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Developmental Biology

Developmental biology is the science that investigates how a variety of interacting processes generate an organism’s heterogeneous shapes, size, and structural features that arise on the trajectory from embryo to adult, or more generally throughout a life cycle. It represents an exemplary area of contemporary experimental biology that focuses on phenomena that have puzzled natural philosophers and scientists for more than two millennia. Philosophers of biology have shown interest in developmental biology due to the potential relevance of development for understanding evolution, the theme of reductionism in genetic explanations, and via increased attention to the details of particular research programs, such as stem cell biology. Developmental biology displays a rich array of material and conceptual practices that can be analyzed to better understand the scientific reasoning exhibited in experimental life science. This entry briefly reviews some central phenomena of ontogeny and then explores four domains that represent some of the import and promise of conceptual reflection on the epistemology of developmental biology.

1.1 Historical Considerations

1.2 developmental phenomena, 1.3 developmental mechanisms, 2.1 no theory of development, 2.2 erotetic organization, 3.1 genetics, 3.2 physics, 3.3 integrating approaches: genetics and physics, 4. model organisms for the study of development, 5.1 functional homology in developmental genetics, 5.2 normal stages and phenotypic plasticity, 6. conclusion, figure credits, other internet resources, related entries, 1. overview.

Developmental biology is the science that investigates how a variety of interacting processes generate an organism’s heterogeneous shapes, size, and structural features that arise on the trajectory from embryo to adult, or more generally throughout a life cycle (Love 2008; Minelli 2011a). It represents an exemplary area of contemporary experimental biology that focuses on phenomena that have puzzled natural philosophers and scientists for more than two millennia. How do the dynamic relations among seemingly homogeneous components in the early stages of an embryo produce a unified whole organism containing heterogeneous parts in the appropriate arrangement and with correct interconnections? More succinctly, how do we explain ontogeny (or, more archaically, generation )? In Generation of Animals , Aristotle provided the first systematic investigation of developmental phenomena and recognized key issues about the emergence of and relationships between hierarchically organized parts (e.g., bone and anatomical features containing bone), as well as the explanatory difficulty of determining how a morphological form is achieved reliably in offspring (e.g., the typical shape and structure of appendages in a particular species). Generation remained a poignant question throughout the early modern period and was explored by many key figures writing at the time, including William Harvey, René Descartes, Robert Boyle, Pierre Gassendi, Nicolas Malebranche, Gottfried Wilhelm Leibniz, Anne Conway, Immanuel Kant, and others (Smith 2006). Observations of life cycle transitions, such as metamorphosis, fed into these endeavors and led to striking conclusions, such as Leibniz’s denial of generation sensu stricto .

Animals and all other organized substances have no beginning … their apparent generation is only a development, a kind of augmentation … a transformation like any other, for instance like that of a caterpillar into a butterfly. (Smith 2011: 186–187)

A major theme that crystallized in this history of investigation is the distinction between epigenesis and preformation (see the entry on theories of biological development ). Proponents of epigenesis claimed that heterogeneous, complex features of form emerge from homogeneous, less complex embryonic structures through interactive processes. Thus, an explanation of the ontogeny of these form features requires accounting for how the interactions occur. Proponents of preformation claimed that complex form preexists in the embryo and “unfolds” via ordinary growth processes. An adequate explanation involves detailing how growth occurs. Although preformation has a lighter explanatory burden in accounting for how form emerges during ontogeny (on the assumption that growth is easier to explain than process interactions), it also must address how the starting point of the next generation is formed with the requisite heterogeneous complex features. This was sometimes accomplished by embedding smaller and smaller miniatures ad infinitum inside the organism ( Figure 1 ). Epigenetic perspectives were often dependent on forms of teleological reasoning (see the entry on teleological notions in biology ) to account for why interactions among homogeneous components eventually result in a complex, integrated whole organism. Though nothing prevents mixing features of these two outlooks in explaining different aspects of development, polarization into dichotomous positions has occurred frequently (Rose 1981; Smith 2006).

Figure 1: An early modern depiction of a tiny person inside of a sperm exemplifying preformationist views.

In the late 19 th and early 20 th century, the topic of development was salient in controversies surrounding vitalism, such as the disagreement between Wilhelm Roux and Hans Driesch over how to explain ontogeny (Maienschein 1991). Roux thought that a fertilized egg contains inherited elements that represent different organismal characteristics. During the process of cellular division, these elements become unequally distributed among daughter cells leading to distinct cell fates. Driesch, in contrast, held that each cell retained its full potential through division even though differentiation occurred. Although this issue is often understood in terms of the metaphysics of life (vitalism versus materialism), Driesch’s interpretation of development and the autonomy of an organism had epistemological dimensions (Maienschein 2000). The explanatory disagreement involved different experimental approaches and divergent views on the nature of differentiation in early ontogeny (e.g., to what degree cells are pre-specified). A familiar philosophical theme running through these discussions, both epistemological and metaphysical, is the status of reductionism in biology . Through the middle of the 20 th century, embryology—the scientific discipline studying development—slowly transformed into developmental biology with a variety of reworked and recalcitrant elements (Berrill 1961). In conjunction with the issue of reductionism, a key aspect of this history is the molecularization of experimental (as opposed to comparative) embryology (Fraser and Harland 2000), with a concomitant emphasis on the explanatory power of genes (see the entry on gene and Section 3.1 ). This complex and fascinating history, including interrelations with medicine and reproductive technology, has been detailed elsewhere (see, e.g., Oppenheimer 1967; Horder et al. 1986; Hamburger 1988; Hopwood 2019; Maienschein 2014; Maienschein et al. 2005; Gilbert 1991; Embryo Project in Other Internet Resources).

Developmental biology has increasingly become an area of exploration for philosophy of biology due to the potential relevance of development for understanding evolution (Love 2015; Section 5 ), the theme of reductionism in biology and explanations from molecular genetics (Robert 2004; Rosenberg 2006; Section 3 ), and via increased attention to the details of particular research programs, such as stem cell biology (Fagan 2013; Laplane 2016). However, it should not be forgotten that ontogeny was on the radar of philosophical scholars in the 20 th century, as seen in Ernest Nagel’s treatment of hierarchical organization and reduction in the development of living systems (Nagel 1961: 432ff). For contemporary philosophy of science, developmental biology displays a rich array of material and conceptual practices that can be analyzed to better understand the scientific reasoning exhibited in experimental life science (see the entry on experiment in biology ). After a brief review of some central phenomena of ontogeny, this entry explores four domains that represent some of the import and promise of conceptual reflection on the epistemology of developmental biology.

Developmental biology is the science that seeks to explain how the structure of organisms changes with time. Structure, which may also be called morphology or anatomy, encompasses the arrangement of parts, the number of parts, and the different types of parts. (Slack 2006: 6)

Most of the properties that developmental biologists attempt to explain are structural rather than functional. For example, a developmental biologist concentrates more on how tissue layers fold or how shape is generated than on what the folded tissue layers do or how the shape functions. The ontogeny of function, at all levels of organization, is an element of developmental biology, but it is often bracketed because of the predominance (both past and present) of questions surrounding the ontogeny of form or structure (Love 2008).

Textbooks (e.g., Gilbert 2010; Slack 2013; Wolpert et al. 2010) typically describe a canonical set of events surrounding the changing structures displayed during animal development. [ 1 ] The first of these is fertilization (in sexually reproducing species), where an already semi-organized egg merges with a sperm cell, followed by the fusion of their nuclei to achieve the appropriate complement of genetic material. Second, the fertilized egg undergoes several rounds of cleavage , which are mitotic divisions without cell growth that subdivide the zygote into many distinct cells ( Figure 2 ). After many rounds of cleavage, this spherical conglomerate of cells (now called a blastula ) begins to exhibit some specification of germ layers (endoderm, mesoderm, and ectoderm) and then proceeds to invaginate at one end, a complex process referred to as gastrulation that eventually yields a through-gut. All three germ layers, from which specific types of cells are derived (e.g., neural cells from ectoderm), become established during gastrulation or shortly after it completes. [ 2 ] Organogenesis refers to the production of tissues and organs through the interaction and rearrangement of cell groups. Events confined to distinct taxonomic groups include neurulation in chordates, whereas other events correlate with mode of development ( metamorphosis from a larval to adult stage) or individual trauma ( regeneration of a limb).

Figure 2: An example of embryonic cleavage in marine snail embryos showing the fate of different cell lineages through developmental time.

Several key processes underlie these distinct developmental events and the resulting features of form that emerge (e.g., the through-gut formed subsequent to gastrulation or the heart formed during organogenesis). These are critical to the ontogeny of form and link directly to major research questions in developmental biology ( Section 2 ). First, cellular properties, such as shape, change during ontogeny. This is a function of differentiation whereby cells adopt specific fates that include shape transformations ( Figure 3 ). Second, regions of cells in the embryo are designated through arrangement and composition alterations that correspond to different axes in different parts of the embryo (e.g., dorsal-ventral, anterior-posterior, left-right, and proximal-distal). The successive establishment of these regions is referred to as pattern formation . Third, cells translocate and aggregate into layers (e.g., endoderm and ectoderm, followed by the mesoderm in many lineages) and later tissues (aggregations of differentiated cell types). Fourth, cells and tissues migrate and interact to produce new arrangements and shapes composed of multiple tissue layers with novel functions (i.e., organs). These last two sets of processes are usually termed morphogenesis (Davies 2005) and occur via many distinct mechanisms ( Section 1.3 ). Fifth, there is growth in the size of different form features in the individual, remarkably obvious when comparing zygote to adult, although proportional change between different features ( allometry ) is also striking.

Figure 3: A simple illustration of the kinds of differentiation related to the cellular components found in blood.

None of these processes occur in isolation and explanations of particular form features usually draw on several of them simultaneously, presuming other features that originated earlier in ontogeny by different instantiations and combinations of the processes. This sets a broad agenda for investigation: how do various iterations and combinations of these processes generate form features during ontogeny? Consider the concrete example of vertebrate cardiogenesis. How does the vertebrate heart, with its internal and external shape and structure originate during ontogeny (Harvey 2002)? How does the heart come to exhibit left/right asymmetry in the body cavity? What causes cells to adopt a muscle cell fate or certain tissues to interact in the prospective region of the heart? How do muscle cells migrate to, aggregate in, and differentiate at the correct location? How does the interior of the heart adopt a particular tubular structure with various chambers (that differs among vertebrate species)? How does the heart grow at a particular rate and achieve a specific size? Solutions relevant to explaining the ontogeny of form characterize causal factors that account for how different processes occur and yield various outcomes ( Section 3 ).

A developmental mechanism is a mechanism or process that operates during ontogeny (see McManus 2012 for discussion). At least two different types of developmental mechanisms can be distinguished (Love 2017a): molecular genetic mechanisms (signaling or gene regulatory networks; Section 3.1 ) and cellular-physical mechanisms (cell migration or epithelial invagination; Section 3.2 ). Philosophical explorations of mechanisms in science and mechanistic explanation have grown dramatically over the past two decades (Craver and Darden 2013; Glennan and Illari 2017; Illari and Williamson 2012). Among different accounts of scientific mechanisms, four shared elements are discernable: (1) what a mechanism is for, (2) its constituents, (3) its organization, and, (4) the spatiotemporal context of its operation. Developmental explanations seek to characterize these four elements through various experimental interventions. Together these elements provide a template for characterizing the two different types of developmental mechanisms.

A well-established molecular genetic mechanism is the initial formation of segments in Drosophila due to the segment polarity network of gene expression (Wolpert et al. 2010, 70-81; Damen 2007). By Stage 8 of development (~3 hours post-fertilization), Drosophila embryos have 14 parasegment units that were defined by pair-rule gene expression in earlier stages. The transcription factor Engrailed accumulates in the anterior portion of each parasegment. This initiates a cascade of gene activity that defines the boundaries of each compartment of cells that will eventually become a segment. One element of this activity is the expression of hedgehog , a secreted signaling protein, in cells anterior to the band of cells where Engrailed has accumulated, which marks the posterior boundary of each nascent segment. This, in turn, activates the expression of wingless , another secreted signaling protein, which maintains the expression of both engrailed and hedgehog in a feedback loop so that segment boundaries persist ( Figure 4 ). The segment polarity network exhibits all four of the shared elements of a mechanism. It is constituted by a number of parts (e.g., Engrailed, Wingless, Hedgehog) and activities or component operations (e.g., signaling proteins bind receptors, transcription factors bind to DNA and initiate gene expression), which are organized into patterns of interacting relationships (feedback loops, signaling pathways) within a spatiotemporal context (in parasegments of the Drosophila embryo, ~3 hours post-fertilization) so as to produce a specific behavior or phenomenon (a set of distinct segments with well-defined boundaries).

Figure 4: Wingless and Hedgehog reciprocal signaling during segmentation of Drosophila embryos.

Next, consider the cellular-physical mechanism of branching morphogenesis, which refers to combinations of cellular proliferation and movement that yield branch-like structures in kidneys, lungs, glands, or blood vessels. There are many types of branching morphogenesis, but one primary mechanism is epithelial folding, which involves cells invaginating at different locations on a structure to yield branches (Davies 2013, ch. 20). Different cellular-physical mechanisms can produce invaginations that lead to branching structures (Varner and Nelson 2014): the constriction of one end of a subset of columnar cells in an epithelium (“apical constriction”); increased cell proliferation of one epithelial sheet in relation to another (“differential growth”); and compression of an epithelium leading to periodic invaginations (“mechanical buckling”). That different mechanisms can lead to the same morphological outcome means it can be difficult to discern which mechanism is operating in an embryonic context. Branching morphogenesis also exhibits all four of the shared elements of a mechanism. The parts are cells and tissues with activities or component operations (e.g., apical constriction, differential growth, mechanical buckling) being organized into patterns of interacting relationships (apical constriction leading to epithelial invagination) within a spatiotemporal context (in tracheal precursors within the Drosophila embryo around Stage 7 and 8). This organization produces a specific behavior or phenomenon (a set of branching structures—the trachea).

Once these types of developmental mechanisms have been distinguished, several conceptual issues become salient. The first pertains to how the two types of mechanisms are interrelated during ontogeny, and how different investigative approaches do or do not successfully provide integrated accounts of them ( Section 3.3 ). A second is their distinct patterns of generality. Molecular genetic mechanisms are widely conserved across phylogenetically disparate taxa as a consequence of evolutionary descent, whereas cellular-physical mechanisms are widely instantiated as a consequence of shared physical organization but not due to evolutionary descent (Love 2017a). The divergence of these patterns has prompted explicit epistemological reflection by developmental biologists. [ 3 ]

2. The Epistemological Organization of Developmental Biology

One recurring theme in the long history of investigations into development is that explaining the ontogeny of form consists of many interrelated questions about diverse phenomena ( Section 1.2 ). Sometimes philosophers have attempted to compress these questions into one broad problem.

The real question concerning metazoan ontogeny is just how a single cell gives rise to the requisite number of differentiated cell lineages with all the right inductive developmental interactions required to reproduce the form of the mature organism (Moss 2002: 97). The central problem of developmental biology is to understand how a relatively simple and homogeneous cellular mass can differentiate into a relatively complex and heterogeneous organism closely resembling its progenitor(s) in relevant aspects (Robert 2004: 1).

This language is not necessarily incorrect but can lead to skewed interpretations. For example, Philip Kitcher has argued that:

In contemporary developmental biology, there is … uncertainty about how to focus the big, vague question, How do organisms develop? (Kitcher 1993: 115)

This is simply false. While it is true that these questions have been manifested with differing frequency and vigor through history, and the ability to answer them (as well as the nature of the questions themselves) has been contingent on different research strategies and methods, the issue has not been an unwieldy central problem. But scrutinizing the structure of developmental biology’s questions is not merely an exercise in clarification. It is crucial for understanding how the science of developmental biology is organized.

Although it is common in philosophy to associate sciences with theories, such that the individuation of a science is dependent on a constitutive theory or group of models, it is uncommon to find presentations of developmental biology that make reference to a theory of development (see discussion in Minelli and Pradeu 2014). Instead, we find references to families of approaches (developmental genetics, experimental embryology, cell biology, and molecular biology ) or catalogues of “key molecular components” (transcription factor families, inducing factor families, cytoskeleton or cell adhesion molecules, and extracellular matrix components). No standard theory or group of models provides theoretical scaffolding in the major textbooks (e.g., Slack 2013; Wolpert et al. 2010; Gilbert 2010). The absence of any reference to a constitutive theory of development or some set of core explanatory models is prima facie puzzling. Three interpretations of this situation are possible: (a) despite the lack of reference to theories, one can reconstruct a theory (or theories) of developmental biology out of the relevant discourse (e.g., multiple allied molecular models); (b) the lack of reference to theories indicates an immaturity in developmental biology because mature sciences always have systematic theories; and, (c) the lack of reference to theories should be taken at face value.

Developmental biology is not an immature science, groping about for some way to explain its phenomena: “some of the basic processes and mechanisms of embryonic development are now quite well understood” (Slack 2013: 7). The impetus for this type of interpretation arises out of commitments to a conception of mature science that presumes theories are abstract systems with a small set of laws or core principles (see the entry on the structure of scientific theories ). On the other hand, holding that developmental biology already has a theory costumed in different guise—not referred to as such by developmental biologists—is a possible interpretation. It arises out of a view that sciences must have theories, which has been expanded to allow for different understandings of theory structure, such as constellations of models without laws, even though the assumption is that theory still plays a similar organizing role in guiding research. However, this assumption should be challenged and rejected on methodological grounds in the case of developmental biology. An analysis of the reasoning in a science should exhibit epistemic transparency and not postulate “hidden” reasoning structure (Love 2012). This criterion is based on the premise that the basis of successes in scientific inquiry must be available to those engaged in its practice (i.e., scientists). If we postulate hidden structure not present in scientific discourse to account for inductive inference, explanation, or other forms of reasoning, then we risk obscuring how scientists themselves access this structure to evaluate it (Woodward 2003: ch. 4). The successes of developmental biology would become mysterious when viewed from the vantage point of its participants.

Epistemic transparency demands a descriptive correspondence between philosophical accounts of science and scientific practice. This does not mean that every claim made by any scientist should be taken with the same credence. A ruling concern is pervasive features of practice. The problem with assuming laws are required for explanation is their relative absence from a variety of successful sciences routinely offering explanations, not that no scientist ever appeals to laws as explanatory. Pervasive features of scientific practice should be prominent in philosophical accounts of sciences. Thus, it is not surprising that the desire for a theory can be found among some developmental biologists: “Developing a theory is of utmost importance for any discipline” (Sommer 2009: 417). But the fact that these calls are rare means we should not assume theories are actually needed to govern and organize inquiry within the domain. [ 4 ]

It was once thought that each science must have laws in order to offer explanations (see the entry on scientific explanations ), but now this is seen as unnecessary (Giere 1999; Woodward 2003). The expectation that a science have a theory to accomplish the task of organizing and guiding inquiry is of similar vintage. It derives from an intuitive expectation of what counts as a mature science in the first place. Even if we find empirically successful and coherent traditions of research without a systematic theoretical framework providing guidance, then the science cannot be mature. One might shrug off these quasi-positivist appeals to maturity by invoking more flexible conceptions of theory and theory structure. But why retain the expectation that theories should accomplish the same epistemic tasks? It is a preconception about knowledge structure that is not plausible in light of the diversity of research practices found across the sciences. The few scientists who favor this philosophical response have different motivations. Instead of maturity, other reasons are salient, such as guidance in the face of a welter of biochemical detail or the need to forge a synthesis between evolution and development. [ 5 ]

Developmental biologists recognize that the “curse of detail” is one of the costs of developmental biology’s meteoric success over the past three decades: “The principal challenge today is that of exponentially increasing detail” (Slack 2013: ix). While something must provide organization and guidance to developmental biology, it need not be theories that accomplish the task. Regarding calls for a synthesis of evolution and development, these often assume that having a developmental theory is a precondition for synthesis (Sommer 2009): “Our troubles … derive from our standing lack of an explicit theory of development” (Minelli 2011a: 4). However, this line of argument relies on the degree to which evolutionary theory exhibits the supposed structure to which developmental biologists should aspire. The actual practice associated with evolutionary theory indicates a more flexible framework with chameleon qualities that is responsively adjusted to the diverse investigative aims of evolutionary researchers (Love 2013). Therefore, it is not clear that evolutionary theory supplies the preferred template. A productive way forward is to relinquish the prior expectation that sciences must have theories of a certain kind to govern and guide their activity. Instead, sciences that display empirical success and fecundity should be studied to discover what features are responsible, without assuming that those features will be the same for all sciences: “Science need not be understood in these terms and, indeed, may be better understood in other terms” (Giere 1999: 4).

The criterion of epistemic transparency ( Section 2.1 ) encourages an exploration of our third interpretive option—the lack of reference to theories should be taken at face value. Developmental biology is organized primarily by stable, broad domains of problems that correspond to abstract representations of major ontogenetic processes (differentiation, pattern formation, growth, and morphogenesis; Section 1.2 ). Yet how do we interpret the “theoretical” aspects of developmental biology (e.g., positional information models of pattern formation) and the utilization of theories from other domains (e.g., biochemistry)? One way is to distinguish between theory-informed science—using theoretical knowledge—and theory-directed science—having a theory that directs inquiry and organizes knowledge (Waters 2007b); developmental biology is theory-informed but not theory-directed. Theories need not be wholly absent from developmental biology but—when present—they play roles very different from standard philosophical expectations. Developmental biology uses theoretical knowledge from biochemistry when appealing to morphogen gradients to explain how segments are established or chemical thermodynamics when invoking reaction–diffusion mechanisms to explain pigmentation patterns. It also uses theoretical knowledge derived from within developmental biology, such as positional information models. Different kinds of theory inform developmental biology, but these do not organize research—they are not necessary to structure the knowledge and direct investigative activities. Developmental biologists are not focused on confirming and extending the theory of reaction–diffusion mechanisms, nor are they typically organizing their research around positional information. [ 6 ] This theoretical knowledge is used in building explanations but does not provide rails of guidance for how to proceed in a research program. All sciences may use theoretical knowledge, but this is not the same as all sciences having a theory providing direction and organization.

Why think that problems provide organizational architecture for the epistemology of developmental biology? They are a pervasive feature of its reasoning practices, illustrated in textbooks that capture substantial community consensus about standards of explanation, experimental methods, essential concepts, and empirical content. Unlike evolutionary biology textbooks that discuss the theory of natural selection or economics textbooks that talk about microeconomic theory, an examination of several editions of major textbooks indicate that developmental biology does not have similar kinds of theories.

Jonathan Slack’s Essential Developmental Biology (Slack 2006, 2013) is organized around four main types of processes, also described as clustered groups of problems, which occur during embryonic development: regional specification (pattern formation), cell differentiation, morphogenesis, and growth. These broad clusters are then fleshed out along a standard timeline of early development, highlighting gametogenesis, fertilization, cleavage, gastrulation, and axis specification ( see Section 1.2 ). Different experimental approaches (cell and molecular biology, developmental genetics, and experimental embryology) are utilized in a specific set of model organisms ( see below, Section 4 ) to dissect the workings of these developmental phenomena. Subsequent chapters cover later aspects of development (e.g., organogenesis), with different systems treated in depth by tissue layer, differentiation and growth, or in relation to evolutionary questions (see below, Section 5 ). Throughout this presentation, no specific theory, set of hypotheses, or dominant model is invoked to organize these different domains of investigation. Instead, broad clusters of questions that reflect generally delineated processes (differentiation, specification, morphogenesis, and growth) set the agenda of research.

Scott Gilbert’s Developmental Biology exhibits a similar pattern (Gilbert 2000 [2003, 2006, 2010]). Developmental biology is constituted by two broad questions (“How does the fertilized egg give rise to the adult body? And how does that adult body produce yet another body?”), which can then be subdivided into further categories, such as differentiation, morphogenesis, growth, reproduction, regeneration, evolution, and environmental regulation. These questions can be parsed more analytically in terms of five variables: abstraction, variety, connectivity, temporality, and spatial composition. The values given to these variables structure the constellation of research questions within the broad problem agendas corresponding to generally delineated processes. For example, research questions oriented around events in zebrafish gastrulation are structured in a way that differs from the research questions oriented around vertebrate neural crest cell migration because they involve different values for the five variables: abstraction (zebrafish vs. vertebrates), temporality (earlier vs. later), spatial composition (tissue layer interactions vs. a distinctive population of cells), variety (epiboly vs. epithelium to mesenchyme transition), and connectivity (gut formation and endoderm vs. organogenesis and ectoderm/mesoderm). These configurations can be adjusted readily in response to shifts in the values for different variables (Love 2014). [ 7 ]

This anatomy of problems, with explicit epistemological structure derived from different values for these variables, operates to organize the science of development. Investigators from different disciplines can be working on the same problem but asking different questions that require distinct but complementary methodological resources. Knowledge and inquiry in developmental biology are intricately organized, just not by a central theory or group of models, and this erotetic organization is epistemologically accessible to the participating scientists. While theoretical knowledge, especially that drawn from molecular biological mechanisms (see the entry on molecular biology ) and mathematical models (e.g., reaction–diffusion models) is ubiquitous ( theory-informed ), the clusters of problems that reappear across the textbooks and correspond to different types of processes provide the governing architecture ( not theory-directed ), which can be characterized explicitly according to the variables described. Further analysis of this problem anatomy is possible, including how it is displayed in regular research articles and not just textbooks, as well as other areas of biology (see, e.g., Brigandt and Love 2012).

3. Explanatory Approaches to Development

Explanations in developmental biology are usually causal, though unlike standard mechanistic explanation there is a constant acquisition of new causal capacities (in terms of constituent entities, activities, and their organization) through development (McManus 2012; Parkkinen 2014). Although much work remains in characterizing different aspects of explanation in developmental biology, there is no doubt that a difference making or manipulability conception of causation (see the entry on causation and manipulability ) provides a core element of the reasoning (Woodward 2003; Strevens 2009; Waters 2007a). Genetic explanations of development ( Section 3.1 ), similar to what is seen in molecular genetics , work by identifying changes in the expression of genes and interactions among their RNA and protein products that lead to changes in the properties of morphological features during ontogeny (e.g., shape or size), while holding a variety of contextual variables fixed. More recently, there has been growing interest in physical explanations of development ( Section 3.2 ) that involve appeals to mechanical forces due to geometrical arrangements of mesoscale materials, such as fluid flow (Forgacs and Newman 2005). Researchers agree on the phenomena that need to be explained ( Section 1.2 and Section 2.2 ), but differ on whether physical rules or genetic factors are more or less explanatory (Keller 2002). [ 8 ] The existence of two different types of causal explanations for developmental phenomena poses an additional question about how they might be combined into a more integrated explanatory framework ( Section 3.3 ).

Many philosophers have turned to explanations of development over the past two decades in an effort to esteem or deflate claims about the causal power of genes (Keller 2002; Neumann-Held and Rehmann-Sutter 2006; Rosenberg 2006; Robert 2004; Waters 2007a). [ 9 ] Genetic explanations touch the philosophical theme of reductionism and appear to constitute the bulk of empirical success accruing to developmental biology over the past several decades. [ 10 ] Statements from developmental biologists reinforce this perspective:

Developmental biology … deals with the process by which the genes in the fertilized egg control cell behavior in the embryo and so determine its pattern, its form, and much of its behavior … differential gene activity controls development. (Wolpert et al. 1998: v, 15)

These types of statements are sometimes amplified in appeals to a genetic program for development.

[Elements of the genome] contain the sequence-specific code for development; and they determine the particular outcome of developmental processes, and thus the form of the animal produced by every embryo. … Development is the execution of the genetic program for construction of a given species of organism (Davidson 2006: 2, 16). [ 11 ]

At other times, statements concentrate on genetics as the primary locus of causation in ontogeny: “Developmental complexity is the direct output of the spatially specific expression of particular gene sets and it is at this level that we can address causality in development” (Davidson and Peter 2015: 2). Whether or not these statements can be substantiated has been the subject of intense debate. [ 12 ] The strongest claims about genetic programs or the genetic control of development have empirical and conceptual drawbacks that include an inattention to plasticity and the role of the environment, an ambiguity about the locus of causal agency, and a reliance on metaphors drawn from computer science (Gilbert and Epel 2009; Keller 2002; Moss 2002; Robert 2004). However, this leaves intact the difference-making principle of genetic explanation exhibited in molecular genetics (Waters 2007a), which yields more narrow and precise causal claims under controlled experimental conditions, and is applicable to diverse molecular entities that play causal roles during development, such as regulatory RNAs, proteins, and environmental signals. We can observe this briefly by reconsidering the example of vertebrate cardiogenesis ( Section 1.2 ).

Are there problems with claiming that genes contain all of the information (see the entry on biological information ) to form vertebrate hearts? Is there a genetic program in the DNA controlling heart development? Are genes the primary supplier and organizer of material resources for heart development, largely determining the phenotypic outcome? Existing studies of heart development have identified a role for fluid forces in specifying the internal form of the heart (Hove et al. 2003) and its left/right asymmetry (Nonaka et al. 2002). Biochemical gradients of extracellular calcium are responsible for activating the asymmetric expression of the regulatory gene Nodal (Raya et al. 2004) and inhibition of voltage gradients scrambles normal asymmetry establishment (Levin et al. 2002). Mechanical cues such as microenvironmental stiffness are crucial for key transitions from migratory cells into organized sheets during heart formation (Jackson et al. 2017). A number of genes are clearly difference makers in these processes (Asp et al. 2019; Srivastava 2006; Brand 2003; Olson 2006), but the conclusion that genes carry all the information needed to generate form features of the heart seems unwarranted. While it may be warranted empirically in some cases to privilege DNA sequence differences as causal factors in specific processes of ontogeny (Waters 2007a), such as hierarchically organized networks of genetic difference makers explaining tissue specification (Peter and Davidson 2011), the diversity of entities appealed to in molecular genetics and the extent of their individual and joint roles in specifying developmental outcomes implies that debates about the meaning, scope, and power of genetic explanations will continue (Griffiths and Stotz 2013). However, a shift away from genetic programs and genetic determinism to DNA, RNA, and proteins as difference makers that operate conjointly suggests that we conceptualize other causal factors in a similar way.

Fluid flow, as a physical force, is also a difference maker during the development of the heart, and ontogeny more generally, and developmental biologists appeal to physical difference makers, which are understood as factors in producing the morphological properties of developmental phenomena (Forgacs and Newman 2005). A physical causation approach was on display in the late 19th century work of Wilhelm His (Hopwood 1999, 2000; Pearson 2018) and especially visible in the early 20th century work of D’Arcy Thompson and others (Thompson 1992 [1942]; Keller 2002: ch. 2; Olby 1986). This occurred in the milieu of increasing attention to the chromosomal theory of inheritance and attempts to explore developmental phenomena via classical genetic methods (Morgan 1923, 1926, 1934). Thompson appealed to differential rates of growth and the constraints of geometrical relationships to explain how organismal morphology originates. Visual representations of abiotic, mechanical analogues provided the plausibility, such as the shape of liquid splashes or hanging drops for the cup and bell configurations of the free-swimming sexual stage of jellyfish. If physical forces generated specific morphologies in viscoelastic materials, then analogous morphologies in living species should be explained in terms of physical forces operating on the viscoelastic materials of the developing embryo. Yet morphogenetic processes that produce the shape and structure of morphology have been seen primarily, if not exclusively, in terms of genetics for the last half-century. Physical approaches moved into the background as molecular genetics approaches went from strength to strength (Fraser and Harland 2000).

The molecularization of experimental embryology is one of the most striking success stories of contemporary biology as genes and genetic interactions (e.g., in transcriptional networks and signaling pathways; see Section 1.3 ) were discovered to underlie specific details of differentiation, morphogenesis, pattern formation, and growth when structure originates during development. Genetic approaches predominate in contemporary developmental biology and physical modes of causation are often neglected. The frustration among researchers interested in physical causation during embryogenesis has been palpable.

To the molecular types, a cause is a molecule or a gene. To explain a phenomenon is to identify genes and characterize proteins without which the phenomenon will fail or be abnormal. A molecule is an explanation: a force is a description; to argue otherwise brings pity, at best (Albert Harris to John Trinkaus, 12 March 1996; Source: Marine Biological Laboratory Library Archives).

Despite this predominance of genetic explanatory approaches and the frustration among researchers utilizing other approaches, a groundswell of interest has been building around physical explanations of development, especially in terms of their integration with genetic explanations (Miller and Davidson 2013; Newman 2015). Some philosophers have argued that the biomechanical modeling of physical causal factors constitutes a rejection of certain forms of reductive explanation in biology (Green and Batterman 2017).

Thompson held that physical forces were explanatory but inadequate in isolation to account for the developmental origin of morphology; heredity (genetics) was also a necessary causal factor. [ 13 ] Yet Thompson was quick to highlight that mechanical modes of causation might be neglected in the midst of growing attention to heredity (genetics):

it is no less of an exaggeration if we tend to neglect these direct physical and mechanical modes of causation altogether, and to see in the characters of a bone merely the results of variation and of heredity. (Thompson 1992 [1942]: 1023)

Despite this latter form of exaggeration manifesting itself through much of the 20 th century, an agenda to combine or integrate the two approaches is now explicit. [ 14 ]

There is no controversy about whether genetic and physical modes of causation are at work simultaneously:

both the physics and biochemical signaling pathways of the embryo contribute to the form of the organism. (Von Dassow et al. 2010: 1)

They are not competing causal explanations of the same phenomenon. Explanations should capture how their productive interactions yield developmental outcomes:

an increasing number of examples point to the existence of a reciprocal interplay between expression of some developmental genes and the mechanical forces that are associated with morphogenetic movements. (Brouzés and Farge 2004: 372)

Genetic causes can lead to physical causation and vice versa . Physical causation brings about genetic causation through mechanotransduction. Stretching, contraction, compression, fluid shear stress, and other physical dynamics are sensed by different molecular components inside and outside of cells that translate these environmental changes into biochemical signals (Hoffman et al. 2011; Wozniak and Chen 2009). Genetic causation brings about physical causation by creating different physical properties of cells and tissues through the presence, absence, or change in frequency of particular proteins. For example, different patterns of expression for cell adhesion molecules (e.g., cadherins) can lead to differential adhesion across epithelial sheets of tissue and thereby generate phase separations or compartments via surface tension variation (Newman and Bhat 2008). If these modes of causation are not competing, then how might one combine genetic and physical difference makers into an integrated causal explanation? How much explanatory unity can be achieved for this “reciprocal interplay”?

Finding philosophical models for the explanatory integration of genetics and physics remains an open question (Love 2017b). Apportioning causal responsibility in the sense of determining relative contributions (e.g., the composition of causal magnitudes among different physical forces in Newtonian mechanics) is problematic because this requires commensurability with respect to how causes produce their effects (Sober 1988). In the context of causation understood in terms of difference makers, the difficulty of integration is a variation on a problem in causal reasoning identified by John Stuart Mill and labeled the “intermixture of effects,” which involves multiple causes contributing in a blended fashion to yield an outcome.

This difficulty is most of all conspicuous in the case of physiological phenomena; it being seldom possible to separate the different agencies which collectively compose an organized body, without destroying the very phenomena which it is our object to investigate. (Mill 1843 [1974]: 456 [book 3, chapter 11, section 1, paragraph 7])

Careful statistical methodology in experiments can answer whether one type of difference maker accounts for more of the variation in the effect variable for a particular population. But a ranking of causal factors with respect to how much of a difference they made is not the same as combining two modes of causation into an integrated account. Another response is to dissolve the integration problem by reducing all of the causal interactions to one of the two distinct modes, thereby achieving a kind of explanatory unity (Rosenberg 2006). However, this approach is eschewed by working biologists who take both genetic and physical modes of causation as significant and not reducible one to the other.

A different strategy is integrative pluralism (Mitchell 2002). This involves a two-step procedure for explaining complex phenomena whose features are the result of multiple causes: (a) formulate idealized models where particular causal factors operate in isolation (“theoretical modeling”); and, (b) integrate idealized models to explain how particular, concrete phenomena originate from these causes in combination. This model is suggestive but also has key drawbacks that include the fact that genetic causal reasoning in developmental biology does not typically involve theoretical modeling and the precise nature of the integration is underspecified. Integration of genetic and physical difference makers in a single mechanism offers a further possibility (Darden 2006; Craver 2007). Although this valuably highlights the productive continuity between difference makers through stages in a sequence (i.e., their reciprocal interplay), it also has handicaps. These include:

Divergent approaches to measuring time. Instead of time “in the mechanism,” time is measured with external standardized stages (see below, Section 5.2 ). Stages facilitate the study of different kinds of developmental mechanisms, with different characteristic rates and durations for their stages, within a common framework for a model organism (e.g., Drosophila ), while also permitting conserved molecular mechanisms to be studied in different species because the corresponding mechanism description is not anchored to the temporal sequence of the model organism.

An expectation that mechanism descriptions “bottom-out” in lowest level activities of molecular entities (Darden 2006). In the case of combining genetic and physical difference makers, the reciprocal interplay means that there is a studious avoidance of bottoming out in one or the other mode of causation.

The requirement of stable, compositional organization for mechanisms:

Mechanistic explanations are constitutive or componential explanations: they explain the behavior of the mechanism as a whole in terms of the organized activities and interaction of its components. (Craver 2007: 128)

But these mechanism descriptions are often embedded in different developmental contexts (at different times in ontogeny) with distinct compositional relations (within and between species). The reciprocal interplay between genetic and physical difference makers is not maintained precisely because these compositional differences alter relationships of physical causation (fluid flow, tension, etc.; see Section 1.3 ). Developmental biologists have been able to generalize relationships of genetic causation (in terms of genetic mechanisms; see Section 1.3 ) across species quite widely, but the attempt to combine these with physical causation has necessitated narrowing the scope of the causal claims.

Adequate philosophical models of the systematic dependence between genetic and physical difference makers in ontogeny need to account for how the temporal relations necessary for making causal claims are anchored in an external periodization used by developmental biologists. The imposition of different temporal scales can lead to distinct factors being significant or salient, which matters for ascertaining how different kinds of causes can be combined into integrated explanations. One possibility is to juxtapose these difference makers at distinct stages via experimental verification such that they exhibit productive continuity within the constraints of the external periodization (Love 2017b). This facilitates representing symmetry between causal factors because genetic difference makers can be placed before or after physical difference makers (and vice versa ). Although this does not provide a way to combine causal magnitudes (as in vector addition from Newtonian mechanics), it offers an explicit strategy for assigning responsibility among different kinds of causes through the vehicle of temporal organization that goes beyond ranking difference makers. The periodization serves as a template from the practices of developmental biologists for providing wholeness or unity to the different modes of causation to yield a kind of integrated explanation of the morphology that results from a sequence of developmental processes.

Not all types of causal explanation involve an external periodization and there are other ways to combine causes in order to produce more integrated explanatory frameworks. One area where combined explanations for developmental phenomena are being analyzed pertains to mechanism descriptions and mathematical modeling in systems biology (Brigandt 2013; Fagan 2013). For example, Fagan (2013: ch. 9) shows how an integrated explanation emerges from a step-wise procedure that starts with a detailed description of a molecular mechanism followed by the formulation of an abstracted wiring diagram of component interactions, which is then translated into a system of equations that can account for changes in component interactions over time. Solutions to these systems of equations and a mapping of solutions for the interactions among components of the system onto the behavior of the overall system within a shared landscape representation more systematically explains cellular differentiation.

Model organisms are central to contemporary biology and studies of embryogenesis (Ankeny and Leonelli 2011; Steel 2008; Bier and McGinnis 2003; Davis 2004). Biologists utilize only a small number of species to experimentally elucidate various properties of ontogeny (e.g., C. elegans , Drosophila , and Brachydanio [zebrafish]; see Figure 5 ). These experimental models permit researchers to investigate development in great depth and facilitate a precise dissection of causal relationships. Critics have questioned whether these models are good representatives of other species because of inherent biases involved in their selection, such as rapid development and short generation time (Bolker 1995), and problematic presumptions about the conservation of gene functions and regulatory networks (Lynch 2009). For example, C. elegans embryogenesis is not representative of nematodes in terms of pattern formation and cell specification (Schulze and Schierenberg 2011) and zebrafish appendage formation is a poor proxy for the development of appendages in tetrapods (Metscher and Ahlberg 1999).

Figure 5: Drosophila melanogaster (the common fruit fly) is one of the standard model organisms used in developmental biology.

One response to this criticism is to emphasize the conserved genetic mechanisms shared by all animals despite differences in developmental phenomena (Gerhart and Kirschner 2007; Ankeny and Leonelli 2011; Weber 2005). Fruit flies may be unrepresentative in exhibiting syncytial development, but they use the collinear expression of Hox genes to specify their anterior-posterior body axis. This response indicates that whether an entire model organism is representative per se is too coarse-grained a criterion to capture the rationale behind their use. We have to ask about representation with respect to what , and some accounts have moved in this direction. Jessica Bolker has distinguished exemplary and surrogate modes of representation (Bolker 2009), where the former serve basic research by exemplifying a larger group and the latter correspond to models designed to provide indirect experimental access to otherwise inaccessible phenomena, such as mouse models of human psychological disorders (e.g., depression). Surrogate models are adopted in biomedical contexts where the phenomena of interest are manifested in humans. Most developmental biologists consider model organisms as exemplars, not surrogates. [ 15 ] Thus, in order to respond to a criticism of non-representativeness, the criterion of representation must be explored in more detail. [ 16 ]

A basic presumption about model organisms is that they bear appropriate similarity relationships to larger groups of animals. This presumption is an instantiation of what is discussed generally for models in science meant to represent phenomena. Model organisms represent developmental phenomena in species that are either studied little or never studied at all: “we study flies and frogs as examples for the development of animals in general” (Nüsslein-Volhard 2006: 87). One source of confidence in treating them as exemplars derives from an inductive inference over discovered patterns of evolutionary conservation with respect to developmental phenomena (e.g., gastrulation or somite formation). If all or most model organisms share a developmental feature, then all or most animals will share the feature. This inference can be circumscribed more or less narrowly (e.g., if all or most vertebrate model organisms share somite formation, then all or most vertebrates will share it).

As a consequence of this confidence, the model organism (“source”) can represent these other unstudied species (“targets”). This basic distinction between the model or source and the phenomena or target it is supposed to represent is ubiquitous in reasoning with model organisms (Ankeny and Leonelli 2011). Zebrafish is a model or representation of vertebrate development, the target phenomena, because we expect to learn about vertebrate development generally by studying ontogeny in zebrafish specifically. We do not invest time and resources into zebrafish as a model organism only because we are interested in zebrafish. Researchers plan to make claims about somite formation from observations in zebrafish that will apply to somite formation in other vertebrates that we will never have the time or money to investigate.

Developmental biologists often speak of investigating mechanisms that account for phenomena in ontogeny (see Section 1.3 ), and focus on conserved genetic and cellular mechanisms in model organisms (Gerhart and Kirschner 2007; Ankeny and Leonelli 2011; Weber 2005). This suggests a distinction between representation with respect to developmental phenomena and representation with respect to genetic and cellular mechanisms operating in development. If we are interested in explaining how hearts ( phenomena ) develop, then we might investigate the molecular or cellular mechanisms occurring in the heart field during zebrafish ontogeny. Some of these mechanisms could be conserved even though the phenomena are not. Drosophila has only one cardiac cell type, no neural crest cells, and a heart with no atrial or ventricular chamber morphology (Kirby 1999). However, cardiogenesis in all invertebrates and vertebrates investigated thus far depends essentially on the expression of the homeobox gene Nkx2-5 / tinman (Gajewski and Schulz 2002). The reverse situation also can hold: similar phenomena may be manifested but genetic and cellular mechanisms might differ. Amphibians form a neural tube (neurulation) through a process of invagination (the folding of an epithelial sheet), whereas teleost fishes form a neural tube via cavitation (the hollowing out of a block of tissue via cell death). The neural tube is homologous across vertebrates (i.e., a conserved phenomenon), but the cellular and genetic mechanisms involved in invagination versus cavitation are distinct (Davies 2013: ch. 4).

The distinction between phenomena and mechanisms assumes specificity; i.e., there are specific phenomena (somite formation in vertebrates) or mechanisms (collinear Hox gene expression) in view when judging the relationship between source (model) and target. But animal development consists of a multitude of different processes that involve a host of different mechanisms. Therefore, another distinction operating in the representational criterion pertains to questions of specificity versus variety when selecting and using model organisms. A model might represent one type of target phenomena (differentiation or growth) or mechanism (cell signaling or cell cycling) but not others—specificity—or may do so better or worse with respect to particular types of phenomena or mechanisms. A model might represent several types of target phenomena and mechanisms simultaneously—variety—with variability in how each type is represented. Trade-offs exist with respect to how well different phenomena or mechanisms are co-instantiated in a model organism. Note that experimental organisms may be selected with respect to variety and specificity simultaneously, such as when a biologist working on a specific phenomenon intends to work on others using the same model in the future. They also may be selected with one or the other of these two aspects predominant. A model might be desirable if it has representational variety in both mechanisms and phenomena even if it is not the best representative for every specific mechanism or phenomenon. Conversely, a model organism might be desirable if it is the best representative for a specific mechanism despite being a very poor model for other phenomena or mechanisms. Variety is indicative of the “whole organism” being the model. [ 17 ] A further distinction can be introduced between “model organisms” and “experimental organisms” (Ankeny and Leonelli 2011) or “general model organisms” and “Krogh-principle model organisms” (Love 2010). General model organisms are selected and used with the variety aspect of the representational criterion preeminent; experimental or Krogh-principle model organisms are selected and used with specificity preeminent.

Other issues relevant to the representation criterion include how individual cells or cell types serve as developmental models (Fagan 2016), how developmental mechanisms in different model organisms are compared and evaluated (Yoshida forthcoming), how the use of model organisms constitutes an example of case-based reasoning (Ankeny 2012), and how model organisms involve idealizations or known departures from features present in the model’s target as the result of laboratory cultivation (Ankeny 2009; Section 5.2 ). Additionally, the question of representation is not the only one germane to understanding model organism use. Because model organisms are utilized for experimental intervention, questions of representation must be juxtaposed with questions of manipulation (see the supplement on Model Organisms and Manipulation ).

5. Development and Evolution

The relationships that obtain between development and evolution are complicated and under ongoing investigation (for a review, see Love 2015). Two main axes dominate within a loose conglomeration of research programs (Raff 2000; Müller 2007): (a) the evolution of development, or inquiry into the pattern and processes of how ontogeny varies and changes over time; and, (b) the developmental basis of evolution, or inquiry into the causal impact of ontogenetic processes on evolutionary trajectories—both in terms of constraint and facilitation. Two examples where the concepts and practices of developmental and evolutionary biology intersect are treated here: the problematic appeal to functional homology in developmental genetics that is meant to underwrite evolutionary generalizations about ontogeny ( Section 5.1 ) and the tension between using normal stages for developmental investigation and determining the evolutionary significance of phenotypic plasticity ( Section 5.2 ). These cases expose some of the philosophical issues inherent in how development and evolution can be related to one another.

The conserved role of Hox genes in axial patterning is referred to as functionally homologous across animals (Manak and Scott 1994), over and above the relation of structural homology that obtains between DNA sequences. And yet “functional homology” is a contradiction in terms (Abouheif et al. 1997) because the definition of a homologue is “the same organ in different animals under every variety of form and function” (Owen 1843: 379)—the descendant, evolutionary distinction between homology (structure) and analogy (function) is founded on this recognition. Therefore, the idea of functional homology appears theoretically confused and there is a conceptual tension in its use by molecular developmental biologists.

Figure 6: Vertebrate wings are homologous as forelimbs; they are derived by common descent from the same structure. The function of vertebrate wings (i.e., flight) is analogous; although the wings fulfill similar functions, their role in flight has evolved separately.

The reference to “organ” in Owen’s definition is indicative of a structure (an entity) found in an organism that may vary in its shape and composition (form) or what it is for (function) in the species where it occurs. Translated into an evolutionary context, sameness is cashed out by reference to common ancestry. Since structures also can be similar by virtue of natural selection operating in similar environments, homology is contrasted with analogy. Homologous structures are the same by virtue of descent from a common ancestor, regardless of what functions these structures are involved in, whereas analogous structures are similar by virtue of selection processes favoring comparable functional outcomes, regardless of common descent ( Figure 6 ).

This is what makes similarity of function an especially problematic criterion of homology (Abouheif et al. 1997). Because functional similarity is the appropriate relation for analogy, it is not necessary for analogues to have the same function as a consequence of common ancestry—similarity despite different origins suffices (Ghiselin 2005). Classic cases of analogy involve taxa that do not share a recent common ancestor that exhibits the structure, such as the external body morphology of dolphins and tuna (Pabst 2000). Thus, functional homology seems to be a category error because what a structure does should not enter into an evaluation of homologue correspondence and similarity of function is often the result of adaptation via natural selection to common environmental demands, not common ancestry.

Although we might be inclined to simply prohibit the terminology of functional homology, its widespread use in molecular and developmental biology should at least make us pause. [ 18 ] While it is important to recognize this pervasive practice, some occurrences may be illicit. Swapping structurally homologous genes between species to rescue mutant or null phenotypes is not a genuine criterion of functional homology, especially when there is little or no attention to establishing a phylogenetic context. This makes a number of claims of functional homology suspect. To not run afoul of the conceptual tension, explicit attention must be given to the meaning of “function.” Biological practice harbors at least four separate meanings of function (Wouters 2003, 2005): activity (what something does), causal role (contribution to a capacity), fitness advantage or viability (value of having something), and selected effect or etiology (origination and maintenance via natural selection). Debate has raged about which of them (if any) is most appropriate for different aspects of biological and psychological reasoning or most general in scope (i.e., what makes them all function concepts?) (see discussion in Garson 2016). Here the issue is whether we can identify a legitimate concept of homology of function.

If we are to avoid mixing homology and analogy, then the appropriate notion of function cannot be based on selection history, which is allied with the concept of analogy and concerns a particular variety of function. Similarly, viability interpretations concentrate on features where the variety of function is critical because of conferred survival advantages. Any interpretation of function that relies on a particular variety of function (because it was selected or because it confers viability) clashes with the demand that homology concern something “under every variety of form and function.” A causal role interpretation emphasizes a systemic capacity to which a function makes a contribution. It too focuses on a particular variety of function, though in a way different from either selected effect or viability interpretations. Only an activity interpretation (‘what something does’) accents the function itself, apart from its specific contribution to a systemic capacity and position in a larger context. Therefore, the most appropriate meaning to incorporate into homology of function is “ activity -function” because it is at least possible for activity-functions to remain constant under every variety. An evaluation of sameness due to common ancestry is made separately from the role the function plays (or its use), whether understood in terms of a causal role, a fitness advantage, or a history of selection. [ 19 ] Activity -functions can be put to different uses while being shared via common descent (i.e., homologous). More precisely, homology of function can be defined as the same activity-function in different animals under every variety of form and use-function (Love 2007). This unambiguously removes the tension that plagued functional homology.

Careful discussions of regulatory gene function in development and evolution recognize something akin to the distinction between activity- and use-function (i.e., between what a gene does and what it is for in some process within the organism).

When studying the molecular evolution of regulatory genes, their biochemical and developmental function must be considered separately. The biochemical function of PAX-6 and eyeless are as general transcription factors (which bind and activate downstream genes), but their developmental function is their specific involvement in eye morphogenesis (Abouheif 1997: 407).

The biochemical function is the activity-function and the developmental function is the use-function. This distinction helps to discriminate between divergent evolutionary trajectories. Biochemical (activity-functions) of genes are often conserved (i.e., homologous), while simultaneously being available for co-option to make causal role contributions (use-functions) to distinct developmental processes. The same regulatory genes are evolutionarily stable in terms of activity-function and evolutionarily labile in terms of use-function. [ 20 ] By implication, claims about use-function homology for genes qua developmental function are suspect compared to those concerning activity-function homology for genes qua biochemical function because developmental functions are more likely to have changed as phylogenetic distance increases.

The distinction between biochemical (activity) function and developmental (use) function is reinforced by the hierarchical aspects of homology (Hall 1994). A capacity defining the use-function of a regulatory gene at one level of organization, such as axial patterning, must be considered as an activity-function itself at another level of organization, such as the differentiation of serially repeated elements along a body axis. (Note that “ level of organization ” need not be compositional and thus the language of “higher” and “lower” levels may be inappropriate.) The developmental roles of Hox genes in axial patterning may be conserved by virtue of their biochemical activity-function homologies but Hox genes are not use-function homologues because of these developmental roles. Instead of focusing on the activity of a gene component and its causal role in axial patterning, we shift to the activity of axial patterning and its causal role elsewhere (or elsewhen) in embryonic development.

Introducing a conceptually legitimate idea of homology of activity-function is not about keeping the ideas of developmental biology tidy. It assists in the interpretation of evidence and circumscribes the inferences drawn. For example, NK-2 genes are involved in mesoderm specification, which underlies muscle morphogenesis. In Drosophila , the expression of a particular NK-2 gene ( tinman ) is critical for both cardiac and visceral mesoderm development. If tinman is knocked out and transgenically replaced with its vertebrate orthologue, Nkx2-5 , only visceral mesoderm specification is rescued; the regulation of cardiac mesoderm is not (Ranganayakulu et al. 1998). A region of the vertebrate protein near the 5′ end of the polypeptide differs enough to prevent appropriate regulation in cardiac morphogenesis. The homeodomains (stretches of sequence that confer DNA binding) for vertebrate Nkx2-5 and Drosophila tinman are interchangeable. The inability of Nkx2-5 to rescue cardiac mesoderm specification is not related to the activity-function of differential DNA binding. One component of the orthologous (homologous) proteins in both species retains an activity-function homology related to visceral mesoderm specification but another component (not the homeodomain) has diverged. This homeobox gene does not have a single use-function (as expected), but it also does not have a single activity-function. Any adequate evaluation of these cases must recognize a more fine-grained decomposition of genes into working units to capture genuine activity-function conservation. We can link activity-function homologues directly to structural motifs within a gene, but there is not necessarily a single activity-function for an entire open reading frame.

Defusing the conceptual tensions between developmental and evolutionary biology with respect to homology of function has a direct impact on the causal generalizations and inferences made from model organisms ( Section 4 ). Activity-function homology directs our attention to the stability or conservation of activities. This conservation is indicative of when the study of mechanisms in model organisms will produce robust and stable generalizations ( Section 1.3 ). The widespread use of functional homology in developmental biology is aimed at exactly this kind of question, which explains its persistence in experimental biology despite conceptual ambiguities. Generalizations concerning molecular signaling cascades are underwritten by the coordinated biochemical activities in view, not the developmental roles (though sometimes they may coincide). Thus, activity-function details about a signaling cascade gleaned from a model organism can be generalized via homology to other unstudied organisms even if the developmental role varies for the activity-function in other species.

All reasoning strategies combine distinctive strengths alongside of latent weaknesses. For example, decomposing a system into its constituents to understand the features manifested by the system promotes a dissection of the causal interactions of the localized constituents, while downplaying interactions with elements external to the system (Wimsatt 1980; Bechtel and Richardson 1993). Sometimes the descriptive and explanatory practices of the sciences are successful precisely because they intentionally ignore aspects of natural phenomena or use a variety of approximation techniques. Idealization is one type of reasoning strategy that scientists use to describe, model, and explain that purposefully departs from features known to be present in nature. For example, the interior space of a cell is often depicted as relatively empty even though intracellular space is known to be crowded (Ellis 2001); the variable of cellular volume takes on a value that is known to be false (i.e., relatively empty). Idealizations involve knowingly ignoring variations in properties or excluding particular values for variables, in a variety of different ways, for descriptive and explanatory purposes (Jones 2005; Weisberg 2007).

“Normal development” is conceptualized through strategies of abstraction that manage variation inherent within and across developing organisms (Lowe 2015, 2016). The study of ontogeny in model organisms ( Section 4 ) is usually executed by establishing a set of normal stages for embryonic development (see Other Internet Resources). A developmental trajectory from fertilized zygote to fully-formed adult is broken down into distinct temporal periods by reference to the occurrence of major events, such as fertilization, gastrulation, or metamorphosis (Minelli 2003: ch. 4; see Section 1.2 ). This enables researchers in different laboratory contexts to have standardized comparisons of experimental results (Hopwood 2005, 2007). They are critical to large communities of developmental biologists working on well-established models, such as chick (Hamburger and Hamilton 1951) or zebrafish (Kimmel et al. 1995): “Embryological research is now unimaginable without such standard series” (Hopwood 2005: 239). These normal stages are a form of idealization because they intentionally ignore kinds of variation in development, including variation associated with environmental variables. While facilitating the study of particular causal relationships, this means that specific kinds of variation in developmental features that might be relevant to evolution are minimized in the process of rendering ontogeny experimentally tractable (Love 2010).

Phenotypic plasticity is a ubiquitous biological phenomenon. It involves the capacity of a particular genotype to generate phenotypic variation, often in the guise of qualitatively distinct phenotypes, in response to differential environmental cues (Pigliucci 2001; DeWitt and Scheiner 2004; Kaplan 2008; Gilbert and Epel 2009). One familiar example is seasonal caterpillar morphs that depend on different nutritional sources (Greene 1989). Some of the relevant environmental variables include temperature, nutrition, pressure/gravity, light, predators or stressful conditions, and population density (Gilbert and Epel 2009). The reaction norm is a summary of the range of phenotypes, whether quantitatively or qualitatively varying, exhibited by organisms of a given genotype for different environmental conditions. When the reaction norm exhibits discontinuous variation or bivalent phenotypes (rather than quantitative, continuous variation), it is often labeled a polyphenism ( Figure 7 ).

Figure 7: A color polyphenism in American Peppered Moth caterpillars that represents an example of phenotypic plasticity.

Phenotypic plasticity has been of recurring interest to biological researchers and controversial in evolutionary theory. Extensive study of phenotypic plasticity has occurred in the context of quantitative genetic methods and phenotypic selection analyses, where the extent of plasticity in natural populations has been demonstrated and operational measures delineated for its detection (Scheiner 1993; Pigliucci 2001). Other aspects of plasticity require different investigative methods to ascertain the sources of plasticity during ontogeny, the molecular genetic mechanisms that encourage plasticity, and the kinds of mapping functions that exist between the genotype and phenotype (Pigliucci 2001; Kirschner and Gerhart 2005: ch. 5). These latter aspects, the origin of phenotypic variation during and after ontogeny, are in view at the intersection of development and evolution: How do molecular genetic mechanisms produce (or reduce) plasticity? What genotype-phenotype mapping functions are prevalent or rare? Does plasticity contribute to the origination of evolutionary novelties (Moczek et al. 2011; West-Eberhard 2003)?

In order to evaluate these questions experimentally, researchers need to alter development through the manipulation of environmental variables and observe how a novel phenotype can be established within the existing plasticity of an organism (Kirschner and Gerhart 2005: ch. 5). This manipulation could allow for the identification of patterns of variation through the reliable replication of particular experimental alterations within different environmental regimes. However, without measuring variation across different environmental regimes, you cannot observe phenotypic plasticity. These measurements are required to document the degree of plasticity and its patterns for a particular trait, such as qualitatively distinct morphs. An evaluation of the significance of phenotypic plasticity for evolution requires answers to questions about where plasticity emerges, how molecular genetic mechanisms are involved in the plasticity, and what genotype-phenotype relations obtain.

Developmental stages intentionally ignore variation associated with phenotypic plasticity. Animals and plants are raised under stable environmental conditions so that stages can be reproduced in different laboratory settings and variation is often viewed as noise that must be reduced or eliminated if one is to understand how development works (Frankino and Raff 2004). This practice also encourages the selection of model organisms that exhibit less plasticity (Bolker 1995). The laboratory domestication of a model organism may also reduce the amount or type of observable phenotypic variation (Gu et al. 2005), though laboratory domestication also can increase variation (e.g., via inbreeding). Despite attempts to reduce variation by controlling environmental factors, some of it always remains (Lowe 2015) and is displayed by the fact that absolute chronology is not a reliable measure of time in ontogeny, and neither is the initiation or completion of its different parts (Mabee et al. 2000; Sheil and Greenbaum 2005). Developmental stages allow this recalcitrant variation to be effectively ignored by judgments of embryonic typicality. Normal stages also involve assumptions about the causal connections between different processes across sequences of stages (Minelli 2003: ch. 4). Once these stages have been constructed, it is possible to use them as a visual standard against which to recognize and describe variation as a deviation from the norm (DiTeresi 2010; Lowe 2016). But, more typically, variation ignored in the construction of these stages is also ignored in the routine consultation of the stages in day-to-day research contexts (Frankino and Raff 2004).

Normal stages fulfill a number of goals related to descriptive and explanatory endeavors that developmental biologists engage in (Kimmel et al. 1995). They yield a way to measure experimental replication, enable consistent and unambiguous communication among researchers, especially if stages are founded on commonly observable morphological features, facilitate accurate predictions of developmental phenomena, and aid in making comparisons or generalizations across species. As idealizations of ontogeny, normal stages allow for a classification of developmental events that is comprehensive with suitably sized and relatively homogeneous stages, reasonably sharp boundaries between stages, and stability under different investigative conditions (Dupré 2001), which encourages more precise explanations within particular disciplinary approaches (Griesemer 1996). Idealizations also can facilitate abstraction and generalization, both of which are a part of extrapolating findings from the investigative context of a model organism to other domains (Steel 2008; see Section 4 and 5.1 ).

There are various weaknesses associated with normal stages that accompany the fulfillment of these investigative and explanatory goals. Key morphological indicators sometimes overlap stages, terminology that is useful for one purpose may be misleading for another, particular terms can be misleading in cross-species comparisons, and manipulation of the embryo for continued observation can have a causal impact on ontogeny. Avoiding variability in stage indicators can encourage overlooking the significance of this variation, or at least provide a reason to favor its minimization.

Thus, there are good reasons for adopting normal stages to periodize model organism ontogeny, and these reasons help to explain why their continued use yields empirical success. However, similar to other standard (successful) practices in science, normal stages are often taken for granted, which means their biasing effects are neglected (Wimsatt 1980), some of which are relevant to evolutionary questions (e.g., systematically underestimating the extent of variation in a population). This is critical to recognize because the success of a periodization is not a function of the eventual ability to relax the idealizations; periodizations are not slowly corrected so that they become less idealized. Instead, new periodizations are constructed and used alongside the existing ones because different idealizations involve different judgments of typicality that serve diverse descriptive and explanatory aims. In addition to the systematic biases involved in developmental staging, most model organisms are poorly suited to inform us about how environmental effects modulate or combine with genetic or other factors in development—they make it difficult to discover details about mechanisms underlying reaction norms. Short generation times and rapid development are tightly correlated with insensitivity to environmental conditions through various mechanisms such as prepatterning (Bolker 1995).

The tension between the specific practice of developmental staging in model organisms and uncovering the relevance of variation due to phenotypic plasticity for evolution can be reconstructed as an argument.

• Variation due to phenotypic plasticity is a normal feature of ontogeny.
• The developmental staging of model organisms intentionally downplays variation in ontogeny associated with the effects of environmental variables (e.g., phenotypic plasticity) by strictly limiting the range of values for environmental variables and by removing variation in characters utilized to establish the comprehensive periodization.
• Therefore, using model organisms with specified developmental stages will make it difficult, if not impossible, to observe patterns of variation due to phenotypic plasticity.

Although this tension obtains even if the focus is not on evolutionary questions, sometimes encouraging developmental biologists to interpret absence of evidence as evidence of the developmental insignificance of phenotypic plasticity, it is exacerbated for evolutionary researchers. The documentation of patterns of variation is precisely what is required to gauge the evolutionary significance of phenotypic plasticity. Practices of developmental staging in model organisms can retard our ability to make either a positive or negative assessment. Developmental staging, in conjunction with the properties of model organisms, tends to encourage a negative assessment of the evolutionary importance of phenotypic plasticity because the variation is not manifested and documented, and therefore is unlikely to be reckoned as substantive. Idealizations involving normal stages discourage a robust experimental probing of phenotypic plasticity, which is an obstacle to determining its evolutionary significance.

The consequences of this tension for the intersection of development and evolution are two-fold. First, the most powerful experimental systems for studying development are set up to minimize variation that may be critical to comprehending how evolutionary processes occur in nature. Second, if evolutionary investigations revolve around a character that was assessed for typicality to underwrite the temporal partitions that we call stages, then much of the variation in this character was conceptually removed as a part of rendering the model organism experimentally tractable. [ 21 ]

The identification of drawbacks that accompany strategies of idealization used to study development invites consideration of ways to address the liabilities identified (Love 2006). We can construct a principled perspective on how to address these liabilities by adding three further premises:

• Reasoning strategies involving idealization, such as (2), are necessary to the successful prosecution of biological investigations of ontogeny.
• Therefore, compensatory tactics should be chosen in such a way as to specifically redress the blind spots arising from the kind of idealizations utilized.
• Given (1)–(3), compensatory tactics must be related to the effects of ignoring variation due to phenotypic plasticity that result from the developmental staging of model organisms.

At least two compensatory tactics can promote observations of variation due to phenotypic plasticity that is ignored when developmental stages are constructed for model organisms: the employment of diverse model organisms and the adoption of alternate periodizations.

Variation often will be observable in non-standard model organisms because experimental organisms that do not have large communities built around them are less likely to have had their embryonic development formally staged, and thus the effects of idealization on phenotypic plasticity are not operative. In turn, researchers are sensitized to the ways in which these kinds of variation are being muted in the study of standard models. Stages can be used then as visual standards to identify variation as deviations from a norm and thereby characterize patterns of variability. [ 22 ]

A second compensatory tactic is the adoption of alternative periodizations. This involves choosing different characters to construct new temporal partitions, thereby facilitating the observation of variation with respect to characteristics previously stabilized in the normal stage periodization. These alternative periodizations often divide a subset of developmental events according to processes or landmarks that differ from those used to construct the normal stages, and they may not map one-one onto the existing normal stages, especially if they encompass events beyond the trajectory from fertilization to a sexually mature adult. This lack of isomorphism between periodizations also will be manifested if different measures of time are utilized, whether sequence (event ordering) or duration (succession of defined intervals), and whether sequences or durations are measured relative to one another or against an external standard, such as absolute chronology (Reiss 2003; Colbert and Rowe 2008). These incompatibilities prevent assimilating the alternative periodizations into a single, overarching staging scheme. In all of these cases, idealization is involved and therefore each new periodization is subject to the liabilities of ignoring kinds of variation. However, alternative periodizations require choosing different characters to stabilize and typify when defining its temporal partitions, which means different kinds of variation will be exposed than were previously observable. [ 23 ]

The compensatory tactics of employing a diversity of model organisms and adopting alternative periodizations may be conceptually appropriate for addressing how the practice of developmental staging has an impact on the detection of phenotypic plasticity, but this does not remove associated costs (human, financial, and otherwise) or controversy. The advantages of a single, comprehensive periodization for a general model organism (e.g., zebrafish normal stages) must be weighed in light of the advantages of alternative, process-specific periodizations. However, by openly scrutinizing these practices in relation to the phenomenon of interest and recognizing both advantages and drawbacks involved in the idealizations utilized, developmental and evolutionary biologists are better positioned to offer systematic descriptions and comprehensive explanations of biological phenomena.

This entry has only sampled a small portion of work relevant to the import and promise of conceptual reflection on the epistemology of developmental biology. Much more could be said about each of the above domains, such as a more fine-grained analysis of how normal stages operate as types in developmental biology (DiTeresi 2010; Lowe 2016). Additionally, little has been said about how evidence works in developmental biological experimentation or differences between confirmatory and exploratory experimentation (Hall 2005; O’Malley 2007; Waters 2007b), nor have I treated the role of metaphors and models that characterize key practices in developmental biology (Fagan 2013; Keller 2002). The latter have been perspicuously analyzed via increased attention to the details of particular research programs. Finally, nothing has been said about the metaphysical implications of developmental phenomena (a key input for Aristotle’s metaphysics ). Concepts of potentiality are very natural in descriptions of embryological phenomena (e.g., the pluripotency of stem cells or the potential of a germ layer to yield different kinds of tissue lineages) and some have argued that empirical advances in developmental biology support a new form of essentialism about biological natural kinds (Austin 2019). This bears on how we understand dispositions (see the entry on dispositions ) because the triggering conditions are often complex and multiply realized (including manifestations without a trigger), as well as the fact that cells exhibit dispositions with multiple possible manifestations (cell types) in specific sequential orderings (Hüttemann and Kaiser 2018; Laplane 2016). Metaphysical issues also arise in the context of human developmental biology, such as how to understand the ontology of pregnancy (Kingma 2018; Sidzinska 2017). Thus, developmental biology displays not only a rich array of material and conceptual practices that can be analyzed to better understand the scientific reasoning exhibited in experimental life science, but also points in the direction of new ideas for metaphysics, especially when that endeavor explicitly considers the input of empirically successful sciences.

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• Figure 1 : “Preformation”, drawn by Nicolaas Hartsoeker ( Essai de Dioptrique , 1694). Licensed under Public domain via Wikimedia Commons. http://commons.wikimedia.org/wiki/File:Preformation.GIF
• Figure 2 : “Spiral cleavage in gastropod Trochus ” by Morgan Q. Goulding, 2009, “Cell Lineage of the Ilyanassa Embryo: Evolutionary Acceleration of Regional Differentiation during Early Development“, PLoS ONE , 4(5): e5506, doi:10.1371/journal.pone.0005506, Figure 1 TIFF. Licensed under Creative Commons Attribution 2.5 via Wikimedia Commons. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0005506 OR http://commons.wikimedia.org/wiki/File:Spiral_cleavage_in_Trochus.png
• Figure 3 : “Hematopoiesis simple” by Mikael Häggström (no attribution required), from original by A. Rad (requires attribution) - Image:Hematopoiesis_(human)_diagram.png by A. Rad. Licensed under Creative Commons Attribution-Share Alike 3.0 via Wikimedia Commons. http://commons.wikimedia.org/wiki/File:Hematopoiesis_simple.svg
• Figure 4 : “Wingless and Hedgehog reciprocal signaling during segmentation of Drosophila embryos” by Fred the Oyster (requires attribution). Licensed under Creative Commons Attribution-Share Alike 4.0 via Wikimedia Commons. https://upload.wikimedia.org/wikipedia/commons/7/7c/Wingless_and_Hedgehog_reciprocal_signaling_during_segmentation_of_Drosophila_embryos.svg
• Figure 5 : “ Drosophila melanogaster —side (aka)” by André Karwath aka Aka - Own work. Licensed under Creative Commons Attribution-Share Alike 2.5 via Wikimedia Commons: http://commons.wikimedia.org/wiki/File:Drosophila_melanogaster_-_side_(aka).jpg
• Figure 6 : “Homology” from George John Romanes, 1892 [1910], Darwin and after Darwin , (fourth edition), Chicago: The Open Court Publishing Company, Figure 5, Chapter 3, p. 56, “Wings of Reptile, Mammal, and Bird. Drawn from nature (Brit. Mus.)”. http://www.talkorigins.org/faqs/precursors/images/homology.jpg . Licensed under Public domain via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Homology.jpg
• Figure 7 : “ Biston betularia caterpillars on birch (left) and willow (right)” by Mohamed A.F. Noor, Robin S. Parnell, Bruce S. Grant, 2008, “A Reversible Color Polyphenism in American Peppered Moth ( Biston betularia cognataria ) Caterpillars”, PLoS ONE , 3(9): e3142. doi:10.1371/journal.pone.0003142. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0003142 Licensed under Creative Commons Attribution 2.5 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Biston_betularia.png

How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

• Embryo Project
• Visible Embryo (Human Development)
• Society for Developmental Biology
• Model Organisms for Biological Research
• Normal Stages for Model Organisms Used in Developmental Biology

Aristotle, General Topics: biology | Aristotle, General Topics: metaphysics | biological development: theories of | biology: experiment in | biology: philosophy of | Boyle, Robert | causation: and manipulability | cell biology, philosophy of | Conway, Lady Anne | Descartes, René | dispositions | epistemology | evolution | evolution: concept before Darwin | evolution: from the Origin of Species to the Descent of Man | Gassendi, Pierre | gene | genetics | genetics: genotype/phenotype distinction | information: biological | Kant, Immanuel | Leibniz, Gottfried Wilhelm | levels of organization in biology | life | Malebranche, Nicolas | mechanism in science | metaphysics | Mill, John Stuart | models in science | molecular biology | reduction, scientific: in biology | scientific explanation | systems and synthetic biology, philosophy of | teleology: teleological notions in biology

Acknowledgments

Thanks to the many philosophical and scientific colleagues who have provided me with extensive comments on different aspects of this material over the past decade. I am grateful to Max Dresow, Kelle Dhein, Nathan Lackey, Lauren Wilson, and Yoshinari Yoshida for insightful recommendations and an anonymous referee for helpful feedback that substantially improved the final version of the entry.

Copyright © 2020 by Alan Love < aclove @ umn . edu >

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9 Evolving Embryology

• Published: September 2003
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This chapter discusses the development of embryology. Experimental embryology, which began in the 1880s, aimed to discover the actual physicochemical processes by which the adult developed from the egg. Referred to by its founders as “developmental mechanics”, it originated from the premise that organisms could be understood in the same way as machines. Its findings however, caused biologists to stand back somewhat as their studies revealed a complexity of development that confronted the general aim of understanding the organism in terms of its parts. Many leaders of the new generation of experimental embryologists opposed the concept of organisms as constituting a cell state.

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Embryology Definition

Embryology is the branch of biology concerned with the development of new organisms. Embryologists track reproductive cells (gametes) as they progress through fertilization , become a single-celled zygote, then an embryo, all the way to a fully functioning organism. There are many subdivisions of embryology, some scientist focusing on human embryos, while others study animals and plants. Evolutionary biologists often use embryology as a means of comparing species, as the development of an organism can give many clues to its evolutionary history. Still other scientists use embryology as a tool to better understand the system or organism they are dealing with, be it conservation of an endangered species or the reproductive disruption of a pest species. Scientists studying human embryology assist with women’s reproductive health, and understand the broad scope of issues which can lead to developmental defects and malformations.

History of Embryology

Early scientists and philosophers were not ignorant, and were aware of sperm as soon as the microscope was invented. However, there have been competing theories in early embryology. The first notions of embryology are as old as the classical philosophers. Aristotle first proposed the correct mechanism for the development of an embryo, without having a microscope to observe his theory. Aristotle suggested that animals form through the process of epigenesis , in which a single cell divides and differentiates into the many tissues and organs of an animal. However, without evidence, a theory is really only a guess.

A second theory, preformation , gained much traction before the invention of microscopes and more advanced imaging techniques. This idea suggested that the embryo was contained, small but fully formed, inside the sperm. An image of this theory can be seen above. This theory also suggested women were simply vessels to carry the growing child, and that girls came from the left testicle, while boys came from the right. Knowing modern biology, it is obvious that this theory is incorrect. At the time, though, lack of proof and religious overtones into science pushed this rather sexist and equally unproven idea. When the microscope finally was invented, one of the first things people looked at was sperm. The sperm were magnified to the limits of early microscopes, and no fully formed small babies were ever found. But, this failed to fully convince the preformation supporters that epigenesis was the right answer.

It wasn’t until 1827 that clear evidence was obtained that female mammals also produce a sex cell, the ovum . The discovery of a female sex cell directly contradicted many aspects of the preformation theory, and led to wider acceptance of the epigenesis theory. Karl Ernst von Baer, discoverer of the ovum, and Heinz Christian Pander then proposed the theory which is still at the heart of embryology today. That theory is the germ layer theory, which postulates that a single cell becomes separate layers of cells as the early organism divides. These germ layers then give rise to the rest of the organism by growing and folding into organs, vessels, and other complex tissues and the cells within differentiate accordingly.

A few more advancements would fully establish the germ layer theory into embryology. The discovery and understanding of DNA led to a more comprehensive understanding of how sperm and egg become a zygote. The development of ultrasound greatly increased the understanding of fetus development in humans, seen in the above image. Many studies were done on simple organisms to understand basic embryology. The flat worm was cultured intensively, as it reproduces sexually and the cells are large enough to watch develop under a good microscope. The fruit fly was also observed extensively, for similar reasons. Studying a polychaete worm, E.B. Wilson developed a coding process to label and understand the movements and divisions of cells during embryogenesis. While the exact process changes depending on the species, this method greatly expedited the understanding of embryology and led to medical and evolutionary science breakthroughs.

Careers in Embryology

An embryologist is a scientist who studies embryology. Any organism that reproduces sexually must create some sort of embryo as it develops into an adult form. An embryologist may study the development of animals, plants, and even fungi. Evolutionary biologists often study embryology as a means of understanding complicated lines of evolution. For instance, all vertebrates including humans go through an embryological phase in which the precursors for gills are present. In humans, these structures develop into structures of the throat. However, the similarity between all vertebrate embryos suggests that all vertebrates arose from a common ancestor which used this form of embryogenesis. A professional embryologist may remain in academia, advancing the science of embryology, or can choose to join the medical profession.

Embryologists are needed anywhere pregnancy is handled, as pregnancy is simply human embryogenesis. Some scientists specialize in disruptions to embryogenesis which result in malformations and disorders. This is called teratology , and covers everything from miscarriages to birth defects. Doctors can specialize solely in embryology and teratology or may choose to cover a broader range of women’s health issues.

Many professions employ knowledge of embryology in their practices. Many pharmaceutical companies develop drugs for both fertility and sterility, and the processes of embryology are key to these efforts. Scientists developing insecticides, or ways to deal with other pests, often turn to embryology to battle the reproductive cycles of the organisms. This is often the most cost-efficient way to battle a large pest problem. Others use embryology for the advantage of a species, like the scientists trying to repopulate endangered species. For instance, researchers at several institutions across the United States are teaming up to save the Black-Footed Ferret. They must understand ferret embryology to fully be successful, as well as their behavior, diet, and mating habits. This is a good example of how embryology plays a small but very important role in a larger scientific endeavor.

Brusca, R. C., & Brusca, G. J. (2003). Invertebrates. Sunderland, MA: Sinauer Associates, Inc. Hyttel, P., Sinowatz, F., & Vejlsted, M. (2010). Essentials of Domestic Animal Embryology. China: Elsevier Limited. Pough, F. H., Janis, C. M., & Heiser, J. B. (2009). Vertebrate Life. Boston: Pearson Benjamin Cummings.

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Evolution of embryology: a synthesis of classical, experimental, and molecular perspectives

Affiliation.

• 1 Departamento de Ciencias Morfológicas I, Facultad de Medicina, Universidad Complutense de Madrid, Spain. [email protected]
• PMID: 11241752
• DOI: 10.1002/1098-2353(200103)14:2<158::AID-CA1025>3.0.CO;2-Q

Embryology as a modern science began at the beginning of the 19th century and continued as the classic period until the 1940s. During this period, a body of basic knowledge was established which, generally, described the events of development. From 1940 to 1970 experimental or causal embryology predominated; explanations of secondary causes were demonstrated for development. The decade of the 1970s was a decade of transition that led to the current revolution in molecular biology that began in the 1980s. Molecular biology and its new branch, molecular genetics, shook up the heretofore serene, but already limited, field of embryology. Today the discipline of embryology is being built on the analysis of the results of genetic expression. Embryology is now concerned with understanding development from the viewpoint of the activation and transcription of DNA sequences, which will allow us to approach the first causes or underlying genetic and epigenetic mechanisms of development. As a result, embryology and genetics have fused into a wider biological subdiscipline, developmental biology. Will this be enough to define the full scope of our knowledge of embryonic development? What is certainly evident is that the molecular period of embryology will help achieve a better understanding of the schemata constructed by classic and experimental embryologists. Furthermore, to the degree that the molecular analysis of whatever phenomenon of development requires additional foundational knowledge, classic and experimental embryology will not have exhausted all their possibilities.

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Quantitative experimental embryology: a modern classical approach.

1. Introduction

2. cell addition as an essential tool in experimental embryology, 2.1. chimaeras, homotypic grafts and size regulation, 2.2. blastula aggregation, 2.3. homotypic grafting experiments, 2.4. inductive reprogramming, 3. cell removal as an essential tool in experimental embryology, 4. tissue embedding as an essential tool in experimental embryology, 4.1. force generation and tissue mechanics during development, 4.2. intrinsic and extrinsic mechanical cues in development, 4.3. control of mechanical and biochemical parameters, 5. conclusions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Busby, L.; Saunders, D.; Serrano Nájera, G.; Steventon, B. Quantitative Experimental Embryology: A Modern Classical Approach. J. Dev. Biol. 2022 , 10 , 44. https://doi.org/10.3390/jdb10040044

Busby L, Saunders D, Serrano Nájera G, Steventon B. Quantitative Experimental Embryology: A Modern Classical Approach. Journal of Developmental Biology . 2022; 10(4):44. https://doi.org/10.3390/jdb10040044

Busby, Lara, Dillan Saunders, Guillermo Serrano Nájera, and Benjamin Steventon. 2022. "Quantitative Experimental Embryology: A Modern Classical Approach" Journal of Developmental Biology 10, no. 4: 44. https://doi.org/10.3390/jdb10040044

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The Historiography of Embryology and Developmental Biology

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• Kate MacCord 6 &
• Jane Maienschein 5 , 6  

Part of the book series: Historiography of Science ((HISTSC,volume 1))

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Embryology is the science of studying how embryos undergo change over time as they grow and differentiate. The unit of study is the unfolding organism, and the timeline upon which embryology is focused is brief compared to the life cycle of the organism. Developmental biology is the science of studying development, which includes all of the processes that are required go from a single celled embryo to an adult. While embryos undergo development, so to do later stages of organisms. Thus, development is broader than embryology, and it focuses on the processes more than on the entities being developed. The fields overlap, and in some senses embryology gave way to developmental biology as new techniques and new questions, in particular genetic analyses and methods, allowed researchers to “see” more inside of organisms and manipulate the processes that are required for their unfolding. This article examines the ways in which developmental biology manifested from embryology, while also retaining aspects of the scientific goals and approaches of the earlier field of embryology. It also looks at the ways in which the study of both embryos and their processes of development have intersected with evolution, both in the nineteenth century and throughout the late-20th century emergence of the field of evolutionary developmental biology.

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MacCord, K., Maienschein, J. (2018). The Historiography of Embryology and Developmental Biology. In: Dietrich, M., Borrello, M., Harman, O. (eds) Handbook of the Historiography of Biology. Historiography of Science, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-74456-8_7-1

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Experimental Embryology

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The organizer concept and modern embryology: anglo-american perspectives., studies on the nature of the amphibian organization centre. ii.--induction by synthetic polycyclic hydrocarbons, yolk utilization in the notochord of newt as studied by electron microscopy., substrate-ex/yme orientation during embryonic development, development of cells in differentiating urodele pituitaries showing rna and alkaline phosphatase characteristics associated with the beginning of secretion, molecular heterochrony in the pattern of fibronectin expression during gastrulation in amphibians, landmarks in developmental biology 1883–1924, a simplified method for the fusion of two or more amphibian embryos, embryonic and larval epiphysectomy in the salamander, taricha torosa, and observations on scoliosis, the revolutionary developmental biology of wilhelm his, sr., related papers.

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• > Understanding Development
• > Defining Development, if Possible

Book contents

• Understanding Development
• Series page
• Copyright page
• Acknowledgements
• 1 Defining Development, if Possible
• 2 Cells and Development
• 3 Development as the History of the Individual
• 4 Revisiting the Embryo
• 5 Developmental Sequences: Sustainability versus Adaptation
• 6 Genes and Development
• 7 Emerging Form
• 8 The Ecology of Development
• Concluding Remarks
• Summary of Common Misunderstandings
• Classification
• References and Further Reading

1 - Defining Development, if Possible

Published online by Cambridge University Press:  29 April 2021

Among biologists and philosophers of biology there is no general agreement on a definition of development. Development is not necessarily the history of the individual, or the sequence of changes from egg to adult (adultocentrism). The notion that the adult stage is the target of development is unacceptable, both because it implicitly gives development a purpose, and because it does not apply to the biology of many organisms. In the common use of the term adult, two different notions are confused: adult as reproductively mature stage and as a stage that maintains its morphological organization until the onset of senescence or death. However, reproductive maturity and the presence of definitive morphological condition are not always associated. The divide between developmental processes and mere metabolic changes is not always clear-cut. Modern developmental biology is not the same as the descriptive and experimental biology of the past. Partly owing to strong focus on genetic control and molecular-level processes, most research effort is restricted to a few model species; but these are not necessarily representative of developmental processes in more or less distant relatives.

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• Defining Development, if Possible
• Alessandro Minelli
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What is the definition of experimental embryology?

i belive that an embryological relationship involves comparing the embryo with other animals that share the same habitat (i.e deserts, oceans, forests) to help taxonomists classify organisms

Heather Tremblay ∙

Experimental embryology is the study of the development of embryos through experimentation, typically in non-human organisms. This field aims to understand the processes involved in embryonic development, such as cell differentiation, growth, and morphogenesis, by manipulating embryos in controlled laboratory settings. It has provided valuable insights into fundamental biological processes and has led to advancements in various fields, including developmental Biology and regenerative medicine.

Developmental biology (and therefore embryology) mostly concerns itself with the study of organ and organ system development. Early studies of developmental biology were chiefly guided by gross anatomical observations of embryo development. For example, the heart was dissected at various levels of development and studied to see where and when certain structures appear and how they relate to the definitive adult heart. The same was done with the nervous system, gastrointestinal system, endocrine system, head and neck, genitourinary system, and other organs and organ systems.

New developments in cell and molecular biology have been exploited in developmental biology. Modern approaches often focus on the molecular signals that are switched on and off during development to control the formation of a particular organ.

Developmental biology studies a range of animals, from humans and other mammals to chickens and fruit flies. Interestingly, much of what is known about human development was first demonstrated in chicks and fruit flies.

Add your answer:

What is the definition of Experimental value?

the values you actually get when you do the procedure, these are then compared to the standard values

What is experimental result?

Experimental result is basically what it says, It's the Result of and experiment. Definition To "Result" = Something that results-effect, consequence-beneficial or discernible effect-something obtained by calculation or 8investigation. Definition To "Experimental" = A controlled proceeder carried out discover or test something. Definitions by the Merriam-Webster Dictionary. Eample: You water a plant to see if it would grow during the week. The experiment is to see if the plant would grow during the week, and at the end of the week you find out that the plant did grow. So the result would be that it did grow during the week you watered it. Simple ;)

What is The study and comparison of the development of young organisms from egg to birth?

This is called embryology or developmental Biology.

Study of growth and development of organisms?

The study of growth and development of organisms is known as developmental biology. It focuses on understanding how organisms grow from a single cell into complex multicellular structures through processes such as cell division, differentiation, and morphogenesis. Developmental biology is important in understanding the genetic and environmental factors that influence organismal development.

How are DNA and biochemical analysis and embryology and morphology used to classify organisms?

Who cares.they have in common the classification and name.

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• Published: 18 June 2024

A single-cell atlas of pig gastrulation as a resource for comparative embryology

• Luke Simpson 1 ,
• Andrew Strange   ORCID: orcid.org/0000-0002-2004-150X 1 ,
• Doris Klisch 1 ,
• Sophie Kraunsoe   ORCID: orcid.org/0000-0003-1813-6670 1   nAff4 ,
• Takuya Azami 2 ,
• Daniel Goszczynski 1 ,
• Triet Le Minh 1 ,
• Benjamin Planells 1 ,
• Nadine Holmes 3 ,
• Fei Sang 3 ,
• Sonal Henson   ORCID: orcid.org/0000-0002-2002-5462 3 ,
• Matthew Loose   ORCID: orcid.org/0000-0002-5264-0929 3 ,
• Jennifer Nichols 2 &
• Ramiro Alberio   ORCID: orcid.org/0000-0001-6560-3919 1  

Nature Communications volume  15 , Article number:  5210 ( 2024 ) Cite this article

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• Cell lineage
• Gastrulation

Cell-fate decisions during mammalian gastrulation are poorly understood outside of rodent embryos. The embryonic disc of pig embryos mirrors humans, making them a useful proxy for studying gastrulation. Here we present a single-cell transcriptomic atlas of pig gastrulation, revealing cell-fate emergence dynamics, as well as conserved and divergent gene programs governing early porcine, primate, and murine development. We highlight heterochronicity in extraembryonic cell-types, despite the broad conservation of cell-type-specific transcriptional programs. We apply these findings in combination with functional investigations, to outline conserved spatial, molecular, and temporal events during definitive endoderm specification. We find early FOXA2 + /TBXT- embryonic disc cells directly form definitive endoderm, contrasting later-emerging FOXA2/TBXT+ node/notochord progenitors. Unlike mesoderm, none of these progenitors undergo epithelial-to-mesenchymal transition. Endoderm/Node fate hinges on balanced WNT and hypoblast-derived NODAL, which is extinguished upon endodermal differentiation. These findings emphasise the interplay between temporal and topological signalling in fate determination during gastrulation.

Introduction

The blueprint of the mammalian body plan is laid down during gastrulation, a fundamental process of embryonic morphogenesis that ends with the establishment of the three basic germ layers. Gastrulation can be sub-divided into “primary gastrulation” describing early germ-layer formation events prior to the formation of the node, and “secondary gastrulation” encompassing convergent extension, the onset of neurulation and somitogenesis 1 . The unfolding of these processes has been mapped using single-cell transcriptomics in the mouse 2 , rabbit 3 , 4 , nonhuman primates 5 , 6 , and partially in humans 7 . Cross-species analyses have identified broadly conserved and divergent features of major lineage emergence, however, detailed investigations of “primary gastrulation” are limited due to the scarcity of cells in these datasets. Furthermore, validation of transcriptomic observations using embryos is limited by the lack of specimens in non-human primates and humans. To address this, here we present a high-resolution single-cell transcriptomic atlas of pig gastrulation and early organogenesis, comprised of 91,232 cells from 62 complete pig embryos collected between embryonic days (E) 11.5 to 15 (equivalent to Carnegie stages, CS, 6 to 10). The pig embryo, like most other mammals, forms a flat embryonic disc (ED) before the onset of gastrulation and represents an accessible species for functional investigations 8 . Importantly, the pig is a valuable model for biomedical research and is increasingly being utilized for the development of transplantable organs for humans 9 , 10 , 11 .

Here, we used this comprehensive dataset to shed light on the salient features of gastrulation in mammals. By performing cross-species comparisons we uncover heterochronic differences in the development of extra-embryonic cell types. Despite variability in differentiation dynamics and pathways regulating cell behaviour, there is broad conservation in cell type-specific programs across pigs, primates and mice. We focussed on the long-standing question of how the definitive endoderm (DE) emerges during mammalian gastrulation. Despite the evidence of mesendodermal progenitors in invertebrates, fish and chick 12 , 13 , 14 , 15 , recent studies in mice demonstrated that epiblast cells give rise to DE independent of mesoderm 16 , 17 . However, evidence from studies using mouse and human embryonic stem cells (hESC) suggests that a common mesendodermal progenitor may also exist in mammals 18 , 19 , 20 , 21 . We combined transcriptomic analysis and embryo imaging to show that soon after the first mesodermal cells appear in the posterior epiblast a group of ED disc cells expressing FOXA2+ delaminate to give rise to DE, these cells differ from later FOXA2/TBXT+ cells which give rise to the node/notochord. Further, both cell types form via a mechanism independent of mesoderm and do not undergo epithelial-to-mesenchymal transition (EMT). Further, functional validations using in vitro differentiation of pluripotent pig embryonic disc stem cells (EDSCs) and hESC 22 , 23 show that a balance of WNT and Activin/NODAL signalling are critical to the acquisition of the endoderm fate. Together, our findings indicate that the temporal dynamics and spatial localisation of WNT, originating from the primitive streak, coupled with hypoblast-derived NODAL play critical roles in orchestrating primary gastrulation in mammals.

Single-cell transcriptome of gastrulation and organogenesis

To investigate cellular diversification during gastrulation and early organogenesis in bilaminar disc embryos, we obtained scRNA-seq profiles from 23 pooled samples encompassing 62 pig embryos, collected at twelve-hour intervals between E11.5 and E15 using the 10X chromium platform (Fig.  1a ; Supplementary Fig.  1 ). The dataset includes early streak up to 10-somites stage, equivalent to Carnegie stages (CS) 6 to 10 (Fig.  1b ). Transcriptomes of 91,232 cells passed quality controls, with a median of 3,221 genes detected per cell (see Methods; Supplementary Fig.  1a–c ). Known cell-type markers and unbiased clustering of all samples (See Supplementary Data  1 ; Supplementary Fig.  1e ) were used to identify 36 major cell populations and subsequently, pig cell-type marker genes (see Methods, Fig.  1c ; Supplementary Data  2 , 3 ). Early embryonic cell types, such as epiblast and primitive streak (PS) cells, decreased in number over time concomitant with their differentiation (Fig.  1d ). Mesoderm and DE progenitors were present as early as E11.5 suggesting that gastrulation may commence before the morphological changes due to A-P patterning become visually apparent. Most mesoderm diversification occurs from E12 followed by an expansion of ectodermal lineages from E13.5 onward. Consistent with the well-documented late emergence of amnion in most domestic animals compared to primates, the amnion cluster was not present in earlier samples but appeared later in gastrulation, from E12.5 onward. While it is possible that amnion may be involved in cell patterning in pig, as in primates, a role in A-P patterning is unlikely, as this occurs prior to amnion formation 24 , 25 .

a Onset and duration of primary and secondary gastrulation in humans, mice, pigs and monkeys. Timepoints where high-resolution single-cell datasets are available, are marked for each species 2,6,28 as well as the time points covered in this atlas. Numbers indicate embryonic day. b Diagrammatic representations of the earliest and latest embryo samples in this dataset with visible embryonic structures/cell types labelled. Epi Epiblast, PS Primitive streak, NM Nascent mesoderm, Hyp Hypoblast, TB Trophoblast, NT Neural tube, Phar Pharyngeal arches, Som Somites, PosNP Posterior neuropore, AntNP Anterior neuropore, YS yolk sac. c Uniform manifold approximation and projection (UMAP) plot showing atlas cells (91,232 cells, 23 biologically independent samples). Cells are coloured by their cell-type annotation and numbered according to the same legend as d below. d Stacked area plot showing the fraction of each cell type at each time point, a progressive increase in cell-type complexity can be seen across time points with mesodermal cell type diversification preceding that of ectoderm. APS Anterior Primitive Streak, ExE extra-embryonic, PS Primitive streak, NM Nascent mesoderm, HE Hematoendothelial, PGC Primordial germ cells.

Similarities between pig, human, monkey and mouse embryos

To gain insights into conserved and divergent features of non-rodent and rodent mammals we compared the transcriptomes of peri-gastrulation stage mouse 2 and Cynomologous monkey 6 embryos with pigs using high-confidence one-to-one orthologues (see methods). Projection of our pig dataset onto mouse 2 , and subsequent transfer of mouse labels showed that the majority of cell-type annotations were well-matched between both species (Fig.  2a and Supplementary Fig.  2a ), with large fractions of each cell type allocated being analogous to their mouse counterparts including cardiac mesoderm, extraembryonic endoderm, spinal cord, primordial germ cells (PGCs), and epiblast 2 (Supplementary Fig.  2a ). By contrast, later emerging neural cell types such as caudal and rostral neurectoderm, neural crest and fore/mid/hindbrain (FB/MB/HB) had fewer matches in our data consistent with neural tissues being more advanced in the mouse at the 10-somite stage compared to pig. In the case of extra-embryonic tissues, such as amnion and trophoblast, a large fraction of these tissues was allocated a surface ectoderm identity. Similarly, a large portion of extraembryonic mesoderm (ExM) was allocated as mesenchyme. Projections of pig stages onto mouse development show our time course aligns approximately between E7 to E8.5 (Fig.  2b ). Projection mapping to the macaque dataset also showed a high degree of similarity of cell type annotations; in contrast to mouse-annotation mapping, this resulted in better agreement between predicted identities and our annotations of extra-embryonic mesodermal tissues (Fig.  2c ; Supplementary Fig.  2b ). Stage mapping also showed expected alignment between all our time points to monkey equivalents except E26 which had no clear match in our dataset (Fig.  2d ). Given the large discrepancies in the methodologies and criteria used for annotating cell types in single-cell data 26 meaningful cross-species comparisons can often be challenging. To overcome these challenges, we used data projection/label transfer to apply our own cell type labels to each dataset for consistent annotation of equivalent cell types for further cross-species comparisons (Fig.  2e ).

a UMAPs showing E6.5–8.5 mouse embryo cell types 2 and Pig E11.5 to E15 with mouse annotations after reciprocal PCA-based projection onto the mouse dataset. b Heat map showing the percentage of pig cells in each stage allocated to a particular mouse stage after label transfer. E Embryonic day. c UMAPs showing E20-29 monkey embryo cell types 6 and Pig E11.5 to E15 with mouse annotations after reciprocal PCA-based projection onto the monkey dataset. d Heat map showing the percentage of pig cells in each stage allocated to a particular monkey stage after label transfer. A percentage of 100 would indicate that all cells of a given cell type were predicted to be analogous to the cell identity in the queried organism. e UMAPs showing the aligned monkey, mouse and pig datasets with pig cell type and subtype annotations.

Hierarchal clustering of individual cell types (Supplementary Fig.  3 ) generally grouped cell types with lower correlation together, these included several extra-embryonic tissues (e.g., ExE Endo 1, 2, 4, 5 and Hypoblast 1 and 2) together corroborating known differences in the morphology and regulation of these tissues 3 , 25 , 27 . We noted that among cell-type specific marker genes, there was a large degree of overlap between monkeys, mice and pigs. This allowed us to determine sets of highly conserved cell type-specific markers: epiblast 1 ( POU5F1, SALL2, OTX2, PHC1, FST, CDH1 and EPCAM ), PS 1 ( CDX1, HOXA1, SFRP2, and GBX2 ), APS ( CHRD, FOXA2, GSC, CER1 and EOMES ), node ( FOXA2, CHRD, SHH and LMX1A ) (Supplementary Fig.  4a–h ), DE/Foregut ( SOX17, FOXA2, PRDM1, OTX2 and BMP7 ) and DE/Hindgut ( SOX17, FOXA2, TNNC1 and ITGA6) . Notably, we also identified sets of genes that were strong cell type identifiers in monkey and pig cell types, but not mice, for example, UPP1, SFRP1, PRKAR2B, APOE and IRX2 in the epiblast, CD9, GPC4 and COX6B2 in the APS and PTN, HIPK2 and FGF8 demarcating node. We identified conserved and divergent markers for other less well-characterised cell types (Supplementary Data  3 – 6 ). These observations highlight the importance of investigating multiple representative animal models to identify conserved gene-regulatory networks relevant to cell fate decisions in mammals.

We then looked for transcriptional differences outside of cell-type specific gene programs, utilising ClusterProfiler to elucidate KEGG term enrichment among differentially expressed genes (DEGs). This revealed a considerable number of genes that were markedly upregulated in pig and monkey epiblasts compared to mice (Supplementary Fig.  5a ). Further examination showed that a significant proportion of these DEGs were replicated across multiple cell-type comparisons. Notably, many of the identified genes were associated with distinct signalling pathways, including the Mitogen-Activated Protein Kinases (MAPK) and the Phosphatidylinositol 3-Kinases (PI3K)/Akt pathways, along with cell adhesion pathways such as those mediating focal adhesions and adherens junctions (Supplementary Fig.  5b ). Given that these differentially expressed genes are part of pathways generally implicated in cell behaviour such as the regulation of cell growth, proliferation, differentiation, and morphogenesis, this may correspond to known differences in embryo size, cell cycle length and morphology between these species.

We next used our dataset to better understand the process of human gastrulation, currently informed by a single gastrula-stage CS7 (E17-19) human embryo 7 . Cross-species reference mapping of the CS7 human embryo onto our own dataset as well as that of mouse and monkey revealed heterochronicity between cell differentiation dynamics across species (Fig.  3a, b ; Supplementary Fig.  6a–e ). Intriguingly, despite the relatively immature stage of the human embryo, the mapping of mesodermal cell types onto their porcine counterparts revealed a considerable degree of alignment with E15 extra-embryonic mesoderm. This alignment potentially suggests that human extraembryonic mesoderm not only envelops the embryo more extensively but also exhibits accelerated maturation when compared with other cell types, such as the epiblast and nascent mesoderm clusters. The latter two were found to correspond more closely with their E13 porcine equivalents, which more closely mirror the morphology of a CS7/8 human embryo. A congruous trend was observed comparing the human embryo to mice 2 , as the human yolk sac mesoderm aligned closely to E8.5 mesenchyme (Fig.  3a ). Comparisons of endodermal cell types also showed asynchronous development of yolk sac endoderm, like ExE mesoderm, human yolk sac endoderm had a higher mapping frequency to pig E15 yolk sac endoderm and E8.5 ExE endoderm (Fig.  3b ). By contrast, nearly all the cell types investigated showed that CS7 human cell types matched CS9 in non-human primates (Supplementary Fig.  6d, e ). While these results might reflect a discrepancy in embryonic staging, they also suggest little asynchrony between human and Cynomolgus  monkey embryos. We also noted that annotations of ectodermal tissues such as amnion and surface ectoderm appeared to differ between human and monkey, while the pig annotations of these tissues aligned more closely to human (Supplementary Fig.  6a–c ). This suggested there is a need to better define the transcriptional profiles of these tissues for further comparisons and that gross morphology alone does not always indicate transcriptional equivalency. These findings suggest that although the programs guiding cell-type specific differentiation are remarkably conserved, the dynamics of differentiation exhibit notable variations across species.

a Heat maps showing the percentage of human mesodermal cells 7 allocated to a pig or mouse 2 cell identity after label transfer. b As with a, except with endodermal cell types. 100% would indicate that all cells of a given cell type were predicted to be analogous to the cell identity in the queried organism. E Embryonic day.

Pig mesoderm and endoderm progenitors are transcriptionally distinct

Given that our results suggested conservation in cell-type specific programs, we reasoned that the core mechanisms of differentiation would be conserved between mice and large mammals. However, one area of controversy is that of endodermal differentiation. Indeed, it has recently been suggested that proliferative, bi-fated “mesendodermal” progenitors are not found in the mouse embryo 16 , 17 , 28 . However, this idea has gained less traction in large mammalian embryology as early in vitro evidence from hESC differentiation studies has suggested that bipotent progenitors may exist 18 , 19 , 20 , 21 . Given the high numbers of cells of early gastrulation, our dataset allowed us to dissect the events of this period at high resolution. We analysed epiblast, PS, APS/node, nascent mesoderm, and DE clusters (Fig.  4a–d ). Sub-clustering of mesoderm and endoderm-fated cells identified 14 subpopulations: four nascent mesoderm, three PS, APS, node, two epiblast, two early caudal epiblast, DE, and midgut (Fig.  4b ). Nascent mesodermal cells expressing MESP1 increased in number between E11.5 to E14. Epiblast, early caudal epiblast and APS clusters decreased throughout time points (Fig.  4c ). High- SOX17 expressing DE cells were present from the earliest time point (E11.5) and throughout the time course (Fig.  4c, d ; Supplementary Fig.  7 ). In contrast, node cells predominantly emerged one day later (E12.5). This finding is in line with previous observations where FOXA2 , TBXT , and GSC -positive node cells can be identified from E12-E13 pigs 29 , 30 , demarcating the start of secondary gastrulation in the pig. Early caudal epiblast could be distinguished by expression of CDX1 and increased EOMES expression compared to epiblast 1 and 5. PS clusters 1 and 3 maintained expression of DNMT3B and had markedly higher expression of TBXT than caudal epiblast clusters. The PS2 cluster, by contrast, was not present until E12.5 onwards and showed reduced pluripotency expression and increased CDX1 expression suggesting this may be later PS forming from the late caudal epiblast population. Nascent mesoderm was identifiable by decreased DNMT3B and FST expression and increased MESP1 . We observed that most cells within the APS expressed both FOXA2 and CHRD . Notably, however, the APS cluster manifested certain heterogeneity. A significant proportion of cells exhibited elevated expression of POU5F1, NANOG, EOMES , and CER1 . Conversely, a subset of cells displayed lower levels of these markers, but higher expression of TBXT . These observations are consistent with the idea that distinct populations of the APS may give rise to the DE and node.

a UMAP plot showing epiblast, PS, APS/node, nascent mesoderm and DE clusters (24,874 cells; 23 biologically independent samples; E11.5-E15) coloured by global cell-type annotation and developmental time points. b As with a, coloured by cell subtypes. c Stacked bar graphs showing the frequency of each subcluster within the subset shown in a & b at specific time points in development. E Embryonic day. d Heat map illustrating the scaled expression of genes within individual cells. Expression of selected markers was used to identify cell subclusters as well as epithelial and mesenchymal marker genes.

We next analysed the expression of epithelial markers and genes related to EMT (Fig.  4d ; Supplementary Fig.  7 ). Tight junction markers OCLN, CLDN6 , and CLDN4 , along with the intermediate filament protein-encoding genes KRT8 and KRT18 , displayed low expression within nascent mesodermal clusters. Except for CLDN6 , these markers also exhibited higher expression in the DE cluster. As with other epithelial markers, CDH1 and cell-cell adhesion-associated EPCAM also showed reduced expression within nascent mesoderm compared to other clusters. In contrast, the expression of the mesenchymal transition regulator SNAI1 and post-EMT marker CITED1 showed elevated expression within the nascent mesoderm. Unexpectedly, the intermediate filament and mesenchyme marker VIM was expressed in most epiblast clusters but reduced in the APS, the node and DE. In addition, CDH2 and FN1 , frequently associated with EMT, were highly expressed within a portion of APS cells and the DE. Likewise, ZEB2 , a transcriptional repressor of CDH1 , was expressed in both nascent mesoderm clusters and the node.

Together these data suggest that cells of the APS, node, and DE, retain a robust epithelial identity throughout their differentiation despite upregulating a small number of genes associated with increased cell motility. This epithelial-to-epithelial transition has been previously described during the formation of the amnion in the mouse 31 . In contrast, PS-derived nascent mesoderm undergoes a divergent process resembling the “classic” model of EMT. Therefore, the processes by which epiblast cells transition from a columnar to a simple epithelium (in the case of DE), or toward a mesenchyme/mesenchyme-like state, as is the case for nascent mesoderm and notochordal process respectively, appear to be highly nuanced and tissue-specific. This observation is especially pertinent for the DE and nascent mesoderm, as we found no evidence supporting a common mechanism of cell ingression, in line with findings in mice 16 , 17 , 32 .

Exploring early somitogenesis in pig embryos

To explore the derivatives of the nascent mesoderm cells we sub-clustered nascent mesoderm, pre-somitic and somitic mesodermal cell types (Supplementary Fig.  8 ). This facilitated the identification of seven subtypes: 3 anterior somitic mesoderm clusters, cranial mesoderm, dermomyotome/sclerotome, posterior somitic mesoderm and pre-somitic mesoderm. We observed several genes with similar dynamics in pigs, as reported in mice and in human in vitro models. For example, TBX6 , is highly expressed in pre-somitic mesoderm and posterior somites, but less so in more mature somitic cell types 33 , 34 , 35 . In contrast, MYF5 and MYOD1 , were expressed at later time points. Additionally, FOXC2 was lowly expressed in all somitic subtypes. Generally, we observed the first mature somite subclusters emerge from day 14 onward while presomitic mesoderm clusters were present throughout, consistent with patterns described in many other species. We have also noted several markers of pig somitogenesis (Supplementary Fig.  8d ).

Spatiotemporal mapping of mesoderm and endoderm in pig

To elucidate the potential causal mechanisms underlying the formation of mesoderm, endoderm and node we applied reversed graph embedding and pseudo-temporal ordering to E11.5 to E13 epiblast, PS, APS/node, nascent mesoderm, and DE subclusters using Monocle3 36 (Fig.  5a–e ). Epiblast and early caudal epiblast cells were positioned at the beginning of the trajectory, preceding the first bifurcation towards either PS/mesoderm or APS fates. Notably, few cells within the early caudal epiblast cluster appeared to be between PS and APS fates (Fig.  5a, b ) suggesting the early caudal epiblast represents the last cells fated toward both mesoderm and endoderm. Comparisons of mesodermal and endodermal trajectories confirmed that in contrast to endoderm progenitors, mesodermal progenitors rapidly loose their epithelial characteristics and undergo “classical EMT” (Supplementary Fig.  9 ) as evidenced by elevated expression of SNAI1 and CITED1 . Trajectory analysis showed a secondary fate decision in the form of a bifurcation within the APS toward DE or node fates (Fig.  5a, b ). Consistent with previous findings, NANOG expression was elevated in epiblast, early caudal epiblast, PS, and APS clusters but decreased sharply in node-fated cells and nascent mesoderm, in contrast to DE (Fig.  5c ). We observed little difference in FOXA2 expression between endoderm and node-fated cells, whereas TBXT expression was far more pronounced in node-fated cells of the APS.

a UMAP plot with reversed graph embedding trajectories projected on top using Monocle3. Black nodes mark trajectory branching points. n  = 16757 cells across 11 biologically independent samples. b UMAP plot showing predicted cell fates inferred from Monocle3. c Bar graphs showing the NANOG, TBXT and FOXA2 expression in lineage-fated cells from a&b. Data are presented as mean values +/- SEM. d Box plots showing epiblast, nascent mesodermal and endodermal lineage scores in selected clusters from a. n  = 2340, 263, 1536 and 1140 cells respectively across 11 biologically independent samples. Centre line represents median, minima and maxima hinges represent the 25 th and 75 th percentiles respectively. Whiskers extend from the quartiles to the last data point that is within 1.5 times the interquartile range. Points beyond this range are shown and are considered outliers. P -values indicate the results of a two-sided Mann-Whitney U test. e Volcano plots showing differential expression between differently fated cells. Primitive streak (mesoderm fated) vs APS fates and Endodermal vs node-fated cells. Cut-off criteria for significant DEGs was a Log2 fold change ≥0.5 and an adjusted p value ≤ 0.01. f Heat map illustrating the scaled average expression of selected genes in each of the cell fates identified in b . g UMAP plots showing cells categorised by FOXA2, NANOG, TBXT and SOX17 expression at selected time points. F FOXA2, N NANOG, T TBXT, S SOX17 cells. Cells are coloured by their F/N/T/S category. APS Anterior primitive streak, E Embryonic day.

Considering the absence of bi-fated mesendodermal progenitors outside the early caudal epiblast and the observed co-expression of TBXT and FOXA2 in the APS/node, we investigated whether the APS fulfils the criteria for mesendoderm. Historically, the node/notochord has been categorized as a mesodermal tissue, as such, earlier descriptions of “mesendoderm” referred to the progenitors of the prechordal plate, notochord, pharyngeal and head endoderm 37 , 38 , 39 . Despite this classification, node cells initially migrate into the hypoblast layer as the notochordal process 40 and tend to cluster near the endoderm in low-dimensional space. Given the limitations of UMAP in inferring transcriptional similarity from spatial proximity, we employed module scoring with significant markers of mesoderm, endoderm, and epiblast to assess tissue similarity (Fig.  5d ). While node cells exhibited a significantly lower endodermal score compared to endoderm itself it was higher than both epiblast and nascent mesoderm clusters. By contrast, the mesodermal score differences between DE and node clusters were not significantly different, suggesting the node is more transcriptionally similar to DE than mesoderm. Module scoring also validated our previous observations that DE cells had a higher epiblast score compared to mesoderm or node, aligning with the expression of pluripotency-associated genes in DE-fated APS cells.

Differential expression analysis highlighted several key factors differentiating PS/mesodermal from APS-fated cells after divergence from an early caudal epiblast state (Fig.  5e ). PS/Mesoderm-biased states exhibited upregulation of WNT8A , WLS (WNT Ligand Secretion Mediator), and epigenetic regulators MSH6 and EZH2 . Conversely, APS-fated cells showed increased expression of NODAL , TGF-beta superfamily and NODAL-related factor: GDF3 , along with TLL1 , a metalloproteinase involved in processing TGF-beta superfamily precursors. Differential expression analysis also provided insights into DE vs. Node fate decisions, with DE-fated cells continuing to display increased expression of NODAL as well as known endodermal regulators such as GSC and CER1 . Node-fated cells showed increased expression of retinoic acid modulator CRABP2 as well as upregulation of caudal factors such as CDX1 and CDX2 . Given the differential expression of several genes involved in signal regulation, we looked at the expression of genes involved in active WNT signalling, NOTCH signalling, Activin/NODAL inhibition and WNT inhibition (Fig.  5f ). In line with many of our previous observations we observed increased expression of factors involved in WNT signalling in mesoderm and node fated cells albeit these tissues showed differential expression of specific WNTs. In contrast to mesoderm, however, node-fated cells showed high expression of Activin/NODAL inhibitors NOG , CHRD and FST and upregulated several genes involved in NOTCH signalling. DE-fated cells showed far greater expression of WNT inhibitors compared to their node-fated counterparts or mesoderm-fated cells.

While we did not observe high levels of TBXT and FOXA2 outside of the APS and node clusters, we looked for rarer cell types that may co-express TBXT and FOXA2 that exist in other cell clusters. As we have previously shown NANOG and SOX17 are exclusively expressed in the ED and hypoblast layer respectively in pig E10-E11.5 embryos 41 , we reasoned that including the expression status of these markers in conjunction with FOXA2 and TBXT could be used to ascertain the position of cells during fate commitment. Of note we identified, 287 FOXA2  + , NANOG  + , TBXT-, SOX17- (FN+) cells, 98 FOXA2  + , NANOG  + , SOX17  + , TBXT - (FNS+), 500 FOXA2  + , TBXT  + , NANOG-, SOX17- (FT+) cells and 110 FOXA2+, TBXT+ SOX17 + (FTS+) cells (Fig.  5g ). We observed that of these groups FN+ cells were most abundant at E11.5-12, while FT+ cells were more abundant at E12.5-13 concurrent with the fate switching of the APS from DE to node that also occurs during this period. In line with the high levels of TBXT and FOXA2 expression in node-fated cells, FT+ cells made up a large percentage of this group and were less abundant in the DE-fated group (Supplementary Fig.  10a, b ). FN+ cells were more abundant in early caudal epiblast, APS and DE-fated cells (Supplementary Fig.  10a ). We subsequently examined whether cells that co-express mesodermal and endodermal factors exhibit concurrent high-level expression of these factors, or if their expression patterns are mutually exclusive (Supplementary Fig.  10b ). TBXT showed a positive correlation with FOXA2 both in cells with detectable SOX17 ( R  = 0.33, p  < 0.0001) and without ( R  = 0.48, p  < 0.0001). FOXA2 also had a significant correlation with both SOX17 ( R  = 0.37, p  < 0.0001) and EOMES ( R  = 0.16, p  < 0.0001) only in FN+ cells which did not have detectable TBXT expression. EOMES expression was negatively correlated with TBXT in cells with both SOX17 positive ( R  = −0.36, p  < 0.001) and SOX17 negative cells ( R  = −0.53, p  < 0.001). CDX2 expression did not correlate with any other factor but TBXT which was limited to FT+ cells ( R  = 0.14, p  < 0.01). Together these data suggest that cells co-expressing high levels of TBXT and FOXA2 primarily contribute to the node/notochord. Given the APS origin of the node, endoderm-like transcriptional signature, and that APS/node cells like endoderm do not appear to undergo classical EMT this also suggests that FT+ cells are not mesendodermal at all. Likewise, it appears that most cells that contribute to DE have no detectable TBXT transcripts or low levels and correspondingly most mesoderm-fated cells are FOXA2 negative. We also find these observations apply in a less binary fashion to mouse embryos, with the high T population being node/notochord fated and a low T population, endoderm fated (Supplementary Fig.  11 ).

FOXA2 and TBXT cells in gastrulating pig embryos

To spatially position mesodermal and endodermal progenitors, we conducted whole-mount immuno-fluorescence imaging of E10.5-E11.5 porcine embryos for SOX2, FOXA2, and TBXT (Fig.  6 ; Supplementary Movies  1 – 3 ). At E10.5, TBXT-positive (T+) cells appear in the posterior epiblast, their numbers increased in E11.5 embryos. Many of these cells extend beyond the posterior ED boundary as ExM by the end of E11.5 (Fig.  6a–d ). A group of FOXA2 - positive TBXT-negative (F+) cells was detected anterior to the FOXA2+ and TBXT+ (FT+) cells at E11.5. A similar population of F+ cells in the epiblast layer was recently reported in mice 16 , 17 . While initially, this F+ population outnumbered FT+ cells by a factor of ~3, this gradually decreased to ~1.4 by the end of E11.5 (Fig.  6c , Supplementary Fig.  12 ). Lateral reconstructions of late E11.5 embryos showed F+ cells part-way delaminating from the epiblast into the hypoblast layer (Fig.  6d ). In line with their transcriptional profiles, early epiblast-derived F+ cells contributing to the hypoblast/DE layer are NANOG + , in contrast to the T+ cells which are NANOG negative. By E12 NANOG+ cells are rarely detected (Fig.  6e ). Importantly, we did not observe any FT+ cells intercalating the hypoblast. In line with their spatial positioning and the transcriptomic profiles (Figs.  4 , 5 , 6 ), these observations indicate that the F+ population is fated to DE while the FT+ cells represent node/notochord precursors. This is further supported by the increased number of double-positive TBXT/FOXA2 (FT+) cells demarcating the medial APS region by the end of E11.5.

a Maximum intensity projection (Dorsal view) of E10.5 ( n  = 1) to E11.5 ( n  = 4) porcine embryos showing TBXT and SOX2 expression. E11.5 embryos are ordered left to right by age. E Embryonic day. b Single z-slice of the embryos shown in a showing FOXA2 and TBXT expression. Scale bar: 50 µm. c In Silico representations of embryos following 3D segmentation of embryos from a and b . d Axial and lateral reconstructed sections of embryos stained for FOXA2 and TBXT. Epiblast layer is oriented above the hypoblast/DE layer. White arrowheads indicate FOXA2 + TBXT- cells that are spatially separated from the TBXT domain. Scale bar: 50 µm. e Lateral sections of E11.5 (n  = 1) and E12 ( n  = 1) embryos, showing expression of NANOG, FOXA2 and TBXT. Epiblast layer is oriented above the hypoblast/DE layer. White arrowheads indicate NANOG+ cells. Scale bar: 50 µm. Please refer to Supplementary Fig.  21 for a colour blind friendly version of the figure.

Extraembryonic signalling correlates with emergence of DE

Previous studies demonstrated the contribution of extra-embryonic endoderm to DE in mice 2 , 42 , 43 , 44 . To establish whether this is true in pig embryos we isolated DE, gut, hypoblast and extraembryonic endoderm (ExE) clusters for further analysis (Supplementary Figs.  13a–d , 14 – 16 ). Through marker expression we identified subclusters of the hypoblast including the posterior hypoblast and AVE as well as yolk-sac endoderm (Supplementary Figs.  13d, e , 14 ). Temporal dynamics and module scoring also revealed a population of cells that appear to be an intermediate (InterHypo) between hypoblast and DE in line with similar observations in mice 42 (Supplementary Fig.  13f–g ). We also confirmed previous observations that suggested the hypoblast is the primary source of NODAL in pig embryos 45 (Supplementary Fig.  13h–i ). However, NODAL signalling in the hypoblast is restricted to E11.5-E12, which coincides with the specification of the DE from epiblast cells. Moreover, the absence of NODAL at E12.5 also correlates with the displacement of the hypoblast with DE and the fate switching of the APS cells from endoderm to node/notochord. These findings are consistent with our observations that APS-fated cells upregulate NODAL expression before node-fated cells upregulate NODAL inhibitors. Indeed, recent reports have suggested that timed Activin/NODAL inhibition promotes notochord formation from DE-competent cells 46 , 47 . Lastly, the isolation of E14-E15 samples allowed for the identification of gut sub-populations that resemble their mouse counterparts 43 (Supplementary Figs.  15 , 16 ).

Organiser-like signalling patterns of porcine cell types

It has been suggested that in mice, the node, PS and hypoblast have functions analogous to the organizer in amphibians 48 , 49 , 50 , yet it is unclear whether the signals leading to A-P patterning are relevant to other mammalian species. One such example is Wnt3 , secreted by the posterior epiblast in response to Bmp4 , which acts as the primary driver of mouse gastrulation 51 . Experiments in hESC have demonstrated that, like in mice, WNT3 is the only WNT orthologue to respond to BMP4 stimulation 52 . However, while mouse Wnt3 knockout embryos fail to gastrulate 51 , humans with a homozygous WNT3 nonsense mutation can complete gastrulation but develop tetra-amelia and urogenital defects 53 . Furthermore, the only demonstration of WNTs organiser function in large mammals comes from in vitro experiments using WNT3A 52 . Given the lack of understanding of the in vivo role of WNTs in large mammals, we investigated canonical WNT crosstalk in E11.5-E12 cell types using CellChat 54 (Supplementary Fig.  17a–c ). In line with findings in mice and in keeping with a role in A-P patterning, many early cell types were highly receptive to WNT ligands via multiple FZD and LRP receptor combinations. The highest WNT3 signals came from the PS and APS (Supplementary Fig.  17a, b ). Conversely, WNT3A was expressed within anterior ED clusters, namely the epiblast and APS.

Cell-cell signalling analysis also showed that multiple cell types were predicted to be receptive to node-produced SHH (Supplementary Fig.  17c ). Notably, the scarcity of node cells identified prior to E12.5, is consistent with the notion that the mammalian node/notochord is principally involved in secondary gastrulation 48 . In line with this hypothesis, node cells and node-fated cells not only secreted SHH, a dorsal tissue patterning ligand, but also expressed high levels of NOG and CHRD , factors primarily associated with axial extension and DV patterning 55 , 56 , 57 .

WNT/NODAL guides DE differentiation directly from Epiblast

WNT and NODAL signalling is critical for endoderm formation 52 , however, the balance of these competing signals required for DE formation has not been fully established. To assess this, we used varying concentrations of Activin A (ActA) and a WNT agonist (CHIR99021, CHIR) to simulate specific microenvironments across the ED in 2D cultures of the pig EDSCs line (EDSCL4) 22 and hESC lines H9 and HNES1 58 . All cells were maintained undifferentiated in AFX medium (see methods). Upon differentiation, a dose-response effect to the addition of ActA was observed with the number of FOXA2 positive cells decreasing when combined with higher CHIR levels at both 24 and 48 hrs in pEDSCs. This effect was not observed in H9, where only a modest increase in FOXA2 protein was determined after 48 hrs. In addition, pEDSCs showed higher SOX17 expression by 48 hrs when exposed to increasing levels of ActA (20–100 ng/ml) in the presence of low CHIR levels but showed a stunting of SOX17 expression in response to higher CHIR. A similar trend is seen in H9, but H9 also showed an increased sensitivity to ActA, with peak SOX17 expression at 20 ng/ml and a comparatively reduced levels when treated with 100 ng/ml. In pEDSC endogenous WNT inhibition using XAV939 resulted in very low SOX17 expression, indicating a moderate level of WNT is required for endoderm differentiation (Fig.  7a, b , Supplementary Fig.  18 ).

a Representative images depicting differentiation conditions for EDSCL4. Images were captured using an Operetta CLS high-throughput microplate imager. Scale bar: 200 µm. b Box plots summarizing 2D differentiation experiments. Data is normalised to well background signal. n  = 3 independent experiments. Centre line represents median, minima and maxima hinges represent the 25 th and 75 th percentiles, respectively. c Proposed model of epiblast-DE differentiation in pig embryos. Please refer to Supplementary Fig.  22 for a colour-blind-friendly version of the figure.

In line with recent reports 59 , TBXT expression required exogenous Activin/NODAL, in addition to WNT, suggesting a role for the hypoblast in streak formation. In both species, higher levels of CHIR increased TBXT expression at 24 hrs, which was largely extinguished by 48 hr, consistent with the TBXT expression profile in the embryo. Co-expression between SOX17 and TBXT or SNAI1 was rarely observed at 24–48 hrs and showed very low Pearson’s correlation scores (Supplementary Fig.  19a–d ). TBXT/SOX17 co-expression was not observed at 8 hrs (Supplementary Fig.  19e, f ).

Previous studies showed that there are differences between using WNT3A and CHIR, both in cellular response and the efficacy of in vitro differentiation of hESC towards DE 60 , 61 . We treated pig EDSC with WNT3A and found minimal response to WNT3A alone, however robust expression of FOXA2 and SOX17 was determined when ActA was added (Supplementary Fig.  20 ). In contrast, H9 hESC exposed to WNT3A alone upregulate FOXA2, however, like in pEDSCs, SOX17 was only upregulated when ActA + WNT were added. In hESC, expression of both FOXA2 and SOX17 was significantly enhanced by WNT stimulation compared to CHIR stimulation, a response not seen in pEDSC. These experiments show that DE can be induced more efficiently with WNT3A, as previously demonstrated 61 , however, the sensitivity to this inducer differs between pig and human lines.

Based on these results we propose a model of DE formation in the pig embryo (Fig.  7c ) whereby between E10.5 and E11, the first TBXT+ cells emerge at the posterior-most point of the ED opposing the anterior-most hypoblast/AVE-produced WNT/NODAL inhibitors (i.e LEFTY2 and DKK1)(Supplementary Fig.  13d, e ) 45 . Driven by a combination of posterior epiblast-secreted WNT and hypoblast-derived NODAL signalling, these cells initiate EMT, thus constituting the nascent mesoderm. Concomitantly, in cells anterior to the TBXT ED domain a combinatorial effect of WNT signal inhibition and increased NODAL activity induces EOMES. EOMES, which is capable of inhibiting TBXT gene regulatory activity 62 , alongside NODAL, promotes FOXA2 expression. The most anterior FOXA2+ cells secrete NODAL inhibitor CER1, delaminate, and intercalate into the hypoblast to form and expand the DE. This results in the termination of NODAL signalling in the newly formed DE and WNT signal inhibition in the APS. The remaining FT+ NODAL-primed cells, still driven by WNT signalling, begin to form the node from approximately E12 onward.

We present a scRNAseq atlas of pig gastrulation and early organogenesis that represents a comprehensive resource for exploring the molecular mechanisms governing cell-fate determination during a crucial juncture of development. We harnessed this resource to investigate the major temporal signalling and differentiation events, which direct primary gastrulation in bilaminar disc embryos. Our findings underscore the nuanced heterochrony in embryonic development across mammals, evidenced by asynchrony in cell type maturation 63 . Further, these heterochronic differences combined with comparisons between pigs, monkeys and mice reinforce the idea that extraembryonic tissues may be less conserved than their embryonic counterparts 3 , 25 , 27 . Through cross-species mapping and comparative transcriptomics, we also elucidated distinctive gene expression patterns associated with cell-cell adhesion and signalling pathways in pig and monkey epiblasts, compared to their mouse counterparts. Such observations may have implications for understanding species-specific aspects of cell differentiation, growth, and morphological features. Our findings also highlight the ongoing need for comprehensive and well-annotated single-cell atlases of mammals to better characterise in vitro embryo models. Frequently, models such as mice have been used to “stage” in vitro models of other species, such as humans. However, given that the stage of differentiation of a given cell-type may differ between species despite morphological/structural similarities, this approach may not always be appropriate.

Despite the observed species differences, we were able to identify highly conserved early cell-type specific programs between mice, primates and pigs. These findings were exemplified by our investigations into the segregation of the endoderm, mesoderm and node, the precursors of which display organizer-like patterns of gene expression. Without the reduced spatial constraints and slower development of the pig embryo compared to mice, we can show a distinct temporal and spatial pattern of lineage specification from ED cells. Contrary to earlier proposed models in hESC, which suggest a bipotent mesoderm-endoderm progenitor undergoes EMT prior to lineage specialisation, we find little evidence of this.

Instead, we observe that classical EMT predominantly occurs in PS cells transitioning to nascent mesoderm. Importantly, this transition occurs following the segregation of the PS and APS from an early caudal epiblast population. Given the expression of pluripotency and epithelial markers, this suggests that the last cells capable of producing both mesodermal and endodermal populations are situated in the epiblast layer. APS and subsequently node and DE populations maintain their epithelial characteristics during their ingression in a mechanism that resembles an epithelial-to-epithelial transition. These observations in the pig embryo are supported by recently proposed murine models 16 , 17 , 28 . Given that the early caudal epiblast population itself can be characterised by the expression of factors that are not associated with ectoderm-fated cells (e.g., EOMES , CDX2 ) this raises questions about the extent to which this epiblast population can be considered bipotent or merely predisposed toward mesendodermal fates.

We also looked for the co-expression of mesodermal and endodermal genes and while most mesodermal and endodermal genes showed mutually exclusive expression profiles, genes such as TBXT and FOXA2 are both highly expressed in node progenitors as well as within the node/notochord. Earlier work describes this population, which gives rise to a portion of anterior endoderm, as mesendodermal 37 , 38 , 39 . This definition is therefore predicated on the notochord itself being a mesodermal tissue. Because of this, we considered whether node/notochord was of mesodermal origin in the pig embryo. Notably, lineage scoring of mesodermal, endodermal and node populations revealed that node/notochord is transcriptionally closer to endoderm than mesoderm. However, despite this result, the unique signalling profiles, transcriptional signatures, and nuanced differences in EMT gene expression within this population suggest the node may not fit classical definitions of either mesoderm or endoderm.

Our findings extend the understanding of mesodermal, endodermal and node progenitor organiser-like signalling patterns. We show that node progenitors in the APS and later arising node do not express A-P patterning genes ( NODAL, CER1, LEFTY2 and DKK1) , but instead express genes related to axial extension. These findings are in contrast with chick models where the node appears to have a clear role in A-P patterning 48 . Combined with both embryo whole mount IF and in vitro experiments we showed that this sequence is determined by a balance of WNT and Activin/NODAL signalling gradients along the A-P and D-V axis, respectively, and equivalent findings were recapitulated in vitro with hESC, suggesting that these findings are representative of human embryos. Altogether, these findings provide evidence that classical EMT occurs as primitive streak cells lose their pseudostratified epithelial organisation as they transition to nascent mesoderm. This classical EMT occurs after the segregation of the primitive streak and anterior primitive streak from a “caudal epiblast” population. This suggests that the last cells capable of producing both mesodermal and endodermal populations are situated in the epiblast layer. Given that the APS and subsequently node and DE populations maintain their epithelial characteristics during their ingression they likely internalise via mechanisms independent of those that govern mesodermal ingression.

While we have identified several conserved and divergent features of mammalian gastrulation it is important to recognise the limitations inherent in such analyses. Firstly, differences in the quality of reference genomes/transcriptomes and annotations, which form the basis for quantifying transcriptional programs vary greatly between species, potentially leading to discrepancies in data quantification and interpretation. Additionally, assumptions about the orthology of genes and indeed the exclusion of genes in which orthology cannot be assumed will undoubtedly mean many factors that may be critical in cell-fate determination in a particular species will be overlooked. Lastly, given the logistics of collecting and sequencing samples of a given species we have made our comparisons with publicly available datasets of which the methodologies surrounding embryo collection, handling, dissection, and single-cell isolation may vary and therefore batch effects may mask biological variation. Therefore, while our findings provide valuable insights into conserved and divergent aspects of development, they also underscore the need for caution in extrapolating results across different species. By addressing these conceptual challenges, we can better harness the potential of such cross-species analyses to reveal fundamental principles of development.

We anticipate that this resource in combination with other recent works 3 and techniques, such as spatial transcriptomics of early embryos 64 , will inform more detailed analyses of species differences and will shed light on the molecular events that underly the phenotypic differences in mammalian embryos. Given their use in agriculture pig embryos represent a highly accessible model for functional investigations relevant to human development. As with comparable datasets in mice 2 , we expect that this dataset will serve as a wild-type reference for comparisons against mutant embryos. Such investigations will serve as a foundation on which to develop more robust in vitro differentiation protocols of pluripotent cells, improved methodologies to produce interspecies chimaeras and to study the development of organs used for xenotransplantation 9 , 10 , 11 .

Ethical statement

All procedures involving animals were approved by the Animal Welfare and Ethics Review Committee (Nbr. 99) of the School of Biosciences, The University of Nottingham. The research conducted adhered to the Home Office Code of Practice guidelines for the Housing and Care of Animals used in Scientific Procedures.

Embryo collection

Animals used in this study were monitored twice daily and sacrificed by electrical stunning followed by exsanguination (Schedule 1). Embryos were collected from crossbred Large White and Landrace sows (2–3 years old) between days 10 to 16 post artificial insemination. Each uterine horn was separated into an upper and lower half before fresh PBS + 5% BSA was used to flush out porcine embryos. Uterine horns were then bisected and searched by hand for any further embryos. Embryos were stored in warm PBS + 5% BSA during. Embryos were either fixed in PFA at 4 °C overnight for IHC or taken for 10x single-cell RNA sequencing. To ensure a representative sampling of cell types and to mitigate the overrepresentation of cells from extraembryonic tissues, the embryos were carefully dissected to remove most of these tissues prior to dissociation. Embryo dissections were performed using forceps, and extraembryonic membranes were carefully dissected, avoiding disrupting ED derivatives.

IF and imaging of whole-mount embryos

Embryos were permeabilized and blocked at the same time for 2 hr in a solution containing 10% donkey serum and 5% BSA with 1% Triton-X (PB buffer) at RT. Samples were incubated with primary antibody O/N at 4 °C in PB buffer. Secondary antibodies were incubated with sample for 2 hr at RT in PB buffer. Washes were performed after primary and secondary antibody incubations 4x15min in PBS with 0.2% Triton-X. Samples were mounted in either VECTASHIELD or Fluoroshield with or without DAPI. If the mounting media did not contain DAPI, it was added at the secondary antibody incubation stage. Antibodies used here are listed in Supplementary Table  1 . Imaging of embryo sectioned and whole mount samples was performed with a confocal Zeiss LSM 900 with Airyscan, samples imaged as z-stacks were done so with a Z resolution of 0.32 µm.

Segmentation and quantification of embryos

Z stack images were segmented using a StarDist/TrackMate pipeline within Fiji. StarDist allows for 2D segmentation, and TrackMate is used to build up each cell in 3D from the totality of the 2D segmentation data. 3D data for each cell was tabulated and the data for each embryo was exported as a.CSV file and further analysed in R using custom code. Thresholding was achieved via Otsu’s method within fiji. Analysis in R allowed for the extraction of location and protein expression information for each cell, which was then used to recapitulate the embryos in silico, as well as directly quantify the number of cells expressing a given protein. Lateral re-slicing performed within Fiji.

Culturing, imaging and quantification of 2D experiments

Both human and pig cells were cultured in AFX medium: N2B27 supplemented with FGF2 (20 ng/ml) + Activin A (12.5 ng/ml) + XAV939 (2 μM) 22 . For experimental procedures, cells were seeded at a range of seeding densities (750–1200 cells/well) into CytoOne TC treated 96 well plates coated with Laminin-511-E8 fragment with additional ROCKi for 48hrs. Cells were differentiated in N2B27 supplemented with CHIR, Activin A, XAV and WNT3A depending on the condition. Cells were then fixed with 4% PFA and immunostained as detailed above. Images were captured using an Operetta CLS high-throughput microplate imager for the CHIR experiment or a CellDiscoverer 7 confocal plate reader for the WNT3A experiment. Images were segmented using Harmony (v4.1) from phenoLOGIC or StarDist2D within Fiji, respectively. Thresholding, cell expression identity and plotting was performed with a custom script in R. Cell lines used were EDSCL4 (porcine), H9 and HNES1 (human) (Supplementary Table  1 ).

Single-cell transcriptomic analysis

Preparation of scrna-seq library and sequencing.

Single-cell libraries were constructed using Single Cell 3 Library & Gel Bead Kit v3 according to the manufacturer’s protocol (10X Genomics). Briefly embryos taken for scRNAseq were either frozen and stored at −20C or sequenced fresh. Given the large size of ExE tissues, to ensure that embryonic cells were well represented, we manually dissected most ExE structures prior to dissociation and sequencing, preserving the hypoblast. Embryos were dissociated into single cells via incubation with TrypLE, for 7 mins at 37 °C. Embryos were further dissociated via pipetting and then washed with a solution of DMEM/F12 0.04%BSA to quench TrypLE activity. Cells were strained though a Flowmi cell strainer into a 15 ml falcon. Once dissociated, cells from pools of same stage embryo were counted using a haemocytometer. Approximately 4000–14000 cells per lane of each 10x chip were transferred. The chip was then loaded into a Chromium Controller for cell lysis, cDNA synthesis and barcode labelling. cDNA libraries were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies). Finally, the libraries underwent 150 bp paired-end sequencing using the NovaSeq 6000 platform.

scRNA-seq data preprocessing

Raw fastq files were processed using cellranger-6.1.2 software with default mapping arguments. Reads were mapped to the Sus scrofa Sscrofa11.1 (GCA_000003025.6) genome. Subsequently, the CellRanger ‘aggr’ command was used to normalize the sequencing depth of different samples. For samples where the number of sequenced cells differed greatly from the counted number of cells prior to loading the ‘force cells’ command was used, and the estimated number of loaded cells was given.

Filtering, dimensionality reduction and clustering

The filtered expression matrix with cell barcodes and gene names was loaded with the ‘Read10X’ function of the Seurat (v.4.0.0) R package 65 . Following this, Seurat objects from each sample (23 samples) were independently created and processed according to standard Seurat protocols. Initially, single cells with a number of detected genes (nFeature_RNA) above 1750 were retained to exclude low-quality cells. Subsequently, doublet or multiplet cells were identified with the DoubletFinder R package 66 and excluded. After normalization of the Seurat object, we selected the 3000 most variably expressed genes using the ‘FindVariableFeatures’ command. These features were used to calculate the first 100 principal components. Given the heterogeneity of cell compositions of early and late embryos, we did not exclude cells based on the percentage of their transcriptomes that were made up of mitochondrial genes. Instead, following clustering, we looked for non-discreet clusters that had significantly higher expression of mitochondrial genes and low ‘nFeature_RNA’. Two clusters of cells were excluded on this basis, we also noted that these cells showed expression of markers of multiple cell-types suggesting that these cells were clustering based on a shared apoptotic identity. We then used the ‘FindIntegrationAnchors’ and ‘IntegrateData’ functions of Seurat to exclude individual heterogeneities between samples. Data integration was done using the reciprocal pca (rpca) method and the first 30 dimensions of each object were used. To construct the main UMAP plot of 91,232 cells in Fig.  1c , we used the first 25 principal components for calculating UMAP 1 and 2, setting the seed at 42, using a minimum distance of 0.5 and the 50 nearest neighbours (n.neighbors). The other parameters were kept as the defaults for UMAP generation. For clustering of the same cells, the ‘k.parameter’ of 20 and ‘n.trees parameter’ of 50 were the default settings during the neighbour-finding process; 25 dimensions were selected via the ElbowPlot method for neighbour finding, and clustering. A resolution of 1.2 was used to identify the 36 major cell-types. For sub-clustering, cell-types of interest were subsetted, objects were re-scaled and then the same numbers of principle components were calculated. The same parameters were used for neighbour finding, however, a resolution of 0.5 was used to cluster. For UMAP creation, again the ElbowPlot method was used to select the number of dimensions however the 30 nearest neighbours were used along with a minimum distance of 0.4. We calculated the DEGs of each cell cluster with RNA assay using the ‘FindAllMarkers’ function in Seurat. Heatmaps were plotted based on the most highly expressed genes (according to fold change) which had an adjusted p-value less than 0.05.

The ‘monocle3’ R package 67 was used to calculate the developmental pseudotime of E11.5-E13 epiblast, PS, nascent mesoderm, APS, DE and node subclusters. The Seurat object was converted to a monocle3/ CellDataSet class by the ‘as.cell_data_set’ command of the SeuratWrappers R package. Cells were then processed according to the developer vignettes and fate information was transferred back to the Seurat object for further analysis.

Gene expression-based categorisation of cells

Cells were categorised as having ‘positive’ expression of NANOG, FOXA2, TBXT or SOX17 cells if they had a scaled expression value greater than 0.1.

Transcription factor regulon analysis (SCENIC)

Gene regulatory networks and regulons were elucidated using the command-line interface (CLI) of the pySCENIC pipeline, including tools such as ‘arboreto_with_multiprocessing.py’, ‘ctx’, and ‘aucell' 68 . Input data consisted of raw gene counts, pre-processed to consider cells detecting between 1,750 to 7,500 genes. Cells bearing over 10% mitochondrial reads, along with genes identified in less than three cells, were excluded from the analysis. For transcription factor binding motif analysis, a custom RcisTarget database was assembled using the ‘create_cistarget_motif_databases.py’ tool. The construction of this database was based on the v109 Ensembl release of the Sscrofa11.1 reference genome. Feather ranking databases were constructed based on two distinct sets of regions: (1) regions encompassing 2,500 bp upstream and 500 bp downstream of each transcription start site (TSS), and (2) regions spanning 10 kb upstream and 10 kb downstream of each TSS. The binding motif list was generated by renaming human binding motifs, obtained from the SCENIC motifs’ v10 public collection ( https://resources.aertslab.org/cistarget/motif_collections/v10nr_clust_public/snapshots/ ), to their pig orthologues. High-confidence, one-to-one orthologues were obtained using the Biomart tool available on the Ensembl website.

Cell–cell communication analysis

Cell annotation information and raw count expression matrix was exported from Seurat, and pig gene names were converted to their human orthologues using the same process as described above. The matrix was then processed according to the standard CellChat 54 protocol using the CellChatDB.human ‘secreted signalling’ database.

Comparison of datasets among mice, humans, monkeys and pigs

To project pig single-cell data onto the mouse, human and, monkey datasets, expression matrices were exported from Seurat and gene names were converted to their human orthologues. Ensembl biomart was used to identify 14,108 high-confidence one-to-one orthologues. All genes that were not present in all four species were excluded from the matrices. Individual matrices were then loaded into Seurat and processed individually as before using the same parameters as for pig samples. Pig, mouse and monkey datasets were then randomly down sampled so that each sample contained 25,000 cells. The transfer of cell labels between each dataset was done in a pairwise fashion using the MapQuery function in Seurat. The anchors between mouse and pig data were found with the FindTransferAnchors function (reference.reduction, ‘pca’; dims, 1:50; k.filter, NA), and the function MapQuery (reference.reduction, ‘pca’; reduction.model, ‘umap’) was used. For comparisons of cell transcriptomes, pig, monkey and mouse datasets were integrated using the IntegrateData function as before and the active identity was set to the pig cell type annotations. For identifying differentially expressed and conserved gene expression three individual objects were made from this parent object, pig-mouse, pig-monkey and mouse-monkey. The ‘FindConservedMarkers’ function was used to find conserved and divergent cell type-specific markers in a pairwise fashion. All comparisons used the “RNA” assay. A marker gene was classified as ‘conserved’ if the marker was significantly increased beyond 2-fold in the cell type of interest compared to all other cell types (adjusted p-value < 0.05) in both species tested. A gene was classified as divergent if it was significantly decreased in the cell type of interest in one species compared to all other cell types but increased in the other. To identify differentially expressed genes that include but were not limited to cell-type specific genes a cell type in each species was compared in a pairwise fashion (Epiblast 1_Pig vs Epiblast 1_Monkey, for example) using a cut-off of <0.05 for the adjusted p-value. For visualisations, the combined species matrix was scaled using the ScaleData function in Seurat.

Generation of Figures

All figures were created in R. The following packages were predominantly used for analysis and figure creation: Seurat 65 , 69 , 70 , 71 , ggplot2 72 , ComplexHeatmap 73 , pheatmap and monocle/monocle3 36 , 67 , 74 . The full list of packages and their versions can be found in the reporting summary.

Statistics and Reproducibility

The statistical tests performed and biological replicates for each experiment are indicated in the figure legends.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The raw and processed single-cell pig gastrulation and early organogenesis data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO) database under accession code GSE236766 . The processed single-cell pig gastrulation and early organogenesis data can be viewed on our website available at https://www.nottingham.ac.uk/biosciences/people/ramiro.alberio . The mouse gastrulation and early organogenesis dataset used as a reference is available at ArrayExpress under accession code E-MTAB-6967 . The monkey gastrulation dataset is available at GEO, under accession code GSE193007 . The human CS7 dataset is available at ArrayExpress under accession no. E-MTAB-9388 and at GEO under accession no. GSE157329 .  Source data are provided with this paper.

Code availability

No new code was created for this study however, we have deposited the scripts used for analysis and figure generation at https://github.com/DrLukeSimpson/A-single-cell-atlas-of-pig-gastrulation-as-a-resource-for-comparative-embryology .

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Acknowledgements

This project was supported by the Biotechnology and Biological Sciences Research Council grants [grant number BB/S000178/1] [grant number BB/T013575/1] to R.A, M.L, and J.N. We thank the staff at the School of Life Sciences Imaging facility as well as at the nMRC core imaging at the University of Nottingham, especially Jacqueline Hicks.

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Sophie Kraunsoe

Present address: The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK

Authors and Affiliations

School of Biosciences, University of Nottingham, Sutton Bonington Campus, Nottingham, LE12 5RD, UK

Luke Simpson, Andrew Strange, Doris Klisch, Sophie Kraunsoe, Daniel Goszczynski, Triet Le Minh, Benjamin Planells & Ramiro Alberio

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, EH4 2XU, UK

Takuya Azami & Jennifer Nichols

School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, UK

Nadine Holmes, Fei Sang, Sonal Henson & Matthew Loose

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L.S., A.S., B.P. and S.K. designed and performed experiments including scRNA-Seq, embryo dissections and wrote the paper; L.S., A.S., S.H., F.S., N.H., D.G. and T.L. performed bioinformatic analysis; A.S., T.A. and D.K. performed IF and functional experiments; M.L. supervised bioinformatic analysis; J.N. supervised the functional experiments and wrote the paper. R.A. supervised the project, designed experiments, performed dissections, and wrote the paper. All authors discussed the results and contributed to the manuscript.

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Correspondence to Ramiro Alberio .

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Simpson, L., Strange, A., Klisch, D. et al. A single-cell atlas of pig gastrulation as a resource for comparative embryology. Nat Commun 15 , 5210 (2024). https://doi.org/10.1038/s41467-024-49407-6

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Received : 21 August 2023

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DOI : https://doi.org/10.1038/s41467-024-49407-6

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