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Early expression onset of tissue-specific effector genes during the specification process in sea urchin embryos

EvoDevo volume 14, Article number: 7 (2023) Cite this article

Abstract

Background

In the course of animal developmental processes, various tissues are differentiated through complex interactions within the gene regulatory network. As a general concept, differentiation has been considered to be the endpoint of specification processes. Previous works followed this view and provided a genetic control scheme of differentiation in sea urchin embryos: early specification genes generate distinct regulatory territories in an embryo to express a small set of differentiation driver genes; these genes eventually stimulate the expression of tissue-specific effector genes, which provide biological identity to differentiated cells, in each region. However, some tissue-specific effector genes begin to be expressed in parallel with the expression onset of early specification genes, raising questions about the simplistic regulatory scheme of tissue-specific effector gene expression and the current concept of differentiation itself.

Results

Here, we examined the dynamics of effector gene expression patterns during sea urchin embryogenesis. Our transcriptome-based analysis indicated that many tissue-specific effector genes begin to be expressed and accumulated along with the advancing specification GRN in the distinct cell lineages of embryos. Moreover, we found that the expression of some of the tissue-specific effector genes commences before cell lineage segregation occurs.

Conclusions

Based on this finding, we propose that the expression onset of tissue-specific effector genes is controlled more dynamically than suggested in the previously proposed simplistic regulation scheme. Thus, we suggest that differentiation should be conceptualized as a seamless process of accumulation of effector expression along with the advancing specification GRN. This pattern of effector gene expression may have interesting implications for the evolution of novel cell types.

Background

The multicellular bodies of animals develop from a fertilized egg through numerous cell divisions [1]. Each cell determines its cell fate and specializes during the course of the developmental process, ultimately producing various differentiated tissues composing an animal body [1, 2]. In a textbook example, specification is regarded as part of the commitment of a cell to a certain fate [1]. Specified cells have the potential to differentiate autonomously in a neutral environment such as a petri dish. Differentiation is defined as the development of specialized cells [1]. This specification, and subsequent differentiation which is generally considered to occur as an endpoint following a period of specification, have been a critical focus in the attempt to understand the genetic principles that control animal development and its evolution [1, 2].

Sea urchins are one of the best model species to investigate the regulation of differentiation as the genetic control of their cell lineages has been well investigated at single cell resolution [2, 3]. Research using sea urchins imply that capturing differentiation as an endpoint of specification may be oversimplified. For example, the pigment cells are often referred to as differentiating when they begin to express the genes for pigmentation [4, 5]. However, it is many hours later before there is evidence that they function in immunosurveillance [4,5,6]. Moreover, they also change their cell state and increase their motility in response to infection or wounding [6, 7]. Referring to the above examples, differentiation processes rather seem to overlap with specification and proceed without clear start and endpoint.

Nevertheless, the current genetic regulatory scheme of specification and differentiation has been described according to the simplistic view of differentiation endpoint model [2, 8,9,10]. During the developmental process, GRN circuits of specification, which consist of complex interactions of regulatory genes such as transcription factors and signaling molecules, generate and establish distinct regulatory states in different spatial regions of an embryo [2, 11, 12]. This eventually leads to cell fate segregation in each specific cellular region (Fig. 1). In parallel with this process, a small set of regulatory genes, termed differentiation driver genes, begin to be expressed in each region [2, 11, 12]. These gene sets ultimately activate the expression of tissue-specific effector genes, which are the cohorts of genes that give rise to the biological identities of differentiated cell types [2, 11, 12]. In this model, they provided the mechanistic definition of specification and differentiation: the process to establish a cellular population with a uniform regulatory state through the interaction of regulatory genes, the installation of the expression of tissue-specific effector genes to perform cellular biological functions, respectively [2].

Fig. 1
figure 1

Cell fate segregation during development of the sea urchin H. pulcherrimus. The timing of cell fate segregation and embryonic stages were defined based on previous work using the sea urchin S. purpuratus [14, 25, 27, 29, 30, 36]. Embryonic stages at each developmental time (0, 6, 8, 10, ... 30 hpf) in H. pulcherrimus were defined with reference to the previous descriptions and our observations [22]. Based on the above information, cell fate segregation along developmental time in H. pulcherrimus was estimated. Egg cleavage generates three cell lineages of mesomeres, macromeres and micromeres, segregating to the apical/nonapical ectoderm, Veg1/2 and skeletogenic/germline lineages, respectively. Each lineage segregates further to establish and differentiate various tissues, such as the apical organ, cilia, mid/hindgut, pigment and skeletogenic cells. Cell lineages (top) and cellular regions in an embryo (bottom). Apical ectoderm, Veg1 endoderm, Veg2 endoderm, NSM and skeletogenic cells are highlighted in blue, gray, green, yellow and orange, respectively. EB early blastula, HB hatched blastula, MB mesenchyme blastula, EG early gastrula and LG late gastrula

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Previous works, however, reported that some tissue-specific effector genes are regulated directly by upstream transcription factors to promote early specification in sea urchin [2, 9, 13, 14]. For example, Mtmmpb (Matrix metalloproteinase) and Cyp1 (Cyclophilin1), which function in the skeletogenesis, are directly regulated by specific transcription factors such as Alx1 and Ets in skeletogenic progenitor cells, respectively [15, 16]. Thus, the Cyp1 gene began to be expressed in the vegetal epithelium of embryos in parallel with the skeletogenic cell specification process [15]. Expression onset during the specification process was also observed in other tissues, such as endoderm cells, in which a marker gene for endoderm Endo16 starts to be expressed at the blastula stage before gastrulation [17]. Surprisingly, some tissue-specific effector genes are reported to be expressed even before cell fate segregation. Pks (Polyketide synthase) and Fmo (Flavin-containing monooxygenase) genes are effector genes of pigment cells of nonskeletogenic mesoderm (NSM) and begin to be expressed at the blastula stage [18], well before the cell fate segregation of NSM cells. In fact, some research has already indicated that the expression of tissue-specific effector genes is also driven by early specification genes [2, 9]. The above examples do not fit well with the view of the differentiation endpoint model. In other words, the nature of differentiation regulation should be reexamined from the viewpoint of expression dynamics of tissue-specific effector genes.

Several pioneering studies have examined the expression dynamics of tissue-specific effector genes in the early development of sea urchin. Rafiq et al. 2014 compared the transcriptomes of normal embryos and embryos that lacked precursors for skeletogenic cells and comprehensively identified the tissue-specific effector genes in the skeletogenic cell lineage [19]. They found that many effector genes showed high expression levels during the late blastula to gastrula stages, when biomineralization is observed [19]. Barsi et al. 2015 obtained cell type-specific transcriptomes of six different lineages from pregastrula and early gastrula sea urchin embryos by using a combination of bacterial artificial chromosome (BAC) recombineering and fluorescence-activated cell sorting (FACS) techniques [20]. They comprehensively identified the effector genes for each cell lineage, such as skeletogenic cells, pigment cells, and apical ectodermal subdomain cells, among others [20]. However, systematic analyses of the temporal profiles of the expression of these effector genes remain to be performed.

In this study, we examined the details of the tissue-specific effector genes, specifically focusing on the expression onset of tissue-specific effector genes, in the sea urchin Hemicentrotus pulcherrimus. This species and the model sea urchin species Strongylocentrotus purpuratus diverged an estimated 9.74–14.0 million years ago [21]. Previous studies have reported the same key regulator functions and their interactions between the developmental GRNs of H. pulcherrimus and S. purpuratus [22, 23]. Based on this, we used public single-cell transcriptomic data from S. purpuratus to identify tissue-specific effector genes [3]. Then, we examined the expression onset of these tissue-specific effector genes through temporal transcriptomic analysis of H. pulcherrimus embryos (egg to early gastrula stage; 0 and 6–30 hpf at 2-h intervals). Our analysis showed the temporal overlapping expression of the tissue-specific effector genes and specification GRN components. Rather, we found that the expression of some of the effector genes commences before cell lineage segregation occurs. We propose that differentiation should be defined as an accumulation process of effector expression overlapping with specification which results in the establishment of the cellular regulatory state. Finally, our finding of dynamic expression of tissue effector genes provides interesting insight into the evolution of new cell types.

Results

Identification of tissue-specific effector gene cohorts in H. pulcherrimus

Extraction of non-transcription factor and non-signaling molecule genes based on domain structures

We first attempted to systematically identify the candidate gene cohort of tissue-specific effector genes in the sea urchin H. pulcherrimus (experimental workflow: Fig. 2). Of 24,860 gene models that were previously identified in the genome of H. pulcherrimus [24], we identified 752 genes encoding transcription factors and cellular signaling proteins based on the gene domain structures. We tentatively considered the remaining 24,108 genes as the first candidate cohort of tissue-specific effector genes (Fig. 2).

Fig. 2
figure 2

Experimental flow for screening tissue-specific effector genes. Screening of tissue-specific effector genes was conducted from the gene models of H. pulcherrimus genome in multiple steps. (1) Transcription factors and signaling molecules were removed based on the domain structure. (2) The genes that did not show reciprocal BLAST best hits between H. pulcherrimus (Hp) and S. purpuratus (Sp) were excluded. (3) Genes whose expression was biased to specific cell lineage(s) in single-cell RNA-seq data from S. purpuratus were selected. The average of expression levels (Ave. exp.) was calculated in each cell cluster (0–14) and gene. We counted the number of cell lineages in which the expression level was higher than 0.3 for each gene (#: the number of cell lineages). Three examples were shown in 0, 1 and 7 lineage(s). (4) Zygotic expression onset of the genes was determined using temporal transcriptomic data from H. pulcherrimus. (5) Gene annotations were checked manually to refine the sets of tissue-specific effector genes. See the text for detailed methods and results

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This candidate list included ubiquitous housekeeping genes with roles in basic life requirements and genes that are not expressed in early developmental processes. Therefore, we next extracted the tissue-specific effector genes based on spatial expression pattern analysis.

Extraction of tissue-specific effector genes based on spatial expression at the early gastrula stage

Tissue-specific effector genes were expected to be expressed in specific territories of the embryo. In particular, because major specification and cell fate segregation are established at the early gastrula stage of sea urchin (Fig. 1), our target genes were assumed to be expressed in specific cell lineage(s) at this stage. Under this assumption, we examined the spatial expression patterns of the candidate genes at the early gastrula stage in a published dataset of single-cell RNA-seq of another sea urchin species, Strongylocentrotus purpuratus [3].

Dataset preparation

We first prepared and reanalyzed the single-cell RNA-seq dataset of early development of S. purpuratus published by Foster et al. 2020. While these authors integrated the data from various early developmental phases, we reanalyzed the data from early gastrula embryos only in order to clarify the cell states at this stage (Fig. 1). As shown in Additional file 1: Fig. S1, 15 cell clusters were reconstructed (Additional file 1: Fig. S1; apical ectoderm; Cluster 11, nonapical ectoderm; Clusters 1–3, 8 and 9, Veg1 ectoderm; Clusters 0, 5, and 6, Veg1/2 endoderm; Clusters 4, 7, nonskeletogenic mesoderm (NSM); Clusters 10, 14, skeletogenic cells; Cluster 12, germline; Cluster 13). Note that we could not technically distinguish the Veg1 and Veg2 endoderm lineage clusters from one another; thus, we identified these lineages as Veg1/2 endoderm here. See the Methods for details about the identification of cell clusters.

Removal of ubiquitously expressed genes to identify tissue-specific effector genes

For subsequent investigations using the dataset of different species, we first restricted our analysis to the 13,446 genes that showed reciprocal BLAST best hits between H. pulcherrimus and S. purpuratus (Fig. 2). We then investigated whether these genes had detectable expression in single-cell RNA-seq of S. purpuratus and found that among 13,446 genes, 11,130 had detectable expression. Finally, the average expression levels of 11,130 genes in each cluster were determined (Fig. 2).

To specify how many cell lineages at the early gastrula stage expressed each gene, the gene expression in a cell cluster was determined according to a threshold of the average expression level (Fig. 2). The median and third quantile of the average expression level of 11,130 genes in each cluster were approximately 0.0529–0.0892 and 0.232–0.356, respectively (Additional file 1: Fig. S2). Referring to these values, we set the threshold of 0.3 average expression level as the cutoff for gene expression [the validity of this threshold was tested in subsequent expression analysis (Additional file 1: Fig. S3) and gene annotation]. When multiple cell clusters were identified as belonging to the same cell lineage (ex. Clusters 0, 1, 5 and 6: Veg1 ectoderm; Clusters 2, 3, 8 and 9: nonapical ectoderm; Clusters 4 and 7: Veg1/2 endoderm; Clusters 10 and 14 for NSM lineage), the cluster that showed the highest average expression level was considered representative of that lineage (Additional file 1: Fig. S1).

We applied the above criteria to 11130 genes and found that 6258 genes did not show expression levels higher than 0.3 in any cell lineage (Fig. 2). In contrast, 1809 genes showed expression levels higher than 0.3 in all seven lineages, suggesting that these genes were housekeeping genes. We finally identified 3063 genes whose expression was restricted to specific cell lineage(s) in the early gastrula (Fig. 2). Among them, 1058 genes showed single cell lineage-specific expression, and 507 showed expression in two cell lineages. The rest of the genes showed expression in more than three lineages (Fig. 2; 494, 329, 348 and 327 genes in 3–6 lineages, respectively). We investigated the spatial expression patterns of 1058 genes whose expression was determined to be restricted to a single cell lineage according to the single-cell transcriptomic data. According to this profile, 1058 genes exhibited expression that was mainly localized to a single cell lineage (Additional file 1: Fig. S3). Note that the typical effector genes for skeletogenesis, such as Msp130L and C-lectin/PMC1, were included in this cohort. Thus, we considered that the threshold of expression level was an adequate cutoff for gene expression, and our target tissue-specific effector genes were extracted (Fig. 2).

A number of tissue-specific effector genes begin their expression before embryonic territories are established in H. pulcherrimus

Determination of expression onset of tissue-specific effector genes during early development of H. pulcherrimus

To determine the zygotic expression onset of the 3063 tissue-specific effector genes that were screened above, we prepared whole-embryo transcriptome data from a total of 14 stages of H. pulcherrimus: fertilized eggs (0 hpf) and embryonic stages (6–30 hpf) from the early blastula to gastrula stage at 2-h intervals (Fig. 1). We obtained three biological replicates from different parents and calculated the average expression level (FPKM value) of each gene at each timepoint.

Expression onset was determined through the identification of the timepoint at which the FPKM value exceeded three for the first time. A total of 2833 of 3063 genes showed FPKM values higher than three at one or more of the developmental time points examined (Fig. 2; 0–30 hpf). Among these 2833 genes, 1843 genes showed FPKM values higher than three at 0 hpf, indicating that the transcripts of these genes were of maternal origin (Fig. 2). Although we aimed to identify the zygotic expression onset of these 1843 genes, we could not technically define their zygotic expression onset. The remaining 910 genes showed FPKM values higher than three for the first time between 6 and 30 hpf (Fig. 2). Of these 910 genes, 516 genes showed FPKM values of less than one at 0 hpf (Fig. 2). These genes were clearly defined as the genes that were zygotically expressed. Thus, we targeted these 516 genes in the subsequent analysis (Fig. 2).

The annotations of the 516 genes were checked manually (Additional file 1: Table S1). As representative examples, the pigmentation marker gene Fmo (Fmo5_1) was listed among the genes that were estimated to be specifically expressed in NSM. The germline marker gene Nanos (Nan2) was also screened as a germline-specific effector gene. Moreover, many skeletogenic/biomineralization marker genes, including Clectins (C-lectin, C-lectin/PMC1, Clect_13, Clect_25 and Clect_76), Msps (Msp130L and Msp130r1), Sm29 and P16, were included in the list of genes that were specifically expressed in skeletogenic cells. Our screening also identified a number of genes that are involved in the biological process of differentiation, such as membrane formation and cellular transportation. This validated our approach for screening tissue-specific effector genes. Notably, this gene list included 9 putative cellular signaling-related genes (Delta, Dkk1, Eph, Frizz4, Frizz9/10, Hh, Vegf3, TgfbrtII and PlexA4_2-like, Tie1/2) and 7 putative transcription factors (Erg, Hnf1aL-like, Zmym1-like, Zmynd11, Zmynd12 and Znhit1). We removed these genes from subsequent analysis. Thus, the zygotic expression onset of a total of 500 genes was determined (Fig. 2).

Expression onset pattern of tissue-specific effector genes during early development of H. pulcherrimus

Of these 500 genes, 246 were expressed in a single cell lineage at the early gastrula stage (Fig. 3A). The remaining genes were found to be expressed in multiple cell lineages (Fig. 3A; 87, 63, 43, 25 and 37 genes in 2–6 cell lineages, respectively). We found that a large number of genes showed expression onset at the early stage, when cell lineage segregation occurs (6–10 hpf; Fig. 3B–C). This early expression onset was clearly observed for the genes that showed expression in multiple cell lineages (Fig. 3B–C). For tissue-specific effector genes that are expressed in a single cell lineage, the expression onset is rather broad; three main peaks of onset were detected at 10, 16 and 24 hpf (Fig. 3B–C). Nevertheless, most importantly, our observations are not consistent with the previous model of tissue-specific effector gene expression activation at the endpoint of specification GRNs after cell lineage territories are established (Fig. 3B–C).

Fig. 3
figure 3

Zygotic expression onset of tissue-specific effector genes during the development of H. pulcherrimus. A Indicates the number of tissue-specific effector genes calculated as expressed in a specific number of cell lineage(s). B and C Show the number of tissue-specific effector genes with zygotic expression onset at each timepoint. The color of each bar in A–C reflects the number of cell lineages in which tissue-specific effector genes were expressed at the early gastrula stage. B Shows the number of genes at each developmental time stacked together, and C shows the number of genes expressed in each number of cell lineages separately. D Shows the number of tissue-specific effector genes whose expression was restricted to a single cell lineage at each zygotic expression onset time and each cell lineage (as shown in the bottom graphs in C). The number and cell lineages are reflected in the size and color of the circles, respectively. Bars 1–4 in D indicate the cell fate segregation timing with reference to Fig. 1. Bar 1: micromere to skeletogenic cells and germline; Bar 2: mesomere to apical ectoderm and macromere to Veg1/Veg2; Bar 3: nonapical ectoderm to ciliary ectoderm/nerve cells and Veg2 to Veg2 endoderm/NSM; Bar 4: Veg1 to Veg1 ectoderm/Veg1 endoderm

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Furthermore, we identified the spatial expression regions of the genes that showed specific expression in a single cell lineage at the early gastrula stage (Fig. 3D). For example, 19 genes began to be expressed at 6 hpf and were expressed in a single cell lineage at the early gastrula stage (Fig. 3D). We found that early onset of gene expression at 6 hpf was observed in genes of all cell lineages: apical ectoderm, 3; nonapical ectoderm, 2; Veg1 ectoderm, 1; Veg1/2 endoderm, 1; NSM, 3; skeletogenic cells, 3; and germline, 6 (Fig. 3D). Surprisingly, we identified several effector genes with expression onset before cell lineage segregation occurs. While cell lineage segregation into the Veg2 endo-mesoderm at approximately 16 hpf in the mesenchyme blastula stage (Fig. 1), some of the specific effector genes for the Veg1/2 endoderm were expressed beginning at 6 hpf (Fig. 3D). Some NSM effectors also show expression from 6 hpf (Fig. 3D). Thus, the effector genes of endoderm and NSM are also coexpressed in the precursor cells (Veg2 blastomeres). Such coexpression was also observed in Veg1 ecto-endoderm lineage. Cell lineage segregation into Veg1 ecto-endoderm occurred at approximately 14 hpf in the hatched blastula stage (Fig. 1), while some of the specific effector genes for the Veg1 ectoderm and Veg1/2 endoderm were expressed beginning at 6 hpf (Fig. 3D). Specifically, we detected the coexpression of the endodermal (LOC582093: Cadherin_6 and LOC575113: Rergl4/Ras-like estrogen-regulated, growth inhibitor like 4) and mesodermal (LOC589279: Got1/Glutamic-oxaloacetic transaminase 1) effectors in Veg2 endo-mesodermal cells at the early blastula stage (Fig. 4). In both cases, some Veg2 cells expressed both endoderm and mesodermal effector genes whose expression was ultimately restricted to a single lineage. Coexpression of the endoderm effector LOC575113 (Rergl4) and ectoderm endoderm effector LOC576910 (Hypp_5866) was also detected in their precursor Veg1 cells (Fig. 4).

Fig. 4
figure 4

Coexpression of tissue-specific effector genes of the Veg2 endo/mesoderm and Veg1 ecto/endoderm in their precursor cells. A–C Show the expression patterns of LOC582093/Cadherin_6 and LOC589279/Got1; LOC575113/Rergl4 and LOC589279/Got1; and LOC575113/Rergl4 and LOC576910/Hypp_5866 in the UMAP projection derived from S. purpuratus single-cell RNA-seq data with the Seurat option FeaturePlot and its blend function. a and b Show the expression of the former and latter genes at the early blastula stage, respectively. c Merges the expression patterns of a and c. d is a magnified view of Veg2 (A and B) or Veg1 (C) cell clusters. e and f Show the expression of the former and latter genes at the early gastrula stage, respectively. h Is the color index showing the strength of the expression level and coexpression in c and g

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Early expression onset of tissue-specific effector genes was validated in the analysis of representative cell lineages

Above, we extracted the expression onset pattern of the tissue-specific effector genes from the comprehensive list of sea urchin genes. We next took a complementary approach to extract tissue-specific effector genes for the representative cell lineages. We examined the marker effector genes for the NSM, skeletogenic, Veg1/2 endoderm and apical ectoderm cell lineages because the regulatory pathways in these cell types have been described in detail (Fig. 1) [2, 14, 25,26,27,28]. Then, we directly compared expression onset among early specification GRN, differentiation driver genes and tissue-specific effector genes in each lineage.

Extraction of marker effector genes and regulatory genes for each cell lineage

In this analysis, we extracted the tissue-specific effector gene cohort for each cell lineage using a statistical approach. We used Seurat to find marker genes of single or multiple cell cluster (s) using single-cell transcriptomic data from S. purpuratus (see the Methods for details). After checking the resulting gene expression patterns manually, we ultimately elucidated 53, 66, 18 and 21 tissue-specific effector genes for NSM, skeletogenic cells, Veg1/2 endoderm and apical ectoderm, respectively (Additional file 1: Table S2). We confirmed that known marker genes were included in our screening list (for example, Msp130 and Sm50 for skeletogenic cells and Pks and Fmo for NSM). Finally, the expression onsets were determined using the criteria mentioned above in 44, 60, 16 and 20 genes, respectively. We also extracted the transcription factors or signaling genes that compose the specification GRN and differentiation drivers in the above four lineages from the literature.

Expression onset of differentiation marker effector genes in four cell lineages

NSM

NSM cells are segregated from the Veg2 cell lineage at the hatched blastula stage (12–16 hpf; Fig. 1) and invaginate into the blastocoel from the tip of the archenteron at the late gastrula stage (Fig. 1) [26, 29, 30]. Such mesenchymal cells are called secondary mesenchymal cells (SMCs) and differentiate into various mesodermal cells, including pigment cells and coelomocytes [29, 30]. We found that early specification genes for the Veg2 lineage, such as Eve and Foxa [26, 28], begin to be expressed at approximately 6–12 hpf (Fig. 5B). A previous study reported that GataE (GataL), Six1/2 and GataC drive the expression of differentiation effector genes [31, 32]. These differentiation driver genes were initially expressed at 12, 10 and 16 hpf (Fig. 5B). This expression onset pattern is consistent with the cell fate segregation of the Veg2 endo-mesoderm occurring at approximately 12–16 hpf.

Fig. 5
figure 5

Zygotic expression onset of tissue-specific effector and specification genes in four representative cell lineages. Zygotic expression onset of tissue-specific effector genes and specification genes are shown in each representative cell lineage: A, B: NSM; C, D: skeletogenic cells; E, F: Veg1/2 endoderm; G, H: apical ectoderm (A, C, E and F: effector genes and B, D, F and H: specification genes). Each dot indicates the timing of expression onset, and dotted lines indicate the timing of cell fate segregation or morphogenesis of a developmental landmark in each cell lineage. Embryonic stages and cell fate segregation are shown in Fig. 1. E egg, Macro/Ma macromere, M micromere, Meso mesomere, Cleav cleavage

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Among the 44 tissue-specific genes that were screened for NSM, 22 marker effector genes were zygotically expressed only during 10–28 hpf. Among these, several effector genes began to be expressed in parallel with the expression of some specification genes; 11 genes were zygotically expressed at 10–14 hpf, corresponding to early and hatched blastula stages (Fig. 5A). In particular, effector genes such as Spsb3, Hypp-0119 and Ars-3 began to be expressed even before cell fate segregation at 10 or 12 hpf (Fig. 5A). The onset of the remaining zygotic genes occurred after cell fate segregation, and several genes considered to function in pigmentation were in this cohort: Fmo3, 16 hpf; Psk1, 16 hpf and Fmo5_1, 18 hpf (Fig. 5A). The remaining 22 effector genes were those whose transcripts were distributed in the egg (Fig. 5A). Although the zygotic expression onset of these genes was unclear in most cases, we could estimate the zygotic expression onset of several maternal genes based on the temporal expression pattern (Additional file 1: Fig. S4). Among these genes, Abcc5D was inferred to undergo zygotic expression onset at 10 hpf before cell fate segregation, as the expression level of Abcc5D decreased from 0 hpf (FPKM: 3.23) to 8 hpf (FPKM: 1.86) and then increased after 10 hpf (FPKM: 4.93; Additional file 1: Fig. S4). Therefore, our data confirmed that some tissue-specific effector genes for NSM begin to be expressed prior to the segregation of the Veg2/NSM lineage.

We further investigated the spatial expression patterns of representative effector genes (Srcr42 and Spsb3) to validate that transcriptome-based analysis accurately captured the marker genes of the NSM lineage and their expression onsets. Srcr42 transcripts were localized in eggs, and Spsb3 was zygotically expressed in the Veg2 cell lineage before the segregation of the endo-mesoderm (Figs. 5A, B, 7A–D). Our subsequent investigation showed Srcr42 as broadly expressed in whole eggs (Fig. 7A, B: 0 hpf) and early blastula embryos (Fig. 7A, B: 6 hpf) but specifically expressed in the NSM region after the mesenchyme blastula stage (Fig. 7A, B: 16, 24 hpf). Spsb3 was specifically expressed in a Veg2 tier at the vegetal pole of the early blastula at 10 hpf, before the Veg2 endo-mesoderm was clearly segregated (Figs. 1, 7C, D). Spsb3 was then expressed in the Veg2 cell lineage at 12, 18 and 24 hpf (Fig. 7). These expression patterns were clearly consistent with the results of our transcriptome-based determinations of expression onset.

Skeletogenic cells

The precursors of skeletogenic cells, PMCs, are specified from micromere lineages by maternal factors, such as beta-catenin polarization at the vegetal pole of embryos during the cleavage stage (Fig. 1) [10, 14, 29]. Then, PMCs ingress into the blastocoel at the mesenchyme blastula stage and finally differentiate into skeletogenic cells through cellular aggregation and mineralization at the early gastrula stage [10, 14, 29].

As the fate of skeletogenic cells is committed at the cleavage stage, we found that early specification genes such as Wnt8, Pmar1a, Nrl and Delta [10, 14] began to be expressed at 6–8 hpf, the earliest timepoint in our dataset (Fig. 5D). Genes such as DriI, Erg, and FoxB were reported to function as differentiation driver genes to activate tissue-specific effector genes in skeletogenic cell lineages [15]. The expression onset of these genes was observed at 12–20 hpf (Fig. 5D). During this timeframe, we detected that some effector genes, such as Spsb3, Sm50 and Enpep_2, began to be expressed at 10 hpf, corresponding to the early blastula stage (Fig. 5C). The expression onset of effector genes such as Pks2, Timp3b and Prss12L_2 occurred at 12–16 hpf, the time at which differentiation driver genes began to be expressed (Fig. 5C). Other genes, such as Otop2L and Hypp-5094, began to be expressed after establishment of the regulatory state, at 22–28 hpf (Fig. 5C). We also found that transcripts of some effector genes were maternally localized in eggs, as observed for NSM (Fig. 5C; 18 of 61 genes), and the zygotic expression onset could be estimated for only two of these genes (Additional file 1: Fig. S4; Sdccag3L: 24 hpf, Anpep_1: 16 hpf). In summary, early expression and accumulation of effector genes were also observed in skeletogenic specification process.

We also clarified the expression onset of effector genes that were previously identified to function in the biomineralization process (i.e., Msp130, Plod2, Sm50 and 3Apcol) [33, 34]. Some biomineralization genes began to be expressed at approximately 12–16 hpf (Fig. 6), and there was no tendency for biomineralization genes to begin to be expressed only after later stages, such as the mesenchyme blastula or early gastrula stage. We also found that genes such as Ttrspn, Plod2, Msp130r2, Fam20c, A2(IV) and 3Apcol were maternally distributed in eggs (Fig. 6). These findings indicated that even effector genes with specific functions in biomineralization began to be expressed prior to regulatory state establishment.

Fig. 6
figure 6

Zygotic expression onset of biomineralization genes in skeletogenic cells. Zygotic expression onset of skeletogenic effector genes that are expected to function in the mineralization process is highlighted in red. The dataset is the same as that shown in Fig. 5C. White dots represent the genes whose functions are unknown or not involved in the biomineralization process

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We investigated the spatial expression patterns of two representative genes for skeletogenic cells: Plod2 and Enpep_2. Consistent with the FPKM values, ubiquitous expression of Plod2 was detected in eggs and early blastula embryos (Fig. 7E, F: 0 and 6 hpf, respectively). Specific expression in skeletogenic cells was also observed in the mesenchyme blastula and early gastrula (Fig. 7F: 16 and 24 hpf, respectively), suggesting that maternal transcripts persisted until the early developmental phase and that zygotic expression specifically occurred in skeletogenic cells. We also detected the specific expression of Empep2 in skeletogenic cells from the early blastula to early gastrula stage (Fig. 7G, H) and confirmed that the calculated expression level reflected the endogenous expression profile.

Fig. 7
figure 7

Spatial expression pattern of the representative tissue-specific effector genes. Temporal (A, C, E, G, I, K) and spatial (B, D, F, H, J, L) expression patterns of each gene were, respectively, obtained from transcriptome data and whole-mount in situ hybridization (A, B: Srcr42; C, D: Spsb3; E, F: Plod2; G, H: Enpep_2; I, J: PppL_224; K, L: Hypp_1056). Developmental timepoints for in situ hybridization are indicated above each photo of B, D, F, H, J and L

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Veg1/2 endoderm cells

Figure 5E shows the expression onset of Veg1/2 endoderm effector genes. As mentioned above, the Veg2 cellular population segregated to NSM and Veg2 endoderm at the hatched blastula stage (Fig. 1) [26, 29, 30]. Veg1 endoderm cells develop from the Veg1 cell lineage at the mesenchyme blastula stage (Fig. 1) [26, 30]. We detected that the expression of early specification genes such as Eve and Foxa and differentiation driver genes such as Bra was initiated at 6–8 hpf and 14 hpf, respectively (Fig. 5F) [26, 28]. Therefore, the regulatory state of Veg1/2 endodermal cells was estimated to be established at approximately 10–14 hpf.

Our data also showed that approximately half of the effector genes for the Veg1/2 endoderm were those whose transcripts were maternally distributed in eggs (Fig. 5E, 9 of 16 genes). Their zygotic expression onset could not be calculated. On the other hand, three genes (Hypp_0973, PppL_224, and Cadherin_6) began to be expressed at 8–10 hpf, at which time the Veg1/2 endodermal regulatory state was not clearly segregated (Fig. 5E). Thus, consistent with the results for NSM, both endodermal and mesodermal effector genes commonly initiate expression in their precursor Veg2 cells.

We detected the coexpression of Spsb3 (a mesodermal effector gene: Fig. 7D) and Pppl_224 (an endoderm effector gene: Fig. 7J) in Veg2 cells at the early blastula stage, at which the Veg2 lineage was not clearly segregated (Fig. 7J). We found that the expression of Pppl_224 could be observed in the epithelium of the vegetal pole, including the Veg2 region, at the early blastula stage (12 hpf: Fig. 7J), similar to the expression of Spsb3 at the same stage (10 hpf: Fig. 7D). Pppl_224 continued to be expressed until the early gastrula stage and finally localized specifically to the endodermal region at the early gastrula stage (20, 24 hpf: Fig. 7J). These spatial expression patterns suggested that the expression of tissue-specific effector genes for endoderm and mesoderm commence simultaneously in their precursor cells.

Apical ectodermal cells

Finally, we investigated the expression onset of tissue-specific effector genes of the apical ectoderm (Fig. 5G, H) and found expression onset patterns similar to those of other lineages. The cell fate of the apical ectoderm is clearly segregated from mesomeres in the early blastula stage (Fig. 1), although several regulators, such as the Foxq2 gene, began to be expressed in the apical ectodermal region at approximately the 32-cell stage [27, 35, 36]. In the later stages, such as the hatched blastula stage, neural progenitor cells develop from the apical ectoderm [36].

We found that early specification genes such as Foxq2, Six3 and Hbn begin to be expressed at 6–10 hpf [27]. Differentiation driver genes such as Z133 (Z133_1) and Ac/Sc began to be expressed at 16 and 18 hpf, respectively [27]. Our data also indicated that the onset of expression of some effector genes (Hypp_1056, Hypp_1641, RapL, Opn5L and Hypp_1863) occurred at 6–10 hpf, at which the cell fate segregation of mesomere occurs (Fig. 5G). Eleven genes were maternal genes whose transcripts were detected in eggs, and their zygotic gene expression onset could not be determined. We finally observed that the gene Hypp-1056, one of the screened effector genes, was expressed in the apical ectoderm in the early, hatched blastula and early gastrula stages (Fig. 7L: 6, 12 and 24 hpf, respectively). Therefore, even in the apical ectodermal region, our data suggested that many effector genes began to be expressed before the regulatory state and cell fate was clearly established.

Finally, we note that the expression onset of some effector and regulator genes has already been reported in sea urchin species such as S. purpuratus, Lytechinus variegatus and Paracentrotus lividus [18, 19, 27, 28, 31, 37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70]. We have summarized the expression onset of these genes in Additional file 1: Table S3. The results validated that most of the genes showed similar expression onset in these sea urchin species compared to H. pulcherrimus. Thus, it is suggested that the early expression onset pattern shown in this study is not specific to H. pulcherrimus, but is generally observed in sea urchin species.

Discussion

According to the differentiation endpoint model, the GRN has been proposed as a regulatory scheme underlying early specification and differentiation in sea urchins [2, 10,11,12, 28]. In this scheme, transcription factors in early specification GRNs, together with some signaling molecules, generate cell lineage territories in embryos and subsequently activate the expression of differentiation driver genes, which also include additional transcription factor genes [2, 11, 12]. Finally, the transcription factors among the differentiation driver genes subsequently regulate the expression of tissue-specific effector genes in each territory [2, 11, 12].

However, contravening this model, the expression onset of some effector genes was reported to be directly regulated by early specification genes [2, 9, 13, 14]. This study suggests that the early expression of some effector genes is not exceptional but rather a general observation in various lineages of sea urchin embryos. In this study, we first comprehensively extracted the tissue-specific effector genes for seven cell lineages (Fig. 3). Although the timing of cell fate segregation is different among cell lineages, tissue-specific effector genes of any lineage commonly begin to be expressed around the blastula stage (Fig. 3). Moreover, we validated the early expression of several tissue-specific effector genes during specification process in representative cell lineages (Fig. 5). In some cell lineages, such as Veg1 and Veg2, tissue-specific effector genes begin to be expressed before cell fate segregation (Figs. 3, 4, 5, 6, 7). For example, both tissue-specific effector genes for NSM and Veg2 endoderm are expressed in their precursor Veg2 cells (Figs. 4, 7). Overall, tissue-specific effector genes generally begin to be expressed in parallel with specification GRN and even before cell fate segregation during the early development of sea urchins. This finding is not consistent with the differentiation endpoint model. We rather support that differentiation overlaps with specification processes and proceeds without clear start and endpoint as mentioned in the examples of pigment cells. In other words, the differentiation and its regulatory scheme should be conceptualized as a seamless process of accumulation of effector expression along with the advancing specification GRN.

Here, we discuss some biological impacts of this early onset of effector gene expression. The first question may be whether effector gene expression in the early phase of differentiation can be harmful. Although we do not have any evidence of when the proteins encoded by effector genes are produced, our observations suggest that the early expression of tissue-specific effector genes is modest or even negligible. The presence of biomineralization matrix proteins in cells during the early differentiation process can be tolerated. Alternatively, the effects of expression may be suppressed by an active degradation mechanism. Recently, it has been recognized that active transcript degradation mechanisms are involved in specification and differentiation [71, 72]. For example, it has been reported that the interposition of specific microRNAs controls the patterning of the larval skeleton in sea urchin embryos [73, 74]. Thus, the mRNAs of tissue-specific effector genes may be selectively degraded or translationally controlled during the early specification process. We finally want to note the possibility that some effector genes are expressed prior to cell fate segregation to perform specific roles in the biological process of asymmetric fate segregation. In the future, a comprehensive genetic control model based on the specification GRN and degradation mechanism should be constructed by focusing on the expression onset of tissue-specific effector genes. In this respect, it is worth noting that a large number of tissue-specific effector genes were deposited as maternal RNAs. Generally, in early animal development, many transcripts and proteins are deposited into oocytes [75]. These transcripts and proteins function to maintain basic cell functions until zygotic gene activation occurs [75,76,77]. Considering that tissue-specific effector genes have specific functions in differentiation, such as skeletogenesis and pigmentation, the maternal transcripts of effector genes are unlikely to function in any biological processes during early developmental stages such as cleavage stages. For example, some maternal transcripts may be localized to a specific cell lineage with selective degradation from all other lineages.

Another implication of the early onset of effector expression may be the evolution of new cell types or functions. We found that some effector genes are coexpressed in precursor cells before cell lineage segregation occurs. This phase of early effector gene expression may provide test cases for novel repertoires of gene expression to perform novel cell functions. It is not rare for the developmental processes of certain cell types to be altered during evolution [78]. For example, echinoderm larval skeletogenesis is likely to have arisen from the co-option of adult skeletogenesis for the larval stage in the sea urchin and brittle star lineage [79, 80]. This co-option allows a developmental process in which biomineralization cells and mobile mesenchyme cells are derived from precursor cells. Then, the precursor cells coexpress the effector genes for biomineralization and those for mobile mesenchyme cells. This may have contributed to the acquisition of the unique larval skeleton. Therefore, such cases of modification of developmental processes may provide their own combination of effector gene expression and provide occasions to explore novel cell functions. In other words, the early specification process may not only serve to provide differentiation driver genes for certain lineages; in an evolutionary sense, it can also function as a showcase of new cell functions.

Conclusions

Differentiation processes underlying animal development have attracted critical interest to understand how animal morphology evolves. Pioneer studies on sea urchin GRN established a simplistic regulatory model which capture differentiation as the endpoint of specification process. This study reexamined the differentiation endpoint model, and found that gene cohorts of tissue-specific effector genes show expression onset patterns that are not well-consistent with the previous regulatory scheme. We suggest that differentiation processes are more dynamic than previously proposed, and imply that the dynamic GRN nature drives evolution of new cell types.

Methods

Preparation of transcriptome data and calculation of expression level

Adult specimens of H. pulcherrimus were collected around Tateyama (Chiba Prefecture, Japan). Artificial fertilization was conducted with reference to previous works [81]. Embryos from three different parents were obtained and cultured using artificial seawater (commercially purchased as Marine Art BR, Osaka Yakken Co, Osaka, Japan) at 14 °C. Total RNA was extracted from living eggs (0 hpf) and embryos (6, 8, 10, ..., 30 hpf) in each replicate using TRIzol reagent (Thermo Fisher Scientific, Massachusetts, U.S.) and then purified with the RNeasy kit (Qiagen, Hilden, Germany). Preparation of paired-end libraries and sequencing (150 bp) on a NovaSeq 6000 were performed by Novogene. The raw reads were deposited in the DDBJ Sequence Read Archives (DRA015433). The quality of raw reads was evaluated by FastQC (version 0.11.5; https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and further filtered by Trimmomatic (version 0.38) [82]. The gene models of H. pulcherrimus, which were previously published by Kinjo et al., 2018, were used as a reference for the calculation of expression levels over developmental time [24]. Read mapping and FPKM calculation were conducted using Rsem/bowtie2 (version 1.2.28) [83]. The FPKM values of each gene in three biological replicates at each developmental timepoint were averaged for the subsequent analysis. This dataset is available in the supplementary file (see Availability of data and materials).

General description of bioinformatic analysis and data visualization

We mainly used the R language (version 4.1.3) and its packages, such as Tidyverse (version 1.3.2), for the analysis of transcriptome data [84, 85]. Data processing was also conducted using the Unix standard command line, for example, AWK in bash/zsh. The ggplot2 (in Tidyverse) and gt packages were utilized to generate graphs (Figs. 3, 5, 6, 7 Additional file 1: S2, S4) and tables (Additional file 1: Tables S1, S2), respectively [85, 86]. Original figures were processed using Adobe Illustration without modifying the results. Code (R scripts), datasets, and original figures are provided in the supplemental files.

Analysis of single-cell RNA-seq data of S. purpuratus

Dataset preparation and cell clustering

We obtained the raw data of single-cell RNA-seq of S. purpuratus early blastula (EB) and early gastrula (EG) embryos from NCBI GEO (GSE149221; GSM4494541: SpEB/early blastula stage; GSM4494544: SpEG/early gastrula) [3]. All data were further processed using Seurat (version 3.2.1) [87]. Filtering, normalization, feature selection, dimension reduction and cell clustering were performed with reference to the online manual, which was provided by the Seurat developer (https://satijalab.org/seurat/archive/v3.2/pbmc3k_tutorial.html) [87]. Essentially, we used the default values to analyze these data in the same way as the authors who published the raw S. purpuratus single-cell RNA-seq data [3]. A total of 10 and 15 cell clusters were found in the EB and EG, respectively (Additional file 1: Fig. S1A, B). Each cluster was annotated according to the expression patterns of the following marker genes with previously characterized expression: Apical ectoderm [35, 88]: Foxq2 (foxq2), Nkx2.1 (NK2.1); nonapical ectoderm [25]: Six3 (LOC576281), Emx (LOC577702), Unvn (LOC373488), Lim1 (Lim1), Gsc (Gsc) and FoxG (FoxG); Veg1 ectoderm [25]: Eve (eve), Vegf3 (LOC100889860); Veg1 endoderm [25]: Eve (eve), Hox7 (Hbox7); skeletogenic cells [14]: Alx1 (Alx1), Sm50 (SM50); NSM [28]: Gcm (gcm), GataE (GATAe); Veg2 endoderm [28]: Blimp1/Krox (blimp1/krox), FoxA (FoxA); germline [89]: Nanos (Nanos2). The expression of these genes was plotted on the uniform manifold approximation and projection (UMAP) projection of EB and EG data (Additional file 1: Fig. S1C).

Spatial expression analysis for screening of tissue-specific effector genes

The spatial expression of the candidate tissue-specific effector genes was investigated using the EG data. We used the dotplot option in Seurat to obtain the comprehensive expression data (average expression in each cluster) of each gene [87]. The expression matrix was further processed to show the distribution of expression levels in each cluster (Fig. S2) and the localization of expression of each gene (Additional file 1: Fig. S3). Coexpression of Veg2 endo-mesoderm and Veg1 ecto-endoderm effector genes was investigated using the option FeaturePlot and its blend function with the default threshold (Fig. 4) [87].

Extraction of marker genes of representative clusters

To obtain the list of marker genes for apical ectoderm, Veg1/2 endoderm, NSM and skeletogenic cells, the FindMarkers option in Seurat was used [87]. Using this option with default parameters, we first extracted the marker genes of cluster(s) 12, 4/7, 10 and 11. From this list, the top 100 genes with the highest p values were extracted. Then, these genes were further filtered by domain search for transcription factors/signaling machinery and BLAST reciprocal hit between S. purpuratus and H. pulcherrimus as described below. Finally, we manually reviewed and selected their spatial expression patterns (Additional file 1: Table S2).

Determination of zygotic expression onset of tissue-specific effector and transcription factor genes in H. pulcherrimus

Tissue-specific effector genes were extracted from two experimental flows as described in the text (screening from comprehensive genomic dataset and extraction of the marker genes of representative cell lineages). Transcription factors were extracted with the domain search described below. Expression onset was investigated as the earliest time at which a FPKM value threshold was met during the developmental time (0, 6–30 hpf). Specifically, for each target gene, we specified the time at which the FPKM value exceeded three for the first time as the time of expression onset. We could not technically distinguish maternal and zygotic transcripts in this study. Thus, zygotic expression onset was determined only for those genes whose FPKM values were less than one at 0 hpf. In exceptional cases, we defined the zygotic expression onset of the genes that did not meet this criterion via manual analysis of representative cell lineages (Additional file 1: Fig. S4). Specifically, we manually defined the zygotic expression onsets in cases with (1) a decrease in FPKM values in early stages, such as 6–10 hpf, and (2) an increase in FPKM values just after these stages.

Other bioinformatic analyses

Domain search

Domain searches of transcription factors and signaling-related genes were carried out using HMMER search (version 3.3.2) [90]. Specifically, the following hmm domains were prepared to extract transcription factors: ARID (PF01388.17), AT_hook (PF02178.15), Basic (PF01586.12), CUT (PF02376.11), DM (PF00751.14), Ets (PF00178.18), Forkhead (PF00250.14), GATA (PF00320.23), GCM (PF03615.11), HLH (PF00010.22), HMG_box (PF00505.15), Hairy_orange (PF07527.9), Homeobox (PF00046.25), Hormone_recep (PF00104.26), OAR (PF03826.13), P53 (PF00870.14), P53_tetramer (PF07710.7), PAX (PF00292.14), Pou (PF00157.13), HPD (PF05044.8), RHD_DNA_bind (PF00554.18), Runt (PF00853.15), SCAN (PF02023.13), SIM_C (PF06621.8), SRF-TF (PF00319.14), T-box (PF00907.18), TBX (PF12598.4), TF_AP-2 (PF03299.10), TF_Otx (PF03529.9), bZIP_1 (PF00170.17), bZIP_2 (PF07716.11), zf-C2H2 (PF00096.22), zf-C2HC (PF01530.14) and zf-C4 (PF00105.14). The following hmm domains were for signaling-related genes: AMH_N (PF04709.8), CSF-1 (PF05337.7), Cbl_N (PF02262.12), Cbl_N2 (PF02761.10), Cbl_N3 (PF02762.10), CheW (PF01584.15), DIX (PF00778.13), Dishevelled (PF02377.11), FGF (PF00167.14), Focal_AT (PF03623.9), G-gamma (PF00631.18), GM_CSF (PF01109.13), Hpt (PF01627.19), IL11 (PF07400.7), IL12 (PF03039.10), IL2 (PF00715.13), IL3 (PF02059.11), IL4 (PF00727.14), IL5 (PF02025.11), IL7 (PF01415.12), MCPsignal (PF00015.17), NPH3 (PF03000.10), Olfactory_mark (PF06554.8), PDGF (PF00341.13), PDGF_N (PF04692.9), PSK (PF06404.8), PTN_MK_C (PF01091.14), PTN_MK_N (PF05196.9), Phe_ZIP (PF08916.7), RGS (PF00615.15), Rabaptin (PF03528.11), STAT_alpha (PF01017.16), STAT_bind (PF02864.11), STAT_int (PF02865.13), TGF_beta (PF00019.16), TGFb_propeptide (PF00688.14), TRADD_N (PF09034.6) and wnt (PF00110.15). We searched for such domains in all gene models of H. pulcherrimus [24].

Reciprocal BLAST

BLAST software (version. 2.12.0 +) was downloaded from NCBI [91]. Amino acid sequences of H. pulcherrimus and S. purpuratus were obtained from HpBase and EchinoBase, respectively (Hp: HpulGenome_v1_prot.fa, Sp: GCF_000002235.4_Spur_4.2_protein.faa) [24, 92]. First, the amino acid sequence of H. pulcherrimus was used as the query of the blastp program against the S. purpuratus database. We then used each top hit gene model as the query for a subsequent blastp search using the H. pulcherrimus database. Among these BLAST searches, the top hit was the same as the original query for 14,080 genes. Reciprocal BLAST top hit genes are shown in the supplemental files.

Whole-mount in situ hybridization (WMISH)

Eggs and embryos of H. pulcherrimus were obtained from the same cohorts used for RNA-seq as described above (0, 6, 8, 10, ..., 30 hpf) and fixed with 4% paraformaldehyde in artificial seawater. We obtained three biological replicates, and the embryos of replicate 1 are presented as the representative samples. Target gene sequences were obtained from the public database, and the genes were amplified using the following primers: Pppl_224_F, ctccgaagctgccatcgaagatattacatt; Pppl_224_R + T3, attaaccctcactaaagggaagctactctcggtgcataat; Hypp_1056_F, attcccgttatcgcttgaagatgattcgta; Hypp_1056_R + T3, attaaccctcactaaagggacaccactcgagagaattttg; Empep_F, ctcggatggagagatgacggttcccatctt; Empep_R + T3, attaaccctcactaaagggaaatgttctttggttctgcat; Srcr42_F, cgttacgtgcaatggtaatattcgcctaca; Srcr42_R + T3, attaaccctcactaaagggacttcttttccggtgcagttc; Plod2_F, catgagatcgaaaatgcagaaatggaagaa; Plod2_R + T3, attaaccctcactaaagggactacgttaatggtgtacgtg; Spsb3_F, aaaagctcgtctgcatgccgattccttcat; HpSpsb3_R + T3, attaaccctcactaaagggattgaataagcgtgtcctctt. WMISH was carried out as described in previous works [80, 93].

Availability of data and materials

The raw reads were deposited in the DDBJ Sequence Read Archives (DRA015433). The supplementary files are provided in Figshare (10.6084/m9.figshare.21776603)

References

  1. Gilbert SF. Developmental biology. Massachusetts: Sinauer Associates Incorporated; 2013.

    Google Scholar 

  2. Peter IS, Davidson EH. Genomic control process: development and evolution. Amsterdam: Elsevier Science; 2015.

    Google Scholar 

  3. Foster S, Oulhen N, Wessel G. A single cell RNA sequencing resource for early sea urchin development. Development. 2020;147(17):dev191528.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Calestani C, Rogers DJ. Cis-regulatory analysis of the sea urchin pigment cell gene polyketide synthase. Dev Biol. 2010;340(2):249–55.

    Article  CAS  PubMed  Google Scholar 

  5. Perillo M, Oulhen N, Foster S, Spurrell M, Calestani C, Wessel G. Regulation of dynamic pigment cell states at single-cell resolution. Elife. 2020;9:e60388.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Allen RL, George AN, Miranda E, Phillips TM, Crawford JM, Kiehart DP, McClay DR. Wound repair in sea urchin larvae involves pigment cells and blastocoelar cells. Dev Biol. 2022;491:56–65.

    Article  CAS  PubMed  Google Scholar 

  7. Hibino T, Loza-Coll M, Messier C, Majeske AJ, Cohen AH, Terwilliger DP, Buckley KM, Brockton V, Nair SV, Berney K, et al. The immune gene repertoire encoded in the purple sea urchin genome. Dev Biol. 2006;300(1):349–65.

    Article  CAS  PubMed  Google Scholar 

  8. Ettensohn CA. The gene regulatory control of sea urchin gastrulation. Mech Dev. 2020;162: 103599.

    Article  CAS  PubMed  Google Scholar 

  9. Martik ML, Lyons DC, McClay DR. Developmental gene regulatory networks in sea urchins and what we can learn from them. F1000Res. 2016;5:203.

    Article  Google Scholar 

  10. Shashikant T, Khor JM, Ettensohn CA. From genome to anatomy: The architecture and evolution of the skeletogenic gene regulatory network of sea urchins and other echinoderms. Genesis. 2018;56(10): e23253.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Erwin DH, Davidson EH. The evolution of hierarchical gene regulatory networks. Nat Rev Genet. 2009;10(2):141–8.

    Article  CAS  PubMed  Google Scholar 

  12. Hinman VF, Cheatle Jarvela AM. Developmental gene regulatory network evolution: Insights from comparative studies in echinoderms. Genesis. 2014;52(3):193–207.

    Article  CAS  PubMed  Google Scholar 

  13. Damle S, Davidson EH. Precise cis-regulatory control of spatial and temporal expression of the alx-1 gene in the skeletogenic lineage of s purpuratus. Dev Biol. 2011;357(2):505–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Oliveri P, Tu Q, Davidson EH. Global regulatory logic for specification of an embryonic cell lineage. Proc Natl Acad Sci USA. 2008;105(16):5955–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Amore G, Davidson EH. cis-Regulatory control of cyclophilin, a member of the ETS-DRI skeletogenic gene battery in the sea urchin embryo. Dev Biol. 2006;293(2):555–64.

    Article  CAS  PubMed  Google Scholar 

  16. Guerrero-Santoro J, Khor JM, Açıkbaş AH, Jaynes JB, Ettensohn CA. Analysis of the DNA-binding properties of Alx1, an evolutionarily conserved regulator of skeletogenesis in echinoderms. J Biol Chem. 2021;297(1):100901.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Nocente-McGrath C, Brenner CA, Ernst SG. Endo16, a lineage-specific protein of the sea urchin embryo, is first expressed just prior to gastrulation. Dev Biol. 1989;136(1):264–72.

    Article  CAS  PubMed  Google Scholar 

  18. Calestani C, Rast JP, Davidson EH. Isolation of pigment cell specific genes in the sea urchin embryo by differential macroarray screening. Development. 2003;130(19):4587–96.

    Article  CAS  PubMed  Google Scholar 

  19. Rafiq K, Shashikant T, McManus CJ, Ettensohn CA. Genome-wide analysis of the skeletogenic gene regulatory network of sea urchins. Development. 2014;141(4):950–61.

    Article  CAS  PubMed  Google Scholar 

  20. Barsi JC, Tu Q, Calestani C, Davidson EH. Genome-wide assessment of differential effector gene use in embryogenesis. Development. 2015;142(22):3892–901.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Kober KM, Bernardi G. Phylogenomics of strongylocentrotid sea urchins. BMC Evol Biol. 2013;13(1):88.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Yamazaki A, Minokawa T. Roles of hesC and gcm in echinoid larval mesenchyme cell development. Dev Growth Differ. 2016;58(3):315–26.

    Article  PubMed  Google Scholar 

  23. Kurokawa D, Kitajima T, Mitsunaga-Nakatsubo K, Amemiya S, Shimada H, Akasaka K. HpEts, an ets-related transcription factor implicated in primary mesenchyme cell differentiation in the sea urchin embryo. Mech Dev. 1999;80(1):41–52.

    Article  CAS  PubMed  Google Scholar 

  24. Kinjo S, Kiyomoto M, Yamamoto T, Ikeo K, Yaguchi S. HpBase: a genome database of a sea urchin, Hemicentrotus pulcherrimus. Dev Growth Differ. 2018;60(3):174–82.

    Article  CAS  PubMed  Google Scholar 

  25. Li E, Cui M, Peter IS, Davidson EH. Encoding regulatory state boundaries in the pregastrular oral ectoderm of the sea urchin embryo. Proc Natl Acad Sci U S A. 2014;111(10):E906–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Peter IS, Davidson EH. The endoderm gene regulatory network in sea urchin embryos up to mid-blastula stage. Dev Biol. 2010;340(2):188–99.

    Article  CAS  PubMed  Google Scholar 

  27. Feuda R, Peter IS. Homologous gene regulatory networks control development of apical organs and brains in Bilateria. Sci Adv. 2022;8(44):eabo2416.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ben-Tabou de-Leon S. Robustness and accuracy in sea urchin developmental gene regulatory networks. Front Genet. 2016. https://doi.org/10.3389/fgene.2016.00016.

    Article  PubMed  PubMed Central  Google Scholar 

  29. McClay DR. Evolutionary crossroads in developmental biology: sea urchins. Development. 2011;138(13):2639–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Davidson EH, Cameron RA, Ransick A. Specification of cell fate in the sea urchin embryo: summary and some proposed mechanisms. Development. 1998;125(17):3269–90.

    Article  CAS  PubMed  Google Scholar 

  31. Materna SC, Ransick A, Li E, Davidson EH. Diversification of oral and aboral mesodermal regulatory states in pregastrular sea urchin embryos. Dev Biol. 2013;375(1):92–104.

    Article  CAS  PubMed  Google Scholar 

  32. Andrikou C, Pai C-Y, Su Y-H, Arnone MI. Logics and properties of a genetic regulatory program that drives embryonic muscle development in an echinoderm. Elife. 2015;4:e07343.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Livingston BT, Killian CE, Wilt F, Cameron A, Landrum MJ, Ermolaeva O, Sapojnikov V, Maglott DR, Buchanan AM, Ettensohn CA. A genome-wide analysis of biomineralization-related proteins in the sea urchin Strongylocentrotus purpuratus. Dev Biol. 2006;300(1):335–48.

    Article  CAS  PubMed  Google Scholar 

  34. Mann K, Wilt FH, Poustka AJ. Proteomic analysis of sea urchin (Strongylocentrotus purpuratus) spicule matrix. Proteome Sci. 2010;8:33.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Yaguchi S, Yaguchi J, Angerer RC, Angerer LM. A Wnt-FoxQ2-nodal pathway links primary and secondary axis specification in sea urchin embryos. Dev Cell. 2008;14(1):97–107.

    Article  CAS  PubMed  Google Scholar 

  36. McClay DR, Miranda E, Feinberg SL. Neurogenesis in the sea urchin embryo is initiated uniquely in three domains. Development. 2018. https://doi.org/10.1242/dev.167742.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Adomako-Ankomah A, Ettensohn CA. P58-A and P58-B: Novel proteins that mediate skeletogenesis in the sea urchin embryo. Dev Biol. 2011;353(1):81–93.

    Article  CAS  PubMed  Google Scholar 

  38. Beeble A, Calestani C. Expression pattern of polyketide synthase-2 during sea urchin development. Gene Expr Patterns. 2012;12(1–2):7–10.

    Article  CAS  PubMed  Google Scholar 

  39. Cheers MS, Ettensohn CA. P16 is an essential regulator of skeletogenesis in the sea urchin embryo. Dev Biol. 2005;283(2):384–96.

    Article  CAS  PubMed  Google Scholar 

  40. Coffman JA, Kirchhamer CV, Harrington MG, Davidson EH. SpRunt-1, a new member of the runt domain family of transcription factors, is a positive regulator of the aboral ectoderm-specific cyiiia gene in sea urchin embryos. Dev Biol. 1996;174(1):43–54.

    Article  CAS  PubMed  Google Scholar 

  41. Croce J, Lhomond G, Gache C. Expression pattern of brachyury in the embryo of the sea urchin Paracentrotus lividus. Dev Genes Evol. 2001;211(12):617–9.

    Article  CAS  PubMed  Google Scholar 

  42. Cui M, Vielmas E, Davidson EH, Peter IS. Sequential response to multiple developmental network circuits encoded in an intronic cis-regulatory module of sea Urchin hox11/13b. Cell Rep. 2017;19(2):364–74.

    Article  CAS  PubMed  Google Scholar 

  43. Famiglietti AL, Wei Z, Beres TM, Milac AL, Tran DT, Patel D, Angerer RC, Angerer LM, Tabak LA. Characterization and expression analysis of galnts in developing Strongylocentrotus purpuratus embryos. PLoS ONE. 2017;12(4): e0176479.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Fujii T, Sakamoto N, Ochiai H, Fujita K, Okamitsu Y, Sumiyoshi N, Minokawa T, Yamamoto T. Role of the nanos homolog during sea urchin development. Dev Dyn. 2009;238(10):2511–21.

    Article  CAS  PubMed  Google Scholar 

  45. George NC, Killian CE, Wilt FH. Characterization and expression of a gene encoding a 30 6-kDa Strongylocentrotus purpuratus spicule matrix protein. Dev Biol. 1991;147(2):334–42.

    Article  CAS  PubMed  Google Scholar 

  46. Gökirmak T, Campanale JP, Reitzel AM, Shipp LE, Moy GW, Hamdoun A. Functional diversification of sea urchin ABCC1 (MRP1) by alternative splicing. Am J Physiol Cell Physiol. 2016;310(11):C911–20.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Harkey MA, Klueg K, Sheppard P, Raff RA. Structure, expression, and extracellular targeting of pm27, a skeletal protein associated specifically with growth of the sea urchin larval spicule. Dev Biol. 1995;168(2):549–66.

    Article  CAS  PubMed  Google Scholar 

  48. Harkey MA, Whiteley HR, Whiteley AH. Differential expression of the msp130 gene among skeletal lineage cells in the sea urchin embryo: a three dimensional in situ hybridization analysis. Mech Dev. 1992;37(3):173–84.

    Article  CAS  PubMed  Google Scholar 

  49. Howard-Ashby M, Materna SC, Brown CT, Chen L, Cameron RA, Davidson EH. Identification and characterization of homeobox transcription factor genes in Strongylocentrotus purpuratus, and their expression in embryonic development. Dev Biol. 2006;300(1):74–89.

    Article  CAS  PubMed  Google Scholar 

  50. Kenny AP, Kozlowski D, Oleksyn DW, Angerer LM, Angerer RC. SpSoxB1, a maternally encoded transcription factor asymmetrically distributed among early sea urchin blastomeres. Development. 1999;126(23):5473–83.

    Article  CAS  PubMed  Google Scholar 

  51. Killian CE, Croker L, Wilt FH. SpSM30 gene family expression patterns in embryonic and adult biomineralized tissues of the sea urchin Strongylocentrotus purpuratus. Gene Expr Patterns. 2010;10(2):135–9.

    Article  CAS  PubMed  Google Scholar 

  52. Livi CB, Davidson EH. Expression and function of blimp1/krox, an alternatively transcribed regulatory gene of the sea urchin endomesoderm network. Dev Biol. 2006;293(2):513–25.

    Article  CAS  PubMed  Google Scholar 

  53. Martínez-Bartolomé M, Range RC. A biphasic role of non-canonical Wnt16 signaling during early anterior-posterior patterning and morphogenesis of the sea urchin embryo. Development. 2019. https://doi.org/10.1242/dev.168799.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Oliveri P, Walton KD, Davidson EH, McClay DR. Repression of mesodermal fate by foxa, a key endoderm regulator of the sea urchin embryo. Development. 2006;133(21):4173–81.

    Article  CAS  PubMed  Google Scholar 

  55. Peterson KJ, Harada Y, Cameron RA, Davidson EH. Expression pattern of brachyury and not in the sea urchin: comparative implications for the origins of mesoderm in the basal deuterostomes. Dev Biol. 1999;207(2):419–31.

    Article  CAS  PubMed  Google Scholar 

  56. Rafiq K, Cheers MS, Ettensohn CA. The genomic regulatory control of skeletal morphogenesis in the sea urchin. Development. 2012;139(3):579–90.

    Article  CAS  PubMed  Google Scholar 

  57. Ragusa MA, Nicosia A, Costa S, Cuttitta A, Gianguzza F. Metallothionein gene family in the sea urchin paracentrotus lividus: gene structure, differential expression and phylogenetic analysis. Int J Mol Sci. 2017;18(4):812.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Range RC, Angerer RC, Angerer LM. Integration of canonical and noncanonical wnt signaling pathways patterns the neuroectoderm along the anterior-posterior axis of sea urchin embryos. PLoS Biol. 2013;11(1): e1001467.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Ransick A, Davidson EH. cis-regulatory processing of Notch signaling input to the sea urchin glial cells missing gene during mesoderm specification. Dev Biol. 2006;297(2):587–602.

    Article  CAS  PubMed  Google Scholar 

  60. Burke RD, Angerer LM, Elphick MR, Humphrey GW, Yaguchi S, Kiyama T, Liang S, Mu X, Agca C, Klein WH, et al. A genomic view of the sea urchin nervous system. Dev Biol. 2006;300(1):434–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kawasaki T, Mitsunaga-Nakatsubo K, Takeda K, Akasaka K, Shimada H. Lim1 related homeobox gene (HpLim1) expressed in sea urchin embryos. Dev Growth Differ. 1999;41(3):273–82.

    Article  CAS  PubMed  Google Scholar 

  62. Ransick A, Rast JP, Minokawa T, Calestani C, Davidson EH. New early zygotic regulators expressed in endomesoderm of sea urchin embryos discovered by differential array hybridization. Dev Biol. 2002;246(1):132–47.

    Article  CAS  PubMed  Google Scholar 

  63. Rizzo F, Fernandez-Serra M, Squarzoni P, Archimandritis A, Arnone MI. Identification and developmental expression of the ets gene family in the sea urchin (Strongylocentrotus purpuratus). Dev Biol. 2006;300(1):35–48.

    Article  CAS  PubMed  Google Scholar 

  64. Röttinger E, Saudemont A, Vr D, Besnardeau L, McClay D, Lepage T. FGF signals guide migration of mesenchymal cells, control skeletal morphogenesis and regulate gastrulation during sea urchin development. Development. 2008;135(2):353–65.

    Article  PubMed  Google Scholar 

  65. Sherwood DR, McClay DR. Identification and localization of a sea urchin Notch homologue: insights into vegetal plate regionalization and Notch receptor regulation. Development. 1997;124(17):3363–74.

    Article  CAS  PubMed  Google Scholar 

  66. Shipp LE, Hamdoun A. ATP-binding cassette (ABC) transporter expression and localization in sea urchin development. Dev Dyn. 2012;241(6):1111–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Slota LA, Miranda EM, McClay DR. Spatial and temporal patterns of gene expression during neurogenesis in the sea urchin Lytechinus variegatus. EvoDevo. 2019;10(1):2.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Tisler M, Wetzel F, Mantino S, Kremnyov S, Thumberger T, Schweickert A, Blum M, Vick P. Cilia are required for asymmetric nodal induction in the sea urchin embryo. BMC Dev Biol. 2016;16(1):28.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Walton KD, Warner J, Hertzler PH, McClay DR. Hedgehog signaling patterns mesoderm in the sea urchin. Dev Biol. 2009;331(1):26–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Yamazaki A, Kawabata R, Shiomi K, Tsuchimoto J, Kiyomoto M, Amemiya S, Yamaguchi M. Krüppel-like is required for nonskeletogenic mesoderm specification in the sea urchin embryo. Dev Biol. 2008;314(2):433–42.

    Article  CAS  PubMed  Google Scholar 

  71. DeVeale B, Swindlehurst-Chan J, Blelloch R. The roles of microRNAs in mouse development. Nat Rev Genet. 2021;22(5):307–23.

    Article  CAS  PubMed  Google Scholar 

  72. Ivey KN, Srivastava D. microRNAs as developmental regulators. Cold Spring Harb Perspect Biol. 2015;7(7): a008144.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Song JL, Stoeckius M, Maaskola J, Friedländer M, Stepicheva N, Juliano C, Lebedeva S, Thompson W, Rajewsky N, Wessel GM. Select microRNAs are essential for early development in the sea urchin. Dev Biol. 2012;362(1):104–13.

    Article  CAS  PubMed  Google Scholar 

  74. Stepicheva NA, Song JL. microRNA-31 modulates skeletal patterning in the sea urchin embryo. Development. 2015;142(21):3769–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Dworkin MB, Dworkin-Rastl E. Functions of maternal mRNA in early development. Mol Reprod Dev. 1990;26(3):261–97.

    Article  CAS  PubMed  Google Scholar 

  76. Farley BM, Ryder SP. Regulation of maternal mRNAs in early development. Crit Rev Biochem Mol Biol. 2008;43(2):135–62.

    Article  CAS  PubMed  Google Scholar 

  77. Pelegri F. Maternal factors in zebrafish development. Dev Dyn. 2003;228(3):535–54.

    Article  CAS  PubMed  Google Scholar 

  78. Arendt D, Musser JM, Baker CVH, Bergman A, Cepko C, Erwin DH, Pavlicev M, Schlosser G, Widder S, Laubichler MD, et al. The origin and evolution of cell types. Nat Rev Genet. 2016;17(12):744–57.

    Article  CAS  PubMed  Google Scholar 

  79. Gao F, Davidson EH. Transfer of a large gene regulatory apparatus to a new developmental address in echinoid evolution. Proc Natl Acad Sci U S A. 2008;105(16):6091–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Morino Y, Koga H, Tachibana K, Shoguchi E, Kiyomoto M, Wada H. Heterochronic activation of VEGF signaling and the evolution of the skeleton in echinoderm pluteus larvae. Evol Dev. 2012;14(5):428–36.

    Article  CAS  PubMed  Google Scholar 

  81. Yamazaki A, Furuzawa Y, Yamaguchi M. Conserved early expression patterns of micromere specification genes in two echinoid species belonging to the orders clypeasteroida and echinoida. Dev Dyn. 2010;239(12):3391–403.

    Article  CAS  PubMed  Google Scholar 

  82. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011;12(1):323.

    Article  CAS  Google Scholar 

  84. R Core Team: R. A language and environment for statistical computing In. Vienna: R Foundation for Statistical Computing; 2022.

    Google Scholar 

  85. Wickham H, Averick M, Bryan J, Chang W, McGowan LDA, François R, Grolemund G, Hayes A, Henry L, Hester J. Welcome to the tidyverse. J Open Source Softw. 2019;4(43):1686.

    Article  Google Scholar 

  86. Iannone R, Cheng J, Schloerke B, Hughes E, Seo J: gt: Easily Create Presentation-Ready Display Tables. In. 2022.

  87. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-1902.e1821.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Takacs CM, Amore G, Oliveri P, Poustka AJ, Wang D, Burke RD, Peterson KJ. Expression of an NK2 homeodomain gene in the apical ectoderm defines a new territory in the early sea urchin embryo. Dev Biol. 2004;269(1):152–64.

    Article  CAS  PubMed  Google Scholar 

  89. Fresques T, Swartz SZ, Juliano C, Morino Y, Kikuchi M, Akasaka K, Wada H, Yajima M, Wessel GM. The diversity of nanos expression in echinoderm embryos supports different mechanisms in germ cell specification. Evol Dev. 2016;18(4):267–78.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Johnson LS, Eddy SR, Portugaly E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinform. 2010;11(1):431.

    Article  Google Scholar 

  91. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421.

    Article  Google Scholar 

  92. Arshinoff BI, Cary GA, Karimi K, Foley S, Agalakov S, Delgado F, Lotay VS, Ku CJ, Pells TJ, Beatman TR, et al. Echinobase: leveraging an extant model organism database to build a knowledgebase supporting research on the genomics and biology of echinoderms. Nucl Acids Res. 2021;50(D1):D970–9.

    Article  PubMed Central  Google Scholar 

  93. Yamakawa S, Morino Y, Honda M, Wada H. The role of retinoic acid signaling in starfish metamorphosis. EvoDevo. 2018;9(1):10.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was funded by the Japan Society for the Promotion of Science (18H04004). We thank Dr. Takeshi Kawashima for his advice on selecting hmm files related to transcription factors and signaling molecules. We also thank Nina Levin for proofreading this manuscript.

Funding

Japan Society for the Promotion of Science (18H04004).

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Authors and Affiliations

  1. Institute of Zoology and Evolutionary Research, Friedrich-Shiller University Jena, Erbertstraße 1, 07747, Jena, Germany

    Shumpei Yamakawa

  2. Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan

    Shumpei Yamakawa

  3. Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan

    Atsuko Yamazaki, Yoshiaki Morino & Hiroshi Wada

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  1. Shumpei Yamakawa
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Contributions

All authors contributed to the design of the experiments. SY, AY and YM collected the samples. SY and AY performed the experiments. SY, AY and YM analyzed the data. WH supervised this study. All authors read and approved the final manuscript.

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Correspondence to Shumpei Yamakawa.

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Supplementary Information

13227_2023_210_MOESM1_ESM.zip

Additional file 1: Fig S1. Identification of cell clusters in the single-cell transcriptomic data. Fig S2. Distribution of averaged expression levels of the candidate cohort of tissue-specific effector genes in each cell cluster. Fig S3. Spatial expression pattern of the tissue-specific effector genes whose expression was estimated to be restricted to a single cell cluster. Fig S4. Temporal expression pattern of the tissue-specific effector genes whose expression was observed at 0 hpf in the representative cell lineages. Table S1. List of screened tissue-specific effector genes. Table S2. List of marker tissue-specific effector genes.

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Yamakawa, S., Yamazaki, A., Morino, Y. et al. Early expression onset of tissue-specific effector genes during the specification process in sea urchin embryos. EvoDevo 14, 7 (2023). https://doi.org/10.1186/s13227-023-00210-2

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EvoDevo

ISSN: 2041-9139