Introduction

Seahorses, pipefishes, and seadragons are extraordinary fishes in the family Syngnathidae with diverse body plans, coloration, and elaborate structures for paternal brooding. The syngnathid clade comprises over 300 diverse species that vary in conservation status, distribution, ecology, and morphology (Leysen et al., 2011; Manning et al., 2019; Schneider, Woltering, et al., 2023; Stiller et al., 2022). Syngnathids have numerous highly altered traits, trait losses, and evolutionary novelties. They have elongated snouts bearing small, toothless jaws (Leysen et al., 2011) specialized for capture of small zooplankton (Van Wassenbergh et al., 2009). Additionally, syngnathids are distinctly protected by a bony dermal armor rather than scales (Jungerson, 1910). Other skeletal differences include a lack of ribs and pelvic fins, and an expansion in the number of vertebrae (Schneider, Woltering, et al., 2023). Finally, syngnathids exhibit male pregnancy, which has involved the evolution of specialized brooding tissues and structures (Whittington & Friesen, 2020). Paternal investment varies among lineages; for example, seadragons tether embryos externally to their tails while seahorses and some pipefishes have enclosed brood pouches proposed to support embryos through nutrient transfer and osmotic regulation (Carcupino, 2002; Melamed et al., 2005; Ripley & Foran, 2006).

Despite advances in understanding the ecology and evolution of syngnathid novelties, the developmental genetic basis for these traits is largely unknown. The recent production of high quality syngnathid genome assemblies (Qu et al., 2021; Ramesh et al., 2023; Small et al., 2022; Wolf et al., 2024) provides initial clues for the developmental genetic basis of some evolutionary changes. Studies have found that syngnathids lack several genes with deeply conserved roles in vertebrate development, including pharyngeal arch development (fgf3), tooth development (fgf3, fgf4, and eve1, and most Scpp genes), fin development (tbx4), and immune function (MHC pathway components) (Lin et al., 2016; Qu et al., 2021; Small et al., 2016; Small et al., 2022; Zhang et al., 2020). Though these gene losses are highly suggestive of leading to unique changes, exploration of the actual developmental consequences of their losses is needed.

To fill this gap in knowledge, we used single cell RNA sequencing (scRNAseq) to investigate how these striking genomic changes have affected the developmental genetic and cellular basis of syngnathids’ derived traits. The Gulf pipefish (Syngnathus scovelli) is an attractive model for this study (Figure 1A). This species has a high-quality reference genome annotated by NCBI and is amenable to laboratory culture (Anderson & Jones, 2019; Ramesh et al., 2023). Furthermore, species from the Syngnathus genus are used worldwide to address questions about syngnathid evolution in microbial, developmental, functional morphological, histological, transcriptomic, ecotoxicological, and genomic studies (Berglund et al., 1986; Carcupino, 2002; Fuiten & Cresko, 2021; Harada et al., 2022; Harlin-Cognato et al., 2006; Partridge et al., 2007; Ripley & Foran, 2006; Rose et al., 2023; Roth et al., 2012; Small et al., 2016; Small et al., 2013). In this paper, we focus on a subset of unique syngnathid traits, including their elongated head, toothlessness, dermal armor, and development of embryos inside the brood pouch. These traits represent the diversity of evolutionary changes observed in the syngnathid clade (highly altered, lost, and novel traits); hypotheses from studies of model organisms suggest developmental pathways involved in their evolution (Lin et al., 2016; Roth et al., 2020; Small et al., 2016; Small et al., 2022).

Figure 1:

scRNAseq atlases are a powerful complement to genomic analyses (Shema et al., 2019; Ton et al., 2020). They can provide crucial insights into types of cells present, genes that distinguish cell types (marker genes), active gene networks, and a means to identify expression of genes of interest within predicted cell types (Farnsworth et al., 2020; Farrell et al., 2018; Williams et al., 2019). Specifically, scRNAseq captures RNA expression profiles from individual cells, allowing cell types to be inferred post-hoc. scRNAseq has successfully been applied to syngnathid adult kidneys (Parker et al., 2022), but there are no published syngnathid developmental atlases.

Here we report the first developmental scRNAseq atlas for syngnathids from late embryogenesis staged Gulf pipefish. We delineate the overall structure of this atlas, which describes 38 cell clusters composed of 35,785 cells, and use these data to make inferences about the morphological evolution of several syngnathid innovations. In addition to inferring present cell types and their underlying genetic networks, we detail spatial expression patterns using in situ hybridization experiments of select marker and other candidate genes in pipefish embryos and juveniles. We found genes of conserved signaling pathways expressed during craniofacial development but did not detect evidence of tooth primordia. The embryonic dermis and epidermis, respectively, expressed genes for the dermal armor development (bone development pathways) and genes potentially involved in interaction with the male brood pouch (e.g., nutrient acquisition genes). Overall, this atlas provides a deeper understanding of the development of Gulf pipefish and identifies gene candidates for understanding the development of syngnathid evolutionary innovations. In addition to these discoveries, this atlas provides a significant resource for researchers studying syngnathid evolution and development.

Results

Valuable scRNAseq atlas for studying syngnathid development

We produced the first developmental scRNAseq atlas for a syngnathid from two samples comprising 20 similarly staged embryos from pregnant, wild-caught Gulf pipefish (Syngnathus scovelli) males. The samples represent a late organogenesis developmental stage (Figure 1B-H). These embryos had a primarily cartilaginous skeleton with minimal mineralization, including jaw cartilages that were at the onset of mineralization and ethmoid elongation. The embryos also possessed cartilaginous dorsal fin pterygiophores but had no signs of dermal armor mineralization. This stage is referred to as “frontal jaws” in the literature on syngnathids (Sommer et al., 2012).

The atlas included 35,785 cells (19,892 and 15,893 cells from each sample; S1:S2), which formed 38 cell clusters (Figure 2A, Supplementary File 1, 2). We classified cells into 4 different broad tissue types – epithelial, connective, neural, and muscle – using Seurat-identified marker genes and published model organism resources (S3:S57). We next used Seurat identified marker genes to pinpoint single marker genes that were most unique to each cluster (Figure S58, Supplementary File 3, 4, 5). We completed in situ hybridization using Gulf and bay pipefish embryos for cell clusters for which examining gene expression would help hone and validate cluster annotations (Figure S59:S71).

Figure 2:

In total, our atlas contained 13,027 connective tissue cells (excluding cells from blood, immune, and the digestive system) from 14 clusters, 10,112 nervous system cells from ten clusters, 4,363 muscle cells from five clusters, 4,133 blood cells from three clusters, 650 immune cells from two clusters, 432 pigment cells from one cluster, 370 epidermal cells from one cluster, and 137 gut cells from one cluster. Within the connective tissue cell types, we also identified cartilage (302 cells), developing bone (442 cells), fins (253 cells), and notochord (693 cells). The number of recovered cells per identity may not necessarily represent organismal cellular proportions because of potential variability in dissociation success for different cell types (Denisenko et al., 2020; Venema et al., 2022).

Discovery of cell cluster function and state using KEGG analysis

To affirm identities and discover potential properties of each cluster, we completed a KEGG pathway analysis for each cluster using Seurat’s marker genes (Figure S72, Figure 2B). For eight of the clusters (1, 4, 6, 9, 11, 15, 19, and 24), we did not find any significantly enriched pathways, possibly due to similar gene expression profiles across cell types that reduced the number of identified markers. However, we found one or more significantly enriched pathways for the other 29 cell clusters. We observed enriched pathway terms that supported cluster annotations. For example, ‘phototransduction’ in the retina cluster, ‘melanogenesis’ in the pigment cluster, ‘cardiac muscle contraction’ in muscle clusters, and ‘neuroactive ligand receptor interaction’ in neuronal clusters.

The inferred KEGG pathways demonstrated some commonalities across the different tissue types, including in signaling pathways and cell states. Notably, our identified KEGG terms delineated progenitor and differentiated cell clusters. Based on their KEGG terms, we classified clusters 8, 10, and 16 as possible neural, muscle, and connective tissue progenitor cells, respectively. We also detected expression of pax3a and pax3b, muscle primordia markers, in cluster 10, supporting this annotation. These clusters had enriched KEGG terms associated with cell division (‘cell cycle’, ‘DNA replication’, ‘nucleotide excision repair’, and ‘homologous recombination’), and lacked enrichment for KEGG pathways present with differentiated cell types of their lineage. Specifically, cluster 8 lacked the neural KEGG term ‘neuroactive ligand receptor interaction’, cluster 10 lacked muscle KEGG terms ‘adrenergic signaling in cardiomyocytes’, ‘calcium signaling pathways’, and ‘cardiac muscle contraction’, and cluster 16 lacked connective tissue term ‘ECM receptor interaction’. To complement these findings, we completed a cell differentiation analysis using CytoTRACE for neural, muscle, and connective clusters (Figure S73). Clusters 8, 10, and 16 had the lowest scores in each respective comparison, which indicated undifferentiated cell states. Thus, it is likely that clusters 8, 10, and 16 represented undifferentiated cells within the major lineages of neural, muscle, and connective cells.

Commonalities of cell clusters, unique networks, and elusive cell types identified in network analysis

We built gene networks/modules from 3,000 variable genes using weighted gene network correlation analysis (WGCNA; Langfelder & Horvath, 2008). This produced 43 gene modules in total (Supplementary File 8, 9), assessed for each cluster-module pair for their strength of association (Figure 3A, Supplementary File 10, 11) and every module’s dependence on each cluster for their network connectivity (Figure S74, Supplementary File 12, 13). Using the genes from each network, we completed a KEGG pathway analysis to identify whether gene modules indicated specific cellular pathways or states (Figure 3B). We initially explored whether these network-cluster associations could reveal commonalities between cell clusters or identify whether particular clusters contained multiple cell identities.

Figure 3:

First, we asked whether gene modules that associate with three or more cell clusters signify commonalities between clusters that have similar cell types. We identified seven gene modules (6, 7, 21, 14, 17, 41, and 42) that each associate with three or more cell clusters. These gene modules do connect clusters of similar identities or cell states. For example, Modules 6 and 7 associate with connective tissue cells. Module 6, the larger of the two modules, contains numerous other KEGG pathways found in most connective cell clusters, such as ‘cytokine-cytokine receptor interaction’ (Figure 3C). Interestingly, module 21, associated with clusters 8, 10, 16, and 25, contains genes from KEGG pathways related to the ‘cell cycle’ and ‘cellular senescence’, supporting the results found in our cluster based KEGG pathway analysis (Figure 3D). Module 21 also contained ‘Notch signaling’ genes, possibly due to similar correlations with cell cycle genes; however, these were only expressed in the neural cell clusters (8 and 25).

Where one cell cluster is associated with multiple gene networks, we wondered if multiple cell identities existed within the cell cluster. We explored this possibility by examining the pigment cell cluster (cluster #20) and whether the five different correlated gene modules (#34, 35, 36, and 37, and 38) are expressed in distinct subgroups of cells within the cluster (S75). We found cases of more than one genetic network (modules 35 and 38) expressed in the same cells. Modules 35 and 38 included conserved pigment genes, pmela, mlana, and dct in module 35 and tryp1b and pmel in module 38, that mark melanocytes (Figure S6; Du et al., 2003; Johnson et al., 2011; Lamason et al., 2005; Thisse & Thisse, 2004).However, we also found non-overlapping expression of networks, notably modules 36 and 37. We inferred that module 36 is associated with xanthophores and xanthoblasts due to the presence of plin6 and scarb1, genes involved with lipid binding and activity in xanthophores (Ahi et al., 2020). On the other hand, module 37 likely represents iridophores and iridoblasts because it contains pnp4a, which is involved in purine-nucleoside phosphorylase activity in iridoblasts (Kimura et al., 2017).

Conserved signaling pathways are active during syngnathid craniofacial development

The specialized pipefish feeding apparatus is composed of an elongate, tubular snout, toothless mandible and pharyngeal jaws, large tendons, and associated muscles. Therefore, numerous cell types contribute to their distinct faces: cartilage, bone, tendon, muscle, and connective tissues as well as their progenitors. We sought to identify markers of these cell types and signaling pathways active in them.

We found marker genes uniquely expressed in the face, genes that mark cell types important to craniofacial development, and markers with potentially relevant functions for craniofacial development using in situ hybridizations of cell cluster marker genes. For instance, we observed the marker for osteochondro-mesenchymal cells (cluster #6), elnb, specifically expressed at the intersection between the ethmoid plate and palatoquadrate as well as on the Meckel’s cartilage (Figure 4B). Although elnb is observed in the zebrafish cranial skeleton, it is primarily studied for its proposed role in teleost heart evolution (Miao et al., 2007; Moriyama et al., 2016).

Figure 4:

Other genes identified here as cell markers were not uniquely craniofacial but provide insights into the cell types that comprise the face. For example, tnmd expression marked tendons and ligaments (cluster #9) throughout the face and body (Figure 4C; Figure S65). Our finding is consistent with tnmd’s role in tenocyte development in model systems, namely zebrafish and mouse (Chen & Galloway, 2014; Docheva et al., 2005). Identifying tenocyte cells is particularly relevant in syngnathid fishes where tendons are enlarged and store elastic energy necessary for their specialized feeding (Van Wassenbergh et al., 2007).

The last category of markers contained genes with regulatory roles important for craniofacial development such as prdm16, the marker for osteochondro-mesenchymal cells (cluster #5). The gene prdm16 mediates the methylation of histones and regulates gene expression, key for promoting craniofacial chondrocyte differentiation (Ding et al., 2013; Kaneda-Nakashima et al., 2022; Lomeli Carpio Shull et al., 2020). We found prdm16 expressed in mesenchymal cells directly above the ethmoid plate and in fins (Figure 4D; Figure S61). Comparison of pipefish with a related, short snouted fish (stickleback) identified similarity in fin expression, but differences in craniofacial staining (S76). Stickleback expressed prdm16 in the hindbrain, gill arches, and lower jaw, consistent with published zebrafish data (Ding et al., 2013), but staining was additionally detected in the ethmoid region in pipefish.

We next examined signaling pathways active in craniofacial development. Our KEGG pathway analysis revealed that MAPK, Wnt, and TGF-beta signaling pathways were significantly enriched in one or more craniofacial contributing cell clusters (module #6; Figure 2B, Figure 3C, Figure 4A). In addition to these KEGG findings, expression of ligands and receptors that are members of these pathways (Fgf, a MAPK pathway, Wnt, and Bmp, a TGF-beta pathway) was observed in cell clusters of both differentiated and undifferentiated states (Figure 4A). We chose three genes, one from each major pathway, for in situ hybridizations: fgf22 (a Fgf ligand present in the actively dividing cells’ module), bmp4 (a Bmp ligand present in the largest connective tissue module), and sfrp1a (a Wnt pathway enabler present in a cartilage gene module). Although mouse and zebrafish studies identified fgf22 expression only in the nervous system (Miyake & Itoh, 2013; Umemori et al., 2004), we found fgf22 is expressed at the tip of the palatoquadrate and Meckel’s cartilage, the gill arches, fins, and brain in pipefish (figure 4E; S77). These same expression patterns were observed in stickleback, suggesting a surprising co-option of fin and craniofacial expression in percomorph fish.

We observed bmp4 and sfrp1a expressed above the ethmoid plate and along the ceratohyal in pipefish (figure 4F-G). The gene bmp4 has a conserved role in craniofacial development, particularly important at later stages for driving chondrocyte differentiation (Wang et al., 2024; Zhou et al., 2013). However, the specificity of bmp4 expression to mesenchyme around the ceratohyal and ethmoid was not observed in stickleback fish, which had broad craniofacial expression (including the jaws, tooth germs, and gill arches; S78). Interestingly, sfrp1a expression has not been observed in the palate of mice or ethmoid region of zebrafish, but sfrp1a craniofacial expression was identified in stickleback (lower jaw and gill arches; S79) and has been observed in other fishes (Ahi et al., 2014; Schneider, Gunter, et al., 2023; Schilling & Kimmel, 1997; Swartz et al., 2011; Wang et al., 2024).

Gulf pipefish retain tooth development genes but likely lack onset of tooth development

Previous papers have identified possible candidate genes for the loss of teeth in syngnathid fishes including those from genes that initiate tooth bud formation (fgf4, eve1), regulate tooth morphogenesis (fgf3, fgf4), and synthesize tooth minerals (scpp4, scpp7, scpp9, odam, and scpp5; Lin et al., 2016; Qu et al., 2021; Small et al., 2016; Small et al., 2022; Zhang et al., 2020). However, it is unknown whether syngnathid tooth development initiates then halts or whether it never begins. We searched for signs of early tooth primordia within our atlas to ask whether tooth development might initiate in syngnathids. Additionally, we examined whether genes present in mature teeth are still expressed in syngnathids and what types of cells express them.

Our thorough examination of cell clusters for identity annotation did not find a tooth primordium cluster. We therefore searched for tooth primordia by examining expression of specific odontogenesis marker genes (aldh1a2, bmp4, dlx2a, dlx3b, lef1, lhx6a, lhx8, msx1a, msx2, and wnt10a; Figure 5A). We observed several primordium genes expressed in our atlas. However, there was no cluster with every marker gene expressed in over 10% of cells. Several markers (dlx3b, lef1, msx1a, msx2, and wnt10a) were expressed in cluster #28, a fin cluster distinguished by hoxa13a and hoxa13b expression. Since we previously noted that cluster #16 seems to be a primordial connective tissue cluster, we wondered if it could contain tooth primordial cells. In this cluster, we found the following percentage of expression of each gene in the cells: bmp4 in 46%, aldh1a2 in 29%, lef1 in 24%, dlx2a in 18%, dlx3b in 16.4%, eda in 12.7%, msx1a in 10.6%, pitx2 in 10%, lhx6a in 8%, wnt10a in 4.6%, msx2 in 2.65%, lhx8 in 1.8%, and shha in 1%.

Figure 5:

We suggest that, given the low expression of most tooth marker genes, cluster #16 is unlikely to contain tooth primordial cells. To test this hypothesis, we examined spatial gene expression using in situ hybridization of bmp4, pitx2, lhx6a, dlx2a, and dlx3b in pipefish to ask whether the two definitive cell types are present, namely the dental epithelium (marked by pitx2 and bmp4) and dental mesenchyme (distinguished by dlx2a, dlx3b, and lhx6a; Figure 5B-F; Gibert et al., 2019; A. Tucker & Sharpe, 2004). Tooth-specific expression of dlx2a is observed solely in the dental mesenchyme in zebrafish and mice, however, it is also expressed in the dental epithelium in medaka (Stock et al., 2006). For this study, we labelled dlx2a as a mesenchyme marker, though it could be expressed in both dental tissues in a syngnathid outgroup. We found expression of all genes except dlx2a in the developing jaws. However, bmp4, pitx2, lhx6a, and dlx3b were expressed throughout the face rather than in the punctate pattern observed in tooth primordia development.

We next investigated scpp genes (enam, scpp1, and spp1), which are expressed in late tooth development and tooth maintenance in other vertebrates (Figure 5A). These genes also have some expression outside of teeth such as in dental bone (scpp1, spp1; Kawasaki, 2009) and fins (enam; Jain et al., 2007). We identified spp1 and scpp1 expression in 54.5% and 24% of bone cells, respectfully, and sparse spp1 expression (<3%) in other connective tissue cell types. We found minimal expression of enam (in 11.4%) in epidermal cells and in less than 8% of muscle cells. In whole mount in situ hybridization, we found that scpp1 is expressed in all developing pipefish bones, both endochondral and dermal (Figure 5G). Since scpp gene losses observed in syngnathids have been hypothesized to be responsible for their tooth loss (Lin et al., 2016; Qu et al., 2021; Zhang et al., 2020), we explored scpp gene family content in the close, tooth-bearing relative to syngnathids, the blue spotted cornetfish, and we found several gene losses (scpp4, scpp7, and scpp9) and a likely pseudogene (scpp5) (Figure S80; Hughes et al., 2018; Stiller et al., 2022).

Exploration of additional tooth maturation genes (col1a1b, col4a1, col5a1, sparc, and mmp20b) similarly found that these genes were expressed in non-tooth derivatives, including connective tissue, smooth muscle, and neural cells.

Tooth and skeletal genes are expressed during dermal armor development

Syngnathid dermal armor is mineralized dermal bone underneath the skin (Figure 6D, G, H). It is unknown when dermal armor primordia initiate and how they are patterned. Spatial expression analysis of pitx2 and dlx3b in search of tooth primordia instead revealed expression of these genes in possible dermal armor primordia (Figure 6A,B, E,F). We found pitx2 staining localized dorsally to striated muscle underneath developing dermal armor. The gene dlx3b is expressed in a repeating pattern along the body in the epidermal and dermal tissues. Both staining patterns were in discrete regions of the muscle and epidermal layers rather than being continuously expressed across the tissues. We did not observe the expression of other tooth primordium genes (bmp4, lhx6a, and dlx2a) in this region.

Figure 6:

Because the embryos from our atlas had not begun dermal armor mineralization, the atlas cannot be directly used for discovery of genes active in dermal armor. However, the atlas contains osteoblasts from craniofacial bones which we used to create osteoblast-specific gene networks. We therefore asked whether these osteoblast genes were present in mineralizing dermal armor at later stages. In situ hybridization expression analysis revealed that scpp1, an osteoblast and tooth mineralization gene, and ifitm5, an osteoblast gene, were expressed in the dermal armor at the onset of mineralization (Figure 6C; Figure S68).

Epithelial expression of immune and nutrient-processing genes may facilitate embryo-paternal interactions in the brood pouch

Within the brood pouch, embryos could interact with male placenta-like tissues, the male brood pouch epithelium, and/or the pouch microbiome. Once the thin chorion is shed, the embryos’ epidermis is directly exposed to the pouch environment. We therefore asked if the embryonic epidermal cells expressed nutrient acquisition and/or immune genes that would indicate active transfer of nutrients and immune response.

Within our larger KEGG analysis, we asked whether nutrient absorption and immune KEGG terms were among the enriched pathways for the epidermal cells. We identified 106 enriched genes in the ‘endocytosis’ pathway (p-value=.036; Figure 7A). Four metabolism pathways (‘galactose’, ‘glutathione’, ‘sphingolipid’, and ‘starch and sucrose’) are also enriched. No immune-related KEGG terms were enriched in the epidermis. For comparison, we investigated whether these KEGG terms are also enriched in epidermal cells of non-brooding fishes. We completed a KEGG pathway analysis on a comparably staged zebrafish single cell RNA sequencing atlas (3 days post fertilization; Lange et al., 2023). The 3dpf zebrafish epidermal cells did not have a significant enrichment of the ‘endocytosis’ pathway (23 up-regulated genes, p-value=.99) or any metabolism pathway. However, there are 11 endocytosis genes up-regulated in both zebrafish and pipefish epidermal cells, suggesting conserved expression of these genes in pipefish.

Figure 7:

We next examined pipefish epidermal gene networks for presence of nutrient absorption or immune genes. We found that the largest epidermal gene network (#16) contained a striking enrichment of C type lectin genes, carbohydrate binding proteins that possess antimicrobial properties (Figure 7B). This network contained 14 total lectin genes expressed in the epidermal cells: five galactose-specific lectin nattectin, 2 alpha-N-acetylgalactosamine-specific lectins, one L-rhamnose-binding lectins, four ladderlectin, one C-type lectin 37Dd-like, and one C-type lectin domain family 4 member G-like. Through examining lectin gene expression in the entire dataset, we found that these genes were specific to epidermal cells. Interestingly, previous literature has identified an upregulation of C-type lectins in brood pouch tissues throughout different stages of syngnathid pregnancy (Roth et al., 2020; Small et al., 2013; Whittington et al., 2015).

We did not find any expression of C-type lectin genes in zebrafish epidermal cells (Figure S81), unlike in pipefish. However, it is possible there are unannotated C-type lectin genes in zebrafish that remained cryptic in the atlas. Interestingly, the ‘C-type lectin receptor signaling pathway’ was significantly enriched in zebrafish epidermal cells (13 genes, p-value=.04) but not in pipefish epidermal cells (34 genes, p-value=.4). Although these results seem paradoxical, the KEGG term ‘C-type lectin signaling pathway’ does not include any of the C-type lectin genes themselves. Overall, these data suggest that the expression of C-type lectin genes in the pipefish embryonic epidermis is potentially unique and warrants further investigation.

Discussion

Our study examines the development of syngnathids, with a particular focus on novel and adaptive characters, using single cell RNA sequencing of Gulf pipefish embryos coupled with in situ experiments of gene expression. Our single cell atlas represents early craniofacial skeleton development in Gulf pipefish, at a stage when the cartilages of the head skeleton were formed but the face has not elongated. We used the atlas to explore craniofacial and dermal armor development and to investigate potential interactions between the embryos and the brood pouch environment. Our dataset is both an opportunity to explore the developmental genetic underpinnings of syngnathid innovations, and a resource for teleost researchers for future studies in this fascinating lineage.

Our atlas represents a novel resource for Evo-Devo research

Developmental single cell atlases have elucidated cell identities and genetic pathways active in model organisms such as zebrafish, mice, and chick (Farnsworth et al., 2020; Farrell et al., 2018; Feregrino et al., 2019; Morrison et al., 2017; Soldatov et al., 2019; Wagner et al., 2018; Williams et al., 2019). In less traditional models, the majority of scRNAseq atlases are produced from adult tissues, allowing investigations into cell types, population differences, and genetic networks (Chari et al., 2021; Fuess & Bolnick, 2023; Hain et al., 2022; Hong et al., 2023; Koiwai et al., 2023; Parker et al., 2022; Potts et al., 2022; Royan et al., 2021; Songco-Casey et al., 2022; Vonk et al., 2023; Woych et al., 2022). Creation of developmental scRNAseq atlases in non-model organisms is just beginning to accelerate, but many emerging models still lack such a resource, limiting our understanding of their development (Healey et al., 2022; Salamanca-Díaz et al., 2022; Steger et al., 2022; Ton et al., 2023). Our single cell atlas is one of the first created to understand the development of derived traits in a non-model organism.

For syngnathids specifically, this atlas represents an important step towards understanding the genetic nature of unique syngnathid traits. Numerous developmental genetic changes can lead to evolutionary innovations, including the evolution of novel genes, gene duplications, gene losses, gene family expansions or contractions, evolution of regulatory elements, co-option of gene regulatory networks, re-wiring of gene networks, assembly of novel gene networks, and/or the emergence of novel cells (Arendt et al., 2016; Cãestro et al., 2007; Teichmann & Babu, 2004; Wagner, 2011; Wagner & Lynch, 2010). Considering these possibilities, we examined select syngnathid traits and speculated on developmental genetic mechanisms influencing their evolution. Continuing to investigate these proposed mechanisms through expanded scRNAseq atlases and other studies will be critical for understanding syngnathid evolution.

Conserved pathways may contribute to derived syngnathid heads

Syngnathids have highly derived heads including an elongated ethmoid region, uniquely shaped hyoid, and altered muscles and tendons to support specialized “pivot feeding”. The developmental underpinnings of these derived traits have remained underexplored. In our atlas, we identified cell types present in the developing pipefish head and genetic pathways active in those cell types. We identified numerous cells that were present in the developing face: cartilage, bone, tendons, ligaments, osteochondrogenic mesenchyme, fibroblasts, and unclassified connective tissue cells. Overall, we did not find any unrecognizable cell types, suggesting that genetic modifications within conserved cell types may drive craniofacial modifications.

We next investigated signaling pathways expressed in these cells to determine whether and to what extent developmental genetic reorganization might have occurred. Specifically, we examined the expression of one gene each from three different highly conserved signaling pathways: Wnt (sfrp1a), TGF-beta (bmp4), and MAPK (fgf22). Using in situ hybridizations, we found sfrp1a and bmp4 expressed dorsal of the elongating ethmoid plate and surrounding the ceratohyal, suggesting that Wnt and Bmp signaling may be active in the lengthening structures. These two genes are proposed to influence the development of elongated and broadened craniofacial morphologies in other species (Ahi et al., 2014; Schneider, Gunter, et al., 2023; Tucker et al., 2000). Pipefish prdm16 is similarly expressed dorsal to the elongating ethmoid plate. Since prdm16 regulates Wnt and TGF-beta signaling and these genes regulate chondrocyte differentiation (Bjork et al., 2010; Kaneda-Nakashima et al., 2022; Shull et al., 2022; Wang et al., 2024), their co-expression might suggest a prolonged period of chondrocyte differentiation along the pipefish ethmoid region. Future studies investigating this testable hypothesis would clarify whether prolonged chondrocyte differentiation broadly underlies craniofacial diversity.

We found fgf22 expressed in the mandible, gill arches, and fins using in situ hybridizations, but not in the elongating regions of the head. Interestingly, fgf22 expression has not been reported in craniofacial development of any other species (Miyake & Itoh, 2013), but we found similar craniofacial and fin expression in stickleback fish. Fgf signaling, however, is a conserved and essential pathway for craniofacial development (Crump et al., 2004; Leerberg et al., 2019; McCarthy et al., 2016; Szabo-Rogers et al., 2008; Walshe & Mason, 2003; Woronowicz & Schneider, 2019), raising the possibility that fgf22 has been co-opted into a role played by different fgf genes in other species. Future work should consider a relationship between the novel craniofacial expression of fgf22 and the loss of Fgf ligands fgf3 and fgf4 in syngnathids. If fgf22 is active in existing gene networks, particularly those where fgf3 or fgf4 is active in other species, then its novel expression may indicate evolved genetic redundancy prior to the gene losses.

Our analysis suggests ways in which unique syngnathid craniofacial structure could have evolved through genetic network evolution. Unusual expression location (e.g., fgf22) and specificity (e.g., sfrp1a, bmp4, and prdm16) in pipefish compared to zebrafish and stickleback suggests that changes in signaling gene deployment and/or content within craniofacial gene networks, particularly genes from Wnt, Fgf, or TGF-beta families, could underly the exceptionally elongated syngnathid face.

Early, not late, tooth development is likely at the root of evolutionary tooth loss

Tooth loss has occurred independently in numerous lineages and has often been studied to understand the developmental basis of character loss. For instance, research in birds and turtles found that tooth programs initiate but are subsequently truncated, explaining toothlessness in mature animals (Chen et al., 2000; Tokita et al., 2013). Additional studies in birds have found losses in tooth maturation genes (specifically scpp genes; Sire et al., 2008). Since numerous primordium and maturation genes are lost in syngnathids (Lin et al., 2016; Qu et al., 2021; Small et al., 2016; Small et al., 2022; Zhang et al., 2020), we asked if syngnathids begin tooth development at all.

We found that early tooth development genes were still expressed in pipefish, which is unsurprising given their pleiotropic roles, but found no convincing evidence either in the atlas or in spatial gene expression analysis of tissues for tooth primordia. The possibility remains, however, that a transient cell population could fall at a different developmental time than assayed here. Barring this caveat, syngnathid fgf3 and fgf4 losses could have resulted in insufficient Fgf signaling from the oral epithelium to the dental mesenchyme, causing failure of tooth initiation (Small et al., 2022; Stock et al., 2006).

If our finding of the loss of earliest stages of tooth development is consistent across developmental stages and syngnathids, why then have syngnathids retained some members of the scpp tooth maturation gene cluster? Since studies in birds propose scpp gene losses can occur from relaxed selection (Sire et al., 2008), we speculated syngnathids lost scpp genes with expression limited to teeth and retained genes with ancestrally pleiotropic expression patterns. In Gulf pipefish, we found the retained genes scpp1, spp1, and enam, are expressed in structures outside of tooth development, suggesting developmental pleiotropic constraint. Specifically, we found spp1 and scpp1 expressed in osteoblasts which is consistent with zebrafish (Bergen et al., 2022; Kawasaki, 2009; Liu et al., 2016) and enam expressed in the epidermis which has not been reported in zebrafish (Goldsmith et al., 2003; Jain et al., 2007; Liu et al., 2016). Through examining the conservation of the scpp genes in close syngnathid relatives, we found that most scpp gene losses (scpp4, scpp7, and scpp9 and a functional scpp5) are shared with a tooth-bearing outgroup to the family, and likely occurred prior to the loss of teeth in syngnathids. Overall, our analysis favors the hypothesis that pleiotropic scpp genes were retained in syngnathids while other, more tooth-specific scpp genes were lost due to relaxed selection.

Redeployment of the bone gene network to build dermal armor

The syngnathid dermal armor is a type of evolutionary novelty, which can arise through either differentiation of serially repeated elements or de novo origination, derived from either the redeployment of existing gene networks, rewiring of existing gene networks, or the assemblage of new gene networks (Wagner & Lynch, 2010). Currently, there is no understanding of the developmental genetic underpinnings of the syngnathid dermal armor.

We identified dlx3b and pitx2 expression in tissues where the dermal plates mineralize later in development. Using in situ hybridizations, we showed that epithelial and dermal layers expressed dlx3b and underlying muscle cells expressed pitx2. In some species, dermal bone, plate, or denticle development occurs from the re-deployment of tooth gene regulatory networks (Mori & Nakamura, 2022). However, this does not appear to be the case in syngnathids because the dermal armor lacks the characteristic epithelial– mesenchyme interactions distinguished by pitx2 expression in the epithelia.

Instead, dermal armor might originate from the co-option of existing bone development gene regulatory networks. Expression of the gene dlx3b has been observed in epithelia and mesenchyme during dermal and perichondral bone development in zebrafish (Verreijdt et al., 2006). In addition to dlx3b, we observed bone development genes scpp1 and ifitm5 expressed in the ossifying dermal armor. Future studies could test our hypothesis that the dermal armor evolved through re-deployed osteoblast networks by examining osteoblast gene network expression over time.

Signatures of embryonic interactions within the novel pouch environment

Syngnathid embryos are reared within the brood pouch, a novel structure and environment composed of male-derived tissues (epithelium and placental-like tissues that include specialized cell types) that harbors a pouch microbiome (Stölting & Wilson, 2007). During pregnancy, the male brood pouch undergoes numerous changes including increased vascularization and altered expression of immune genes (Harada et al., 2022; Ripley et al., 2010; Roth et al., 2020; Small et al., 2013; Whittington et al., 2015). Researchers predict that these changes relate to nutrient and waste transfer and prevention of embryonic rejection and bacterial infection (Dudley et al., 2021; Whittington & Friesen, 2020). However, there are few studies that examine whether and how embryos interact with the brood pouch environment (Kvarnemo et al., 2011; Ripley & Foran, 2006). To consider whether embryos have specializations for life in this brood pouch environment, we asked about cell type-specific expression of nutrient acquisition and/or immune genes.

Pipefish embryos uptake paternally derived carbohydrates, proteins, and lipids (Kvarnemo et al., 2011; Ripley & Foran, 2006). Our data suggest that this uptake could occur through the embryonic epidermis. Specifically, we noticed an enrichment of endocytosis and metabolism genes in epidermal cells. Epidermal absorption of maternally derived nutrients has been suggested in viviparous fishes (Tengfei et al., 2021; Wourms, 1981). Interestingly, microvilli, a type of cellular projection, have been observed on the anal fin of developing seahorses (Wetzel & Wourms, 2004). In light of our findings in pipefish, possibly these seahorse microvilli could be functionally equivalent to those on the small intestine, maximizing nutrient absorption from the environment.

During pregnancy, the male brood pouch increases expression of C-type lectin genes (Roth et al., 2020; Small et al., 2013; Whittington et al., 2015). These genes are transmembrane or secreted receptors that sense self or non-self and are primarily studied for their role in innate and adaptive immunity (Brown et al., 2018). We identified 14 C-type lectin genes expressed in the embryonic epidermis. Our work suggests that lectin genes are produced by both the father and the embryos, but their function is still unclear. Syngnathid research has primarily suggested that lectin genes are produced to prevent bacterial infection (Melamed et al., 2005), though they could be important for male-embryo recognition.

Overall, our findings suggest that pipefish embryos have evolved to be specialized for development within the brood pouch by expressing genes related to nutrient acquisition and immunity. Future studies could provide insights into when nutrient acquisition and lectin genes are expressed in development, their functional role, and how their expression varies across syngnathid lineages that have exposed versus enclosed embryos, for example, to examine how embryonic development has been impacted by the brood pouch.

Conclusions

Our study represents the first scRNAseq developmental atlas in syngnathids, and one of the first non-model developmental scRNAseq atlases, providing a major step forward for evo-devo research. We used our atlas to begin addressing questions on the evolution and development of syngnathid innovations including their unique craniofacial structure, loss of teeth, dermal armor, and development within the male brood pouch. By combining scRNAseq analysis with spatial expression data from in situ hybridization, we made important discoveries in cell type identity and distribution as well as spatial expression of marker and signaling genes. We found that syngnathids express conserved signaling pathways during craniofacial development, suggesting that alterations within these pathways may be important for the evolution of their craniofacial skeletons. We did not find evidence for tooth primordia within syngnathids and propose that genetic changes early in tooth development could have led to their loss of teeth. We propose that the re-deployment of bone gene networks, but probably not tooth gene networks, could play a role in the dermal armor development. Finally, we observed an enrichment of endocytosis genes and many C-type lectin genes in epidermal cells, which suggests ways these cells might interact with the brood pouch environment. Our atlas advances our understanding of syngnathid development and evolution and provides resources for developmental genetic analysis in nascent evo-devo model species.

Methods

Single Cell RNA Sequencing Libraries Preparation

We created scRNAseq atlases from embryos of wild caught Gulf pipefish (Syngnathus scovelli, acquired from collaborator Emily Rose using collection permit SAL-21-0182-E), and all work was performed under an approved IACUC protocol. We harvested 20 embryos per pouch from two wild caught male pipefish. The embryos were at a stage before the tubular face was fully elongated, and while the head skeleton is cartilaginous with minimal signs of mineralization of superficial intramembranous bones. This corresponds to a stage termed “frontal jaws” in a recent description of pipefish development (Sommer et al., 2012).

We dissociated the embryos using 460ul of .25% trypsin in water and 40ul 100 mg/mL collagenase I (Sigma C0130-200mg) for 16 minutes. We filtered cells using a 40uM cell strainer (Thomas Scientific #1181X52). We quantified cell concentrations using the TC20 Automated Cell Counter (Biorad) then diluted the samples to 800 cells/ul in .04% BSA in PBS. The University of Oregon Genomics and Cell Characterization Core (GC3F; https://gc3f.uoregon.edu) prepared single cell libraries for each sample using 10X Genomics Single Cell 3’ Genome Expression mRNAseq kit with NextGEM v3.1 chemistry. We sequenced these libraries on an S4 lane on the NovaSeq 6000 at the GC3F. To improve the 3’ UTR genome annotations, we also prepared scISOrSeq libraries from the first embryonic sample and from dissociated pouch cells from pregnant and nonpregnant males. These libraries were produced in accordance with (Healey et al., 2022). Embryonic, pregnant pouch and non-pregnant pouch libraries were sequenced separately on PacBio Sequel II - SMRT Cells 8M.

To turn the scISOrSeq reads into gene models, we followed the pipeline from Healey et al. 2022. We ran the script (scISOr_Seq_processing.py from https://github.com/hopehealey/scISOseq_processing) to remove barcodes, identify cell barcodes, and demultiplex with the single cell flag and appropriate barcodes (5’ CCCATGTACTCTGCGTTGATACCACTGCT and 3’ CTACACGACGCTCTTCCGATCT). We aligned the reads to the 2022 Gulf pipefish genome (GenBank: GCA_024217435.2) using minimap v2.9 (Li, 2018). We filtered the reads using cDNA cupcake to remove duplicate transcripts (Tseng, 2021). We used SQANTI3 to identify gene models and filter them (Tardaguila et al., 2018). We merged the SQANTI3 annotations with the Gulf pipefish genome (NCBI GenBank: GCF_024217435.2) using TAMA merge (Kuo et al., 2017). Since the Gulf pipefish genome does not contain mitochondrial genes, we appended the annotation and fasta files with the Gulf pipefish mitochondrial genome (NCBI RefSeq: NC_065499.1).

Single Cell Atlas Construction

We ran Cell Ranger (10X Genomics 3.0.2) using our scRNAseq reads, the Gulf pipefish genome assembly with mitochondrial genome, and the modified gene annotations. Cell Ranger estimated 20,733 cells for sample one, 23,682 genes expressed, and 21,039 mean reads per cell. For sample two, Cell Ranger predicted 17,626 cells, 23,740 genes expressed, and 29,804 mean reads per cell. We analyzed Cell Ranger’s output using Seurat (v4.1.0) on R (v4.0.2; Butler et al., 2018; Hafemeister & Satija, 2019).

To remove extraneous RNA counts from the dataset, we used SoupX (v1.5.2; Young & Behjati, 2020). We identified doublet scores for our dataset using scrublet (v0.2.3). The doublet removal step reduced the first sample by 114 cells (from 20,733 cells to 20,619 cells) and second sample by 167 cells (from 17,626 cells to 17,459 cells). We finally removed cells with less than 500 features, greater than 9000 features, greater than 1E5 RNA counts, with a scrublet score greater than the detected threshold (.76 for sample 2 and .21 for sample 2), or greater than 10% mitochondrial reads. The second filtering step removed 727 cells from sample one (20,619 cells to 19,892 cells) and 1,566 cells from sample two (from 17,459 cells to 15,893 cells).

We normalized the datasets with SCTransform (v0.3.3). We used Seurat’s integration tools, SelectIntegrationFeatures using 3,000 feature genes, FindIntegrationAnchors using SCT normalization, and IntegrateData using SCT normalization, to integrate the two datasets. After integration, our combined atlas had 35,785 cells (Supplementary File 1,2). We then used the integrated dataset to complete PCA analysis. We tested using a variety of principle components for further analysis and chose 30 PCs for our analysis based on the clear delineation of major cell types. We next clustered the cells using 30 PCs and plotted the data on a UMAP with Seurat.

Single Cell Atlas Cluster Identification

To identify cluster identities, we used the RNA assay of the scRNAseq data to find cluster markers with Seurat’s FindAllMarkers command with the parameters only.pos = TRUE and logfc.threshold = 0.25, requiring markers to be upregulated in the cluster and have a log fold change of at least .25. We found a second set of cluster markers through our custom function which searched through all genes and identified genes uniquely expressed in greater than 60% of cells in the cluster and in less than 10% of cells in every other cluster using Seurat’s DotPlots. We searched for our identified markers in available zebrafish datasets (Fabian et al., 2022; Farnsworth et al., 2020; Lange et al., 2023), ZFIN (Howe et al., 2013), NCBI, medlineplus.gov, and genecards to give the clusters initial annotations. For each cluster, we examined multiple genes using DotPlots and FeaturePlots to propose the cluster identity.

Next, we identified one gene for each cluster which marked the cluster best (expressed in the most cells in the focal cluster and expressed in as few of the other clusters as possible) through consulting the two marker gene lists and examining markers with Dot Plots (Supplementary File 3, 4, 5). Using these markers, we completed a set of in situ hybridizations to hone our cluster annotations. Due to challenges culturing Gulf pipefish, we used both embryos and larvae from Syngnathus leptorhynchus, a pipefish from the same genus that lives in Oregon coastal habitats and allowed for easy collection, and from cultured Gulf pipefish for the cluster annotation in situ experiments. We caught pregnant male Syngnathus leptorhynchus using a beach seine near Coos Bay, Oregon under Oregon Department of Fisheries and Wildlife permit number 26987.

Syngnathus scovelli used for in situ experiments were purchased from Alyssa’s Seahorse Saavy and Gulf Specimens Marine Lab then reared in our facility at 25 degrees C water and 25-28 PPT Salinity. We designed probes using NCBI Primer Blast with the Gulf pipefish genome assembly and produced these probes using Gulf pipefish embryonic cDNA pools. To create the probes, we completed two rounds of PCR. The first round used a gene specific forward primer with a reverse gene specific primer that had 10 nucleotides of T7 promotor sequence attached. PCR products were cleaned with Zymo clean and concentrator DNA kit and eluted in 15 ul of elution buffer. Round two of PCR used the same gene specific forward primer with a modified T7 promoter sequence (TGGACTAATACGACTCACTATAGGG) as the reverse primer, and finally the product was cleaned again with Zymo clean and concentrator DNA kit and eluted in 15 ul of elution buffer.

Round one PCR conditions are as followed: 95 degrees Celsius for 3:00 minutes, 40 cycles of denaturation (95 degrees for 30 seconds), annealing (30 seconds, annealing temperature varied by probe), and extension (72 degrees for 1:00 minute), and a final extension step of 3:00 minutes. Round two PCR conditions are as followed: 98 degrees Celsius for 30 seconds, 30 degrees for 10 seconds, 72 degrees for 50 seconds, then 35 rounds of denaturation (98 degrees for 10 seconds), annealing (50 degrees for 10 seconds), and extension (72 degrees for 50 seconds), and a final extension step of 10 minutes at 72 degrees. For round two PCRs with multiple bands (specifically, ifitm5), the band of the expected size was excised with a razorblade and DNA was extracted with Zymoclean gel DNA recovery kit.

The round two PCR product was sanger sequenced to confirm identity. All the marker genes primers and successful PCR conditions are in Supplementary File 6. Alignments of the probes to the unpublished Syngnathus leptorhynchus genome assembly are included in Supplementary File 7. The probes were transcribed with T7 polymerase for 2-6 hours then cleaned with Zymo RNA clean and concentrator and eluted into 30 ul of water. For the in situ hybridizations, we selected embryos and newly spawned larvae close to the developmental stage used in the atlas. We completed in situ hybridizations in keeping with (Thisse & Thisse, 2007), leaving the embryos in stain until background was observed. For spawned larvae, we completed a bleaching step (1% H2O2 and .5% KOH for 8 minutes) prior to the proteinase K digest. After imaging, we used the levels tool in Adobe Photoshop (v23.4.2) to white balance the pictures.

Single Cell KEGG Analysis

To identify pathways upregulated in cell clusters, we completed a KEGG analysis. We downloaded Gulf pipefish KEGG pathways (https://www.kegg.jp). For the KEGG analysis, we used the marker genes identified from our FindAllMarkers list as the input genes. We converted these gene ids using keggConv to KEGG ids. We used a Wilcoxon enrichment test to ask whether cluster marker genes were enriched for each KEGG pathway.

Differentiation State Analysis

To assess whether proposed primordial cell clusters were composed of undifferentiated cells relative to other clusters from their lineages, we completed a differentiation analysis using CytoTRACE (v0.3.3, Gulati et al., 2020). Clusters from similar lineages (neural: 0, 3, 7, 8, 12, 22, 25, 33, and 35; muscle: 2, 10, 17, and 37; connective: 4, 5, 6, 9, 15, 16, 18, 24, 27, 28, and 29) were isolated using subset. Cell counts were gathered using as.Matrix(GetAssayData), then CytoTRACE was run on these counts. Data was plotted on a VlnPlot to show the variability.

Single Cell Atlas Network Analysis

To identify genetic networks present in our atlas, we completed a weighted gene network correlation analysis using WGCNA (v1.72-1, Langfelder & Horvath, 2008). We selected 3,000 variable features from the integrated assay of the single cell dataset for the WGCNA. We created an adjacency matrix from the data using bicor with a maxPOutliers of .05. To decide on a beta value or the soft threshold power, we created an adjacency matrix plot using pickSoftThreshold and picked the threshold where the scale free topology model fit leveled off. We selected the value of two. Then, we raised the adjacency matrix to the power of two.

We calculated the dissimilarity matrix by calculating TOM similarity of the adjacency matrix and subtracting it from one. We then created a gene tree through hclust of the dissimilarity matrix with the average method. From the gene tree, we created modules using cutreeDynamic with deepSplit of two and the minClusterSize of 15 (setting the smallest cluster size to 15). We calculated module eigengenes using moduleEigengenes of the adjacency matrix then calculated the dissimilarity of the module eigengenes with cor of the module eigengenes subtracted from one and clustered the module eigengenes with hclust. We then chose a dissimilarity of 35% as the cut off for merging modules; however, no modules had dissimilarity scores with each other below 40%. Since the module eigengenes are calculated for each cell in the dataset, we used these values to calculate the t-statistic for each cell cluster (considered each sample) in the module (all of the module eigengene scores were used as the population).

We next tested the hypothesis that certain cell clusters were strongly associated with specific gene modules through a two-way permutation test (1000 permutations) and corrected p-values to control the False Discovery Rate (FDR). Additionally, we tested whether specific cell clusters drove underlying gene network structure by measuring network connectivity. The network connectivity was first calculated using the entire dataset, then each cell cluster was progressively dropped from the dataset and the connectivity was remeasured. This resulted in change in connectivity scores for each module-cluster pair. To assess whether these changes were significant, we completed 1000 permutations whereby cells were randomly dropped (the number of cells dropped was equal to the cell cluster size of the focal cluster), connectivity measured, and the change in connectivity recorded. The p-value is the number of instances where the change in connectivity is greater in the permutations than in the focal cell cluster run. P-values were corrected to control the FDR.

Using the module gene lists, we identified the number of genes from each module that were found in each KEGG pathway. Since the KEGG modules do not have p-values associated with the genes, we could not complete a Wilcoxon enrichment test. We instead removed any pathways where there were less than three genes present for any pathway and note that there is no statistical test run on these KEGG results. We visualized the networks using Cytoscape (v3.10.0).

In situ Hybridization

For genes chosen for further follow up analysis, we completed in situ hybridizations in Gulf pipefish (Syngnathus scovelli) or bay pipefish (Syngnathus leptorhynchus). Primer sequences were designed using NCBI Primer Blast with the Gulf Pipefish genome and synthesized using Gulf pipefish embryonic cDNA. The sfrp1a probe was synthesized using the PCR based probe preparation protocol described in the Single Cell Identification section. All other probes (bmp4, dlx2a, dlx3b, fgf22, lhx6a, pitx2, and scpp1) were prepared using TOPO cloning. Probe primer sequences as well as the species used for the in situ experiments are described in Supplementary file 6. Pregnant male bay pipefish were caught as described above. For Gulf pipefish embryos, we allowed the fish to mate in our facility then harvested embryos once they reached the appropriate stages. Fish were reared in 25 degrees C water with 25-28 ppt salinity. Embryos and larvae were fixed in 4% PFA, dehydrated through a series of PBT/MeOH washes, and stored in MeOH at -20C. 5-12 embryos were used for each probe. We completed in situ hybridizations in keeping with (Thisse & Thisse, 2007), leaving the embryos in stain until background was observed. After imaging, we used the levels tool in Adobe Photoshop (v23.4.2) to white balance the pictures.

Bone and Cartilage Staining

We used alcian and alizarin stains to mark cartilage and bones. Specifically, we assayed cartilage and bone development in siblings of the scRNAseq samples. These embryos were fixed in 4% PFA then stored at -20C in MeOH. We followed the protocol from Walker and Kimmel (2007) with minor alterations. We stored samples in 50% glycerol/0.1% KOH at 4C and imaged them in 100% glycerol. After imaging, we white balanced the photographs using Photoshop (v23.4.2) levels tool.

Gene Cluster Analysis

To examine close syngnathid outgroups, we downloaded the 2023 mandarin dragonet genome (GenBank assembly accession: GCA_027744825.1) and the 2024 cornetfish genome assembly (GenBank assembly accession: GCA_037954325.1) from NCBI. Since these genomes were unannotated, we manually identified scpp genes. We searched for scpp genes using BLASTN with medaka and additional fish sequences as the query. We also searched for these genes with mVISTA plots across conserved gene synteny regions (LAGAN alignment using translated anchoring) with medaka as the focal species. To identify scpp genes in additional species, we gathered cluster information from NCBI and ensembl. Additionally, we used mVISTA plots to further search for unannotated scpp genes.

Acknowledgements

We are indebted to Emily Rose for her collection of the Gulf pipefish specimens for the single cell libraries. We are grateful for everyone in the Cresko Lab for their ideas and participation in early morning pipefish collection trips. We are additionally thankful for Emily Beck for her assistance collecting pipefish. In particular, we are immensely appreciative of Mark Currey and Tiffany Thornton’s valiant efforts to culture Gulf pipefish. Additionally, we are grateful for Balan Ramesh and Adam Jones for collaborating with the Cresko Lab to produce new Syngnathiformes genomes. We thank Tina Arredondo from the UO GC3F for her preparation of our single cell RNA sequencing libraries. This work was funded by the National Science Foundation Grant OPP-2015301 (to WAC, SB, and CMS), University of Oregon Research Excellence funds (WAC) and National Institute of Health Fellowship F31DE032559-02 (to HMH). Additionally, HMH was supported by the Genetics Training Program (NIH T32GM007413). Finally, VG received funding and mentorship from the Hui Undergraduate Research Scholars and MW was supported by the Knight Campus Undergraduate Scholars program at the University of Oregon.

Data Availability statement

All raw sequencing data associated with this study are published via NCBI (PRJNA1168967). The integrated single cell RNA sequencing atlas is also available through NCBI (GSE278814). The fasta file and updated Gulf pipefish annotation are stored on Dryad (10.5061/dryad.f7m0cfz60). Code used for the analysis is available on github (https://github.com/hopehealey/ssc_emb_atlas_scripts).

Supplementary figure captions

Figure S1: Two Gulf pipefish samples have similar contributions to cell clusters. This UMAP features cells plotted by sample (sample one in orange and sample two in cyan) in the first two UMAP dimensions.

Figure S2: QC metrics for both Gulf pipefish samples show similar sample quality after initial filtering. Sample quality was assessed using the number of genes identified (nFeatureRNA), the total RNA counts (nCountRNA), the percentage of mitochondrial reads (percentmt), and the likelihood of being a doublet (doubvalues). Gulf pipefish sample data post-filtering is displayed for each of these metrics.

Figure S3: Zebrafish muscle cell marker genes expression patterns correspond with annotated muscle cells in Gulf pipefish atlas. Feature plots of zebrafish muscle cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated muscle cell clusters in pipefish are distinguished by a dashed outline.

Figure S4: Zebrafish muscle satellite cell marker genes expression patterns correspond with annotated muscle primordia cells in Gulf pipefish atlas. Feature plots of zebrafish muscle satellite cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated muscle primordia cell cluster in pipefish is distinguished by a dashed outline.

Figure S5: Zebrafish cardiac muscle cell marker genes expression patterns correspond with annotated cardiac muscle cells in Gulf pipefish atlas. Feature plots of zebrafish cardiac muscle cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated cardiac muscle cell cluster in pipefish is distinguished by a dashed outline.

Figure S6: Zebrafish retinal cell marker genes expression patterns correspond with annotated retinal cells in Gulf pipefish atlas. Feature plots of zebrafish retinal cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated retinal cell cluster in pipefish is distinguished by a dashed outline.

Figure S7: Zebrafish neuronal cell marker genes expression patterns correspond with annotated neuronal cells in Gulf pipefish atlas. Feature plots of zebrafish neuronal cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated neuronal cell clusters in pipefish are distinguished by a dashed outline.

Figure S8: Zebrafish chondroblast cell marker genes expression patterns correspond with annotated chondroblast cells in Gulf pipefish atlas. Feature plots of zebrafish chondroblast cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated chondroblast cell cluster in pipefish is distinguished by a dashed outline.

Figure S9: Zebrafish osteoblast cell marker genes expression patterns correspond with annotated osteoblast cells in Gulf pipefish atlas. Feature plots of zebrafish osteoblast cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated osteoblast cell cluster in pipefish is distinguished by a dashed outline.

Figure S10: Zebrafish notochord cell marker genes expression patterns correspond with annotated notochord cells in Gulf pipefish atlas. Feature plots of zebrafish notochord cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated notochord cell cluster in pipefish is distinguished by a dashed outline.

Figure S11: Zebrafish fin cell marker genes expression patterns correspond with annotated fin cells in Gulf pipefish atlas. Feature plots of zebrafish fin cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated fin cell cluster in pipefish is distinguished by a dashed outline.

Figure S12: Zebrafish mesenchymal cell marker genes expression patterns correspond with annotated mesenchymal cells in Gulf pipefish atlas. Feature plots of zebrafish mesenchymal cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated mesenchymal cell clusters in pipefish are distinguished by a dashed outline.

Figure S13: Zebrafish tenocyte cell marker genes expression patterns correspond with annotated tenocyte cells in Gulf pipefish atlas. Feature plots of zebrafish tenocyte cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated tenocyte and ligament cell cluster in pipefish is distinguished by a dashed outline.

Figure S14: Zebrafish erythrocyte cell marker genes expression patterns correspond with annotated erythrocyte cells in Gulf pipefish atlas. Feature plots of zebrafish erythrocyte cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated erythrocyte cell clusters in pipefish are distinguished by a dashed outline.

Figure S15: Zebrafish endothelial cell marker genes expression patterns correspond with annotated endothelial cells in Gulf pipefish atlas. Feature plots of zebrafish endothelial cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated endothelial cell cluster in pipefish is distinguished by a dashed outline.

Figure S16: Zebrafish gut and liver cell marker genes expression patterns correspond with annotated gut and liver cells in Gulf pipefish atlas. Feature plots of zebrafish gut and liver cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated gut and liver cell cluster in pipefish is distinguished by a dashed outline.

Figure S17: Zebrafish epidermal cell marker genes expression patterns correspond with annotated epidermal cells in Gulf pipefish atlas. Feature plots of zebrafish epidermal cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated epidermal cell cluster in pipefish is distinguished by a dashed outline.

Figure S18: Zebrafish glial cell marker genes expression patterns correspond with annotated glial cells in Gulf pipefish atlas. Feature plots of zebrafish glial cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated glial cell cluster in pipefish is distinguished by a dashed outline.

Figure S19: Zebrafish immune cell marker genes expression patterns correspond with annotated immune cells in Gulf pipefish atlas. Feature plots of zebrafish immune cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated immune cell clusters in pipefish are distinguished by a dashed outline.

Figure S20: Zebrafish pigment cell marker genes expression patterns correspond with annotated pigment cells in Gulf pipefish atlas. Feature plots of zebrafish pigment cell marker genes identified from the Daniocell scRNAseq database demonstrate Gulf pipefish expression. Annotated pigment cell clusters in pipefish are distinguished by a dashed outline.

Figure S21: Gulf pipefish cluster 0 markers. Feature plots of top markers for cluster 0 identified from the marker gene lists.

Figure S22: Gulf pipefish cluster 1 markers. Feature plots of top markers for cluster 1 identified from the marker gene lists.

Figure S23: Gulf pipefish cluster 2 markers. Feature plots of top markers for cluster 2 identified from the marker gene lists.

Figure S24: Gulf pipefish cluster 3 markers. Feature plots of top markers for cluster 3 identified from the marker gene lists.

Figure S25: Gulf pipefish cluster 4 markers. Feature plots of top markers for cluster 4 identified from the marker gene lists.

Figure S26: Gulf pipefish cluster 5 markers. Feature plots of top markers for cluster 5 identified from the marker gene lists.

Figure S27: Gulf pipefish cluster 6 markers. Feature plots of top markers for cluster 6 identified from the marker gene lists.

Figure S28: Gulf pipefish cluster 7 markers. Feature plots of top markers for cluster 7 identified from the marker gene lists.

Figure S29: Gulf pipefish cluster 8 markers. Feature plots of top markers for cluster 8 identified from the marker gene lists.

Figure S30: Gulf pipefish cluster 9 markers. Feature plots of top markers for cluster 9 identified from the marker gene lists.

Figure S31: Gulf pipefish cluster 10 markers. Feature plots of top markers for cluster 10 identified from the marker gene lists.

Figure S32: Gulf pipefish cluster 12 markers. Feature plots of top markers for cluster 12 identified from the marker gene lists.

Figure S33: Gulf pipefish cluster 13 markers. Feature plots of top markers for cluster 13 identified from the marker gene lists.

Figure S34: Gulf pipefish cluster 14 markers. Feature plots of top markers for cluster 14 identified from the marker gene lists.

Figure S35: Gulf pipefish cluster 15 markers. Feature plots of top markers for cluster 15 identified from the marker gene lists.

Figure S36: Gulf pipefish cluster 16 markers. Feature plots of top markers for cluster 16 identified from the marker gene lists.

Figure S37: Gulf pipefish cluster 17 markers. Feature plots of top markers for cluster 17 identified from the marker gene lists.

Figure S38: Gulf pipefish cluster 18 markers. Feature plots of top markers for cluster 18 identified from the marker gene lists.

Figure S39: Gulf pipefish cluster 19 markers. Feature plots of top markers for cluster 19 identified from the marker gene lists.

Figure S40: Gulf pipefish cluster 20 markers. Feature plots of top markers for cluster 20 identified from the marker gene lists.

Figure S41: Gulf pipefish cluster 21 markers. Feature plots of top markers for cluster 21 identified from the marker gene lists.

Figure S42: Gulf pipefish cluster 22 markers. Feature plots of top markers for cluster 22 identified from the marker gene lists.

Figure S43: Gulf pipefish cluster 23 markers. Feature plots of top markers for cluster 23 identified from the marker gene lists.

Figure S44: Gulf pipefish cluster 24 markers. Feature plots of top markers for cluster 24 identified from the marker gene lists.

Figure S45: Gulf pipefish cluster 25 markers. Feature plots of top markers for cluster 25 identified from the marker gene lists.

Figure S46: Gulf pipefish cluster 26 markers. Feature plots of top markers for cluster 26 identified from the marker gene lists.

Figure S47: Gulf pipefish cluster 27 markers. Feature plots of top markers for cluster 27 identified from the marker gene lists.

Figure S48: Gulf pipefish cluster 28 markers. Feature plots of top markers for cluster 28 identified from the marker gene lists.

Figure S49: Gulf pipefish cluster 29 markers. Feature plots of top markers for cluster 29 identified from the marker gene lists.

Figure S50: Gulf pipefish cluster 30 markers. Feature plots of top markers for cluster 30 identified from the marker gene lists.

Figure S51: Gulf pipefish cluster 31 markers. Feature plots of top markers for cluster 31 identified from the marker gene lists.

Figure S52: Gulf pipefish cluster 32 markers. Feature plots of top markers for cluster 32 identified from the marker gene lists.

Figure S53: Gulf pipefish cluster 33 markers. Feature plots of top markers for cluster 33 identified from the marker gene lists.

Figure S54: Gulf pipefish cluster 34 markers. Feature plots of top markers for cluster 34 identified from the marker gene lists.

Figure S55: Gulf pipefish cluster 35 markers. Feature plots of top markers for cluster 35 identified from the marker gene lists.

Figure S56: Gulf pipefish cluster 36 markers. Feature plots of top markers for cluster 36 identified from the marker gene lists.

Figure S57: Gulf pipefish cluster 37 markers. Feature plots of top markers for cluster 37 identified from the marker gene lists.

Figure S58: Marker genes define cell identity and are a resource for cell cluster exploration. This dotplot shows the marker genes on the x-axis, and the cell cluster on the y-axis. The size of each dot indicates the percentage of cells that express the gene in each cluster. The darkness of each dot represents the average expression of the gene.

Figure S59: slc25a4, a marker for cluster 2, is expressed in muscle cells in bay pipefish. Atlas expression is limited to cluster 2 (A). In situ hybridization experiments revealed muscle staining of slc25a4 in bay pipefish (B-D). Wild caught bay pipefish at the onset of craniofacial elongation were used for the experiment. Embryos were mounted laterally to highlight expression domains.

Figure S60: scg2a, a marker for cluster 3, is expressed in the brain of bay pipefish. Atlas expression is limited to cluster 3 (A). In situ hybridization experiments revealed brain staining of scg2a in bay pipefish (B-D). Wild caught bay pipefish at the onset of craniofacial elongation were used for the experiment. Embryos were mounted laterally to highlight expression domains.

Figure S61: prdm16, a marker for cluster 5, is expressed in the head and fins of bay pipefish. Strong expression of prdm16 in cluster 5, osteochondrogenic mesenchyme (A). In situ hybridization experiments revealed mesenchymal staining of prdm16 in the fins and face of bay pipefish (B-D). Wild caught bay pipefish at the onset of craniofacial elongation were used for the experiment. Embryos were mounted laterally (B, D, E) and with a dorsal view (C) to highlight expression domains.

Figure S62: elnb, a marker for cluster 6, is expressed in the head of bay pipefish. Strong expression of elnb in cluster 6, osteochondrogenic mesenchyme (A). In situ hybridization experiments revealed mesenchymal staining of elnb in the face of bay pipefish (B-D). Wild caught bay pipefish at the onset of craniofacial elongation were used for the experiment. Embryos were mounted dorsally to showcase the craniofacial staining (B) and laterally (C-E) to show the lack of staining in the ceratohyal, dorsal fin, and caudal fin.

Figure S63: fndc9, a marker for cluster 7, is expressed in the brain of Gulf pipefish. Expression of fndc9 is limited to cluster 7, brain cells, in the atlas (A). In situ hybridization experiments revealed brain staining of fndc9 in Gulf pipefish (B-C). One day post spawn Gulf pipefish larvae were used for this experiment. Embryos were mounted to highlight two views of the brain staining, one dorsal (B) and one lateral (C).

Figure S64: insm1b, a marker for cluster 8, is expressed in the brain of Gulf pipefish. Strong expression of insm1b is observed in cluster 8, brain progenitor cells, in the atlas (A). In situ hybridization experiments revealed brain staining of insm1b in Gulf pipefish (B). Five day post spawn Gulf pipefish larvae were used for this experiment. Embryos were mounted laterally to highlight the brain staining.

Figure S65: tnmd, a marker for cluster 9, is expressed in tendons and ligaments of bay pipefish. Strong expression of tnmd is observed in cluster 9, tenocyte and ligament cells, in the atlas (A). In situ hybridization experiments revealed tendon and ligament staining of tnmd in bay pipefish (B-E). Wild caught embryos from bay pipefish at the onset of craniofacial elongation were used for the experiment. Embryos were mounted laterally (B,E) and ventrally (C,D) to showcase tendon and ligament staining.

Figure S66: myf5, a marker for cluster 10, is expressed in the muscle of bay pipefish. Strong expression of myf5 is observed in cluster 10, muscle progenitor cells, in the atlas (A). In situ hybridization experiments revealed muscle staining of myf5 in bay pipefish (B-D). Wild caught embryos from bay pipefish at the onset of craniofacial elongation were used for the experiment. Embryos were mounted laterally to highlight muscle staining.

Figure S67: hpdb, a marker for cluster 13, is expressed in somites of bay pipefish. Strong expression of hpbd is observed in cluster 13, fibroblasts, in the atlas (A). In situ hybridization experiments revealed somite staining of hpbd in bay pipefish (B). Wild caught embryos from bay pipefish at the mid-somitogenesis stage were used for the experiment. Embryos were mounted laterally to highlight muscle staining.

Figure S68: ifitm5, a marker for cluster 18, is expressed in bay pipefish osteoblasts. Specific expression of ifitm5 is observed in cluster 18, osteoblasts, in the atlas (A). In situ hybridization experiments revealed bone staining of ifitm5 in bay pipefish (B-E). Wild caught embryos from bay pipefish at the onset of craniofacial elongation were used for the experiment. Embryos were mounted laterally to highlight muscle staining.

Figure S69: plaat4, a marker for cluster 23, is weakly expressed in bay pipefish epidermal cells. Specific expression of plaat4 is observed in cluster 23, epidermal cells, in the atlas (A). In situ hybridization experiments revealed epidermal staining of plaat4 in bay pipefish (B-D). Wild caught embryos from bay pipefish at the onset of craniofacial elongation were used for the experiment. Embryos were mounted ventrally (B) and laterally (C,D) to highlight epidermal staining.

Figure S70: hyal6, a marker for cluster 33, is strongly expressed in bay pipefish hair cells. Specific expression of hyal6 is observed in cluster 33, hair cells, in the atlas (A). In situ hybridization experiments revealed staining of hyal6 in bay pipefish nares and ears (B-C). Wild caught embryos from bay pipefish from one day post spawn larvae were used for the experiment. Embryos were mounted dorsally (B) and laterally (C) to highlight staining in nares and ears respectively.

Figure S71: soat2, a marker for cluster 36, is strongly expressed in bay pipefish muscle cells. Expression of soat2 is observed in cluster 36, muscle cells, in the atlas (A). In situ hybridization experiments revealed staining of soat2 in bay pipefish muscle and fins (B-E). Wild caught embryos from bay pipefish at the onset of craniofacial elongation were used for the experiment. Embryos were mounted laterally to highlight staining in muscle and fin.

Figure S72: KEGG analysis reveals pathways significantly enriched in cell clusters. KEGG pathways are listed on the x-axis. For each cluster, the number of genes that belong to the specific KEGG pathway is represented by the height of each bar. Cell clusters are labeled by both color and their designated number. For clarity, only cell clusters with more than three annotated genes in each pathway are shown and certain pathways involving infection and metabolism are removed.

Figure S73: Clusters 8,10, and 16 are composed of largely undifferentiated cells. Differentiation state of cell clusters was measured using CytoTRACE, where an output of 1 indicates an undifferentiated cell and a value of 0 suggests a cell is fully differentiated. Cells are plotted on a violin plot with individual cell predictions shown as dots. Panels show results from muscle (A), neural (B), and connective (C) clusters.

Figure S74: Cell clusters drive gene module connectivity. The heatmap shows the change in gene network connectivity when individual cell clusters are removed. Cell clusters are in the columns and gene modules are in rows. The coloration scale is based on the change in connecitvity when a cell cluster is removed, with yellow indicating a large change in connectivity. The modules and clusters are clustered using the Pearson distance method. Asterisks indicate cluster-module pairs that are significant using a one way permutation test and FDR correction. The size of the gene modules are shown to the right.

Figure S75: Gene module expression comparison with the Pigment Cluster reveals potential elusive cell types within the cluster. Genes within the network are combined into one feature then plotted on a feature plot. In this case, UMAP dimensions one and two are the x and y-axis accordingly, dots represent each pigment cell, and the color of the dots represents the expression of the module. Cells that express gene modules are either colored red or green (when modules are plotted on separate plots). When plotted on the same plot, cells that express both gene modules are yellow. Panels A through J show each gene module comparison.

Figure S76: Conserved fin prdm16 expression domains in stickleback and pipefish, but fishes have expression in different regions of the head. The gene prdm16 expression was probed using in situ hybridization. Embryos used in this assay were similarly staged (9dpf in threespine stickleback and mid-craniofacial elongation in bay pipefish). Stickleback (A-C) expressed prdm16 in the lower jaw, brain, gill arches, and fins. Bay pipefish also have fin staining (F), but craniofacial expression is limited to the mesenchyme above the ethmoid region.

Figure S77: Conserved expression domains of fgf22 in pipefish and stickleback fish. Expression of fgf22 was probed using in situ hybridization. Embryos used in this assay were similarly staged (9dpf threespine stickleback and 10dpf Gulf pipefish). Staining in the jaws (A,E), brain (B,F), gill arches (C,G), and fins (D, H) suggests similar \\textit{fgf22} expression domains in both species.

Figure S78: Broad bmp4 craniofacial expression in stickleback, but bmp4 is restricted to ethmoid and ceratohyal regions in pipefish. Expression of bmp4 was probed using in situ hybridization. Embryos used in this assay were similarly staged (9dpf threespine stickleback and mid-craniofacial elongation staged bay pipefish). Stickleback have bmp4 staining in tooth germs (A), the jaws (A-B), and gill arches (C). However, bmp4 expression is restricted in bay pipefish to mesenchyme above the ethmoid and ceratohyal.

Figure S79: sfrp1a has conserved fin expression in pipefish and stickleback, but craniofacial expression differs between the species. Expression of sfrp1a was probed using in situ hybridization. Embryos used in this assay were similarly staged (9dpf threespine stickleback and mid-craniofacial elongation staged bay pipefish). Both species had sfrp1a staining in the fins (D,H). However, stickleback have broad craniofacial expression (in lower jaws, A-B, and gill arches, C) while expression is limited to mesenchyme above the ethmoid (E), the jaw joint (F), and around the ceratohyal (G) in bay pipefish.

Figure S80: scpp gene cluster analysis identifies most scpp losses are not unique to syngnathids. A) denotes the Syngnathoidei lineage which contains syngnathids and blue spotted cornetfish (among other fish) to highlight that this clade contains various tooth alterations. Syngnathids lack teeth completely and cornetfish have small teeth limited to the lower jaw. B) denotes solely the syngnathid clade, which completely lacks teeth. The phylogenetic tree was time calibrated using Hughes et al. 2018 and Stiller et al. 2022.

Figure S81: 3dpf Zebrafish do not express lectin genes in epidermal cells. This dotplot shows the marker genes on the x-axis and y-axis, organized by cell type. The size of the dot represents the percentage of cells that express the gene in each cluster. The darkness of the dot represents the average expression of the genes.

Supplementary file captions

SupplementaryFile1_scRNAseq_cellranger_info.csv, a table containing quality metrics for the single cell libraries.

SupplementaryFile2_cell_cluster_identity_and_sizes. csv, a table containing the number of cells in each cell cluster and cluster identities.

SupplementaryFile3_seurat_findallmarker_output.csv, a file containing marker genes identified using Seurat FindAllMarkers for each cluster.

SupplementaryFile4_dotplotmethod_marker_genes.csv, a file containing marker genes identified using the DotPlot method.

SupplementaryFile5_ select_marker_gene_detailed_information.xlsx, a file containing additional information on the marker gene identified for every cluster.

SupplementaryFile6_insitu_probe_sequences_and_prep_information.xlsx, a list of the in situ hybridization probes used in this study, the conditions used to prepare the probes, and the staging/sample information for the embryos.

SupplementaryFile7_insitu_probe_alignment.zip, a zipped folder containing alignments of in situ hybridization probes with the unpublished Syngnathus leptorhynchus genome.

SupplementaryFile8_WGCNA_module_number_to_color_translation_table.csv, genetic networks were initially labeled with colors, we converted these labels to numeric annotations for simplicity using this conversion table. The table also contains the number of genes in each network.

SupplementaryFile9_WGCNA_networks_with_gene_descriptions.xlsx, a file containing all the genetic networks, the genes inside of them, and additional information for the genetic networks highlighted in this paper.

SupplementaryFile10_WGCNA_mod_association_wClus_tstat.csv, this file contains the t-statistics derived for each module-cell cluster pair. The cell clusters are in the rows and the gene modules are in the columns.

SupplementaryFile11_WGCNA_associat_mods_w_clus_tstat_pvalues_corrected.csv, p-values for the t-statistics of the strength of association between gene modules and cell clusters. P-values are corrected for multiple testing hypotheses using fdr. The cell clusters are in the rows and the gene modules are in the columns.

SupplementaryFile12_WGCNA_connectivity_change.csv, the change in connectivity for gene modules when individual cell clusters are removed. The cell clusters are in the columns and the gene networks are in the rows.

SupplementaryFile13_Connectivity_Permutation_Pvalues_Corrected.csv, the p-values for the change in connectivity are found in this csv. The cell clusters are in the columns and the gene networks are in rows.