students.
Ebony McGee
, December 2018
The authors argues that both racial groups endure emotional distress because each group responds to its marginalization with an unrelenting motivation to succeed that imposes significant costs.
Barbara Means, Haiwen Wang, Xin Wei, Emi Iwatani, Vanessa Peters
, November 2018
Students overall and from under-represented groups who had attended inclusive STEM high schools were significantly more likely to be in a STEM bachelor’s degree program two years after high school graduation.
Paulo Tan, Kathleen King Thorius
, November 2018
Results indicate identity and power tensions that worked against equitable practices.
Caesar R. Jackson
, November 2018
This study investigated the validity and reliability of the Motivated Strategies for Learning Questionnaire (MSLQ) for minority students enrolled in STEM courses at a historically black college/university (HBCU).
Tuan D. Nguyen, Christopher Redding
, September 2018
The results highlight the importance of recruiting qualified STEM teachers to work in high-poverty schools and providing supports to help them thrive and remain in the classroom.
Joseph A. Taylor, Susan M. Kowalski, Joshua R. Polanin, Karen Askinas, Molly A. M. Stuhlsatz, Christopher D. Wilson, Elizabeth Tipton, Sandra Jo Wilson
, August 2018
The meta-analysis examines the relationship between science education intervention effect sizes and a host of study characteristics, allowing primary researchers to access better estimates of effect sizes for a priori power analyses. The results of this meta-analysis also support programmatic decisions by setting realistic expectations about the typical magnitude of impacts for science education interventions.
Brian A. Burt, Krystal L. Williams, Gordon J. M. Palmer
, August 2018
Three factors are identified as helping them persist from year to year, and in many cases through completion of the doctorate: the role of family, spirituality and faith-based community, and undergraduate mentors.
Anna-Lena Rottweiler, Jamie L. Taxer, Ulrike E. Nett
, June 2018
Suppression improved mood in exam-related anxiety, while distraction improved mood only in non-exam-related anxiety.
Gabriel Estrella, Jacky Au, Susanne M. Jaeggi, Penelope Collins
, April 2018
Although an analysis of 26 articles confirmed that inquiry instruction produced significantly greater impacts on measures of science achievement for ELLs compared to direct instruction, there was still a differential learning effect suggesting greater efficacy for non-ELLs compared to ELLs.
Heather C. Hill, Mark Chin
, April 2018
In this article, evidence from 284 teachers suggests that accuracy can be adequately measured and relates to instruction and student outcomes.
Darrell M. Hull, Krystal M. Hinerman, Sarah L. Ferguson, Qi Chen, Emma I. Näslund-Hadley
, April 20, 2018
Both quantitative and qualitative evidence suggest students within this culture respond well to this relatively simple and inexpensive intervention that departs from traditional, expository math instruction in many developing countries.
Erika C. Bullock
, April 2018
The author reviews CME studies that employ intersectionality as a way of analyzing the complexities of oppression.
Angela Calabrese Barton, Edna Tan
, March 2018
Building a conceptual argument for an equity-oriented culture of making, the authors discuss the ways in which making with and in community opened opportunities for youth to project their communities’ rich culture knowledge and wisdom onto their making while also troubling and negotiating the historicized injustices they experience.
Sabrina M. Solanki, Di Xu
, March 2018
Researchers found that having a female instructor narrows the gender gap in terms of engagement and interest; further, both female and male students tend to respond to instructor gender.
Susanne M. Jaeggi, Priti Shah
, February 2018
These articles provide excellent examples for how neuroscientific approaches can complement behavioral work, and they demonstrate how understanding the neural level can help researchers develop richer models of learning and development.
Danyelle T. Ireland, Kimberley Edelin Freeman, Cynthia E. Winston-Proctor, Kendra D. DeLaine, Stacey McDonald Lowe, Kamilah M. Woodson
, 2018
Researchers found that (1) identity; (2) STEM interest, confidence, and persistence; (3) achievement, ability perceptions, and attributions; and (4) socializers and support systems are key themes within the experiences of Black women and girls in STEM education.
Ann Y. Kim, Gale M. Sinatra, Viviane Seyranian
, 2018
Findings indicate that young women experience challenges to their participation and inclusion when they are in STEM settings.
Guan Saw, Chi-Ning Chang, and Hsun-Yu Chan
, 2018
Results indicated that female, Black, Hispanic, and low SES students were less likely to show, maintain, and develop an interest in STEM careers during high school years.
Di Xu, Sabrina Solanki, Peter McPartlan, and Brian Sato
, 2018
This paper estimates the causal effects of a first-year STEM learning communities program on both cognitive and noncognitive outcomes at a large public 4-year institution.
Christina S. Chhin, Katherine A. Taylor, and Wendy S. Wei
, 2018
Data showed that IES has not funded any direct replications that duplicate all aspects of the original study, but almost half of the funded grant applications can be considered conceptual replications that vary one or more dimensions of a prior study.
Okhee Lee
, 2018
As federal legislation requires that English language proficiency (ELP) standards are aligned with content standards, this article addresses issues and concerns in aligning ELP standards with content standards in English language arts, mathematics, and science.
Jordan Rickles, Jessica B. Heppen, Elaine Allensworth, Nicholas Sorensen, and Kirk Walters
, 2018
Researchers found no statistically significant differences in longer term outcomes between students in the online and face-to-face courses. Implications of these null findings are discussed.
Colleen M. Ganley, Casey E. George, Joseph R. Cimpian, Martha B. Makowski
, December 2017
Researchers found that perceived gender bias against women emerges as the dominant predictor of the gender balance in college majors.
James P. Spillane, Megan Hopkins, Tracy M. Sweet
, December 2017
This article examines the relationship between teachers’ instructional ties and their beliefs about mathematics instruction in one school district working to transform its approach to elementary mathematics education.
Susan A. Yoon, Sao-Ee Goh, Miyoung Park
, December 6, 2017
Results revealed needs in five areas of research: a need to diversify the knowledge domains within which research is conducted, more research on learning about system states, agreement on the essential features of complex systems content, greater focus on contextual factors that support learning including teacher learning, and a need for more comparative research.
Candace Walkington, Virginia Clinton, Pooja Shivraj
, November 2017
Textual features that make problems more difficult to process appear to differentially negatively impact struggling students, while features that make language easier to process appear to differentially positively impact struggling students.
Rebecca L. Matz, Benjamin P. Koester, Stefano Fiorini, Galina Grom, Linda Shepard, Charles G. Stangor, Brad Weiner, Timothy A. McKay
, November 2017
Biology, chemistry, physics, accounting, and economics lecture courses regularly exhibit gendered performance differences that are statistically and materially significant, whereas lab courses in the same subjects do not.
Adam V. Maltese, Christina S. Cooper
, August 2017
The results reveal that although there is no singular pathway into STEM fields, self-driven interest is a large factor in persistence, especially for males, and females rely more heavily on support from others.
Brian R. Belland, Andrew E. Walker, Nam Ju Kim
, August 2017
Scaffolding has a consistently strong effect across student populations, STEM disciplines, and assessment levels, and a strong effect when used with most problem-centered instructional and educational levels.
Di Xu, Shanna Smith Jaggars
, July 2017
The findings indicate a robust negative impact of online course taking for both subjects.
Maisie L. Gholson, Charles E. Wilkes
, June 2017
This chapter reviews two strands of identity-based research in mathematics education related to Black children, exemplified by Martin (2000) and Nasir (2002).
Sarah Theule Lubienski, Emily K. Miller, and Evthokia Stephanie Saclarides
, November 2017
Using data from a survey of doctoral students at one large institution, this study finds that men submitted and published more scholarly works than women across many fields, with differences largest in natural/biological sciences and engineering.
David Blazar, Cynthia Pollard
, October 2017
Drawing on classroom observations and teacher surveys, researchers find that test preparation activities predict lower quality and less ambitious mathematics instruction in upper-elementary classrooms.
Nicole M. Joseph, Meseret Hailu, Denise Boston
, June 2017
This integrative review used critical race theory (CRT) and Black feminism as interpretive frames to explore factors that contribute to Black women’s and girls’ persistence in the mathematics pipeline and the role these factors play in shaping their academic outcomes.
Benjamin L. Wiggins, Sarah L. Eddy, Daniel Z. Grunspan, Alison J. Crowe
, May 2017
Researchers describe the results of a quasi-experimental study to test the apex of the ICAP framework (interactive, constructive, active, and passive) in this ecological classroom environment.
Sean Gehrke, Adrianna Kezar
, May 2017
This study examines how involvement in four cross-institutional STEM faculty communities of practice is associated with local departmental and institutional change for faculty members belonging to these communities.
Lawrence Ingvarson, Glenn Rowley
, May 2017
This study investigated the relationship between policies related to the recruitment, selection, preparation, and certification of new teachers and (a) the quality of future teachers as measured by their mathematics content and pedagogy content knowledge and (b) student achievement in mathematics at the national level.
Will Tyson, Josipa Roksa
, April 2017
This study examines how course grades and course rigor are associated with math attainment among students with similar eighth-grade standardized math test scores.
Anne K. Morris, James Hiebert
, March 2017
Researchers investigated whether the content pre-service teachers studied in elementary teacher preparation mathematics courses was related to their performance on a mathematics lesson planning task 2 and 3 years after graduation.
Laura M. Desimone, Kirsten Lee Hill
, March 2017
Researchers use data from a randomized controlled trial of a middle school science intervention to explore the causal mechanisms by which the intervention produced previously documented gains in student achievement.
Okhee Lee
, March 2017
This article focuses on how the Common Core State Standards (CCSS) and the Next Generation Science Standards (NGSS) treat “argument,” especially in Grades K–5, and the extent to which each set of standards is grounded in research literature, as claimed.
Cory Koedel, Diyi Li, Morgan S. Polikoff, Tenice Hardaway, Stephani L. Wrabel
, February 2017
Researchers estimate relative achievement effects of the four most commonly adopted elementary mathematics textbooks in the fall of 2008 and fall of 2009 in California.
Mary Kay Stein, Richard Correnti, Debra Moore, Jennifer Lin Russell, Katelynn Kelly
, January 2017
Researchers argue that large-scale, standards-based improvements in the teaching and learning of mathematics necessitate advances in theories regarding how teaching affects student learning and progress in how to measure instruction.
Alan H. Schoenfeld
, December 2016
The author begins by tracing the growth and change in research in mathematics education and its interdependence with research in education in general over much of the 20th century, with an emphasis on changes in research perspectives and methods and the philosophical/empirical/disciplinary approaches that underpin them.
Marcia C. Linn, Libby Gerard, Camillia Matuk, Kevin W. McElhaney
, December 2016
This chapter focuses on how investigators from varied fields of inquiry who initially worked separately began to interact, eventually formed partnerships, and recently integrated their perspectives to strengthen science education.
: Are Teachers’ Implicit Cognitions Another Piece of the Puzzle?
Almut E. Thomas
, December 2016
Drawing on expectancy-value theory, this study investigated whether teachers’ implicit science-is-male stereotypes predict between-teacher variation in males’ and females’ motivational beliefs regarding physical science.
: A By-Product of STEM College Culture?
Ebony O. McGee
, December 2016
The researcher found that the 38 high-achieving Black and Latino/a STEM study participants, who attended institutions with racially hostile academic spaces, deployed an arsenal of strategies (e.g., stereotype management) to deflect stereotyping and other racial assaults (e.g., racial microaggressions), which are particularly prevalent in STEM fields.
James Cowan, Dan Goldhaber, Kyle Hayes, Roddy Theobald
, November 2016
Researchers discuss public policies that contribute to teacher shortages in specific subjects (e.g., STEM and special education) and specific types of schools (e.g., disadvantaged) as well as potential solutions.
: A Sociological Analysis of Multimethod Data From Young Women Aged 10–16 to Explore Gendered Patterns of Post-16 Participation
Louise Archer, Julie Moote, Becky Francis, Jennifer DeWitt, Lucy Yeomans
, November 2016
Researchers draw on survey data from more than 13,000 year 11 (age 15/16) students and interviews with 70 students (who had been tracked from age 10 to 16), focusing in particular on seven girls who aspired to continue with physics post-16, discussing how the cultural arbitrary of physics requires these girls to be highly “exceptional,” undertaking considerable identity work and deployment of capital in order to “possibilize” a physics identity—an endeavor in which some girls are better positioned to be successful than others.
Jeremy Roschelle, Mingyu Feng, Robert F. Murphy, Craig A. Mason
, October 2016
In a randomized field trial with 2,850 seventh-grade mathematics students, researchers evaluated whether an educational technology intervention increased mathematics learning.
: Making Research Participation Instructionally Effective
Sherry A. Southerland, Ellen M. Granger, Roxanne Hughes, Patrick Enderle, Fengfeng Ke, Katrina Roseler, Yavuz Saka, Miray Tekkumru-Kisa
, October 2016
As current reform efforts in science place a premium on student sense making and participation in the practices of science, researchers use a close examination of 106 science teachers participating in Research Experiences for Teachers (RET) to identify, through structural equation modeling, the essential features in supporting teacher learning from these experiences.
Brian R. Belland, Andrew E. Walker, Nam Ju Kim, Mason Lefler
, October 2016
This review addresses the need for a comprehensive meta-analysis of research on scaffolding in STEM education by synthesizing the results of 144 experimental studies (333 outcomes) on the effects of computer-based scaffolding designed to assist the full range of STEM learners (primary through adult education) as they navigated ill-structured, problem-centered curricula.
Vaughan Prain, Brian Hand
, October 2016
Researchers claim that there are strong evidence-based reasons for viewing writing as a central but not sole resource for learning, drawing on both past and current research on writing as an epistemological tool and on their professional background in science education research, acknowledging its distinctive take on the use of writing for learning.
June Ahn, Austin Beck, John Rice, Michelle Foster
, September 2016
Researchers present analyses from a researcher-practitioner partnership in the District of Columbia Public Schools, where the researchers are exploring the impact of educational software on students’ academic achievement.
Barbara King
, September 2016
This study uses nationally representative data from a recent cohort of college students to investigate thoroughly gender differences in STEM persistence.
Ryan C. Svoboda, Christopher S. Rozek, Janet S. Hyde, Judith M. Harackiewicz, Mesmin Destin
, August 2016
This longitudinal study draws on identity-based and expectancy-value theories of motivation to explain the socioeconomic status (SES) and mathematics and science course-taking relationship.
Mathematics Course Placements in California Middle Schools, 2003–2013
Thurston Domina, Paul Hanselman, NaYoung Hwang, Andrew McEachin
, July 2016
Researchers consider the organizational processes that accompanied the curricular intensification of the proportion of California eighth graders enrolled in algebra or a more advanced course nearly doubling to 65% between 2003 and 2013.
Lina Shanley
, July 2016
Using a nationally representative longitudinal data set, this study compared various models of mathematics achievement growth on the basis of both practical utility and optimal statistical fit and explored relationships within and between early and later mathematics growth parameters.
Mimi Engel, Amy Claessens, Tyler Watts, George Farkas
, June 2016
Analyzing data from two nationally representative kindergarten cohorts, researchers examine the mathematics content teachers cover in kindergarten.
F. Chris Curran, Ann T. Kellogg
, June 2016
Researchers present findings from the recently released Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011 that demonstrate significant gaps in science achievement in kindergarten and first grade by race/ethnicity.
Rachel Garrett, Guanglei Hong
, June 2016
Analyzing the Early Childhood Longitudinal Study–Kindergarten cohort data, researchers find that heterogeneous grouping or a combination of heterogeneous and homogeneous grouping under relatively adequate time allocation is optimal for enhancing teacher ratings of language minority kindergartners’ math performance, while using homogeneous grouping only is detrimental.
Jennifer Gnagey, Stéphane Lavertu
, May 2016
This study is one of the first to estimate the impact of “inclusive” science, technology, engineering, and mathematics (STEM) high schools using student-level data.
Hanna Gaspard, Anna-Lena Dicke, Barbara Flunger, Isabelle Häfner, Brigitte M. Brisson, Ulrich Trautwein, Benjamin Nagengast
, May 2016
Through data from a cluster-randomized study in which a value intervention was successfully implemented in 82 ninth-grade math classrooms, researchers address how interventions on students’ STEM motivation in school affect motivation in subjects not targeted by the intervention.
Rebecca M. Callahan, Melissa H. Humphries
, April 2016
Researchers employ multivariate methods to investigate immigrant college going by linguistic status using the Educational Longitudinal Study of 2002.
Federick Ngo, Tatiana Melguizo
, March 2016
Researchers take advantage of heterogeneous placement policy in a large urban community college district in California to compare the effects of math remediation under different policy contexts.
: An Analysis of German Fourth- and Sixth-Grade Classrooms
Steffen Tröbst, Thilo Kleickmann, Kim Lange-Schubert, Anne Rothkopf, Kornelia Möller
, February 2016
Researchers examined if changes in instructional practices accounted for differences in situational interest in science instruction and enduring individual interest in science between elementary and secondary school classrooms.
: A Mixed-Methods Study
David F. Feldon, Michelle A. Maher, Josipa Roksa, James Peugh
, February 2016
Researchers offer evidence of a similar phenomenon to cumulative advantage, accounting for differential patterns of research skill development in graduate students over an academic year and explore differences in socialization that accompany diverging developmental trajectories.
: The Influence of Time, Peers, and Place
Luke Dauter, Bruce Fuller
, February 2016
Researchers hypothesize that pupil mobility stems from the (a) student’s time in school and grade; (b) student’s race, class, and achievement relative to peers; (c) quality of schooling relative to nearby alternatives; and (4) proximity, abundance, and diversity of local school options.
: How Workload and Curricular Affordances Shape STEM Faculty Decisions About Teaching and Learning
Matthew T. Hora
, January 2016
In this study the idea of the “problem space” from cognitive science is used to examine how faculty construct mental representations for the task of planning undergraduate courses.
Jessaca Spybrook, Carl D. Westine, Joseph A. Taylor
, January 2016
This article provides empirical estimates of design parameters necessary for planning adequately powered cluster randomized trials (CRTs) focused on science achievement.
Paul L. Morgan, George Farkas, Marianne M. Hillemeier, Steve Maczuga
, January 2016
Researchers examined the age of onset, over-time dynamics, and mechanisms underlying science achievement gaps in U.S. elementary and middle schools.
: Opportunity Structures and Outcomes in Inclusive STEM-Focused High Schools
Lois Weis, Margaret Eisenhart, Kristin Cipollone, Amy E. Stich, Andrea B. Nikischer, Jarrod Hanson, Sarah Ohle Leibrandt, Carrie D. Allen, Rachel Dominguez
, December 2015
Researchers present findings from a three-year comparative longitudinal and ethnographic study of how schools in two cities, Buffalo and Denver, have taken up STEM education reform, including the idea of “inclusive STEM-focused schools,” to address weaknesses in urban high schools with majority low-income and minority students.
: How Do They Interact in Promoting Science Understanding?
Jasmin Decristan, Eckhard Klieme, Mareike Kunter, Jan Hochweber, Gerhard Büttner, Benjamin Fauth, A. Lena Hondrich, Svenja Rieser, Silke Hertel, Ilonca Hardy
, December 2015
Researchers examine the interplay between curriculum-embedded formative assessment—a well-known teaching practice—and general features of classroom process quality (i.e., cognitive activation, supportive climate, classroom management) and their combined effect on elementary school students’ understanding of the scientific concepts of floating and sinking.
: An International Perspective
William H. Schmidt, Nathan A. Burroughs, Pablo Zoido, Richard T. Houang
, October 2015
In this paper, student-level indicators of opportunity to learn (OTL) included in the 2012 Programme for International Student Assessment are used to explore the joint relationship of OTL and socioeconomic status (SES) to student mathematics literacy.
Xueli Wang
, September 2015
This study examines the effect of beginning at a community college on baccalaureate success in science, technology, engineering, and mathematics (STEM) fields.
: Trends and Predictors
David M. Quinn, North Cooc
, August 2015
With research on science achievement disparities by gender and race/ethnicity often neglecting the beginning of the pipeline in the early grades, researchers address this limitation using nationally representative data following students from Grades 3 to 8.
Shaun M. Dougherty, Joshua S. Goodman, Darryl V. Hill, Erica G. Litke, Lindsay C. Page
, May 2015
Researchers highlight a collaboration to investigate one district’s effort to increase middle school algebra course-taking.
David F. Feldon, Michelle A. Maher, Melissa Hurst, Briana Timmerman
, April 2015
This mixed-method study investigates agreement between student mentees’ and their faculty mentors’ perceptions of the students’ developing research knowledge and skills in STEM.
: Reviving Science Education for Civic Ends
John L. Rudolph
, December 2014
This article revisits John Dewey’s now-well-known address “Science as Subject-Matter and as Method” and examines the development of science education in the United States in the years since that address.
Dermot F. Donnelly, Marcia C. Linn Sten Ludvigsen
, December 2014
The National Science Foundation–sponsored report Fostering Learning in the Networked World called for “a common, open platform to support communities of developers and learners in ways that enable both to take advantage of advances in the learning sciences”; we review research on science inquiry learning environments (ILEs) to characterize current platforms.
: A Longitudinal Case Study of America’s Chemistry Teachers
Gregory T. Rushton, Herman E. Ray, Brett A. Criswell, Samuel J. Polizzi, Clyde J. Bearss, Nicholas Levelsmier, Himanshu Chhita, Mary Kirchhoff
, November 2014
Researchers perform a longitudinal case study of U.S. public school chemistry teachers to illustrate a diffusion of responsibility within the STEM community regarding who is responsible for the teacher workforce.
: Relations Between Early Mathematics Knowledge and High School Achievement
Tyler W. Watts, Greg J. Duncan, Robert S. Siegler, Pamela E. Davis-Kean
, October 2014
Researchers find that preschool mathematics ability predicts mathematics achievement through age 15, even after accounting for early reading, cognitive skills, and family and child characteristics.
T. Jared Robinson, Lane Fischer, David Wiley, John Hilton, III
, October 2014
The purpose of this quantitative study is to analyze whether the adoption of open science textbooks significantly affects science learning outcomes for secondary students in earth systems, chemistry, and physics.
: 1968–2009
Robert N. Ronau, Christopher R. Rakes, Sarah B. Bush, Shannon O. Driskell, Margaret L. Niess, David K. Pugalee
, October 2014
We examined 480 dissertations on the use of technology in mathematics education and developed a Quality Framework (QF) that provided structure to consistently define and measure quality.
Andrew D. Plunk, William F. Tate, Laura J. Bierut, Richard A. Grucza
, June 2014
Using logistic regression with Census and American Community Survey (ACS) data ( = 2,892,444), researchers modeled mathematics and science course graduation requirement (CGR) exposure on (a) high school dropout, (b) beginning college, and (c) obtaining any college degree.
Corey Drake, Tonia J. Land, Andrew M. Tyminski
, April 2014
Building on the work of Ball and Cohen and that of Davis and Krajcik, as well as more recent research related to teacher learning from and about curriculum materials, researchers seek to answer the question, How can prospective teachers (PTs) learn to read and use educative curriculum materials in ways that support them in acquiring the knowledge needed for teaching?
Lorraine M. McDonnell, M. Stephen Weatherford
, December 2013
This article draws on theories of political and policy learning and interviews with major participants to examine the role that the Common Core State Standards (CCSS) supporters have played in developing and implementing the standards, supporters’ reasons for mobilizing, and the counterarguments and strategies of recently emerging opposition groups.
: Motivation, High School Learning, and Postsecondary Context of Support
Xueli Wang
, October 2013
This study draws upon social cognitive career theory and higher education literature to test a conceptual framework for understanding the entrance into science, technology, engineering, and mathematics (STEM) majors by recent high school graduates attending 4-year institutions.
Philip M. Sadler, Gerhard Sonnert, Harold P. Coyle, Nancy Cook-Smith, Jaimie L. Miller
, October 2013
This study examines the relationship between teacher knowledge and student learning for 9,556 students of 181 middle school physical science teachers.
: Teaching Critical Mathematics in a Remedial Secondary Classroom
Andrew Brantlinger
, October 2013
The researcher presents results from a practitioner research study of his own teaching of critical mathematics (CM) to low-income students of color in a U.S. context.
Jason G. Hill, Ben Dalton
, October 2013
This study investigates the distribution of math teachers with a major or certification in math using data from the National Center for Education Statistics’ High School Longitudinal Study of 2009 (HSLS:09).
Kristin F. Butcher, Mary G. Visher
, September 2013
This study uses random assignment to investigate the impact of a “light-touch” intervention, where an individual visited math classes a few times during the semester, for a few minutes each time, to inform students about available services.
Janet M. Dubinsky, Gillian Roehrig, Sashank Varma
, August 2013
Researchers argue that the neurobiology of learning, and in particular the core concept of , have the potential to directly transform teacher preparation and professional development, and ultimately to affect how students think about their own learning.
: The Impact of Undergraduate Research Programs
M. Kevin Eagan, Jr., Sylvia Hurtado, Mitchell J. Chang, Gina A. Garcia, Felisha A. Herrera, Juan C. Garibay
, August 2013
Researchers’ findings indicate that participation in an undergraduate research program significantly improved students’ probability of indicating plans to enroll in a STEM graduate program.
Okhee Lee, Helen Quinn, Guadalupe Valdés
, May 2013
This article addresses language demands and opportunities that are embedded in the science and engineering practices delineated in “A Framework for K–12 Science Education,” released by the National Research Council (2011).
Liliana M. Garces
, April 2013
This study examines the effects of affirmative action bans in four states (California, Florida, Texas, and Washington) on the enrollment of underrepresented students of color within six different graduate fields of study: the natural sciences, engineering, social sciences, business, education, and humanities.
: Learning Lessons From Research on Diversity in STEM Fields
Shirley M. Malcom, Lindsey E. Malcom-Piqueux
, April 2013
Researchers argue that social scientists ought to look to the vast STEM education research literature to begin the task of empirically investigating the questions raised in the case.
Roslyn Arlin Mickelson, Martha Cecilia Bottia, Richard Lambert
, March 2013
This metaregression analysis reviewed the social science literature published in the past 20 years on the relationship between mathematics outcomes and the racial composition of the K–12 schools students attend.
Jeffrey Grigg, Kimberle A. Kelly, Adam Gamoran, Geoffrey D. Borman
, March 2013
Researchers examine classroom observations from a 3-year large-scale randomized trial in the Los Angeles Unified School District (LAUSD) to investigate the extent to which a professional development initiative in inquiry science influenced teaching practices in in 4th and 5th grade classrooms in 73 schools.
:
Angela Calabrese Barton, Hosun Kang, Edna Tan, Tara B. O’Neill, Juanita Bautista-Guerra, Caitlin Brecklin
, February 2013
This longitudinal ethnographic study traces the identity work that girls from nondominant backgrounds do as they engage in science-related activities across school, club, and home during the middle school years.
: A Review of the State of the Field
Shuchi Grover, Roy Pea
, January 2013
This article frames the current state of discourse on computational thinking in K–12 education by examining mostly recently published academic literature that uses Jeannette Wing’s article as a springboard, identifies gaps in research, and articulates priorities for future inquiries.
Catherine Riegle-Crumb, Barbara King, Eric Grodsky, Chandra Muller
, December 2012
This article investigates the empirical basis for often-repeated arguments that gender differences in entrance into science, technology, engineering, and mathematics (STEM) majors are largely explained by disparities in prior achievement.
Richard M. Ingersoll, Henry May
, December 2012
This study examines the magnitude, destinations, and determinants of mathematics and science teacher turnover.
: How Families Shape Children’s Engagement and Identification With Science
Louise Archer, Jennifer DeWitt, Jonathan Osborne, Justin Dillon, Beatrice Willis, Billy Wong
, October 2012
Drawing on the conceptual framework of Bourdieu, this article explores how the interplay of family habitus and capital can make science aspirations more “thinkable” for some (notably middle-class) children than others.
Erin Marie Furtak, Tina Seidel, Heidi Iverson, Derek C. Briggs
, September 2012
This meta-analysis introduces a framework for inquiry-based teaching that distinguishes between cognitive features of the activity and degree of guidance given to students.
Jaekyung Lee, Todd Reeves
, June 2012
This study examines the impact of high-stakes school accountability, capacity, and resources under NCLB on reading and math achievement outcomes through comparative interrupted time-series analyses of 1990–2009 NAEP state assessment data.
: Toward a Theory of Teaching
Paola Sztajn, Jere Confrey, P. Holt Wilson, Cynthia Edgington
, June 2012
Researchers propose a theoretical connection between research on learning and research on teaching through recent research on students’ learning trajectories (LTs).
: The Perspectives of Exemplary African American Teachers
Jianzhong Xu, Linda T. Coats, Mary L. Davidson
, February 2012
Researchers argue both the urgency and the promise of establishing a constructive conversation among different bodies of research, including science interest, sociocultural studies in science education, and culturally relevant teaching.
Rebecca M. Schneider, Kellie Plasman
, December 2011
This review examines the research on science teachers’ pedagogical content knowledge (PCK) in order to refine ideas about science teacher learning progressions and how to support them.
Brian A. Nosek, Frederick L. Smyth
, October 2011
Researchers examined implicit math attitudes and stereotypes among a heterogeneous sample of 5,139 participants.
Libby F. Gerard, Keisha Varma, Stephanie B. Corliss, Marcia C. Linn
, September 2011
Researchers’ findings suggest that professional development programs that engaged teachers in a comprehensive, constructivist-oriented learning process and were sustained beyond 1 year significantly improved students’ inquiry learning experiences in K–12 science classrooms.
: Teaching and Learning Impacts of Reading Apprenticeship Professional Development
Cynthia L. Greenleaf, Cindy Litman, Thomas L. Hanson, Rachel Rosen, Christy K. Boscardin, Joan Herman, Steven A. Schneider, Sarah Madden, Barbara Jones
, June 2011
This study examined the effects of professional development integrating academic literacy and biology instruction on science teachers’ instructional practices and students’ achievement in science and literacy.
Paul Cobb, Kara Jackson
, May 2011
The authors comment on Porter, McMaken, Hwang, and Yang’s recent analysis of the Common Core State Standards for Mathematics by critiquing their measures of the focus of the standards and the absence of an assessment of coherence.
P. Wesley Schultz, Paul R. Hernandez, Anna Woodcock, Mica Estrada, Randie C. Chance, Maria Aguilar, Richard T. Serpe
, March 2011
This study reports results from a longitudinal study of students supported by a national National Institutes of Health–funded minority training program, and a propensity score matched control.
: Three Large-Scale Studies
Jeremy Roschelle, Nicole Shechtman, Deborah Tatar, Stephen Hegedus, Bill Hopkins, Susan Empson, Jennifer Knudsen, Lawrence P. Gallagher
, December 2010
The authors present three studies (two randomized controlled experiments and one embedded quasi-experiment) designed to evaluate the impact of replacement units targeting student learning of advanced middle school mathematics.
: Examining Disparities in College Major by Gender and Race/Ethnicity
Catherine Riegle-Crumb, Barbara King
, December 2010
The authors analyze national data on recent college matriculants to investigate gender and racial/ethnic disparities in STEM fields, with an eye toward the role of academic preparation and attitudes in shaping such disparities.
Mary Kay Stein, Julia H. Kaufman
, September 2010
This article begins to unravel the question, “What curricular materials work best under what kinds of conditions?” The authors address this question from the point of view of teachers and their ability to implement mathematics curricula that place varying demands and provide varying levels of support for their learning.
Andy R. Cavagnetto
, September 2010
This study of 54 articles from the research literature examines how argument interventions promote scientific literacy.
Victoria M. Hand
, March 2010
The researcher examined how the teacher and students in a low-track mathematics classroom jointly constructed opposition through their classroom interactions.
Terrence E. Murphy, Monica Gaughan, Robert Hume, S. Gordon Moore, Jr.
, March 2010
Researchers evaluate the association of a summer bridge program with the graduation rate of underrepresented minority (URM) students at a selective technical university.
International Journal of STEM Education volume 7 , Article number: 17 ( 2020 ) Cite this article
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Taking publicly funded projects in STEM education as a special lens, we aimed to learn about research and trends in STEM education. We identified a total of 127 projects funded by the Institute of Education Sciences (IES) of the US Department of Education from 2003 to 2019. Both the number of funded projects in STEM education and their funding amounts were high, although there were considerable fluctuations over the years. The number of projects with multiple principal investigators increased over time. The project duration was typically in the range of 3–4 years, and the goals of these projects were mostly categorized as “development and innovation” or “efficacy and replication.” The majority of the 127 projects focused on individual STEM disciplines, especially mathematics. The findings, based on IES-funded projects, provided a glimpse of the research input and trends in STEM education in the USA, with possible implications for developing STEM education research in other education systems around the world.
The rapid development of science, technology, engineering, and mathematics (STEM) education and research since the beginning of this century has benefited from strong, ongoing support from many different entities, including government agencies, professional organizations, industries, and education institutions (Li, 2014 ). Typically, studies that summarized the status of research in STEM education have used publications as the unit of their analyses (e.g., Li et al., 2019 ; Li et al., 2020 ; Margot & Kettler, 2019 ; Minichiello et al., 2018 ; Otten, Van den Heuvel-Panhuizen, & Veldhuis, 2019 ; Schreffler et al., 2019 ). Another approach, which has been used less frequently, is to study research funding. Although not all research publications were generated from funded projects and not all funded projects have been equally productive, as measured by publications, research funding and publications present two different, but related perspectives on the state of research in STEM education. Our review focuses on research funding.
There are different types of sources and mechanisms in place to allocate, administer, distribute, and manage funding support to education. In general, there are two sources of funding: public and private.
Public funding sources are commonly government agencies that support education program development and training, project evaluation, and research. For example, multiple state and federal agencies in the USA provide and manage funding support to education research, programs and training, including the US Department of Education (ED), the National Science Foundation (NSF), and the National Endowment for the Humanities—Division of Education Programs. Researchers seeking support from public funding sources often submit proposals that are vetted through a well-structured peer-review process. The process is competitive, and the decision to fund a project validates both its importance and alignment with the funding agency’s development agenda. Changes in the agencies’ agendas and funding priorities can reflect governmental intentions and priorities for education and research.
Private funding sources have played a very important role in supporting education programs and research with a long history. Some private funding sources in the USA can be sizeable, such as the Bill & Melinda Gates Foundation ( https://www.gatesfoundation.org ), while many also have specific foci, such as the Howard Hughes Medical Institute ( https://www.hhmi.org ) that is dedicated to advancing science through research and science education. At the same time, private funding sources often have their own development agendas, flexibility in deciding funding priorities, and specific mechanisms in making funding decisions, including how funds can be used, distributed, and managed. Indeed, private funding sources differ from public funding sources in many ways. Given many special features associated with private funding sources, including the lack of transparency, we chose to examine projects that were supported by public funding sources in this review.
One approach to studying public research funding support to STEM education would be to examine requests-for-proposals (RFPs) issued by different government agencies. However, those RFPs tend to provide guidelines, which are not sufficiently concrete to learn about specific research that is funded. In contrast, reviewing those projects selected for funding can provide more detailed information on research activity. Figure 1 shows a flowchart of research activity and distinguishes how funded projects and publications might provide different perspectives on research. In this review, we focus on the bolded portion of the flowchart, i.e., projects funded to promote STEM education.
A general flowchart of RFPs to publications
Why focus on research funding in the usa.
Recent reviews of journal publications in STEM education have consistently revealed that scholars in the USA played a leading role in producing and promoting scholarship in STEM education, with about 75% of authorship credits for all publications in STEM education either in the International Journal of STEM Education alone from 2014 to 2018 (Li et al., 2019 ) or in 36 selected journals published from 2000 to 2018 (Li et al., 2020 ). The strong scholarship development in the USA is likely due to a research environment that is well supported and conducive to high research output. Studying public funding support for STEM education research in the USA will provide information on trends and patterns, which will be valuable both in the USA and in other countries.
The tremendous development of STEM education in the USA over the past decades has benefited greatly from both national policies and strong funding support from the US governmental agencies as well as private funding sources. Federal funding for research and development in science, mathematics, technology, and engineering-related education in the USA was restarted in the late 1980s, in the latter years of the Reagan administration, which had earlier halted funding. In recent years, the federal government has strongly supported STEM education research and development. For example, the Obama administration in the USA (The White House, 2009 ) launched the “Educate to Innovate” campaign in November 2009 for excellence in STEM education as a national priority, with over 260 million USD in financial and in-kind support commitment. The Trump administration has continued to emphasize STEM education. For example, President Trump signed a memorandum in 2017 to direct ED to spend 200 million USD per year on competitive grants promoting STEM (The White House, 2017 ). In response, ED awarded 279 million USD in STEM discretionary grants in Fiscal Year 2018 (US Department of Education, 2018 ). The Trump administration took a step further to release a report in December 2018 detailing its five-year strategic plan of boosting STEM education in the USA (The White House, 2018 ). The strategic plan envisions that “All Americans will have lifelong access to high-quality STEM education and the USA will be the global leader in STEM literacy, innovation, and employment.” (Committee on STEM Education, 2018 , p. 1). Consistently, current Secretory of Education DeVos in the Trump administration has taken STEM as a centerpiece of her comprehensive education agenda (see https://www.ed.gov/stem ). The consistency in national policies and public funding support shows that STEM education continues to be a strategic priority in the USA.
Among many federal agencies that funded STEM education programs, the ED and NSF have functioned as two primary agencies. For ED, the Institute of Education Sciences (Institute of Education Sciences (IES), n.d. , see https://ies.ed.gov/aboutus/ ) was created by the Education Sciences Reform Act of 2002 as its statistics, research, and evaluation arm. ED’s support to STEM education research has been mainly administered and managed by IES since 2003. In contrast to the focus of ED on education, NSF (see https://www.nsf.gov/about/ ) was created by Congress in 1950 to support basic research in many fields such as mathematics, computer sciences, and social sciences. Education and Human Resources is one of its seven directorates that provides important funding support to STEM education programs and research. In addition to these two federal agencies, some other federal agencies also provide funding support to STEM education programs and research from time to time.
Any study of public funding support to STEM education research in the USA would need to limit its scope, given the complexity of various public funding sources available in the system, the ambiguity associated with the meaning of STEM education across different federal agencies (Li et al., 2020 ), and the number of programs that have funded STEM education research over the years. For the purpose of this review, we have chosen to focus on the projects in STEM education funded by IES.
Given the preceding research approach decision to focus on research projects funded by IES, we generated the following questions:
What were the number of projects, total project funding, and the average funding per project from 2003 to 2019 in STEM education research?
What were the trends of having single versus multiple principal investigator(s) in STEM education?
What were the types of awardees of the projects?
What were the participant populations in the projects?
What were the types of projects in terms of goals for program development and research in STEM education?
What were the disciplinary foci of the projects?
What research methods did projects tend to use in conducting STEM education research?
Based on the above discussion to focus on funding support from IES, we first specified the time period, and then searched the IES website to identify STEM education research projects funded by IES within the specified time period.
As discussed above, IES was established in 2002 and it did not start to administer and manage research funding support for ED until 2003. Therefore, we considered IES funded projects from 2003 to the end of 2019.
Given the diverse perspectives about STEM education across different agencies and researchers (Li et al., 2020 ), we did not discuss and define the meaning of STEM education. Instead, we used the process described in the following paragraph to identify STEM education research projects funded by IES.
On the publicly accessible IES website ( https://ies.ed.gov ), one menu item is “FUNDING OPPORTUNITIES”, and there is a list of choices within this menu item. One choice is “SEARCH FUNDED RESEARCH GRANTS AND CONTRACTS.” On this web search page, we can choose “Program” under “ADDITIONAL SEARCH OPTIONS.” There are two program categories related to STEM under the option of “Program.” One is “Science, Technology, Engineering, and Mathematics (STEM) Education” under one large category of “Education Research” and the other is “Science, Technology, Engineering, and Mathematics” under another large category of “Special Education Research.” We searched for funded projects under these two program categories, and the process returned 98 funded projects in “Science, Technology, Engineering, and Mathematics (STEM) Education” under “Education Research” and 29 funded projects in “Science, Technology, Engineering, and Mathematics” under “Special Education Research,” for a total of 127 funded projects in these two programs designated for STEM education by IES Footnote 1 .
To address questions 1, 2, 3, and 4, we collected the following information about these projects identified using above procedure: amount of funding, years of duration, information about the PI, types of awardees that received and administered the funding (i.e., university versus those non-university including non-profit organization such as WestEd, Educational Testing Service), and projects’ foci on school level and participants. When a project’s coverage went beyond one category, the project was then coded in terms of its actual number of categories being covered. For example, we used the five categories to classify project’s participants: Pre–K, grades 1–4, grades 5–8, grades 9–12, and adult. If a funded project involved participants from Pre-school to grade 8, then we coded the project as having participants in three categories: Pre-K, grades 1–4, and grades 5–8.
To address question 5, we analyzed projects based on goal classifications from IES. IES followed the classification of research types that was produced through a joint effort between IES and NSF in 2013 (Institute of Education Sciences (IES) and National Science Foundation (NSF), 2013 ). The effort specified six types of research that provide guidance on the goals and level of funding support: foundational research, early-stage or exploratory research, design and development research, efficacy research, effectiveness research, and scale-up research. Related to these types, IES classified goals for funded projects: development and innovation, efficacy and replication, exploration, measurement, and scale-up evaluation, as described on the IES website.
To address question 6, we coded the disciplinary focus using the following five categories: mathematics, science, technology, engineering, and integrated (meaning an integration of any two or more of STEM disciplines). In some cases, we coded a project with multiple disciplinary foci into more than one category. The following are two project examples and how we coded them in terms of disciplinary foci:
The project of “A Randomized Controlled Study of the Effects of Intelligent Online Chemistry Tutors in Urban California School Districts” (2008, https://ies.ed.gov/funding/grantsearch/details.asp?ID=601 ) was to test the efficacy of the Quantum Chemistry Tutors, a suite of computer-based cognitive tutors that are designed to give individual tutoring to high school students on 12 chemistry topics. Therefore, we coded this project as having three categories of disciplinary foci: science because it was chemistry, technology because it applied instructional technology, and integrated because it integrated two or more of STEM disciplines.
The project of “Applications of Intelligent Tutoring Systems (ITS) to Improve the Skill Levels of Students with Deficiencies in Mathematics” (2009, https://ies.ed.gov/funding/grantsearch/details.asp?ID=827 ) was coded as having three categories of disciplinary foci: mathematics, technology because it used intelligent tutoring systems, and integrated because it integrated two or more of STEM disciplines.
To address question 7, all 127 projects were coded using a classification category system developed and used in a previous study (Wang et al., 2019 ). Specifically, each funded project was coded in terms of research type (experimental, interventional, longitudinal, single case, correlational) Footnote 2 , data collection method (interview, survey, observation, researcher designed tests, standardized tests, computer data Footnote 3 ), and data analysis method (descriptive statistics, ANOVA*, general regression, HLM, IRT, SEM, others) Footnote 4 . Based on a project description, specific method(s) were identified and coded following a procedure similar to what we used in a previous study (Wang et al., 2019 ). Two researchers coded each project’s description, and the agreement between them for all 127 projects was 88.2%. When method and disciplinary focus-coding discrepancies occurred, a final decision was reached after discussion.
In the following sections, we report findings as corresponding to each of the seven research questions.
Figure 2 shows the distribution of funded projects over the years in each of the two program categories, “Education Research” and “Special Education Research,” as well as combined (i.e., “STEM” for projects funded under “Education Research,” “Special STEM” for projects funded under “Special Education Research,” and “Combined” for projects funded under both “Education Research” and “Special Education Research”). As Fig. 2 shows, the number of projects increased each year up to 2007, with STEM education projects started in 2003 under “Education Research” and in 2006 under “Special Education Research.” The number of projects in STEM under “Special Education Research” was generally less than those funded under the program category of “Education Research,” especially before 2011. There are noticeable decreases in combined project counts from 2009 to 2011 and from 2012 to 2014, before the number count increased again in 2015. We did not find a consistent pattern across the years from 2003 to 2019.
The distribution of STEM education projects over the years. (Note: STEM refers to projects funded under “Education Research,” Special STEM refers to projects funded under “Special Education Research,” and “Combined” refers to projects funded under both “Education Research” and “Special Education Research.” The same annotations are used in the rest of the figures.)
A similar trend can be observed in the total funding amount for STEM education research (see Fig. 3 ). The figure shows noticeably big year-to-year swings from 2003 to 2019, with the highest funding amount of more than 33 million USD in 2007 and the lowest amount of 2,698,900 USD in 2013 from these two program categories. Although it is possible that insufficient high-quality grant proposals were available in one particular year to receive funding, the funded amount and the number of projects (Fig. 2 ) provide insights about funding trends over the time period of the review.
Annual funding totals
As there are diverse perspectives and foci about STEM education, we also wondered if STEM education research projects might be funded by IES but in program options other than those designated options of “Science, Technology, Engineering, and Mathematics (STEM) Education.” We found a total of 54 funded projects from 2007 to 2019, using the acronym “STEM” as a search term under the option of “SEARCH FUNDED RESEARCH GRANTS AND CONTRACTS” without any program category restriction. Only 2 (3.7%) out of these 54 projects were in the IES designated program options of STEM education in the category of “Education Research.” Further information about these 54 projects and related discussion can be found as additional notes at the end of this review.
Results from two different approaches to searching for IES-funded projects will likely raise questions about what kinds of projects were funded in the designated program option of “Science, Technology, Engineering, and Mathematics (STEM) Education,” if only two funded projects under this option contained the acronym “STEM” in a project’s title and/or description. We shall provide further information in the following sub-sections, especially when answering question 6 related to projects’ disciplinary focus.
Figure 4 illustrates the trend of average funding amount per project each year in STEM education research from 2003 to 2019. The average funding per project varied considerably in the program category “Special Education Research,” and no STEM projects were funded in 2014 and 2017 in this category. In contrast, average funding per project was generally within the range of 1,132,738 USD in 2019 to 3,475,975 USD in 2014 for the projects in the category of “Education Research” and also for project funding in the combined category.
The trend of average funding amount per project funded each year in STEM education research
Figure 5 shows the number of projects in different funding amount categories (i.e., less than 1 million USD, 1–2 million USD, 2–3 million USD, 3 million USD or more). The majority of the 127 projects obtained funding of 1–2 million USD (77 projects, 60.6%), with 60 out of 98 projects (61.2%) under “Education Research” program and 17 out of 29 projects (58.6%) in the program category “Special Education Research.” The category with second most projects is funding of 3 million USD or more (21 projects, 16.5%), with 15 projects (15.3% of 98 projects) under “Education Research” and 6 projects (20.7% of 29 projects) under “Special Education Research.”
The number of projects in terms of total funding amount categories
Figure 6 shows the average amount of funding per project funded across these different funding amount and program categories. In general, the projects funded under “Education Research” tended to have a higher average amount than those funded under “Special Education Research,” except for those projects in the total funding amount category of “less than 1 million USD.” Considering all 127 funded projects, the average amount of funding was 1,960,826.3 USD per project.
The average amount of funding per project across different total funding amount and program categories
Figure 7 shows that the vast majority of these 127 projects were 3- or 4-year projects. In particular, 59 (46.5%) projects were funded as 4-year projects, with 46 projects (46.9%) under “Education Research” and 13 projects (44.8%) under “Special Education Research.” This category is followed closely by 3-year projects (54 projects, 42.5%), with 41 projects (41.8%) under “Education Research” and 13 projects (44.8%) under “Special Education Research.”
The number of projects in terms of years of project duration. (Note, 2: 2-year projects; 3: 3-year projects; 4: 4-year projects; 5: 5-year projects)
Figure 8 shows the distribution of projects over the years grouped by a single PI or multiple PIs where the program categories of “Education Research” and “Special Education Research” have been combined. The majority of projects before 2009 had a single PI, and the trend has been to have multiple PIs for STEM education research projects since 2009. The trend illustrates the increased emphases on collaboration in STEM education research, which is consistent with what we learned from a recent study of journal publications in STEM education (Li et al., 2020 ).
The distribution of projects with single versus multiple PIs over the years (combined)
Separating projects by program categories, Fig. 9 shows projects funded in the program category “Education Research.” The trends of single versus multiple PIs in Fig. 9 are similar to the trends shown in Fig. 8 for the combined programs. In addition, almost all projects in STEM education funded under this regular research program had multiple PIs since 2010.
The distribution of projects with single versus multiple PIs over the years (in “Education Research” program)
Figure 10 shows projects funded in the category “Special Education Research.” The pattern in Fig. 10 , where very few projects funded under this category had multiple PIs before 2014, is quite different from the patterns in Figs. 8 and 9 . We did not learn if single PIs were appropriate for the nature of these projects. The trend started to change in 2015 as the number of projects with multiple PIs increased and the number of projects with single PIs declined.
The distribution of projects with single versus multiple PIs over the years (in “Special Education Research” program)
Besides the information about the project’s PI, the nature of the awardees can help illustrate what types of entity or organization were interested in developing and carrying out STEM education research. Figure 11 shows that the university was the main type of awardee before 2012, with 80 (63.0%) projects awarded to universities from 2003 to 2019. At the same time, non-university entities received funding support for 47 (37.0%) projects and they seem to have become even more active and successful in obtaining research funding in STEM education over the past several years. The result suggests that diverse organizations develop and conduct STEM education research, another indicator of the importance of STEM education research.
The distribution of projects funded to university versus non-university awardees over the years
Figure 12 indicates that the vast majority of projects were focused on student populations in preschool to grade 12. This is understandable as IES is the research funding arm of ED. Among those projects, middle school students were the participants in the most projects (70 projects), followed by student populations in elementary school (48 projects), and high school (38 projects). The adult population (including post-secondary students and teachers) was the participant group in 36 projects in a combined program count.
The number of projects in STEM education for different groups of participants (Note: Pre-K: preschool-kindergarten; G1–4: grades 1–4; G5–8: grades 5–8; G9–12: grades 9–12; adult: post-secondary students and teachers)
If we separate “Education Research” and “Special Education Research” programs, projects in the category “Special Education Research” focused on student populations in elementary and middle school most frequently, and then adult population. In contrast, projects in the category “Education Research” focused most frequently on middle school student population, followed by student populations in high school and elementary school.
Given the importance of funded research in special education Footnote 5 at IES, we considered projects focused on participants with disabilities. Figure 13 shows there were 28 projects in the category “Special Education Research” for participants with disabilities. There were also three such projects funded in the category “Education Research,” which together accounted for a total of 31 (24.4%) projects. In addition, some projects in the category “Education Research” focused on other participants, including 11 projects focused on ELL students (8.7%) projects and 37 projects focused on low SES students (29.1%).
The number of funded projects in STEM education for three special participant populations (Note: ELL: English language learners, Low SES: low social-economic status)
Figure 14 shows the trend of projects in STEM education for special participant populations. Participant populations with ELL and/or Low SES gained much attention before 2011 among these projects. Participant populations with disabilities received relatively consistent attention in projects on STEM education over the years. Research on STEM education with special participant populations is important and much needed. However, related scholarship is still in an early development stage. Interested readers can find related publications in this journal (e.g., Schreffler et al., 2019 ) and other journals (e.g., Lee, 2014 ).
The distribution of projects in STEM education for special participant populations over the years
Figure 15 shows that “development and innovation” was the most frequently funded type of project (58 projects, 45.7%), followed by “efficacy and replication” (34 projects, 26.8%), and “measurement” (21 projects, 16.5%). The pattern is consistent across “Education Research,” “Special Education Research,” and combined. However, it should be noted that all five projects with the goal of “scale-up evaluation” were in the category “Education Research” Footnote 6 and funding for these projects were large.
The number of projects in terms of the types of goals
Examining the types of projects longitudinally, Fig. 16 shows that while “development and innovation” and “efficacy and replication” types of projects were most frequently funded in the “Education Research” program, the types of projects being funded changed longitudinally. The number of “development and innovation” projects was noticeably fewer over the past several years. In contrast, the number of “measurement” projects and “efficacy and replication” projects became more dominant. The change might reflect a shift in research development and needs.
The distribution of projects in terms of the type of goals over the years (in “Education Research” program)
Figure 17 shows the distribution of project types in the category “Special Education Research.” The pattern is different from the pattern shown in Fig. 16 . The types of “development and innovation” and “efficacy and replication” projects were also the dominant types of projects under “Special Education Research” program category in most of these years from 2007 to 2019. Projects in the type “measurement” were only observed in 2010 when that was the only type of project funded.
The distribution of projects in terms of goals over the years (in “Special Education Research” program)
Figure 18 shows that the majority of the 127 projects under such specific programs included disciplinary foci on individual STEM disciplines: mathematics in 88 projects, science in 51 projects, technology in 43 projects, and engineering in 2 projects. The tremendous attention to mathematics in these projects is a bit surprising, as mathematics was noted as being out of balance in STEM education (English, 2016 ) and also in STEM education publications (Li, 2018b , 2019 ). As noted above, each project can be classified in multiple disciplinary foci. However, of the 88 projects with a disciplinary focus on mathematics, 54 projects had mathematics as the only disciplinary focus (38 under “Education Research” program and 16 under “Special Education Research” program). We certainly hope that there will be more projects that further scholarship where mathematics is included as part of (integrated) STEM education (see Li & Schoenfeld, 2019 ).
The number of projects in terms of disciplinary focus
There were also projects with specific focus on integrated STEM education (i.e., combining any two or more disciplines of STEM), with a total of 55 (43.3%) projects in a combined program count. The limited number of projects on integrated STEM in the designated STEM funding programs further confirms the common perception that the development of integrated STEM education and research is still in its initial stage (Honey et al., 2014 ; Li, 2018a ).
In examining possible funding trends, Fig. 19 shows that mathematics projects were more frequently funded before 2012. Engineering was a rare disciplinary focus. Integrated STEM was a disciplinary focus from time to time among these projects. No other trends were observed.
The distribution of projects in terms of disciplinary focus over the years
Figure 20 indicates that “interventional” (in 104 projects, 81.9%) and “experimental research” (in 89 projects, 70.1%) were the most frequently funded types of research. The percentages of projects funded under the regular education research program were similar to those funded under “Special Education Research” program, except that projects funded under “Special Education Research” tended to utilize correlational research more often.
The number of projects in terms of the type of research conducted
Research in STEM education uses diverse data collection and analysis methods; therefore, we wanted to study types of methods (Figs. 21 and 22 , respectively). Among the six types of methods used for data collection, Fig. 21 indicates that “standardized tests” and “designed tests” were the most commonly used methods for data collection, followed by “survey,” “observation,” and “interview.” The majority of projects used three quantitative methods (“standardized tests,” “researcher designed tests,” and “survey”). The finding is consistent with the finding from analysis of journal publications in STEM education (Li et al., 2020 ). Data collected through “interview” and “observation” were more likely to be analyzed using qualitative methods as part of a project’s research methodology.
The number of projects categorized by the type of data collection methods
The number of projects categorized by the type of data analysis methods
Figure 22 shows the use of seven (including others) data analysis methods among these projects. The first six methods (i.e., descriptive, ANOVA*, general regression, HLM, IRT, and SEM) as well as some methods in “others” are quantitative data analysis methods. The number of projects that used these quantitative methods is considerably larger than the number of projects that used qualitative methods (i.e., included in “others” category).
The systematic analysis of IES-funded research projects in STEM education presented an informative picture about research support for STEM education development in the USA, albeit based on only one public funding agency from 2003 to 2019. Over this 17-year span, IES funded 127 STEM education research projects (an average of over seven projects per year) in two designated STEM program categories. Although we found no discernable longitudinal funding patterns in these two program categories, both the number of funded projects in STEM education and their funding amounts were high. If we included an additional 52 projects with the acronym “STEM” funded by many other programs from 2007 to 2019 (see “ Notes ” section below), the total number of projects in STEM education research would be even higher, and the number of projects with the acronym “STEM” would also be larger. The results suggested the involvement of many researchers with diverse expertise in STEM education research was supported by a broad array of program areas in IES.
Addressing the seven questions showed several findings. Funding support for STEM education research was strong, with an average of about 2 million USD per project for a typical 3–4 year duration. Also, our analysis showed that the number of projects with multiple PIs over the years increased over the study time period, which we speculate was because STEM education research increasingly requires collaboration. STEM education research is still in early development stage, evidenced by the predominance of project goals in either “development and innovation” or “efficacy and replication” categories. We found very few projects (5 out of 127 projects, 4.0%) that were funded for “scale-up evaluation.” Finally, as shown by our analysis of project participants, IES had focused on funding projects for students in grades 1–12. Various quantitative research methods were frequently used by these projects for data collection and analyses.
These results illustrated how well STEM education research was supported through both the designated STEM education and many other programs during the study time period, which helps to explain why researchers in the USA have been so productive in producing and promoting scholarship in STEM education (Li et al., 2019 ; Li et al., 2020 ). We connected several findings from this study to findings from recent reviews of journal publications in STEM education. For example, publications in STEM education appeared in many different journals as many researchers with diverse expertise were supported to study various issues related to STEM education, STEM education publications often have co-authorship, and there is heavy use of quantitative research methods. The link between public funding and significant numbers of publications in STEM education research from US scholars offers a strong argument for the importance of providing strong funding support to research and development in STEM education in the USA and also in many other countries around the world.
The systematic analysis also revealed that STEM education, as used by IES in naming the designated programs, did not convey a clear definition or scope. In fact, we found diverse disciplinary foci in these projects. Integrated STEM was not a main focus of these designated programs in funding STEM education. Instead, many projects in these programs had clear subject content focus in individual disciplines, which is very similar to discipline-based education research (DBER, National Research Council, 2012 ). Interestingly enough, STEM education research had also been supported in many other programs of IES with diverse foci Footnote 7 , such as “Small Business Innovation Research,” “Cognition and Student Learning,” and “Postsecondary and Adult Education.” This funding reality further suggested the broad scope of issues associated with STEM education, as well as the growing need of building STEM education research as a distinct field (Li, 2018a ).
Inspired by our recent review of journal publications as research output in STEM education, this review started with an ambitious goal to study funding support as research input for STEM education. However, we had to limit the scope of the study for feasibility. We limited funding sources to one federal agency in the USA. Therefore, we did not analyze funding support from private funding sources including many private foundations and corporations. Although public funding sources have been one of the most important funding supports available for researchers to develop and expand their research work, the results of this systematic analysis suggest the importance future studies to learn more about research support and input to STEM education from other sources including other major public funding agencies, private foundations, and non-profit professional organizations.
Among these 54 funded projects containing the acronym “STEM” from 2007 to 2019, Table 1 shows that only 2 (3.7%) were in the IES designated program option of STEM education in the category of “Education Research.” Forty-nine projects were in 13 other program options in the category of “Education Research,” with surprisingly large numbers of projects under the “Small Business Innovation Research” option (17, 31.5%) and “Cognition and Student Learning” (11, 20.4%). Three of the 54 funded projects were in the program category of “Special Education Research.” To be specific, two of the three were in the program of “Small Business Innovation Research in Special Education,” and one was in the program of “Special Topic: Career and Technical Education for Students with Disabilities.”
The results suggest that many projects, focusing on various issues and questions directly associated with STEM education, were funded even when researchers applied for funding support in program options not designated as “Science, Technology, Engineering, and Mathematics (STEM) Education.” It implies that issues associated with STEM education had been generally acknowledged as important across many different program areas in education research and special education research. The funding support available in diverse program areas likely allowed numerous scholars with diverse expertise to study many different questions and publish their research in diverse journals, as we noted in the recent review of journal publications in STEM education (Li et al., 2020 ).
A previous study identified and analyzed a total of 46 IES funded projects from 2007 to 2018 (with an average of fewer than 4 projects per year) that contain the acronym “STEM” in a project’s title and/or description (Wang et al., 2019 ). Finding eight newly funded projects in 2019 suggested a growing interest in research on issues directly associated with STEM education in diverse program areas. In fact, five out of these eight newly funded projects specifically included the acronym “STEM” in the project’s title to explicitly indicate the project’s association with STEM education.
The data and materials used and analyzed for the review are publicly available at the IES website, White House website, and other government agency websites.
In a previous study (Wang, Li, & Xiao, 2019), we used the acronym “STEM” as a search term under the option of “SEARCH FUNDED RESEARCH GRANTS AND CONTRACTS” without any program category restriction, and identified and analyzed 46 funded projects from 2007 to 2018 that contain “STEM” in a project’s title and/or description after screening out unrelated key words containing “stem” such as “system”. To make comparisons when needed, we did the same search using the acronym “STEM” and found 8 more funded projects in 2019 for a total of 54 funded projects across many different program categories from 2007 to 2019.
The project of “A Randomized Controlled Study of the Effects of Intelligent Online Chemistry Tutors in Urban California School Districts” (2008). In the project description, its subtitle shows intervention information. We coded this project as “interventional.” Then, the project also included the treatment group and the control group. We coded this project as “experimental.” Finally, this project was to test the efficacy of computer-based cognitive tutors. This was a correlational study. We thus coded it as “correlational.”
Computer data means that the project description indicated this kind of information, such as log data on students.
Descriptive means “descriptive statistics.” General regression means multiple regression, linear regression, logistical regression, except hierarchical linear regression model. ANOVA* is used here as a broad term to include analysis of variance, analysis of covariance, multivariate analysis of variance, and/or multivariate analysis of variance. Others include factor analysis, t tests, Mann-Whitney tests, and binomial tests, log data analysis, meta-analysis, constant comparative data analysis, and qualitative analysis.
Special education originally was about students with disabilities. It has broadened in scope over the years.
The number of students under Special Education was 14% of students in public schools in the USA in 2017–2018. https://nces.ed.gov/programs/coe/indicator_cgg.asp
For example, “Design Environment for Educator-Student Collaboration Allowing Real-Time Engineering-centric, STEM (DESCARTES) Exploration in Middle Grades” (2017) was funded as a 2-year project to Parametric Studios, Inc. (awardee) under the program option of “Small Business Innovation Research” (here is the link: https://ies.ed.gov/funding/grantsearch/details.asp?ID=1922 ). “Exploring the Spatial Alignment Hypothesis in STEM Learning Environments” (2017) was funded as a 4-year project to WestEd (awardee) under the program option of “Cognition and Student Learning” (link: https://ies.ed.gov/funding/grantsearch/details.asp?ID=2059 ). “Enhancing Undergraduate STEM Education by Integrating Mobile Learning Technologies with Natural Language Processing” (2018) was funded as a 4-year project to Purdue University (awardee) under the program option of “Postsecondary and Adult Education” (link: https://ies.ed.gov/funding/grantsearch/details.asp?ID=2130 ).
Analysis of variance
Discipline-based education research
Department of Education
Hierarchical linear modeling
Institute of Education Sciences
Item response theory
National Science Foundation
Pre-school–grade 12
Requests-for-proposal
Structural equation modeling
Science, technology, engineering, and mathematics
Committee on STEM Education, National Science & Technology Council, the White House (2018). Charting a course for success: America’s strategy for STEM education . Washington, DC. https://www.whitehouse.gov/wp-content/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf Accessed on 18 Jan 2019.
English, L. D. (2016). STEM education K-12: perspectives on integration. International Journal of STEM Education, 3 , 3 https://doi.org/10.1186/s40594-016-0036-1 .
Article Google Scholar
Honey, M., Pearson, G., & Schweingruber, A. (2014). STEM integration in K-12 education: status, prospects, and an agenda for research . Washington DC: National Academies Press.
Google Scholar
Institute of Education Sciences (IES) (n.d.). About IES: connecting research, policy and practice. Retrieved from https://ies.ed.gov/aboutus/ Accessed on 2 Feb 2020.
Institute of Education Sciences (IES) & National Science Foundation (NSF). (2013). Common guidelines for education research and development. Washington, DC: The authors. Retrieved from https://www.nsf.gov/pubs/2013/nsf13126/nsf13126.pdf Accessed on 2 Feb 2020.
Lee, A. (2014). Students with disabilities choosing science technology engineering and math (STEM) majors in postsecondary institutions. Journal of Postsecondary Education and Disability, 27 (3), 261–272.
Li, Y. (2014). International journal of STEM education – a platform to promote STEM education and research worldwide. International Journal of STEM Education, 1 , 1 https://doi.org/10.1186/2196-7822-1-1 .
Li, Y. (2018a). Journal for STEM Education Research – promoting the development of interdisciplinary research in STEM education. Journal for STEM Education Research, 1 (1-2), 1–6 https://doi.org/10.1007/s41979-018-0009-z .
Li, Y. (2018b). Four years of development as a gathering place for international researchers and readers in STEM education. International Journal of STEM Education, 5 , 54 https://doi.org/10.1186/s40594-018-0153-0 .
Li, Y. (2019). Five years of development in pursuing excellence in quality and global impact to become the first journal in STEM education covered in SSCI. International Journal of STEM Education, 6 , 42 https://doi.org/10.1186/s40594-019-0198-8 .
Li, Y., Froyd, J. E., & Wang, K. (2019). Learning about research and readership development in STEM education: a systematic analysis of the journal’s publications from 2014 to 2018. International Journal of STEM Education, 6 , 19 https://doi.org/10.1186/s40594-019-0176-1 .
Li, Y., & Schoenfeld, A. H. (2019). Problematizing teaching and learning mathematics as ‘given’ in STEM education. International Journal of STEM Education, 6 , 44 https://doi.org/10.1186/s40594-019-0197-9 .
Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020). Research and trends in STEM education: a systematic review of journal publications. International Journal of STEM Education, 7 , 11 https://doi.org/10.1186/s40594-020-00207-6 .
Margot, K. C., & Kettler, T. (2019). Teachers’ perception of STEM integration and education: a systematic literature review. International Journal of STEM Education, 6 , 2 https://doi.org/10.1186/s40594-018-0151-2 .
Minichiello, A., Hood, J. R., & Harkness, D. S. (2018). Bring user experience design to bear on STEM education: a narrative literature review. Journal for STEM Education Research, 1 (1-2), 7–33.
National Research Council. (2012). Discipline-based education research: understanding and improving learning in undergraduate science and engineering . Washington DC: National Academies Press.
Otten, M., Van den Heuvel-Panhuizen, M., & Veldhuis, M. (2019). The balance model for teaching linear equations: a systematic literature review. International Journal of STEM Education, 6 , 30 https://doi.org/10.1186/s40594-019-0183-2 .
Schreffler, J., Vasquez III, E., Chini, J., & James, W. (2019). Universal design for learning in postsecondary STEM education for students with disabilities: a systematic literature review. International Journal of STEM Education, 6 , 8 https://doi.org/10.1186/s40594-019-0161-8 .
The White House (2009). President Obama launches “Educate to Innovate” campaign for excellence in science, technology, engineering & math (Stem) education. Retrieved from https://obamawhitehouse.archives.gov/the-press-office/president-obama-launches-educate-innovate-campaign-excellence-science-technology-en Accessed on 2 Feb 2020.
The White House (2017). Presidential memorandum for the secretary of Education. Retrieved from https://www.whitehouse.gov/presidential-actions/presidential-memorandum-secretary-education/ Accessed on 2 Feb 2020.
The White House (2018). President Donald J. Trump is working to ensure all Americans have access to STEM education. Retrieved from https://www.whitehouse.gov/briefings-statements/president-donald-j-trump-is-working-to-ensure-all-americans-have-access-to-stem-education/ Accessed on 2 Feb 2020.
U.S. Department of Education (2018). U.S. Department of Education fulfills administration promise to invest $200 million in STEM education. Retrieved from https://www.ed.gov/news/press-releases/us-department-education-fulfills-administration-promise-invest-200-million-stem-education Accessed on 2 Feb 2020.
Wang, K., Li, Y., & Xiao, Y. (2019). Exploring the status and development trends of STEM education research: the case of IES funded projects on STEM education in the U.S. 数学教育学报 . Journal of Mathematics Education, 28 (3), 53–61.
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This review was supported by a grant from the National Science Foundation (DUE-1852942). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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Yeping Li, Yu Xiao & Sandra B. Nite
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YL conceptualized the study and drafted the manuscript. KW contributed with data collection, coding, analyses, and manuscript reviews. YX contributed to data collection, coding, and manuscript reviews. JEF and SBN contributed to manuscript improvement through manuscript reviews and revisions. All authors read and approved the final manuscript.
Correspondence to Yeping Li .
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Li, Y., Wang, K., Xiao, Y. et al. Research and trends in STEM education: a systematic analysis of publicly funded projects. IJ STEM Ed 7 , 17 (2020). https://doi.org/10.1186/s40594-020-00213-8
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DOI : https://doi.org/10.1186/s40594-020-00213-8
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“Did you know only 28% of college graduates in the Philippines get degrees in STEM fields? Finding good research topics is vital to getting more Filipino students curious about quantitative studies.
With limited research money and resources, it can be hard for STEM students to find quantitative projects that are possible, new, and impactful. Often, researchers end up feeling apart from local issues and communities.
This blog post offers a unique collection of quantitative research topics for STEM students in the Philippines. Thus, drawing from current events, social issues, and the country’s needs, these project ideas will feel relevant and help students do research that creates positive change.
Philippines students can find inspiration for quantitative studies that make a difference at home through many examples across science, technology, engineering, and math.
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Here are the top quantitative research topics for STEM students in the Philippines in 2024
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Analyze how changing weather affects the growth of crops like rice and corn in different parts of the Philippines. Use numbers to find ways and suggest ways farmers can adapt.
2. Using Drones to Watch Nature
See how well drones with special sensors can watch over forests and coasts in the Philippines. Look at the data they gather to figure out how to save these places.
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Experiment with various ways to make more power with solar panels in sunny, humid places like the Philippines. Utilize math to guess how well they’ll work.
4. Checking How Pollution Hurts Coral Reefs
Count how much damage pollution does to coral reefs in the Philippines. Try to predict how bad it’ll get if we don’t stop polluting.
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Look at how cars move in big cities like Manila. Use math to figure out how to make traffic flow better and help people get around faster.
6. at Air and Sick People
Measure how clean the air is in various parts of the Philippines and see if it affects how many people get sick. Find out which areas need help to stay healthy.
7. Guessing When Earthquakes Might Happen
Look at data from sensors all over the Philippines to see if we can tell when earthquakes might come. Try to guess where they’ll occur next.
8. Making Water Pipes Better
Use math tricks to design cheap pipes that bring clean water to small towns in the Philippines. Think about things like hills and how many people need water.
9. Checking If Planting Trees Helps
Measure if planting trees helps stop the shore from washing away during storms. Use photos from far away and math to see if it’s working.
10. Teaching Computers to Find Sickness
Teach computers to look at pictures and records from hospitals to see if people are sick. Check if they’re good at spotting problems in the Philippines.
11. Finding Better Bags That Break Down
Test different materials like banana leaves to see which ones can be made into bags that don’t hurt the environment. Compare them to regular plastic bags.
12. Making Gardens in the City
See if we can grow vegetables in tall buildings in big cities like Manila. Use numbers to figure out if it’s a good idea.
13. Checking If Bugs Spread Easily in Crowded Places
Use computers to see if diseases spread fast in busy places in the Philippines. Look at how people move around to stop diseases from spreading.
14. Storing Energy for Islands Without Power
Think about ways to save power for small islands without electricity. Try out different ways to save energy and see which one works best.
15. Seeing How Much Storms Hurt Farms
Calculate how much damage storms do to farms in the Philippines. Use numbers to see how much money farmers lose.
16. Testing Ways to Stop Dirt from Washing Away
Try out different ways to stop dirt from being washed away when it rains. Use math to see which way works best on hills in the Philippines.
17. Checking How Healthy Local Food Is
Look at the vitamins and minerals in local foods like sweet potatoes and moringa leaves. See if eating them is good for people in the Philippines.
18. Making Cheap Water Cleaners
Build simple machines that clean dirty water in small towns. Notice if they work better than expensive ones.
19. Seeing How Hot Cities Get
Use satellites to see how hot cities like Manila get compared to places with more trees. Think about how this affects people.
20. Thinking About Trash in Cities
Look at how much trash cities in the Philippines make and find ways to deal with it. Consider what people can do to make less trash.
21. Checking If We Can Use Hot Rocks for Power
Look at rocks under the ground to see if we can get power from them. Consider whether it is beneficial for the environment.
22. Counting Animals in the Forest
Use cameras to count how many animals are in forests in the Philippines. Notice which places need the most help to keep animals safe.
23. Making Fishing Fair
Look at how many fish are caught in the Philippines and see if it’s fair. Think about ways to make sure there will always be enough fish to catch.
24. Making Power Lines Smarter
Design power lines that can change how much power they use. Try to make sure power goes where it’s needed most.
25. Looking at Dirty Water
Find out if chopping down trees and building things by rivers makes the water dirty. Think about what this means for people and animals.
26. Thinking About Big Waves
Use computers to see if big waves could hit the Philippines and what might happen. Think about how to keep people safe.
27. Seeing If Parks Help Cities
Ask people if they like having parks in their city and see what animals live there. Think about if parks make cities better.
28. Making Houses That Don’t Break in Storms
Make houses that don’t fall when there are big storms. Try to make them cheap so more people can have them.
29. Stopping Food from Going Bad
Look at how food gets from farms to people’s houses and see if we can stop it from going bad. Think about how to make sure people have enough to eat.
30. Seeing How Hot Cities Get
Put machines around cities to see how hot they get. Consider how this affects people and what we can do to help.
These topics will help you to make a good project that assists you in getting better scores.
Read why quantitative research matters to Filipino students.
It’s time to see what challenges students face with their quantitative research.
Here are the common challenges that students face with their quantitative research topics:
Doing quantitative research needs access to equipment, software , datasets etc, which can be costly. Many students lack funding and access to these resources.
Quantitative research relies heavily on math and statistical skills. However, many students haven’t developed strong enough skills in these areas yet.
Students need access to academic journals and databases for literature reviews. However, these can be costly for people to access.
Many of the academic literature is in English. This can make reading and learning complex statistical concepts more difficult.
Having an experienced mentor to provide guidance is invaluable. However, not all students have access to mentorship in quantitative research.
Collecting, cleaning and analyzing large datasets requires advanced technical skills. Students may struggle without proper guidance.
Learning how to visualize and communicate statistical findings effectively is an important skill that takes practice.
Ensuring quantitative studies are designed ethically can be difficult for novice researchers.
Adopting the formal, precise writing style required in quantitative research is challenging initially.
Quantitative research is complex and time-consuming. Students may lose motivation without a strong support network.
While quantitative research presents many challenges, Philippines STEM students can overcome these through access to proper resources and support. With hard work, mentorship and collaborative opportunities, students can build essential skills and contribute to the quantitative research landscape.
When conducting research in a new cultural context like the Philippines, it is vital to take time to understand local norms and build trust. Approaching research openly and collaboratively will lead to more meaningful insights.
1. Get Required Approvals
Be sure to get any necessary ethics reviews or approvals from local governing boards before conducting the analysis. It is wise to follow proper protocols and permissions.
2. Hire Local Assistants
Hire local research helpers to help navigate logistics, translation, and cultural sensitivities. This provides jobs and insider insights.
3. Use Multiple Research Methods
Triangulate findings using interviews, focus groups, surveys, participant observation, etc. Multiple methods provide more potent and well-rounded results.
4. Verify Information
Politely verify information collected from interviews before publication. Follow up to ensure accurate representation and context.
5. Share Results
Report back to participants and communities on research findings and next steps. This shows respect and accountability for their contributions.
6. Acknowledge Limitations
Openly acknowledge the limitations of perspective and methods as an outside researcher. Remain humble and keep improving approaches.
Keep in mind, when entering a new community to conduct research, taking an open, patient, and collaborative approach leads to more ethical and meaningful results. Thus, making the effort to understand and work within cultural norms demonstrates respect.
STEM students in the Philippines have many possible research topics using numbers. They could look at renewable energy, sustainability, pollution, environment, disease prevention, farming improvements, preparing for natural disasters, building projects, transportation, and technology access.
By carefully analyzing statistics and creating mathematical models, young Filipino researchers can provide key ideas to guide future policies and programs. Quantitative research allows real observations and suggestions based on evidence to make the country better now and later.
Number-based methods help young researchers in the Philippines give tangible recommendations to improve their communities.
Think about what you enjoy and what you’re skilled at. Consider if your topic is meaningful and if you have the resources to study it. Get advice from teachers or friends to help you decide.
Problems might include: 1. Finding data. 2. Make sure your measurements are correct. 3. Following rules about ethics. 4. Handling big sets of data.
Plan your study carefully, use the correct methods and tools, write down everything you do, and think about the strengths and weaknesses of your work.
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This study explored research trends in science, technology, engineering, and mathematics (STEM) education. Descriptive analysis and co-word analysis were used to examine articles published in Social Science Citation Index journals from 2011 to 2020. From a search of the Web of Science database, a total of 761 articles were selected as target samples for analysis. A growing number of STEM-related publications were published after 2016. The most frequently used keywords in these sample papers were also identified. Further analysis identified the leading journals and most represented countries among the target articles. A series of co-word analyses were conducted to reveal word co-occurrence according to the title, keywords, and abstract. Gender moderated engagement in STEM learning and career selection. Higher education was critical in training a STEM workforce to satisfy societal requirements for STEM roles. Our findings indicated that the attention of STEM education researchers has shifted to the professional development of teachers. Discussions and potential research directions in the field are included.
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Akgunduz, D. (2016). A Research about the placement of the top thousand students placed in STEM fields in Turkey between the years 2000 and 2014. EURASIA Journal of Mathematics, Science and Technology Education, 12 (5), 1365–1377.
Google Scholar
Appianing, J., & Van Eck, R. N. (2018). Development and validation of the Value-Expectancy STEM Assessment Scale for students in higher education. International Journal of STEM Education , 5 , article 24.
Assefa, S. G., & Rorissa, A. (2013). A bibliometric mapping of the structure of STEM education using co-word analysis. Journal of the American Society for Information Science and Technology, 64 (12), 2513–2536.
Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2017). Synthesizing results from empirical research on computer-based scaffolding in STEM education: A meta-analysis. Review of Educational Research, 87 (2), 309–344.
Brotman, J. S., & Moore, F. M. (2008). Girls and science: A review of four themes in the science education literature. Journal of Research in Science Teaching, 45 (9), 971–1002.
Brown, R. E., & Bogiages, C. A. (2019). Professional development through STEM integration: How early career math and science teachers respond to experiencing integrated STEM tasks. International Journal of Science and Mathematics Education, 17 (1), 111–128.
Burt, B. A., Williams, K. L., & Palmer, G. J. M. (2019). It takes a village: The role of emic and etic adaptive strengths in the persistence of black men in engineering graduate programs. American Educational Research Journal, 56 (1), 39–74.
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22 (1), 155–205.
Carlisle, D. L. & Weaver, G. C. (2018). STEM education centers: Catalyzing the improvement of undergraduate STEM education. International Journal of STEM Education, 5 , article 47.
Chang, D. F., & ChangTzeng, H. C. (2020). Patterns of gender parity in the humanities and STEM programs: The trajectory under the expanded higher education system. Studies in Higher Education, 45 (6), 1108–1120.
Charleston, L. J. (2012). A qualitative investigation of African Americans’ decision to pursue computing science degrees: Implications for cultivating career choice and aspiration. Journal of Diversity in Higher Education, 5 (4), 222–243.
Charleston, L. J., George, P. L., Jackson, J. F. L., Berhanu, J., & Amechi, M. H. (2014). Navigating underrepresented STEM spaces: Experiences of black women in US computing science higher education programs who actualize success. Journal of Diversity in Higher Education, 7 (3), 166–176.
Chien, Y. H., & Chu, P. Y. (2018). The different learning outcomes of high school and college students on a 3D-printing STEAM engineering design curriculum. International Journal of Science and Mathematics Education, 16 (6), 1047–1064.
Dehdarirad, T., Villarroya, A., & Barrios, M. (2014). Research trends in gender differences in higher education and science: A co-word analysis. Scientometrics, 101 (1), 273–290.
Dickerson, D. L., Eckhoff, A., Stewart, C. O., Chappell, S., & Hathcock, S. (2014). The examination of a pullout STEM program for urban upper elementary students. Research in Science Education, 44 (3), 483–506.
Eccles, J., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J., & Midgley, C. (1983). Expectancies, values and academic behaviors. In J. T. Spence (Ed.), Achievement and Achievement Motives . W. San Francisco: H. Freeman.
Ellison, S., & Allen, B. (2018). Disruptive innovation, labor markets, and Big Valley STEM School: Network analysis in STEM education. Cultural Studies of Science Education, 13 (1), 267–298.
Erdogan, N., Navruz, B., Younes, R., & Capraro, R. M. (2016). Viewing how STEM project-based learning influences students’ science achievement through the implementation lens: A latent growth modeling. Eurasia Journal of Mathematics, Science and Technology Education, 12 (8), 2139–2154.
European Commission, Directorate-General for Education, Youth, Sport and Culture (2016). Does the EU need more STEM graduates? Final report . Retrieve from https://data.europa.eu/doi/10.2766/000444
Fredricks, J. A., Hofkens, T., Wang, M. T., Mortenson, E., & Scott, P. (2018). Supporting girls’ and boys’ engagement in math and science learning: A mixed methods study. Journal of Research in Science Teaching, 55 (2), 271–298.
Fry, R., Kennedy, B., & Funk, C. (2021). Stem jobs see uneven progress in increasing gender, racial and ethnic diversity. Retrieve from https://www.pewresearch.org/science/wp-content/uploads/sites/16/2021/03/PS_2021.04.01_diversity-in-STEM_REPORT.pdf
Ganley, C. M., George, C. E., Cimpian, J. R., & Makowski, M. B. (2018). Gender equity in college majors: Looking beyond the STEM/non-STEM dichotomy for answers regarding female participation. American Educational Research Journal, 55 (3), 453–487.
Gehrke, S., & Kezar, A. (2019). Perceived outcomes associated with engagement in and design of faculty communities of practice focused on STEM reform. Research in Higher Education, 60 (4), 844–869.
Gilmore, J., Vieyra, M., Timmerman, B., Feldon, D., & Maher, M. (2015). The relationship between undergraduate research participation and subsequent research performance of early career STEM graduate students. Journal of Higher Education, 86 (6), 834–863.
Godwin, A., Potvin, G., Hazari, Z., & Lock, R. (2016). Identity, critical agency, and engineering: An affective model for predicting engineering as a career choice. Journal of Engineering Education, 105 (2), 312–340.
Han, S., Yalvac, B., Capraro, M. M., & Capraro, R. M. (2015). In-service teachers’ implementation and understanding of STEM project based learning. Eurasia Journal of Mathematics Science and Technology Education, 11 (1), 63–76.
Heras, M., Ruiz-Mallén, I., & Gallois, S. (2020). Staging science with young people: Bringing science closer to students through stand-up comedy. International Journal of Science Education, 42 (12), 1968–1987.
Hernandez, P. R., Estrada, M., Woodcock, A., & Schultz, P. W. (2017). Protégé perceptions of high mentorship quality depend on shared values more than on demographic match. Journal of Experimental Education, 85 (3), 450–468.
Hinojo Lucena, F. J., Lopez Belmonte, J., Fuentes Cabrera, A., Trujillo Torres, J. M., & Pozo Sanchez, S. (2020). Academic effects of the use of flipped learning in physical education. International journal of Environmental Research and Public Health , 17 (1), article 276.
Holmes, K., Gore, J., Smith, M., & Lloyd, A. (2018). An integrated analysis of school students’ aspirations for STEM careers: Which student and school factors are most predictive? International Journal of Science and Mathematics Education, 16 (4), 655–675.
Huang, X., & Qiao, C. (2022). Enhancing computational thinking skills through artificial intelligence education at a STEAM high school. Science & Education . https://doi.org/10.1007/s11191-022-00392-6
Article Google Scholar
Hughes, R. M., Nzekwe, B., & Molynearx, K. J. (2013). The single sex debate for girls in science: A comparison between two informal science programs on middle school students’ STEM identity formation. Research in Science Education, 43 , 1979–2007.
Hughes, B. S., Corrigan, M. W., Grove, D., Andersen, S. B., & Wong, J. T. (2022). Integrating arts with STEM and leading with STEAM to increase science learning with equity for emerging bilingual learners in the United States. International Journal of STEM Education , 9 , article 58.
Johnson, A. M. (2019). “I can turn it on when I need to”: Pre-college integration, culture, and peer academic engagement among black and Latino/a engineering students. Sociology of Education, 92 (1), 1–20.
Kayan-Fadlelmula, F., Sellami, A., Abdelkader, N., & Umer, S. (2022). A systematic review of STEM education research in the GCC countries: Trends, gaps and barriers. International Journal of STEM Education, 9 , article 2.
Kelly, R., Mc Garr, O., Leahy, K., & Goos, M. (2020). An investigation of university students and professionals’ professional STEM identity status. Journal of Science Education and Technology, 29 (4), 536–546.
Kezar, A., Gehrke, S., & Bernstein-Sierra, S. (2017). Designing for success in STEM communities of practice: Philosophy and personal interactions. The Review of Higher Education, 40 (2), 217–244.
Kezar, A., Gehrke, S., & Bernstein-Sierra, S. (2018). Communities of transformation: Creating changes to deeply entrenched issues. The Journal of Higher Education, 89 (6), 832–864.
Kricorian, K., Seu, M., Lpoez, D., Ureta, E., & Equils, O. (2020). Factors influencing participation of underrepresented students in STEM fields: Matched mentors and mindsets. International Journal of STEM Education, 7 , article 16.
Ku, C. J., Hsu, Y. S., Chang, M. C., & Lin, K. Y. (2022). A model for examining middle school students’ STEM integration behavior in a national technology competition. International Journal of STEM Education, 9 (1), 3.
Leydesdroff, L. (1989). Words and co-words as indicators of intellectual organization. Research Policy, 18 (4), 209–223.
Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020a). Research and trends in STEM education: A systematic review of journal publications. International Journal of STEM Education, 7 , article 11.
Li, Y., Wang, K., Xiao, Y., Froyd, J. E., Nite, S. B. (2020b). Research and trends in STEM education: A systematic analysis of publicly funded projects. International Journal of STEM Education, 7 , article 17.
Lin, T. C., Lin, T. J., & Tsai, C. C. (2014). Research trends in science education from 2008 to 2012: A systematic content analysis of publications in selected journals. International Journal of Science Education, 36 (8), 1346–1372.
Lin, T. J., Lin, T. C., Potvin, P., & Tsai, C. C. (2019). Research trends in science education from 2013 to 2017: A systematic content analysis of publications in selected journals. International Journal of Science Education, 41 (3), 367–387.
Lin, T. C., Tang, K. Y., Lin, S. S., Changlai, M. L., & Hsu, Y. S. (2022). A co-word analysis of selected science education literature: Identifying research trends of scaffolding in two decades (2000–2019). Frontiers in Psychology, 13 , 844425.
Liu, J. S., & Lu, L. Y. (2012). An integrated approach for main path analysis: Development of the Hirsch index as an example. Journal of the American Society for Information Science and Technology, 63 (3), 528–542.
Liu, C. Y., & Wu, C. J. (2022). STEM without art: A ship without a sail. Thinking Skills and Creativity, 43 , 100977.
Lou, S. H., Shih, R. C., Diez, C. R., & Tseng, K. H. (2011). The impact of problem-based learning strategies on STEM knowledge integration and attitudes: An exploratory study among female Taiwanese senior high school students. International Journal of Technology and Design Education, 21 (2), 195–215.
Lynch, S. J., Burton, E. P., Behrend, T., House, A., Ford, M., Spillane, N., Matray, S., Han, E., & Means, B. (2018). Understanding inclusive STEM high schools as opportunity structures for underrepresented students: Critical components. Journal of Research in Science Teaching, 55 (5), 712–748.
Maass, K., Geiger, V., Ariza, M. R., & Goos, M. (2019). The Role of mathematics in interdisciplinary STEM education. ZDM-Mathematics Education, 51 (6), 869–884.
Mansfield, K. C. (2014). How listening to student voices informs and strengthens social justice research and practice. Educational Administration Quarterly, 50 (3), 392–430.
Margot, K. C., & Kettler, T. (2019). Teachers’ perception of STEM integration and education: A systematic literature review. International Journal of STEM education , 6 , article 2.
Marín-Marín, J. A., Moreno-Guerrero, A. J., Dúo-Terrón, P., & López-Belmonte, J. (2021). STEAM in education: A bibliometric analysis of performance and co-words in Web of Science. International Journal of STEM Education , 8 , article 41.
Martín-Páez, T., Aguilera, D., Perales-Palacios, F. J., & Vílchez-González, J. M. (2019). What are we talking about when we talk about STEM education? A Review of Literature. Science Education, 103 (4), 799–822.
McGee, E. O. (2020). Interrogating structural racism in STEM higher education. Educational Researcher, 49 (9), 633–644.
Meho, L. I., & Yang, K. (2006). A new era in citation and bibliometric analyses: Web of Science, Scopus, and Google Scholar. arXiv preprint cs/0612132 .
Mejias, S., Thompson, N., Sedas, R. M., Rosin, M., Soep, E., Peppler, K., Roche, J., Wong, J., Hurley, M., Bell, P., & Bevan, B. (2021). The trouble with STEAM and why we use it anyway. Science Education, 105 (2), 209–231.
Micari, M., Van Winkle, Z., & Pazos, P. (2016). Among friends: The role of academic-preparedness diversity in individual performance within a small-group STEM learning environment. International Journal of Science Education, 38 (12), 1904–1922.
Millar, V. (2020). Trends, issues and possibilities for an interdisciplinary STEM curriculum. Science & Education, 29 (4), 929–948.
Nadelson, L. S., Callahan, J., Pyke, P., Hay, A., Dance, M., & Pfiester, J. (2013). Teacher STEM perception and preparation: Inquiry-based STEM professional development for elementary teachers. Journal of Educational Research, 106 (2), 157–168.
Nakatoh, T., & Hirokawa, S. (2019, July). Evaluation index to find relevant papers: Improvement of focused citation count. In International Conference on Human-Computer Interaction (pp. 555–566). Springer, Cham.
National Science Technology Council. (2012). Coordinating federal science, technology, engineering, and mathematics (STEM) education investments: Progress report. Retrieved from https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/nstc_federal_stem_education_coordination_report.pdf
National Science Technology Council. (2013). Federal Science, Technology, Engineering, and Mathematics (STEM) Education 5-Year Strategic Plan. Retrieved from https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/stem_stratplan_2013.pdf
Ong, M., Smith, J. M., & Ko, L. T. (2018). Counterspaces for women of color in STEM higher education: Marginal and central spaces for persistence and success. Journal of Research in Science Teaching, 55 (2), 206–245.
Organisation for Economic Cooperation and Development, OECD (2021). Education at A Glance 2021. Retrieve from https://read.oecd.org/10.1787/b35a14e5-en?format=pdf
Perez-Felkner, L., Felkner, J. S., Nix, S., & Magalhaes, M. (2020). The puzzling relationship between international development and gender equity: The case of STEM postsecondary education in Cambodia. International Journal of Educational Development, 72 , 102102.
Perignat, E., & Katz-Buonincontro, J. (2019). STEAM in practice and research: An integrative literature review. Thinking Skills and Creativity, 31 , 31–43.
Quigley, C. F., & Herro, D. (2016). “Finding the joy in the unknown”: Implementation of steam teaching practices in middle school science and math classrooms. Journal of Science Education and Technology, 25 (3), 410–426.
Ramey, K. E., & Stevens, R. (2019). Interest development and learning in choice-based, in-school, making activities: The case of a 3D printer. Learning, Culture and Social Interaction, 23 , 100262.
Salami, M. K., Makela, C. J., & de Miranda, M. A. (2017). Assessing changes in teachers’ attitudes toward interdisciplinary STEM teaching. International Journal of Technology and Design Education, 27 (1), 63–88.
Sanders, M. (2009). Integrative STEM education primer. The Technology Teacher, 68 (4), 20–26.
Saorín, J. L., Melian-Díaz, D., Bonnet, A., Carrera, C. C., Meier, C., & De La Torre-Cantero, J. (2017). Makerspace teaching-learning environment to enhance creative competence in engineering students. Thinking Skills and Creativity, 23 , 188–198.
Simon, R. M., Wagner, A., & Killion, B. (2017). Gender and choosing a STEM major in college: Femininity, masculinity, chilly climate, and occupational values. Journal of Research in Science Teaching, 54 (3), 299–323.
Stolle-McAllister, K., Domingo, M. R. S., & Carrillo, A. (2011). The Meyerhoff way: How the Meyerhoff scholarship program helps black students succeed in the sciences. Journal of Science Education and Technology, 20 (1), 5–16.
Thomas, B., & Watters, J. J. (2015). Perspectives on Australian, Indian and Malaysian approaches to STEM education. International Journal of Educational Development, 45 , 42–53.
Tosun, C. (2022). Analysis of the last 40 years of science education research via bibliometric methods. Science & Education . https://doi.org/10.1007/s11191-022-00400-9
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84 (2), 523–538.
Vencent-Ruz, P., & Schunn, C. D. (2017). The increasingly important role of science competency beliefs for science learning in girls. Journal of Research in Science Teaching, 54 (6), 790–822.
Wang, S., Chen, Y., Lv, X., & Xu, J. (2022). Hot topics and frontier evolution of science education research: A bibliometric mapping from 2001 to 2020. Science & Education . https://doi.org/10.1007/s11191-022-00337-z
Weeden, K. A., Gelbgiser, D., & Morgan, S. L. (2020). Pipeline dreams: Occupational plans and gender differences in STEM major persistence and completion. Sociology of Education, 93 (4), 297–314.
Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25 (1), 68–81.
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Graduate Institute of Science Education, National Taiwan Normal University, No. 88, Ting-Jou Rd., Sec. 4, Taipei City, 116, Taiwan
Ying-Shao Hsu
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Graduate Institute of Library & Information Science, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City, 402, Taiwan
Kai-Yu Tang
Center for Liberal Arts, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist, Kaohsiung City, 807618, Taiwan
Tzu-Chiang Lin
Center for Teacher Education, National Kaohsiung University of Science and Technology, No. 415, Jiangong Rd., Sanmin Dist, Kaohsiung City, 807618, Taiwan
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Hsu, YS., Tang, KY. & Lin, TC. Trends and Hot Topics of STEM and STEM Education: a Co-word Analysis of Literature Published in 2011–2020. Sci & Educ 33 , 1069–1092 (2024). https://doi.org/10.1007/s11191-023-00419-6
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We have previously demonstrated the significant reliance of pancreatic Cancer Stem Cells (PaCSCs) on mitochondrial oxidative phosphorylation (OXPHOS), which enables versatile substrate utilization, including fatty acids (FAs). Notably, dysregulated lipid scavenging and aberrant FA metabolism are implicated in PDAC progression.
Our bioinformatics analyses revealed elevated expression of lipid metabolism-related genes in PDAC tissue samples compared to normal tissue samples, which correlated with a stemness signature. Additionally, PaCSCs exhibited heightened expression of diverse lipid metabolism genes and increased lipid droplet accumulation compared to differentiated progenies. Treatment with palmitic, oleic, and linolenic FAs notably augmented the self-renewal and chemotherapy resistance of CD133 + PaCSCs. Conversely, inhibitors of FA uptake, storage and metabolism reduced CSC populations both in vitro and in vivo. Mechanistically, inhibition of FA metabolism suppressed OXPHOS activity, inducing energy depletion and subsequent cell death in PaCSCs. Importantly, combining a FAO inhibitor and Gemcitabine treatment enhanced drug efficacy in vitro and in vivo, effectively diminishing the CSC content and functionality.
Targeting FAO inhibition represents a promising therapeutic strategy against this highly tumorigenic population.
Pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, is a disease with an unfavorable prognosis due to its late diagnosis, as symptoms are nonspecific even at advanced disease stages, and its intrinsic resistance to established therapeutic options such as chemotherapy and radiotherapy [ 1 ]. Despite its relatively low incidence, PDAC is the seventh leading cause of cancer-related deaths worldwide and one of the most lethal solid tumors [ 2 ] with a poor long-term outcome: the estimated one-year overall survival rate of these patients is 24% [ 3 ].
The main malignant features of PDAC, i.e., intrinsic chemoresistance and elevated metastasis rate, can be partially attributed to specific subpopulations of cancer cells with tumor and metastasis-initiating properties, known as pancreatic cancer stem cells (PaCSCs) [ 4 , 5 ]. CSCs are characterized by the capacity to undergo unlimited cell division while retaining their stem cell identity (self-renewal) and the ability to differentiate into diverse specialized cell types [ 4 , 5 ]. Although migratory and invasive abilities are not restricted to CSCs, only metastatic stem-like cells would be able to initiate secondary lesions upon surviving in the bloodstream as circulating tumor cells (CTCs) [ 6 ]. Considering their intrinsic chemoresistance leading to tumor relapse, the design of combined treatments targeting both PaCSCs and non-CSC populations may represent a promising strategy for improving the long-term survival of PDAC patients.
In the recent years, our group reported that mitochondria are essential organelles for stemness maintenance and tumorigenicity, representing a key vulnerability for PaCSCs. Indeed, perturbations in various processes throughout the mitochondrial life cycle, ranging from biogenesis [ 7 ], fission [ 8 ] and recycling via mitophagy [ 9 ], to interference with mitochondrial activity via oxidative phosphorylation (OXPHOS) inhibition [ 7 ] and alteration of redox state [ 10 ], all of which significantly impair the tumorigenicity and chemoresistance of PaCSCs.
ATP production via OXPHOS requires a large amount of acetyl-CoA, which is commonly supplied by glycolysis, but can also be produced by β-oxidation of fatty acids (FAs). FAs from exogenous sources can be internalized via membrane transporters or lipid receptors such as CD36 or LRPs and then directly metabolized or stored in lipid droplets (LDs). Importantly, increased lipid uptake and aberrant FA metabolism have been linked to disease progression and poor prognosis in PDAC patients [ 11 , 12 , 13 ]. Additionally, studies in other cancers have suggested that lipid metabolism plays a critical role in CSC maintenance, thereby supporting cell membrane formation and energy production [ 14 , 15 ]. Accordingly, we hypothesized that lipid metabolism, particularly FA metabolism, represents a pharmacologically targetable vulnerability for PaCSCs.
Indeed, we showed that several lipid metabolism genes are upregulated in PaCSCs and are correlated with stemness and poor survival in PDAC patients. PaCSCs show increased lipid storage in LDs and FA oxidation (FAO), and FAO inhibition markedly impaired OXPHOS activity, leading to an energy crisis and cell death. Finally, FAO inhibition improved the response to Gemcitabine both in vitro and in vivo, suggesting a new therapeutic strategy that may help improve the outcome of PDAC patients.
Human data analysis.
Expression data from human PDAC tissue and normal pancreatic tissue were analyzed using the webserver GEPIA2 (TCGA and the GTEx project databases; http://gepia2.cancer-pku.cn/ ) [ 16 ]. The Pearson correlation coefficient was calculated to study the association of the individual genes corresponding to lipid metabolism with a stemness signature defined by the combined expression of the pluripotency-related genes KLF4 , OCT4 , NANOG and SOX2 . Additionally, we calculated the overall survival for pancreatic cancer patients from the respective upper and lower quartiles of the expression of these specific lipid metabolism genes; the hazard ratio (HR) was calculated from GEPIA2 using the Cox proportional hazards model.
PDAC patient-derived xenografts (PDXs): A6L, 185, 215, 253, 265 and 354 were obtained from the Biobank of the Spanish National Cancer Research Centre (CNIO), Madrid, Spain (MTAs #CNIO20-027, #CNIO21-253). PDAC PDX-derived cultures were established as previously described [ 17 ]. Pancreatic circulating tumor cells (CTCs): The metastatic model CTCA was established from circulating tumor cells and obtained through the Barts Pancreas Tissue Bank of the Barts Cancer Institute ( https://www.bartspancreastissuebank.org.uk/ ; BCI, London, United Kingdom; 2019/02/IISA/PS/E/Cellcultures).
The cells were grown in RPMI 1640 medium (61870044) supplemented with 10% FBS and 50 U/mL penicillin/streptomycin (all from Gibco, Life Technologies, Carlsbad, CA, USA). The cells were cultured under standard conditions of 5% CO2, 95% humidity, and 37 °C, propagated by treatment with 1X trypsin with 0.2% EDTA (Corning, Oneonta, NY, USA) and subjected to a maximum of 15 passages. For experiments, the medium was changed to sphere medium [DMEM/F-12 (31331028) supplemented with 2% B27 (both from Gibco) and 20 ng/mL FGFbasic (Pan-Biotech, Aidenbach, Germany)], ensuring proper comparison of cells grown in adherent conditions with cells grown as spheres and minimizing any interference resulting from the different concentrations of glucose and other factors present in each media.
For enrichment of CSCs, cells were grown as spheres as previously described [ 7 ]. Briefly, the cells were trypsinized, centrifuged at 1200 rpm for 5 min and resuspended in sphere medium [DMEM/F-12 supplemented with 2% B27 (both from Gibco) and 20 ng/mL FGFbasic (Pan-Biotech)]. The cells were then seeded at a density of 10 5 cells/mL in flasks covered with 10% poly-HEMA (2-hydroxyethyl methacrylate, Sigma–Aldrich, Saint Louis, MO, USA) in 96% ethanol. First generation spheres were grown for seven days. For serial passaging, spheres were harvested using a 40 μm cell strainer, dissociated with trypsin (Corning, Oneonta, NY, USA) and regrown at 10 5 cells/mL for five more days.
FA inhibitors included 200 µM Etomoxir (CPT1A inhibitor) [ 18 ] (E1905, Sigma–Aldrich), 100 µM Mildronate (carnitine synthesis inhibitor) [ 19 ] (15997, Cambridge Biosciences, Cayman, UK), 100 µM Ranolazine (3-ketoacyl-CoA thiolase inhibitor) [ 18 ] (15604, Cambridge Biosciences), all of which were dissolved in dH 2 O, and 1 µM Perhexiline (SML010, CPT1/CPT2 dual inhibitor) [ 18 ] (Sigma-Aldrich) which was dissolved in DMSO following the manufacturer’s instructions. The cells were treated for 24 to 72 h.
FA supplementation included 50 and 100 µM oleic acid (OA) (O3008, Sigma Aldrich), 50 µM sodium palmitate (P9767, Sigma Aldrich) or 200 µM linolenic acid (LNA) (L2376, Sigma Aldrich) conjugated with bovine serum albumin, Fraction V, Fatty Acid-Free, Nuclease-Free and Protease-Free (126609 Sigma-Aldrich). The cells were treated for 24 to 72 h.
Chemotherapy: Gemcitabine 0.9% sodium chloride (Eli Lilly and Company, IN, USA) was used at concentrations ranging from 10 to 5000 nM for 48 h.
RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. The RNA concentration and purity were determined by spectrophotometry (Nanodrop™ 2000, ThermoFisher Scientific). 1 µg of RNA was used for cDNA synthesis using Maxima H minus cDNA synthesis Master Mix with dsDNase kit (Thermo Fisher Scientific), followed by SYBR Green RTqPCR (PowerUp™ SYBR Green Master Mix, Applied Biosystems, Thermo Fisher Scientific) according to the manufacturer’s instructions. The primers used are detailed in Table 1 .
Sample preparation.
Blood and pancreatic tissue samples from mice bearing orthotopic tumors were harvested and processed for FACS sorting. Total blood samples were centrifuged and resuspended in ACK lysing buffer (Thermo Fisher Scientific) for 5 min. Pancreatic tumors were minced mechanically and enzymatically digested with collagenase P for 15 min at 37 °C followed by trypsin for 3 min at 37 °C. Next, cell suspensions were blocked in Flebogamma (1:10 dilution; Grifols) for 15 min at 4 °C and incubated with an hEpCAM-APC antibody (Miltenyi Biotec) or an appropriate isotype-matched control antibody (IgG2a-APC, BD Bioscience) for 30 min at 4 °C. Then, 96 single cells double positive for hEpCAM-APC and GFP and negative for DAPI (Sigma-Aldrich), were sorted in a BD FACSAria™ II into a 96-well plate containing 100 µl of GTC mix [4.19 M Guanidine thiocyanate; 25 mM Na Citrate pH = 7.3; 15 mM Sarcosyl; 11 mM 2-Mercaptoetanol; 18 µM Glycogen] and plates stored at -80 °C.
Individual cells were mixed with 10 µl of 2 M NaOAc and 100 µl 100% Phenol/Chloroform (both at pH 4) and then centrifuged for 13,000 rpm at 4 °C for 10 min. The upper aqueous phase was transferred to low binding tubes and mixed with 2 µl of 5 mg/mL linear acrylamide (Amresco®). After precipitation with isopropanol and ethanol washes, the RNA pellets were resuspended in RNasefree water and immediately reverse-transcribed with SuperScript® VILO cDNA Synthesis Kit (Thermo Fisher Scientific) following manufacturer´s instructions. Then, the cDNAs were mixed with the appropriate primers (CPT1A and HPRT) and a standard PCR using AmpliTaq Gold® 360 Master Mix (Thermo Fisher) was performed.
The ddPCR was performed following the official ddPCR™ application guide performed in a QX100™ Droplet Digital™ PCR system (Bio-Rad). Briefly, mixes containing the pre-amplified cDNA, the specific primers used for each reaction, together with QX200™ ddPCR™ EvaGreen Supermix and QX200™ Droplet Generation Oil for EvaGreen (Bio-Rad), were plated into ddPCR™ cartridges (DG8™ Cartridges for QX200™/QX100™, Bio-Rad) and incubated in a QX100™ Droplet Generator (Bio-Rad). After droplet generation, 40 µl of the generated droplet emulsions were transferred into a new 96-well PCR plate (Eppendorf), foil sealed (PX1™ PCR Plate Sealer, Bio-Rad) and amplified into a C1000 Touch™ Thermal Cycler (Bio-Rad) following manufacturer´s instructions. Following PCR amplification, the plates were measured in a QX100™ Droplet Reader (Bio-Rad) and data analyzed using the software QuantaSoft 1.3.2.0 (Bio-Rad).
Cells grown on coverslips were fixed in 4% paraformaldehyde for 30 min at 4°C. Then, the cells were incubated with BODIPY® 493/503 (Thermo Fisher Scientific) at 2.5 µg/mL for 1 h at 4°C, inside a humid chamber. Coverslips were then washed with PBS and mounted with ProLong® with DAPI (1 µg/mL). Images were taken with a Zeiss LSM 710 confocal microscope.
The cells were resuspended in blocking buffer (2% FBS, 0.5% BSA in PBS) for 15 min on ice under agitation. The cells were stained for 30 min at 4°C with APC-conjugated anti-CD133 or anti-Epcam antibodies (BioLegend, San Diego, USA) or the corresponding control immunoglobulin G1 antibody (IgG1, BioLegend). When indicated, the cells were also incubated for 15 min at 4°C (LD540, Nile red, LipidTOX™, Bodipy®) or 90 min at 37°C (FAO Blue) (Table 2 ). Annexin V-FITC staining was performed on attached and floating cells according to the manufacturer’s instructions (550474 & 556454, BD Biosciences, San Diego, CA, USA). Zombie Violet Dye (77477, Biolegend) or DAPI were used to exclude nonviable cells. A total of 50,000 cells per sample were analyzed using a FACSCanto II (BD, Franklin Lakes, NJ, USA) or ImageStream X Mark II (Amnis, Seattle, WA, USA) and analyzed with FlowJo 9.2 software (Ashland, OR, USA).
The oxygen consumption rate (OCR) was determined by using the XF Mito Cell Stress Test, XF Long Chain Fatty Acid Oxidation Stress Test, or Palmitate Oxidation Stress Test (Agilent Technologies, Santa Clara, CA, USA). A total of 30,000 cells per well were cultured in an XF 96-well cell culture microplate (Agilent Technologies) previously coated with Cell-Tak (BD Biosciences) in growth medium for 24 h. Then, the cells were incubated for 1 h in base assay medium (D5030, Sigma Aldrich) supplemented with 2 mM glutamine, 10 mM glucose, and 1 mM pyruvate at 37 °C. The concentrations of Oligomycin and FCCP were adjusted for each primary cell type as follows: Oligomycin, 1.2 mM for 215 and 253 cells; and 0.8 µM for 354 cells; FCCP 1.2 µM for 215 and 253; and 0.4 µM for 354 cells. Oligomycin, FCCP, Rotenone (1 µM) and Antimycin A (1 µM) were dissolved in DMSO (all from Sigma-Aldrich). For the Long Chain Fatty Acid Oxidation Stress Test, Etomoxir (40 or 100 µM) or Ranolazine (50 µM) were injected into port A prior to Oligomycin, FCCP and Rotenone + Antimycin. For the Palmitate Oxidation Stress Test, cells were incubated for 1 h with FAO medium containing 1 mM glutamine, 2.5 mM glucose and 0.5 mM carnitine, and BSA-palmitate (100 µM) was injected in port A prior to Oligomycin, FCCP and Rotenone + Antimycin.
The percentage of complex I inhibition was calculated as the percentage of OCR inhibited upon compound injection with respect to the inhibition obtained with Rotenone, the latter used as 100%, as described previously [ 7 ]. Experiments were run in a XF96 analyzer (Seahorse Bioscience, Agilent Technologies), and the raw data were normalized to the protein content using the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific).
Cells were seeded at 20,000 cells/well in 96-well culture plates in 100 µL complete RPMI medium. After 24 h, the cells were subjected to different treatments together with LD540 (25 ng/ml) or Annexin V-FITC (1:100) and incubated for 15 min, after which the plate was inserted into an IncuCyte® Live-Cell Analysis System (Sartorius, Göttingen, Germany) for real-time imaging, with two fields imaged per well at 10x magnification every 2 h for a total of 4 days. The fluorescence and confluence data were analyzed using IncuCyte® Confluence version 2021 C software. The data shown represent fluorescence normalized to confluence at each time point.
For cytosolic ATP, PDX265 and 215 cells were infected with the Incucyte® CytoATP Lentivirus Reagent Kit following the manufacturer’s instructions and subjected to puromycin selection to generate a stably expressing population. PDX cells stably expressing CytoATP or the non-binding control were seeded at 10,000 cells/well in a 96-well microplate. After 24 h, the cells were exposed to different treatments and the ATP ratio was analyzed by luminescence using IncuCyte® Confluence version 2021 C software.
A total of 10 4 cells were seeded in triplicate in sphere medium using polyHEMA-coated 24-well plates in the presence of different treatments. Seven days later, spheres were harvested for subsequent assays or counted with an inverted EVOS FL microscope (Thermo Fisher Scientific) using a 10x objective with phase contrast.
Colony formation assay
For the colony formation assay (CFA), 500 or 1000 cells were seeded per well in 2 mL of complete RPMI medium. After 24 h, the treatments were added to sphere medium. The media and treatments were refreshed every 7 days. After 21 days, the cells were stained with crystal violet (Acros Organics, Thermo Fisher Scientific) and the number of colonies was manually counted.
The cell pellets were washed with PBS, resuspended in ultrapure water (10977035, Invitrogen) and frozen for lysis. ATP was quantified using an ATP Determination Kit (A22066, Invitrogen) following the manufacturer’s instructions. Bioluminiscence was determined using a Synergy™ HT Multi-Mode Microplate Reader (BioTek, Winooski, Vermont, USA). The results were normalized to the protein concentration measured for the same samples with the Pierce™ BCA Protein Assay Kit.
Cell pellets were lysed with RIPA buffer (Sigma Aldrich) supplemented with protease and phosphatase inhibitors (both from Alfa Aesar, Thermo Fisher Scientific). The protein concentration was determined with the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific). Proteins (30 µg protein/lane) were separated on Novex™ WedgeWell™ 10% Tris-Glycine precast gels using BenchMark™ pre-stained protein ladder (Invitrogen) and transferred to PVDF membranes (Thermo Fisher Scientific). The membranes were blocked in 5% BSA-1X PBS-0.1% Tween 20 (Thermo Fisher Scientific) for 1 h at room temperature and incubated overnight at 4°C with the following primary antibodies: pAMPK (Thr172), AMPK (both from Cell Signaling Technology, Inc, Danvers, MA, USA), NANOG, DGAT1, MGLL, PPARD (all from Santa Cruz Biotechnology, Inc, Dallas, Texas, USA) and β-actin as loading control (clone AC74, Sigma Aldrich). Afterwards, the membranes were subjected to peroxidase–conjugated secondary antibody (Invitrogen) and developed by chemiluminescence (Pierce™ ECL Western Blotting Substrate) using CL-XPosure™ (Thermo Fisher Scientific) films. The bands were analyzed using ImageJ software.
A total of 10 4 cells were seeded in triplicate in 96-well plates 24 h before treatment. 72 h after treatment, resazurin (Alfa Aesar) was added to the cells at a concentration of 10 µM in 1X PBS and the cells were incubated for 1 h at 37°C with 5% CO 2 . The fluorescence was assessed according to the manufacturer’s instructions by using a Synergy HT plate reader.
Mice were housed according to institutional guidelines and all experimental procedures were performed in compliance with the institutional guidelines for the welfare of experimental animals as approved by the University of Zaragoza Ethics Committee (CEICA PI22/17, PI35/19, PI41/20) and in accordance with the guidelines for Ethical Conduct in the Care and Use of Animals as stated in The International Guiding Principles for Biomedical Research involving Animals, developed by the Council for International Organizations of Medical Sciences (CIOMS).
For the ddPCR, a total of 5 × 10 4 PDX354-GFP cells were orthotopically injected into NU-Foxn1nu (Charles River, UK) nude mice ( n = 8). Once the mice showed signs of disease, they were humanely sacrificed, and pancreatic tumors and total blood were harvested.
For the tumorigenicity assay (Extreme-Limiting Dilution Assay, ELDA), cells were treated in vitro for 48 h, trypsinized and resuspended in sphere medium with Matrigel™ (Corning) (50:50). Two cell densities (10 4 and 10 3 ) were subcutaneously injected into both the top and bottom flanks of six week-old Foxn1 nu nude mice of both sexes ( n = 4 mice per group, n = 8 injections per group). Tumor size was monitored once a week using a digital caliper and tumor volume was calculated using the formula (length*width 2 )/2. All the mice were sacrificed at the same time, when one of the tumors had reached the humane endpoint. ELDA calculations were performed at https://bioinf.wehi.edu.au/software/elda/ .
For in vivo treatment, tumor pieces of approximately 15 mm 3 were soaked in Matrigel™ prior to subcutaneous implantation in both flanks of six week-old Foxn1 nu nude female mice (Envigo, IN, USA) ( n = 5 mice per group, n = 10 implants per group) under isofluorane-induced anesthesia. When the tumor size was approximately 300 mm 3 , the mice were treated with 70 mg/kg Gemcitabine (i.p.) once a week for three weeks with or without 130 mg/kg Ranolazine or the corresponding dose of vehicle (PBS) (oral gavage) once a day until the endpoint. Tumor size was monitored twice a week using a caliper and tumor volume was calculated using the formula (length*width 2 )/2. After 13 weeks, when the control tumors had reached the humane endpoint criteria (maximum 1000 mm 3 ), the mice were euthanized, the tumors were collected and weighed and pictures were taken. A small piece of tumor tissue was processed for RNA to assess lipid metabolism genes by RTqPCR as described above, or for protein extraction to assess p-AMPK/AMPK by Western blot. The remaining tumor tissues were dissociated as previously reported [ 7 ] and stained with EpCAM-FITC, CD133-PE and CD44-APC antibodies for FACS analysis as described above.
The data are expressed as the mean value of at least three experiments ± SEM. The statistical analysis was carried out with GraphPad Prism 8 (GraphPad Software Inc, USA). The significant differences were determined using, in general, analysis of variance (ANOVA, Chicago, IL, USA) and post hoc Bonferroni correction or Kruskal-Wallis tests, depending on the results of the Shapiro-Wilk normality test; p < 0.05 was considered to indicate statistical significant. Significant differences were classified as follows: ∗ : p < 0.05; ∗ ∗ : p < 0.01; ∗ ∗ ∗ : p < 0.001.
To explore the possible association of lipid metabolism with aggressiveness and stemness in PDAC, we first carried out correlation analyses of human gene expression data from the TCGA and GTEx datasets (normal pancreas/PDAC). For these analyses, we included all the genes related to lipid metabolism (GO pathways: sphingolipid acid metabolic process, fatty acid oxidation, fatty acid biosynthetic process, fatty acid metabolic process, medium/long-chain fatty acid catabolic process, positive/negative regulation of fatty acid biosynthetic process and fatty acid transporters) and our established stemness signature composed of the combination of NANOG , KLF4 , OCT3/4 and SOX2 [ 7 ]. Among the genes with greater correlations with stemness, we identified lipoprotein receptors ( LDLR , APOBR , APOER2 ), fatty acid transporters ( SLC27A1 , SLC27A4 ), genes related to LD maintenance and metabolism ( LPCAT2 , LPCAT4 , MGLL , DGAT1 ), sphingolipid and arachidonic acid metabolism ( SPTLC2 , SGPP2 , PTGES3 , ALOX5 , PLA2G4 , TBXAS1 ), FAO ( PEX13 , HADHA , BDH2 , ASAH1 ) and key general lipid metabolism regulators ( PPARD , PPARG ) (Fig. 1 A). Importantly, all these genes were commonly overexpressed in PDAC tissue compared to normal tissue and we found that most of these overexpressed genes were predictive of poor overall survival in PDAC patients, which further suggests their important role in the aggressiveness of PDAC. Next, we further corroborated our finding that lipid metabolism genes with a greater correlation with stemness in PDAC patients were also significantly upregulated in PaCSC-enriched cultures (spheres, with increased expression of stemness genes and CD133 (Figure S1 )) from five primary PDAC PDXs compared to their non-CSC counterparts (Fig. 1 B). Figure 1 C depicts some of the genes commonly upregulated in PDAC vs. normal tissue and CSCs vs. differentiated cells in their corresponding cellular pathways.
Lipid metabolism genes correlate with stemness in PDAC. ( A ) Bioinformatics analyses of lipid metabolism genes in human data included in the TCGA and GTEx project databases, were carried out with the webserver GEPIA2 ( http://gepia2.cancer-pku.cn/ ). The genes were sorted by their correlation (R, first column) with a stemness signature composed of the genes NANOG , KLF4 , OCT3/4 and SOX2 . Data relative to expression in tumor vs. normal tissue and overall survival in the highest and lowest expression quartiles for each gene are shown in the second and third columns, respectively. ( B ) Heatmap of lipid metabolism genes overexpressed in PaCSC-enriched cultures (spheres) from five PDAC PDXs (A6L, 185, 215, 253, 354), evaluated by RTqPCR. In bold, genes determined in A with R > 0.7 or significantly correlated with poor survival. ( C ) Schematic representation of the main genes and pathways correlated with stemness in PDAC. Genes indicated in bold in A and B are highlighted in red in the drawing, indicating their corresponding pathways. FA: Fatty acid; LD: Lipid droplets; TAG: Triglycerides. The data are presented as the mean ± SEM of at least three independent experiments. * p < 0.05; *** p < 0.001
To confirm that PaCSC overexpress genes related with lipid storage in LDs, we first analyzed the LD content in our PDX models. The LD content was heterogeneous, with only a minority of cells showing significant enrichment of LDs, both in vitro (Fig. 2 A) and in vivo (Fig. 2 B). Interestingly, PaCSC-enriched conditions (spheres or CD133 + cells) showed a significantly greater LD content than non-CSCs (adherent cells or CD133 − ) (Fig. 2 C and D), suggesting that the differential distribution of lipid content is dependent on stemness. We also confirmed this observation in a highly metastatic PDAC PDX model (CTCA), derived from circulating tumor cells of a stage IV PDAC patient (Fig. 2 D). Interestingly, the percentage of cells with high lipid content was significantly greater in CTCs isolated from the blood of mice bearing orthotopic PDX tumors than in cells isolated from the primary tumor (Fig. 2 E), implying a potential survival advantage in blood for cells with high lipid storage. Single-cell ddPCR analysis of CTCs demonstrated significant CPT1A overexpression, particularly in the CSC compartment of CTCs (Fig. 2 F). Considering the important regulatory role of CPT1A in FAO via mitochondrial uptake of long-chain FAs, our results suggest that PaCSCs with active FA metabolism could be capable of initiating metastasis.
Increased lipid storage in PaCSCs. ( A ) Confocal microscopy of PDX215 and 354 stained with BODIPY (green) and DAPI (blue). ( B ) Imaging flow cytometry of a fresh PDX215 tumor stained with BODIPY (neutral lipids, green), EpCAM (human PDAC cells, red) and DAPI (live/dead cells). Left, representative flow cytometry plot for BODIPY and EpCAM expression in DAPI negative cells. Right, representative images of two individual cells. ( C ) Flow cytometry of Nile red staining (lipid droplets) in the indicated PDXs cultured in monolayers (adherent) or spheres. Left, representative plots. Right, pooled data. ( D ) Flow cytometry of LD540 staining (lipid droplets) in CD133 - and CD133 + populations in the indicated PDXs. Left, representative histograms. Right, pooled data. ( E ) Imaging flow cytometry of cells isolated from the blood (CTCs) or primary tumors (Tumor) of mice bearing orthotopic PDX354-GFP tumors ( n = 4). The cells were stained with LipidTox (neutral lipids, magenta), EpCAM (red) and DAPI (blue). Left, representative images of two individual CTCs. Right, quantification of the percentage of LipidTox + cells in the blood and primary tumors of each mouse. ( F ) Single-cell mRNA expression by ddPCR of CPT1A in individual cells isolated from blood (CTCs), classified as non-CSCs and CSCs based on the expression of the pluripotency genes NANOG , KLF4 , OCT3/4 and SOX2 . The data are presented as mean ± SEM of at least three independent experiments. ** p < 0.01; *** p < 0.001
Next, we aimed to functionally validate our expression data, which suggested increased FAO activity in PaCSCs. FAO Blue staining assessed by flow cytometry confirmed that CD133 + cells exhibited increased FAO activity, which was abrogated by the CPT1A inhibitor Etomoxir (Fig. 3 A). In addition, we measured the oxygen consumption rate (OCR) upon sequential injections of Etomoxir in cells previously grown either in adherent or in CSC-enriched sphere conditions. We found a greater inhibition of the OCR in CSC-enriched cultures (spheres) upon treatment with Etomoxir than in adherent cells (Fig. 3 B). When cells were treated for Etomoxir for 30 min and subjected to the Long Chain FAO Stress Test, only sphere-derived cells exhibited a significant reduction in ATP-linked and maximal respiration, as well as spare respiratory capacity (Fig. 3 C), suggesting increased FAO-dependent mitochondrial respiration in PaCSCs. Moreover, injection of the FA palmitate led to a significant increase in mitochondrial respiration parameters in sphere-derived cells only (Fig. 3 D). These results suggest that PaCSCs exhibit enhanced FA metabolism compared with differentiated cells, both at the basal level and upon FA supplementation.
PaCSCs show enhanced mitochondrial FAO. ( A ) Median fluorescence intensity of FAO Blue staining as assessed by flow cytometry in cells gated for CD133 expression. Pooled data for PDX185 and 354 cells. ( B ) Oxygen consumption rate (OCR) by Seahorse analysis upon consecutive injections of 40 µM Etomoxir (Eto) and a final injection of the complex III inhibitor Antimycin A and the complex I inhibitor Rotenone (A + R, 1 µM). Left panel, kinetics in adherent vs. sphere-derived PDX215 cells. Right, percentage of mitochondrial OCR inhibition by Etomoxir, with respect to A + R, considered as 100% inhibition. ( C ) Long Chain FAO Stress Test in adherent and sphere-derived PDX215 cells pre-treated for 30 min with Etomoxir. Left, OCR kinetic profile. O, ATP synthase inhibitor Oligomycin; F, mitochondrial oxidative phosphorylation uncoupler FCCP. Right, ATP-linked and maximal respiration, and spare respiratory capacity, shown as percentages of the control for each condition. ( D ) Palmitate Oxidation Stress Test in adherent vs. sphere-derived PDX215 cells upon injection of palmitic acid conjugated with BSA (BSA-PA, 100 µM). Left panel, OCR kinetic profile. ATP-linked, maximal respiration and spare respiratory capacity are shown as percentages of the control for each condition. The data are presented as mean ± SEM of at least three independent experiments. * p < 0.05; *** p < 0.001
Free FAs are essential sources of energy within cells. Among them, the saturated nonesterified FA palmitic acid (PA), the monounsaturated FA oleic acid (OA) and the polyunsaturated FA linolenic acid (LNA) are the most common. Interestingly, treatment for 48 h with PA, OA and LNA in 2D cultures increased the expression of CPT1A , PPARD, LPCAT4 , DGAT1 and MGLL (Fig. 4 A), suggesting that exogenous FA supplementation may be able to induce the expression of genes that we previously found to be correlated with a high stemness signature (Fig. 1 A). These changes were confirmed at the protein level, together with increased expression of the stemness marker NANOG (Fig. 4 B). Considering these results together with the increased OXPHOS in PaCSCs upon incubation with PA (Fig. 3 D), we next evaluated the effects of FA supplementation on CSC functionality. First, we confirmed by flow cytometry and live microscopy that treatment with exogenous FAs increased the LD content in PDAC cells (Fig. 4 C and D). Interestingly, lipid storage in LDs was particularly relevant in the CD133 + population (Fig. 4 E and data not shown). Treatment with exogenous FAs significantly increased the sphere and colony formation abilities of different PDX models (Fig. 4 F and G), indicating enhanced self-renewal capacity. Crucially, in vitro pretreatment with FAs also enhanced in vivo tumorigenicity, as assessed by ELDA (Fig. 4 H). Notably, the most consistent results were obtained with OA supplementation, while PA and LNA only showed significant results in some of the assays used. In summary, we concluded that supplementation with free FAs increases the accumulation of LDs and enhances self-renewal capacity in vitro and tumorigenicity in vivo.
Fatty acid supplementation enhances CSC functionality. ( A ) Gene expression determined by RTqPCR of the indicated genes after 48 h of treatment with palmitic acid (PA) (50 µM), oleic acid (OA 100 µM) or linolenic acid (LNA) (200 µM). ( B ) Western blot analysis of PPARD, DGAT1, MGLL and NANOG after 48 h of treatment with OA at 50 and 100 µM. Left, representative experiment in PDX185. Right, densitometric analyses of Western blots from PDX185 and PDX354. β-Αctin was used as loading control. ( C ) Representative flow cytometry plots of LD540 for PDX253 cells treated with increasing concentrations of OA (1 and 5%) for 24 h. ( D ) LD540 staining by IncuCyte imaging for PDX185 cells upon treatment with PA, OA or LNA as indicated in A. Left, representative images. Right, quantification, calculated as a percentage of the control conditions. ( E ) LD540 staining by flow cytometry in CD133 + cells from the indicated PDX models treated as described in A. ( F ) Sphere formation after seven days of treatment with PA, OA or LNA. Left, representative phase contrast images. Right, quantification, shown as a percentage of the control conditions. ( G ) Colony formation after 21 days of treatment with PA, OA or LNA. Left, representative images of colonies stained with crystal violet. Right, quantification of colony number, shown as compared percentage of the control conditions. ( H ) In vivo ELDA upon injection of the indicated number of PDX185 cells pretreated with PA, OA or LNA as indicated in A. Pictures of the tumors at the endpoint. ELDA calculations were performed on https://bioinf.wehi.edu.au/software/elda/ . The data are presented as mean ± SEM of at least three independent experiments. * p < 0.05; ** p < 0.01; *** p < 0.001
Considering that CSC-enriched cultures showed enhanced FAO activity and that FA supplementation increased stemness in PDAC cells, we next evaluated whether stemness is critically related to FAO and whether the respective inhibitors may be a potential new treatment strategy. For this purpose, we treated PDAC cells for 48 h with Etomoxir [ 18 ], Mildronate (carnitine synthesis inhibitor) [ 19 ], Perhexiline (CPT1/CPT2 dual inhibitor) [ 18 ] and Ranolazine (3-ketoacyl-CoA thiolase inhibitor) [ 18 ]. Interestingly, while Mildronate and Perhexiline were not effective, Etomoxir and Ranolazine both significantly reduced the percentage of CD133 + cells (Fig. 5 A). However, this effect was not accompanied by a reduction in LD content, neither in the CSC population (Fig. 5 B) nor in the total cell population (Figure S2 A). Although only Etomoxir significantly increased apoptosis in the CSC population (Fig. 5 C), but not in the total cell population (Figure S2 B), treatment with either Etomoxir or Ranolazine consistently reduced sphere formation (Fig. 5 D) and colony formation in vitro (Fig. 5 E) and tumorigenicity in vivo (Fig. 5 F). Therefore, Etomoxir and Ranolazine are able to target CSC functionality in PDAC.
FAO inhibition impairs PaCSC functionality. Unless otherwise specified, the cells were treated for 48 h with Etomoxir (Eto) (200 µM), Mildronate (Mild) (100 µM), Perhexiline (Perh) (1 µM) or Ranolazine (Rano) (100 µM). ( A ) Percentage of CD133 + cells by FACS. ( B ) LD540 staining of CD133 + cells. ( C ) Annexin V staining in CD133 + cells. Left panels, mean value of each cell type. Right panels, pooled data. ( D ) Sphere formation assay after seven days of treatment with the inhibitors. ( E ) Colony formation assay after 21 days of treatment with inhibitors. Upper panel representative images of crystal violet staining of PDX185 cells. Lower panel, colony number quantification. ( F ) Tumors at the endpoint after the inoculation of pretreated PDX185 cells. ELDA calculations were performed at https://bioinf.wehi.edu.au/software/elda/ . The data are presented as mean ± SEM of at least three independent experiments. The dashed lines represent the control conditions. * p < 0.05; ** p < 0.01; *** p < 0.001
Interestingly, both compounds inhibited mitochondrial oxygen consumption rate when administered acutely, impacting mitochondrial respiration parameters (Fig. 6 A). These effects were durable, since we detected mitochondrial OCR inhibition even after 72 h of treatment (Fig. 6 B). As expected, the inhibition of mitochondrial activity resulted in a significant decrease in intracellular ATP (Fig. 6 C and D) and a subsequent significant increase in AMPK stress kinase phosphorylation (Fig. 6 E). In summary, FAO inhibition impairs CSC functionality by inhibiting mitochondrial respiration and consequently inducing energy stress.
Treatment with Etomoxir and Ranolazine impairs mitochondrial respiration, inducing energy stress. ( A ) Long Chain FAO Stress Test with acute injections of 100 µM Etomoxir (Eto) or 50 µM Ranolazine (Rano). Left, Oxygen Consumption Rate (OCR) kinetics. Right, ATP-linked, maximal and spare respiration. Pooled data for 354 and CTCA cells. O, Oligomycin; F, FCCP; A + R, Antimycin A + Rotenone. ( B ) Mito Stress after 72 h of treatment of PDX215 cells with Etomoxir or Ranolazine. Left, OCR kinetics. Right, ATP-linked, maximal and spare respiration. ( C ) Cytosolic ATP levels in PDX215 cells visualized by time-lapse fluorescence microscopy (0–96 h). Left, representative images at the indicated times. Right, quantification. ( D ) Total ATP levels quantified by bioluminescence at 48 h. ( E ) Western blot analysis of phospho-AMPK and total AMPK after 48 h of treatment. Left, representative experiment in PDX185. Right, densitometric analysis. β-Αctin was used as loading control. The data are presented as mean ± SEM of at least three independent experiments. * p < 0.05; ** p < 0.01; *** p < 0.001
Finally, we explored whether modulating FAO activity also affects another key feature of CSCs, i.e., chemoresistance [ 4 , 5 ]. Indeed, pretreatment with exogenous FAs for 24 h increased the IC50 of the chemotherapeutic agent Gemcitabine (Fig. 7 A), thus attenuating the effects of the drug on self-renewal (Fig. 7 B). Consistently, OA supplementation protected PDAC cells from Gemcitabine-induced apoptosis, as measured by FACS or IncuCyte (Fig. 7 C and D, S3 A). In contrast, cotreatment with either Etomoxir or Ranolazine significantly enhanced the response to Gemcitabine (Fig. 7 D). Interestingly, the chemosensitizing effect of Etomoxir or Ranolazine was also observed when lipid inhibitors were applied after treatment with Gemcitabine for 48 h (Fig. 7 E and Figure S3 B). Importantly, in vivo treatment with Gemcitabine (3 weeks) combined with Ranolazine (until the endpoint) significantly delayed tumor growth rate, but only at late time points when the tumors reached approximately 2.5-3 times the initial tumor size (Fig. 7 F). At the endpoint, the number of CD44 + /CD133 + CSCs was significantly lower in the Ranolazine-treated tumors (Fig. 7 G). Interestingly, the expression profiles of lipid metabolism genes significantly changed in the Ranolazine-treated tumors (Fig. 7 H), and the p-AMPK/AMPK ratio increased (Fig. 7 I), suggesting that tumors were suffering metabolic stress similar to what we observed in vitro (Fig. 6 E). Therefore, combined treatment with FAO inhibitors and Gemcitabine improves the response to Gemcitabine alone in PDAC PDXs, providing a new perspective for a more effective treatment.
Fatty acid metabolism determines the response to Gemcitabine. ( A ) Percentage of metabolic activity determined by resazurin measurement and the IC50 of Gemcitabine after 72 h of treatment alone and in combination with palmitic acid (PA) (50 µM), oleic acid (OA) (100 µM) or linolenic acid (LNA) (200 µM) (pretreatment 24 h). ( B ) Sphere formation assay of cells pretreated with FAs for 24 h followed by treatment with Gemcitabine (1000 nM) under sphere forming conditions. ( C ) Annexin V levels were determined by flow cytometry after 48 h of treatment with PA, OA or LNA with or without 100 nM Gemcitabine. Pooled data of the indicated PDXs. ( D ) Annexin V levels were measured by IncuCyte imaging at 48 h in PDX185. ( E ) Annexin V measured by Incucyte imaging after 48 h treatment with Gemcitabine and 72 h additional hours in the presence of absence of FAO inhibitors in PDX185. In F-I, tumor pieces of PDX185 were subcutaneously implanted in nude mice, and when they reached around 300 mm 3 they were treated for 3 weeks with Gemcitabine (70 mg/kg) with or without Ranolazine (130 mg/kg), which was administered daily for the whole duration of the experiment. ( F ) Tumor size is shown as the fold change vs. day 1 of treatment. ( G ) CSC content was measured as the percentage of CD44 + /CD133 + double positive cells in tumors at the endpoint. ( H ) Lipid metabolism genes determined by RTqPCR. (I ) Western blot analysis of phospho-AMPK and total AMPK in total lysates from tumors at the endpoint. β-Αctin was used as loading control. The data are presented as mean ± SEM of at least three independent experiments. * p < 0.05; ** p < 0.01; *** p < 0.001
The concept of metabolic rewiring is now widely accepted as one of the hallmarks of cancer [ 20 ], but it has increasingly been recognized as a highly complex and intertwined process in recent years. Indeed, metabolic heterogeneity within tumors, related to fluctuating local microenvironments and distinct cellular populations, is becoming apparent. Specifically, we (and others) have described the unique metabolic features of the CSC population in PDAC compared to their more differentiated progenies: while CSCs acquire a more glycolytic metabolism in some cancer types [ 21 , 22 ], most CSCs, including PaCSCs, rely on mitochondrial OXPHOS for maintenance of stemness [ 7 , 23 , 24 ]. Additionally, recent reports also highlight lipid metabolism as one of the main metabolic pathways regulating CSC functions in several tumor types [ 25 ]. Here, we describe the significance of mitochondrial FAO activity in regulating the self-renewal, tumorigenicity and, most importantly, chemoresistance of PaCSCs. Our findings are in line with the essential role of mitochondrial respiration in stemness in PDAC, as OXPHOS requires acetyl-CoA as a substrate for ATP production, which can be obtained not only via glucose metabolism but also through lipid catabolism via FAO.
Increased lipid uptake and aberrant FA metabolism have previously been associated with tumor progression and poor prognosis in PDAC patients [ 11 , 12 , 13 ]. Indeed, we found that several genes related to FA transport, storage and metabolism are correlated with a stemness signature in PDAC patients, some of which are significantly correlated with overall survival (Fig. 1 ). These genes were overexpressed under CSC-enriched culture conditions in several PDAC PDX models, strongly suggesting a causal relationship between active lipid metabolism and the malignant properties of CSCs. Among these genes, MGLL [ 26 , 27 ], PPARD [ 28 ] and CPT1A [ 29 ] have been correlated with poor prognosis in various cancers. Indeed, increased CPT1A expression specifically in CSCs has been associated with worse outcomes in breast [ 30 ] and colon [ 31 ] cancer, suggesting a key role for mitochondrial FAO activity in CSC functionality. Our results demonstrate that this gene expression profile indeed translates into enhanced FAO activity in CD133 + and sphere-derived cells in PDAC, as previously demonstrated for CSCs in breast cancer [ 32 ] and hepatocellular carcinoma [ 33 ], where NANOG directly upregulates the expression of genes related to FAO.
Our results further indicated increased lipid accumulation in LDs of CD133 + PaCSCs (Fig. 2 ), corroborating our initial gene expression analyses. While similar results were previously reported in vitro for CSCs isolated from primary cultures of colorectal cancer [ 34 ] and breast and ovarian cancer cell lines [ 35 , 36 ], we now also demonstrate differential LD storage for CSCs isolated from fresh tumors. Interestingly, we also detected an enrichment in LD content and CPT1A expression in CTCs isolated from an orthotopic PDX model. CPT1A overexpression was particularly prominent in circulating PaCSCs compared to primary tumors, suggesting a survival advantage for cells with increased lipid storage and metabolism in the blood. In fact, LD staining has been reported to significantly improve CTC detection in a prostate cancer model [ 37 ], suggesting that a similar approach might improve CTC detection in liquid biopsies from PDAC patients.
The addition of exogenous FAs upregulated the expression of several genes involved in lipid transport, storage and metabolism similar to their upregulation in sphere-derived cells (Fig. 4 A and B). Interestingly, lipid droplet accumulation upon exposure to FAs was especially relevant for CD133 + PaCSCs (Fig. 4 C and D). Moreover, PaCSCs also showed a significant increase in FAO activity coupled to mitochondrial respiration in response to PA (Fig. 3 C). This preferential effect of exogenous FAs on CSC metabolism translated into significantly increased self-renewal (measured as sphere and colony formation; Fig. 4 F and G) and in vivo tumorigenicity (Fig. 4 H). Notably, incubation with OA most consistently enhanced self-renewal and chemoresistance, while the effects of incubation with PA and LNA were more variable (Figs. 4 F-H and 7 A-D) and incubation with linoleic acid did not have any significant effect (data not shown). In this sense, our data are in agreement with a previous report suggesting that a panel of FAs exerted differential effects on PDAC viability and growth in vitro and in vivo [ 38 ]. Nevertheless, our results strongly suggest that FA supplementation promoted stemness in PDAC, in agreement with previous reports in different cancer types. OA supplementation was previously shown to maintain self-renewal in breast CSCs treated with soraphen A, a de novo FA synthesis inhibitor [ 39 ]. A similar phenotype has been reported for metastasis initiation in oral carcinomas, melanoma and breast cancer where a high-fat diet or PA supplementation favored sphere formation and tumor initiation in metastatic sites [ 40 , 41 , 42 ]. However, epigenetic histone modifications and the activation of cellular signaling pathways independent of energy production seem to mediate the prometastatic effects of PA in these models.
In addition, FA supplementation, particularly OA, also protected PDAC cells from the cytotoxic effects of Gemcitabine treatment (Fig. 7 A-D, Fig. S3 A). Therefore, our data add to the growing body of evidence indicating that FAs support chemoresistance in different cancer types. Specifically, OA reduced the cytotoxic effects of docetaxel treatment in prostate cancer cells [ 43 ], while PA supplementation protected against cisplatin treatment in ovarian cancer [ 44 ], both via activation of ERK/AKT-mediated survival signals. On the other hand, saturated FAs modulate the response to 5-fluorouracil in colorectal cancer by regulating cell membrane fluidity [ 45 ].
Although we cannot formally exclude the relevance of other protective effects in our experimental settings, our results clearly indicate that FA-mediated chemoresistance in pancreatic PDX models depends on FA catabolism, as FAO inhibition restores response to Gemcitabine (Fig. 7 E, S2 B). This finding suggested that FAO inhibitors may be successful adjuvant drugs for improving PDAC chemotherapy efficacy. Considering that Etomoxir is not suitable for use in humans, we also tested alternative FAO inhibitors that are already approved in different countries for the treatment of angina pectoris: Mildronate (carnitine synthesis inhibitor) [ 46 ], Perhexiline (CPT1/CPT2 dual inhibitor) [ 18 ] and Ranolazine (3- ketoacyl -CoA thiolase inhibitor) [ 47 ]. Among these inhibitors, Ranolazine was the only compound that significantly reduced CD133 + expression, self-renewal and in vivo tumorigenicity, similar to Etomoxir (Fig. 5 ). We confirmed that the mechanism of action of these inhibitors was OXPHOS inhibition, which decreased oxygen consumption and mitochondrial spare respiratory capacity, resulting in a significant reduction in ATP production, and an increase in the pAMPK/AMPK ratio. Indeed, FAO has been shown to contribute to the mitochondrial spare respiratory capacity associated with survival under chemotherapeutic stress conditions [ 48 , 49 ]. Interestingly, Ranolazine sensitized PDAC cells to Gemcitabine both in vitro and in vivo (Fig. 7 ), similar to previous results for human leukemia cells [ 50 , 51 ], prostate cancer [ 47 ], and melanoma [ 52 ], where enhanced OXPHOS via FAO was also described as the main mechanism for resistance to apoptosis induced by chemotherapy and immunotherapy both in vitro and in vivo.
Interestingly, the fact that not only FA synthesis but also external supplementation of FA resulted in increased chemoresistance, suggests an interesting link between a high-fat diet/obesity and chemoresistance. Indeed, Incio et al. demonstrated that obesity reduced drug delivery and toxicity in PDAC [ 53 ]. Remarkably, a previous publication reported that cross-talk between adipose tissue and chronic myeloid leukemia cells results in lipolysis to fuel FAO, inducing chemoresistance in chronic myeloid leukemia cells [ 54 ]. Further research in this area may improve current treatment designs that also consider systemic metabolism, which has become a topic of particular interest in the recent years.
Our results demonstrated that PaCSCs accumulate lipids that serve as substrates for FAO to sustain mitochondrial respiration, which is necessary for maintaining stemness. CSC functionality such as self-renewal and tumorigenicity can be enhanced via FA supplementation or reduced by pharmacological inhibition of FAO. Importantly, we showed that existing FAO inhibitors approved for clinical use under other conditions (e.g. Ranolazine) could represent potential tools for overcoming pancreatic CSC-associated chemoresistance.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Cancer stem cell
Circulating tumor cells
Hazard ratio
Lipid droplet
Linolenic acid
Oxygen consumption rate
Palmitic acid
Pancreatic cancer stem cells
Pancreatic ductal adenocarcinoma
Patient-derived xenografts
Kleeff J, Korc M, Apte M, et al. Pancreatic cancer. Nat Rev Dis Primers. 2016;2(1):16022. https://doi.org/10.1038/nrdp.2016.22 .
Article PubMed Google Scholar
Sung H, Ferlay J, Siegel RL, et al. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660 .
Article CAS PubMed Google Scholar
Rawla P, Sunkara T, Gaduputi V. Epidemiology of pancreatic Cancer: global trends, etiology and risk factors. World J Oncol. 2019;10(1):10–27. https://doi.org/10.14740/wjon1166 .
Article PubMed PubMed Central Google Scholar
Ishiwata T, Matsuda Y, Yoshimura H, et al. Pancreatic cancer stem cells: features and detection methods. Pathol Oncol Res. 2018;24(4):797–805. https://doi.org/10.1007/s12253-018-0420-x .
Li C, Heidt DG, Dalerba P, et al. Identification of pancreatic Cancer stem cells. Cancer Res. 2007;67(3):1030–7. https://doi.org/10.1158/0008-5472.CAN-06-2030 .
Amantini C, Morelli MB, Nabissi M, et al. Expression profiling of circulating Tumor cells in pancreatic ductal adenocarcinoma patients: biomarkers Predicting overall survival. Front Oncol. 2019;9. https://doi.org/10.3389/fonc.2019.00874 .
Sancho P, Burgos-Ramos E, Tavera A, et al. MYC/PGC-1α balance determines the metabolic phenotype and plasticity of pancreatic Cancer stem cells. Cell Metab. 2015;22(4):590–605. https://doi.org/10.1016/j.cmet.2015.08.015 .
Courtois S, de Luxán-Delgado B, Penin-Peyta L, et al. Inhibition of mitochondrial dynamics preferentially targets pancreatic Cancer cells with enhanced tumorigenic and invasive potential. Cancers (Basel). 2021;13(4):698. https://doi.org/10.3390/cancers13040698 .
Alcalá S, Sancho P, Martinelli P, et al. ISG15 and ISGylation is required for pancreatic cancer stem cell mitophagy and metabolic plasticity. Nat Commun. 2020;11(1):2682. https://doi.org/10.1038/s41467-020-16395-2 .
Article CAS PubMed PubMed Central Google Scholar
Jagust P, Alcalá S Jr, Heeschen BS, Sancho C. Glutathione metabolism is essential for self-renewal and chemoresistance of pancreatic cancer stem cells. World J Stem Cells. 2020;12(11):1410–28. https://doi.org/10.4252/wjsc.v12.i11.1410 .
Tadros S, Shukla SK, King RJ, et al. De NovoLipid Synthesis facilitates Gemcitabine Resistance through endoplasmic reticulum stress in pancreatic Cancer. Cancer Res. 2017;77(20):5503–17. https://doi.org/10.1158/0008-5472.CAN-16-3062 .
Swierczynski J. Role of abnormal lipid metabolism in development, progression, diagnosis and therapy of pancreatic cancer. World J Gastroenterol. 2014;20(9):2279. https://doi.org/10.3748/wjg.v20.i9.2279 .
Guillaumond F, Bidaut G, Ouaissi M, et al. Cholesterol uptake disruption, in association with chemotherapy, is a promising combined metabolic therapy for pancreatic adenocarcinoma. Proc Natl Acad Sci. 2015;112(8):2473–8. https://doi.org/10.1073/pnas.1421601112 .
Jagust P, de Luxán-Delgado B, Parejo-Alonso B, Sancho P. Metabolism-based therapeutic strategies targeting Cancer Stem cells. Front Pharmacol. 2019;10. https://doi.org/10.3389/fphar.2019.00203 .
Liu Q, Luo Q, Halim A, Song G. Targeting lipid metabolism of cancer cells: a promising therapeutic strategy for cancer. Cancer Lett. 2017;401:39–45. https://doi.org/10.1016/j.canlet.2017.05.002 .
Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556–60. https://doi.org/10.1093/nar/gkz430 .
Mueller M, Hermann PC, Witthauer J, et al. Combined targeted treatment to Eliminate Tumorigenic Cancer Stem cells in human pancreatic Cancer. Gastroenterology. 2009;137(3):1102–13. https://doi.org/10.1053/j.gastro.2009.05.053 .
Ma Y, Wang W, Devarakonda T, et al. Functional analysis of molecular and pharmacological modulators of mitochondrial fatty acid oxidation. Sci Rep. 2020;10(1):1450. https://doi.org/10.1038/s41598-020-58334-7 .
Wang JX, Rahimnejad S, Zhang YY, et al. Mildronate triggers growth suppression and lipid accumulation in largemouth bass (Micropterus salmoides) through disturbing lipid metabolism. Fish Physiol Biochem. 2022;48(1):145–59. https://doi.org/10.1007/s10695-021-01040-6 .
Hanahan D. Hallmarks of Cancer: New dimensions. Cancer Discov. 2022;12(1):31–46. https://doi.org/10.1158/2159-8290.CD-21-1059 .
Mamouni K, Kim J, Lokeshwar BL, Kallifatidis G. ARRB1 regulates metabolic reprogramming to promote glycolysis in Stem Cell-Like bladder Cancer cells. Cancers (Basel). 2021;13(8):1809. https://doi.org/10.3390/cancers13081809 .
O’Neill S, Porter RK, McNamee N, Martinez VG, O’Driscoll L. 2-Deoxy-D-Glucose inhibits aggressive triple-negative breast cancer cells by targeting glycolysis and the cancer stem cell phenotype. Sci Rep. 2019;9(1):3788. https://doi.org/10.1038/s41598-019-39789-9 .
Raggi C, Taddei ML, Sacco E, et al. Mitochondrial oxidative metabolism contributes to a cancer stem cell phenotype in cholangiocarcinoma. J Hepatol. 2021;74(6):1373–85. https://doi.org/10.1016/j.jhep.2020.12.031 .
Valle S, Alcalá S, Martin-Hijano L, et al. Exploiting oxidative phosphorylation to promote the stem and immunoevasive properties of pancreatic cancer stem cells. Nat Commun. 2020;11(1):5265. https://doi.org/10.1038/s41467-020-18954-z .
Royo-García A, Courtois S, Parejo-Alonso B, Espiau-Romera P, Sancho P. Lipid droplets as metabolic determinants for stemness and chemoresistance in cancer. World J Stem Cells. 2021;13(9):1307–17. https://doi.org/10.4252/wjsc.v13.i9.1307 .
Ye L, Zhang B, Seviour EG, et al. Monoacylglycerol lipase (MAGL) knockdown inhibits tumor cells growth in colorectal cancer. Cancer Lett. 2011;307(1):6–17. https://doi.org/10.1016/j.canlet.2011.03.007 .
Zhang J, Liu Z, Lian Z, et al. Monoacylglycerol lipase: a novel potential therapeutic target and Prognostic Indicator for Hepatocellular Carcinoma. Sci Rep. 2016;6(1):35784. https://doi.org/10.1038/srep35784 .
Yoshinaga M, Taki K, Somada S, et al. The expression of both Peroxisome Proliferator-Activated Receptor Delta and Cyclooxygenase-2 in tissues is Associated with Poor Prognosis in Colorectal Cancer patients. Dig Dis Sci. 2011;56(4):1194–200. https://doi.org/10.1007/s10620-010-1389-9 .
Das M, Giannoudis A, Sharma V. The role of CPT1A as a biomarker of breast cancer progression: a bioinformatic approach. Sci Rep. 2022;12(1):16441. https://doi.org/10.1038/s41598-022-20585-x .
Han S, Wei R, Zhang X, et al. CPT1A/2-Mediated FAO Enhancement—A metabolic target in radioresistant breast Cancer. Front Oncol. 2019;9. https://doi.org/10.3389/fonc.2019.01201 .
Xiong X, Wen YA, Fairchild R, et al. Upregulation of CPT1A is essential for the tumor-promoting effect of adipocytes in colon cancer. Cell Death Dis. 2020;11(9):736. https://doi.org/10.1038/s41419-020-02936-6 .
Wang T, Fahrmann JF, Lee H, et al. JAK/STAT3-Regulated fatty acid β-Oxidation is critical for breast Cancer Stem Cell Self-Renewal and Chemoresistance. Cell Metab. 2018;27(1):136–e1505. https://doi.org/10.1016/j.cmet.2017.11.001 .
Chen CL, Uthaya Kumar DB, Punj V, et al. NANOG metabolically reprograms Tumor-initiating stem-like cells through tumorigenic changes in oxidative phosphorylation and fatty acid metabolism. Cell Metab. 2016;23(1):206–19. https://doi.org/10.1016/j.cmet.2015.12.004 .
Tirinato L, Liberale C, Di Franco S, et al. Lipid droplets: a New Player in Colorectal Cancer Stem cells unveiled by Spectroscopic Imaging. Stem Cells. 2015;33(1):35–44. https://doi.org/10.1002/stem.1837 .
Hershey BJ, Vazzana R, Joppi DL, Havas KM. Lipid droplets define a sub-population of breast Cancer stem cells. J Clin Med. 2019;9(1):87. https://doi.org/10.3390/jcm9010087 .
Li J, Condello S, Thomes-Pepin J, et al. Lipid desaturation is a metabolic marker and therapeutic target of ovarian Cancer stem cells. Cell Stem Cell. 2017;20(3):303–e3145. https://doi.org/10.1016/j.stem.2016.11.004 .
Mitra R, Goodman OB, Le TT. Enhanced detection of metastatic prostate cancer cells in human plasma with lipid bodies staining. BMC Cancer. 2014;14(1):91. https://doi.org/10.1186/1471-2407-14-91 .
Yu M, Liu H, Duan Y, Zhang D, Li S, Wang F. Four types of fatty acids exert differential impact on pancreatic cancer growth. Cancer Lett. 2015;360(2):187–94. https://doi.org/10.1016/j.canlet.2015.02.002 .
Corominas-Faja B, Cuyàs E, Gumuzio J, et al. Chemical inhibition of acetyl-CoA carboxylase suppresses self-renewal growth of cancer stem cells. Oncotarget. 2014;5(18):8306–16. https://doi.org/10.18632/oncotarget.2059 .
Pascual G, Domínguez D, Elosúa-Bayes M, et al. Dietary palmitic acid promotes a prometastatic memory via Schwann cells. Nature. 2021;599(7885):485–90. https://doi.org/10.1038/s41586-021-04075-0 .
Pascual G, Avgustinova A, Mejetta S, et al. Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature. 2017;541(7635):41–5. https://doi.org/10.1038/nature20791 .
Palmitate oxidation drives a pro-metastatic post-translational modification. Nat Cancer . Published Online Febr 15, 2023. https://doi.org/10.1038/s43018-023-00514-1
Liotti A, Cosimato V, Mirra P, et al. Oleic acid promotes prostate cancer malignant phenotype via the G protein-coupled receptor FFA1/GPR40. J Cell Physiol. 2018;233(9):7367–78. https://doi.org/10.1002/jcp.26572 .
Bauerschlag DO, Maass N, Leonhardt P, et al. Fatty acid synthase overexpression: target for therapy and reversal of chemoresistance in ovarian cancer. J Transl Med. 2015;13(1):146. https://doi.org/10.1186/s12967-015-0511-3 .
MORITA HIRAIDET, HORIKAWA Y. Saturated fatty acids in cell membrane lipids induce resistance to 5-Fluorouracil in Colorectal Cancer cells. Anticancer Res. 2022;42(7):3313–24. https://doi.org/10.21873/anticanres.15819 .
Agency EM. SMH. Opinion of the Paediatric Committee on the Granting of a Product-Specific Waiver EMEA-002212-PIP01-17 .; 2017.
Flaig TW, Salzmann-Sullivan M, Su LJ, et al. Lipid catabolism inhibition sensitizes prostate cancer cells to antiandrogen blockade. Oncotarget. 2017;8(34):56051–65. https://doi.org/10.18632/oncotarget.17359 .
Pfleger J, He M, Abdellatif M. Mitochondrial complex II is a source of the reserve respiratory capacity that is regulated by metabolic sensors and promotes cell survival. Cell Death Dis. 2015;6(7):e1835–1835. https://doi.org/10.1038/cddis.2015.202 .
Marchetti P, Fovez Q, Germain N, Khamari R, Kluza J. Mitochondrial spare respiratory capacity: mechanisms, regulation, and significance in non-transformed and cancer cells. FASEB J. 2020;34(10):13106–24. https://doi.org/10.1096/fj.202000767R .
Farge T, Saland E, de Toni F, et al. Chemotherapy-resistant human acute myeloid leukemia cells are not enriched for leukemic stem cells but require oxidative metabolism. Cancer Discov. 2017;7(7):716–35. https://doi.org/10.1158/2159-8290.CD-16-0441 .
Samudio I, Harmancey R, Fiegl M, et al. Pharmacologic inhibition of fatty acid oxidation sensitizes human leukemia cells to apoptosis induction. J Clin Invest. 2010;120(1):142–56. https://doi.org/10.1172/JCI38942 .
Redondo-Muñoz M, Rodriguez-Baena FJ, Aldaz P, et al. Metabolic rewiring induced by ranolazine improves melanoma responses to targeted therapy and immunotherapy. Nat Metab. 2023;5(9):1544–62. https://doi.org/10.1038/s42255-023-00861-4 .
Incio J, Liu H, Suboj P, et al. Obesity-Induced inflammation and Desmoplasia promote pancreatic Cancer progression and resistance to Chemotherapy. Cancer Discov. 2016;6(8):852–69. https://doi.org/10.1158/2159-8290.CD-15-1177 .
Ye H, Adane B, Khan N, et al. Leukemic stem cells evade chemotherapy by metabolic adaptation to an adipose tissue niche. Cell Stem Cell. 2016;19(1):23–37. https://doi.org/10.1016/j.stem.2016.06.001 .
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The authors would like to acknowledge the use of the CIBA (Centro de Investigación Biomédica de Aragón) Flow Cytometry and Animal Facilities (Servicios Científico-Técnicos, IACS Universidad de Zaragoza). We also thank Laura Sancho Andrés for proofreading the manuscript.
The research was supported by the Instituto de Salud Carlos III through the Miguel Servet Program (CP16/00121 and CPII21/00005, to P.S.), the pFIS program (FI21/00031, to P. E-R) and Fondo de Investigaciones Sanitarias (PI17/00082 and PI20/00921, to P.S.) (all co-financed by European funds (FSE: “El FSE invierte en tu futuro” and FEDER: “Una manera de hacer Europa”, respectively), the Worldwide Cancer Research (WCR) Charity together with Asociación Española contra el Cáncer (AECC) (19–0250, to P.S.), and a LAB AECC grant (LABAE223389SANC, to P.S.). M.M. was recipient of a Margarita Salas fellowship from the Universidad Autónoma de Madrid (CA1/RSUE/202100646). A.R-G. was a recipient of a predoctoral contract from the Spanish AECC (PRDAR222458ROYO). I.V. was a recipient of a predoctoral contract from the Aragon Government.
Marta Mascaraque and Sarah Courtois contributed equally to this work.
Instituto de Investigación Sanitaria Aragón (IIS Aragón), Hospital Universitario Miguel Servet, Zaragoza, Spain
Marta Mascaraque, Sarah Courtois, Alba Royo-García, Andrei M. Stoian, Isabel Villaoslada, Pilar Espiau-Romera, Ansooya Bokil & Patricia Sancho
Department of Biology, Universidad Autónoma de Madrid, Madrid, Spain
Marta Mascaraque
Centre for Stem Cells in Cancer & Ageing, Barts Cancer Institute, Queen Mary University of London, London, EC1M 6BQ, UK
David Barneda, Andrés Cano-Galiano & Petra Jagust
Pancreatic Cancer Heterogeneity, Candiolo Cancer Institute – FPO – IRCCS, Candiolo, TO, Italy
Christopher Heeschen
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MM: investigation, formal analysis, writing-original draft, writing-review and editing. SC: conceptualization, investigation, formal analysis and writing-review. AR-G: investigation and writing-editing. DB: conceptualization and investigation. AMS, IV, PE, AB, ACG, PJ: investigation. CH: conceptualization, resources, writing-review and editing. PS: conceptualization, project administration, supervision, funding acquisition, investigation, writing-original draft, writing-review and editing.
Correspondence to Christopher Heeschen or Patricia Sancho .
Ethical approval.
Human material: PDAC patient-derived xenografts (PDXs): A6L, 185, 215, 253, 265 and 354 were obtained from the Biobank of the Spanish National Cancer Research Centre (CNIO), Madrid, Spain (MTAs #CNIO20-027, #CNIO21-253). PDAC PDX-derived cultures were established as previously described [ 17 ]. Pancreatic circulating tumor cells (CTCs): The metastatic model CTCA was established from circulating tumor cells and obtained through the Barts Pancreas Tissue Bank of the Barts Cancer Institute ( https://www.bartspancreastissuebank.org.uk/ ; BCI, London, United Kingdom; 2019/02/IISA/PS/E/Cellcultures). In vivo experiments: Mice were housed according to institutional guidelines and all experimental procedures were performed in compliance with the institutional guidelines for the welfare of experimental animals as approved by the University of Zaragoza Ethics Committee (CEICA PI22/17, PI35/19, PI41/20) and in accordance with the guidelines for Ethical Conduct in the Care and Use of Animals as stated in The International Guiding Principles for Biomedical Research involving Animals, developed by the Council for International Organizations of Medical Sciences (CIOMS).
The authors declare that they have no competing interests.
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Mascaraque, M., Courtois, S., Royo-García, A. et al. Fatty acid oxidation is critical for the tumorigenic potential and chemoresistance of pancreatic cancer stem cells. J Transl Med 22 , 797 (2024). https://doi.org/10.1186/s12967-024-05598-6
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Published : 28 August 2024
DOI : https://doi.org/10.1186/s12967-024-05598-6
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