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Single-cell RNA analysis identifies pre-migratory neural crest cells expressing markers of differentiated derivatives

  1. Ezra Lencer  Is a corresponding author
  2. Rytis Prekeris
  3. Kristin Bruk Artinger
  1. Department of Craniofacial Biology, University of Colorado, United States
  2. Department of Cell and Developmental Biology, University of Colorado, United States
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Cite this article as: eLife 2021;10:e66078 doi: 10.7554/eLife.66078

Abstract

The neural crest is a migratory population of stem-like cells that contribute to multiple traits including the bones of the skull, peripheral nervous system, and pigment. How neural crest cells differentiate into diverse cell types is a fundamental question in the study of vertebrate biology. Here, we use single-cell RNA sequencing to characterize transcriptional changes associated with neural crest cell development in the zebrafish trunk during the early stages of migration. We show that neural crest cells are transcriptionally diverse and identify pre-migratory populations already expressing genes associated with differentiated derivatives, specifically in the xanthophore lineage. Further, we identify a population of Rohon–Beard neurons in the data. The data presented identify novel genetic markers for multiple trunk neural crest cell populations and Rohon–Beard neurons providing insight into previously uncharacterized genes critical for vertebrate development.

Introduction

A fundamental question in developmental biology concerns how multipotent cell precursors produce diverse cell types (Waddington, 1957). This question is particularly relevant for the study of the vertebrate neural crest, a transient population of stem-like cells that contribute to multiple traits and form numerous cell types. Neural crest cells (NCCs) make the cartilages and bones of the skull, neurons of the peripheral nervous system (PNS), glial cells, and pigment cells among other derivatives (Anderson, 1989; Theveneau and Mayor, 2012). How and when NCCs acquire these diverse fates are long-standing questions in the study of vertebrate development and have direct implications for both our understanding of vertebrate evolution as well as the origins of NCC-derived hereditary diseases such as cleft lip and palate.

NCC development is classically conceived as a stepwise series of bifurcating cell fate decisions (Anderson, 1989; Klymkowsky et al., 2010; Simoes-Costa et al., 2014). NCCs are specified at the neural plate border, a region of ectoderm in the gastrula stage embryo that also produces cranial placodes, and in fishes and frogs, Rohon–Beard sensory neurons. Once specified, NCCs undergo an epithelial-to-mesenchymal transition, migrate throughout the embryo, and differentiate into multiple cell types. When and how NCCs differentiate into these derivative lineages remains unclear. Early studies in chick and frog suggested that migratory NCCs are multipotent, requiring cues from the migratory environment to initiate differentiation (Bronner-Fraser and Fraser, 1988; Bronner-Fraser and Fraser, 1989; Collazo et al., 1993). Other studies in zebrafish and quail suggested that NCCs are often lineage restricted prior to migration, and thus interactions among NCCs in the neural tube must somehow specify cells to different lineages before cells become migratory (Henion and Weston, 1997; Raible and Eisen, 1994; Raible and Eisen, 1996; Schilling and Kimmel, 1994).

Current understanding of the transcriptional changes associated with different stages of NCC development has largely been built from analysis of gene expression in whole tissues (Simoes-Costa et al., 2014). However, developing tissues are often transcriptionally heterogeneous, a fact highlighted by recent developments in single-cell RNA sequencing (scRNA-seq) (Briggs et al., 2018; Farnsworth et al., 2020; Farrell et al., 2018; Morrison et al., 2020; Morrison et al., 2017; Saunders et al., 2019; Soldatov et al., 2019; Wagner et al., 2018; Zalc et al., 2021). Furthermore, as cells within tissues are often at different stages of fate specification, scRNA-seq has the potential to expose transcriptional changes associated with differentiation that would have been missed by bulk sequencing experiments (Farrell et al., 2018; Soldatov et al., 2019). For instance, in chick, scRNA-seq studies of migrating cranial NCCs identified unique transcriptional signatures associated with a morphologically cryptic population of cells at the leading migratory edge (Morrison et al., 2020; Morrison et al., 2017). Thus, an emerging picture is that NCCs are more transcriptionally heterogeneous than once thought, and that a complete understanding of NCC biology must account for this heterogeneity.

Here, we use scRNA-seq to characterize the transcriptional landscape of trunk neural crest cells (tNCCs) during the early stages of migration in zebrafish. We sampled NCCs at a stage in zebrafish development when both migratory and pre-migratory tNCC populations are present in the embryo. In the trunk, NCCs migrate along one of two paths that are temporally and spatially separated. Early migrating tNCCs move between the neural tube and somitic mesoderm along the medial path (Figure 1A). These cells give rise to neurons of the PNS, glia, and pigment cells. Later migrating tNCCs follow the dorsolateral path between the skin ectoderm and somitic mesoderm to produce pigment cells. Thus, our experiment aims to capture transcriptional changes associated with the onset of migration and the splitting of NCCs into a neuronal or pigment lineage. We show that tNCCs are a transcriptionally diverse population of cells, and that some pre-migratory tNCCs are already expressing genes associated with a differentiated xanthophore pigment lineage. In addition, we identify a unique population of Rohon–Beard neurons (RBs) that expresses sox10 and comment on the developmental similarity among RBs and NCCs. Ultimately these data create a foundation for future genetic work studying the roles of previously uncharacterized genes during NCC and RB development.

Figure 1 with 4 supplements see all
Single-cell RNA analysis identifies transcriptionally heterogeneous sox10:eGFP population.

(A) Schematic of zebrafish trunk neural crest cell (tNCC) migration. Medial route migrating tNCCs produce peripheral nervous system and pigment cells, while tNCCs migrating along the dorsal-lateral path produce pigment cells. (B) Schematic of experimental design. NCCs were sampled by dissecting the trunks of 80 sox10:eGFP+ zebrafish at 24 hr post fertilization. GFP+ NCCs were FAC sorted and sequenced using 10X Genomics. Representative image shows sox10:eGFP transgene in a dissected trunk used for cell dissociation. YE: yolk extension. (C) Uniform Manifold Approximation and Projection (UMAP) and clustering of cells reveals multiple transcriptionally unique sox10:eGFP+ cell clusters. Numbers and labels correspond to main text. (D) Expression of select genes in UMAP space shows variable expression across different clusters. (E) RNA velocity connects cells in developmental pseudotime. Direction and length of arrows represent predicted direction of differentiation as calculated by Velocyto (see Materials and methods). Note cells predicted to be differentiating from tNCC progenitor cluster one into xanthoblast cluster 3. (F) Heatmap shows expression of top 10 most differentially expressed genes (log2Fold) for each cluster.

Figure 1—source code 1

Annotated text file of R code used to generate analyses presented in Figures 1 and 2.

Data deposited in NCBI GEO GSE112294.

https://cdn.elifesciences.org/articles/66078/elife-66078-fig1-code1-v1.zip

Results

scRNA-seq identifies transcriptionally unique populations of NCCs

We used 10X Genomics to characterize the transcriptomes of 607 tg(–4.9sox10:eGFP)-positive cells from the dissected trunks of 20–24 hr post fertilization (hpf) zebrafish embryos (25 somite stage; hereafter 24 hpf; Figure 1B). At this developmental stage, the sox10:eGFP transgene labels NCCs as well as the otic organ and lateral line (Carney et al., 2006). tNCCs in the anterior segments of the body are beginning to migrate along the medial path (Figure 1A and B), while most tNCCs that populate the posterior segments of the body and dorsolateral pathway are pre-migratory. Thus our sample represents the early stages of tNCC migration and includes a mixed population of migratory and pre-migratory tNCCs.

Our scRNA-seq analysis identified a transcriptionally heterogeneous population of sox10:eGFP+ cells (Figure 1C–F). Most cells were assigned to the NCC lineage based on expression of genes known to label NCCs at this stage (crestin, sox10, foxd3, and tfap2a; Figure 1C and D). We also identified a cluster of cells (cluster 7, 39 cells, Figure 1C) that belongs to the otic organ or lateral line (epcam, cldn7b, f11r.1, otomp, otog). A small cluster of cells (cluster 6, 28 cells), expressing multiple collagen genes, were assigned to a ‘mesenchymal’ NCC type found in other datasets (Howard et al., 2021; Soldatov et al., 2019), and some cells were assigned as posterior arch cranial NCCs based on the expression of genes known to mark these regions of the embryo (prdm1a, grem2b, dlx2a, and fli1a).

These posterior arch cells were split across two clusters (clusters 4 and 5, 116 and 94 cells, respectively), one of which (cluster 5) are proliferating cells expressing genes involved in cell cycle (e.g., cdk1, mki67, plk1; Figure 1F). Regressing out cell cycle confirms that most cluster 5 cells are mitotic posterior arch cells, though a small number of these mitotic cells are assigned as tNCCs (Figure 1—figure supplement 1). Importantly, we recover the same biologically relevant groupings of cells and marker genes whether regressing out variables such as cell cycle or not indicating that the biological signal of cell type in the data is robust to different analysis parameters (Figure 1—figure supplement 1; Materials and methods). Since our main interest is on NCCs in the trunk, we do not further analyze the otic, mesenchymal, and posterior arch tissues at length below.

Despite sampling zebrafish embryos during the early stages of tNCC migration, we observed multiple transcriptionally unique populations of tNCCs. One cluster of tNCCs (cluster 1, 112 cells) expresses genes that mark NCCs (e.g., crestin, sox10, foxd3, sox9a, tfap2a), and multiple Hox genes (e.g., hoxb8a, hoxa11b, hoxb7a) that place these cells in the posterior section of the zebrafish trunk (Figure 1C, D and F). Cells in cluster 1 express genes associated with tNCC migration (e.g., snai1b, prickle1a, wnt11) and genes with unknown function including a predicted cdc42 effector protein (zgc:150493) and cytokine-associated factor crlf1a. As tNCCs in this cluster lack expression of genes identifying these cells to a differentiated lineage, we hypothesize cluster 1 as multipotent tNCC progenitors.

We found a large number of tNCCs already expressing genes associated with differentiated pigment cells spread across two clusters (Figure 1C, D and F). This was surprising as these cells are presumably pre-migratory based on the sampled stage. Cells in cluster 2 (79 cells) express melanophore/pan-pigment genes including mitfa, gpr143, trpm1b. We hypothesize these cells as pan-pigment-cell progenitors. In contrast to these pigment progenitors, cells in cluster 3 (82 cells) express genes enriched in differentiated xanthophores (red/orange pigment cells) including aox5, pax7a/b, and gch. We hypothesize cluster 3 as xanthoblast cells (e.g., pre-xanthophore), and these data identify novel markers of the early xanthophore lineage including solute carrier paralogs slc2a15a/b and gap junction protein gjp8 (Figure 1—figure supplement 2).

These data suggest that a subset of tNCCs begin expressing gene regulatory networks (GRNs) associated with pigment cell lineages prior to migration. This raises the possibility that the sequencing data reflect transcriptional profiles of cells at different stages of differentiation. To explore this further, we inferred RNA velocity (Velocyto) as a measure of developmental pseudotime (Bergen et al., 2020). This method uses estimates of RNA splicing to infer how cells may be transitioning across different transcriptional states. RNA velocity showed a strong signal of tNCCs differentiating from NCC progenitor cluster 1 into xanthoblast pigment cluster 3 (Figure 1E) consistent with a hypothesis that some tNCCs initiate a pigment differentiation program prior to migration.

Identification of neural-like cells and Rohon–Beard neurons

Two clusters (clusters 8 and 9) are distinguished from other cells by expression of genes enriched in the developing spinal cord and differentiated neurons (e.g., elavl3, elavl4). Cells in cluster 8 (21 cells) express genes associated with the neural plate and neural tube including olig3, olig4, sox3, boc, cdon, and multiple zic family transcription factors (zic1, zic2a, zic2b, zic4, zic5, zic6). Intriguingly some of these genes (e.g., zic2a/b, zic5) are also expressed by a subset of tNCC progenitors (Figure 1—figure supplement 3). Cells assigned to this neural cluster may represent ‘early’ NCCs still enriched in genes associated with the neural plate/neural plate border. Alternatively, these cells may be differentiating glia or other neuronal subtype precursors.

Cells in cluster 9 (36 cells) were transcriptionally distinct from all other cells in the dataset and express genes known to mark RBs including isl1, isl2a/b, scrt2, prdm14, and drgx (Figure 1C, D and F; Figure 1—figure supplement 2). RBs are a non-migratory population of cells in the dorsal neural tube that innervate the skin of embryonic and larval zebrafish. Like NCCs, RBs are also specified at the neural plate border and genes critical for the proper specification of NCCs are also critical for specification of RBs (Artinger et al., 1999; Hernandez-Lagunas et al., 2005; Hernandez-Lagunas et al., 2011). While we cannot fully rule out that RB neurons are represented in our dataset due to the proximity of RBs and NCCs to each other in the neural tube, we find this explanation unlikely. Our FACS sorting gated for doublets, and our bioinformatic pipeline further filtered out putative doublets based (Figure 1—figure supplement 4). With the exception of three mitfa expressing cells, RBs do not express genes associated with other NCC clusters, which would be expected if these cells are due to incomplete dissociation from NCC tissue. Instead, our data recovering a putative RB population in an experiment targeting tNCCs may reflect a common developmental origin between these two cell types.

Cross-dataset integration confirms cell identities and expression of marker genes

To further investigate the transcriptional identities of sox10:eGFP+ cells in our dataset, we integrated our data with an existing scRNA-seq atlas of sox10:eGFP tNCCs from 48 to 50 (hereafter 48 hpf) and 68-70 hpf (hereafter 68 hpf) zebrafish embryos (Howard et al., 2021). These complementary data sampled tNCCs using the same transgene allowing us to directly study tNCC identity across developmental time. For these analyses, we used the Seurat integrate datasets function to combine data from cells isolated from the trunks of 24 hpf, 48 hpf, and 68 hpf zebrafish.

Our analysis of these combined datasets found tNCCs clustering by cell type across developmental time (Figure 2). Cells assigned to the otic organ from 24 hpf embryos cluster with cells independently assigned to the otic organ from 48 and 68 hpf embryos. Similarly, mitotic cells from all three datasets group together, as do mesenchymal cell types. Cells assigned as tNCC progenitors at 24 hpf (cluster 1) group proximate to migratory tNCCs from 48 hpf embryos (e.g., ‘Migratory/enteric neural crest’). Posterior arch cells from 24 hpf embryos are split across these NCC progenitor groups and mesenchymal cell types, perhaps reflecting the early developmental stage sampled. Importantly, NCCs from 24 hpf embryos do not group with differentiated neural derivatives from 48 and 68 hpf embryos, suggesting that tNCCs delay initiating a neural differentiation program until post-migratory developmental stages.

Figure 2 with 2 supplements see all
Integrated analysis of sox10:eGFP+ cells from 24, 48, and 68 hr post fertilization (hpf) zebrafish embryos shows pre-migratory trunk neural crest cells (tNCCs) expressing markers of derivative cell types.

(A) Uniform Manifold Approximation and Projection (UMAP) of integrated datasets showing cells colored by age. (B) UMAP of integrated datasets with cells colored and labeled by annotated category. Annotations of 48 and 68 hpf cells from Howard et al., 2021 are indicated with regular text. Annotations for 24 hpf cells from the current study are boxed and bolded. Points representing cells from 24hpf embryos are larger and outlined in black for visualization. Note that cells group by cell identity across developmental time. (C) Same UMAP as in panel (B) with cells split by age. (D) Representative genes expressed at higher levels in mature xanthophores from 68 hpf embryos than xanthoblasts from 24 hpf embryos. Expression is shown in UMAP space and as violin plots for the 24 hpf xanthoblast and 68 hpf xanthophore cluster cells. p values are from Wilcoxon rank-sum tests. (E) Dotplot of select genes that mark NCCs, pigment progenitors, xanthophores, and Rohon–Beard neurons. Clusters from 24 hpf sox10:eGFP+ cells are bolded. Note that xanthoblasts and xanthophores express the same marker genes. Rohon–Beard neurons express a unique set of genes unlike other neural derivatives from older embryos.

Concordance across tNCCs sampled at different stages is striking for pigment cells (Figure 2). Pigment cell progenitors from 24 hpf embryos group with pigment cell progenitors and melanophores from 48 hpf and 68 hpf embryos (Figure 2B and C). Xanthoblasts sampled from 24 hpf embryos form a bridge connecting this pigment progenitor/melanophore cluster to differentiated xanthophores sampled from 68 hpf embryos. Critically, we find that differentiated xanthophores at 68 hpf and xanthoblasts at 24 hpf express the same set of genes including gjb8, rgs2, pax7a/b, aox5, slc2a15a/b (Figure 2E; Figure 2—figure supplement 1). These data further indicate that 24 hpf xanthoblasts are expressing a differentiated xanthophore GRN.

While 24 hpf pigment cells are expressing markers of differentiated derivatives, some differences between early and late pigment cells exist (Figure 2—figure supplement 1). For instance, pigment cells at all developmental stages express critical transcription factors such as mitfa, but downstream targets such as dct and tyrp1a/b that effect melanin biosynthesis are only expressed in cells isolated from 48 hpf and 68 hpf embryos (Figure 2—figure supplement 1). To investigate gene expression changes associated with xanthophore maturation, we looked for differentially expressed genes between 24 hpf xanthoblasts and 68 hpf xanthophores. This test identified genes upregulated in xanthophores at 68 hpf including aqp3a, slc22a7a, cilia-associated protein ccdc39, and prdx1/2/5/6 genes associated with redox state (Figure 2D; Figure 2—figure supplement 1). Xanthoblasts in contrast express genes associated with EMT and melanophore cell types at higher levels including tfap2e and zeb2a (Figure 2—figure supplement 1). Thus, these data suggest that there is biological maturation that occurs during this time frame of xanthophore development. Xanthoblasts at 24 hpf retain a pan-pigment transcriptional state while also expressing a mostly complete differentiated xanthophore GRN.

In these analyses, RBs cluster with neuronal cell types (e.g., enteric neurons and spinal cord neurons) sampled from 48 and 68 hpf embryos (Figure 2B). This reflects similar transcriptomes across different neurons. While these other neuronal cells co-express some shared genes with RBs (e.g., elavl3, elavl4, vim), RBs express a unique set of genes (Figure 2E). For instance, RBs and enteric neuron cells from 68 hpf embryos both express fgf13a/b ligands at appreciable levels. RBs, however, also express cxcr4b and islet genes that are lacking in enteric neurons. Sensory neuronal progenitor cells (potential dorsal root ganglia) sampled at 48 hpf express islet genes, but these same cells lack expression of other RB genes such as pou4f4 and express cxcr4b and fgf13a/b at very low levels (Figure 2E).

To further investigate cell types in our dataset, and RB cell identity, we next integrated our data with a scRNA-seq atlas of whole zebrafish embryos sampled at 14 hpf, 18 hpf, and 24 hpf (Wagner et al., 2018). Both NCCs and RBs are annotated in these whole embryo datasets. As expected, NCCs from our sox10:eGFP dataset grouped with annotated NCCs sampled from whole embryos at these other stages. We again find that cells assigned to the xanthoblast cluster group with annotated ‘xanthophores’ from these other data. RBs isolated from sox10:eGFP cells cluster with cells annotated as RBs in these other datasets (Figure 2—figure supplement 2). Furthermore, we recover a putative RB GRN across datasets and time points. RBs in all four datasets predict restricted expression of a unique RB set of genes that includes cxcr4b, fgf13a/b, isl1/2a/2b, prdm14, and pou4f4.

In situ hybridization confirms novel genetic markers in xanthoblast and RB lineages

Single-cell transcriptomes identify previously uncharacterized genes that are uniquely expressed in pre-migratory xanthoblast and RB cell populations at 24 hpf. We sought to confirm expression of some novel genes predicted to these cell populations using fluorescent quantitative hybridization chain reaction (qHCR) in situ hybridization (Choi et al., 2018).

slc2a15b and gjb8 are co-expressed in pre-migratory xanthoblast tNCCs

In addition to known markers of the xanthophore lineage, scRNA-seq identified slc2a15b and gjb8 as among the most highly expressed and restricted to the xanthoblast lineage cluster (Figure 1D and F). The solute carrier slc2a15b is known to affect leucophore (white pigment cells) development in medaka (Fukamachi et al., 2006; Kimura et al., 2014; Lynn Lamoreux et al., 2005), and is expressed in the xantholeuocophore cells of adult zebrafish (Lewis et al., 2019). Leucophores and xanthophores are thought to be developmentally related (Kimura et al., 2014; Lewis et al., 2019), suggesting that slc2a15b is a good candidate for future study. The connexin protein, gjb8 (previously cx30.3), is known to be expressed in the skin, otic, and neural tube of zebrafish (Chang-Chien et al., 2014; Tao et al., 2010). To our knowledge, no functional role for gjb8 in NCC development has been proposed, but the importance of other gap junction proteins in pigment pattern development in zebrafish (Irion et al., 2014) makes this gene a good candidate for future study.

To confirm expression of these novel xanthoblast genes to NCCs, we used aox5 expression to label putative xanthoblasts, and the tg(sox10:TagRFP) zebrafish line to mark NCCs. At 24 hpf, we found that both slc2a15b and gjb8 were co-expressed with aox5 in a subset of tNCCs (Figure 3A–G; Pearson’s r = 0.91 and 0.95, respectively). We also observed gjb8 expression in the skin as previously reported (Tao et al., 2010), and both gjb8 and slc2a15b expression in medial tissue ventral to the neural tube. Within the NC, many of these aox5/slc2a15b/gjb8 expressing cells were in the dorsal neural tube. We first observe expression of these genes in the NC at the 15–20 somite stage, suggesting that these genes are expressed by NCCs as they become migratory (Figure 3—figure supplement 1). Furthermore, we observed higher expression of aox5/slc2a15b/gjb8 in anterior NCCs of 20 somite stage embryos, suggesting that these genes are expressed in an anterior-posterior wave associated with the onset of NCC migration (Figure 3—figure supplement 1). Thus aox5, slc2a15b, and gjb8 are co-expressed in pre-migratory and early-migrating tNCCs supporting a hypothesis that tNCCs initiate a xanthoblast GRN prior to migrating from the neural tube.

Figure 3 with 2 supplements see all
Expression of novel marker gene expression in the pre-migratory xanthoblast and Rohon–Beard cell populations.

(A) Both slc2a15b and gjb8 are predicted to be expressed in pre-migratory xanthoblast cells. (B–G) Quantitative single-molecule in situ hybridization (hybridization chain reaction [HCR]) confirms expression of these genes to aox5 expressing xanthoblasts. (B) Schematic of 24 hr post fertilization (hpf) embryos labeling region of embryos shown in subsequent panels. (C) Quantification of RNA expression (corrected total cell fluorescence [CTCF]) in neural crest cells (NCCs) from sections shows novel marker genes slc2a15b and gjb8 are strongly co-expressed with aox5. Sections were taken every 15 µM and analyses were limited to sections over the yolk extension. Each dot represents a cell (slc2a15b = 3 embryos, 9 slides, 49 cells; gjb8 = 3 embryos, 9 slides, 58 cells). (D–G) Representative whole mount (D, F) and sections (E, G) show expression of slc2a15b (D, E) and gjb8 (F, G) in 24 hpf embryos. DAPI (blue, E, G) labels nuclei. Arrows point to NCCs co-expressing xanthoblast marker genes, while arrowheads point to NCCs that lack expression of these same genes. Note that a number of aox5/slc2a15b/gjb8 NCCs are pre-migratory in the dorsal neural tube (NT). (H) Both fgf13a and cxcr4b are predicted to be expressed in Rohon–Beard neurons (RBs). (I–P) HCR confirms expression of these genes to RBs. (I) Schematic labeling region of trunk shown in subsequent panels. (J) Percent of HNK-1-positive RBs also expressing cxcr4b and fgf13a indicates that these genes are co-expressed in RB cells. The number of HNK-1, cxcr4b, and fgf13a-positive cells in the dorsal NT was counted. Note almost all fgf13a/cxcr4b expressing cells co-localize to HNK-1-positive RB cells. Sample size reported above bars (#cells/#embryos). (K, L) Representative confocal images of whole-mount in situ hybridization for elavl3, fgf13a, and cxcr4b show expression of fgf13a and cxcr4b as two rows of cells in the dorsal NT (arrows). Elavl3 marks the NT, and DAPI (gray) labels nuclei. Note ventral NT expression of cxcr4b (arrowheads in L’). (M, N) Dual immunolabeling for HNK-1 and in situ hybridization for fgf13a (M) or cxcr4b (N) shows gene expression overlaps with HNK-1 labeling in RBs in the dorsal NT (arrows). Arrowheads mark ventral expression of cxcr4b in panel (N). (O, P) While fgf13a and cxcr4b expression was never observed in NCCs, a subset of putative RB cells express the tg(sox10:tagRFP) transgene (gray in panels O and P). Shown are whole-mount 3D projections (O, P) and single slice through z-stack (O’, P’) showing fgf13a/cxcr4b/elavl3-positive cells co-labeled with the sox10 transgene. Note that sox10-positive cells are positioned topologically similar to sox10-negative RB cells. Scale bars for all panels are 20 µM.

Figure 3—source data 1

_Excel file of quantified hybridization chain reaction intensity of aox5/slc2a15b/gjb8 cells used to generate plots shown in Figure 3C.

https://cdn.elifesciences.org/articles/66078/elife-66078-fig3-data1-v1.xlsx
Figure 3—source data 2

_Excel file of quantified HNK-1/cxcr4b/fgf13a co-localization used to generate plot in Figure 3J.

https://cdn.elifesciences.org/articles/66078/elife-66078-fig3-data2-v1.xlsx

Rohon–Beard neurons express fgf13a/b and cxcr4b

Our data identify a number of genes not previously known to be expressed by RBs, including non-canonical fgf13a/b ligands, the chemokine receptor cxcr4b, and the transcription factor pou4f4 (Figure 1—figure supplement 1). Our analyses also found these genes co-expressed in predicted RB cell clusters in independent zebrafish scRNA-seq datasets further supporting these genes as putatively novel RB markers (Figure 1—figure supplement 2). While many novel RB genes appear intriguing, two stand out in particular. Cxcr4b is a chemokine receptor import for cell-cell signaling. Fgf13a/b were among the most differentially expressed RB genes and are non-canonical Fgf ligands that play roles in neuron function and migration, possibly through binding microtubules or voltage-gated sodium channels (Ornitz and Itoh, 2015; Pablo et al., 2016). Thus both genes are candidates for future functional studies.

To confirm the expression of predicted novel RB marker genes, we investigated the expression of fgf13a and cxcr4b using HCR in situ hybridization. We found that fgf13a and cxcr4b were expressed in two rows of cells running along the lateral edges of the dorsal neural tube (elavl3 expression) coincident with the known location of RB neurons (Figure 3F–N). We additionally observed strong cxcr4b expression in the ventral neural tube (arrowheads in Figure 3L) and diffuse expression of fgf13a in the body. We never observed appreciable levels of fgf13a/cxcr4b expression in migrating NCCs. To confirm the identity of these fgf13a/cxcr4b expressing cells, we labeled RBs with HNK-1 immunofluorescence (Olesnicky et al., 2010). In all embryos, dorsal trunk expression of both fgf13a and cxcr4b co-localized to HNK-1 labeled cells (Figure 3J, M and N), confirming these fgf13a/cxcr4b expressing cells as RBs. We observed expression of these genes in earlier segmentation stage embryos, suggesting that both fgf13a and cxcr4b are expressed by RBs as these cells differentiate (Figure 3—figure supplement 1). The function of fgf13 ligands or the cxcr4b receptor in RBs is unknown and a focus of future research.

These data raise an intriguing question: why are RBs present in a dataset that used FAC sorting to enrich for NCCs? The answer may be that during our experimentation we occasionally observed RBs labeled with the tg(sox10:TagRFP) transgene (Figure 3O–P). In this zebrafish line, these RFP+ RBs were in the same topological position in the neural tube with other RBs, indicating to us that these cells are unlikely to be NCCs. We thus hypothesize that RBs may be represented in our dataset because a small percentage of these cells express the sox10 transgene similar to NCCs. This hypothesis is supported by low-level expression of both sox10 and eGFP by RBs in the scRNA-seq data (Figure 3—figure supplement 2). Further, we note that RBs in an independent scRNA-seq dataset (Wagner et al., 2018) also express some NCC genes including crestin at low levels (Figure 2—figure supplement 2). These data are intriguing as RBs and NCCs share a developmental origin in the neural plate border (Artinger et al., 1999; Cornell and Eisen, 2000; Fritzsch and Northcutt, 1993; Hernandez-Lagunas et al., 2005), and thus our data are consistent with a literature of developmental similarity between these two cell types.

Expression of novel xanthophore and RB neuron markers is lost in Prdm1a mutants

Both NCCs and RBs are specified in the neural plate border during gastrulation. Fate determination for both cell types requires expression of the transcription factor prdm1a (Figure 4A). Loss of prdm1a function leads to loss of both RB and NCC identity (Hernandez-Lagunas et al., 2005; Hernandez-Lagunas et al., 2011; Olesnicky et al., 2010; Powell et al., 2013). To confirm expression of slc2a15b/gjp8 in NCC-derived xanthoblasts and fgf13a/cxcr4b in RBs, and to place these genes within known NCC and RB GRNs, we examined the expression of these genes in the NRD/prdm1a mutant zebrafish line. Prior studies have shown that mutant prdm1a-/- fail to specify NCCs and RBs at 24 hpf and have a reduction of melanocytes at 48 hpf (Artinger et al., 1999; Hernandez-Lagunas et al., 2005; Olesnicky et al., 2010). We thus reasoned that if expression of these genes is restricted to NCCs and RBs respectively then we should see loss of expression of these genes in prdm1a-/- mutants and this is exactly what we found. Dorsal neural tube expression of slc2a15b, gjp8, fgf13a, and cxcr4b were all lost in prdm1a-/- embryos (Figure 4B–I). Expression of these genes in areas of the embryo other than NCCs and RBs was not affected (e.g., cxcr4b expression in ventral neural tube shown in Figure 4H,I). These data suggest that loss of expression was due to loss of NCC and RB cell specification, confirming expression of these genes to NCCs and RBs and placing these genes downstream of the neural plate border specifier Prdm1a.

Expression of novel xanthoblast and Rohon–Beard neuron (RB) marker genes is lost in the Prdm1a mutant.

(A) Schematic of neural crest cell (NCC) and RB specification. Both NCCs and RBs are specified in the neural plate border (NPB) of gastrulating embryos. NCC and RB specification share a similar gene regulatory network (GRN) that includes notch/delta and prdm1a expression in the NPB. RBs may transiently express sox10 at low levels (gray). (B–H) Whole-mount confocal images of hybridization chain reaction (HCR) shows expression of xanthoblast (aox5, slc2a15b, gjp8) and RB marker genes (fgf13a, cxcr4b) in wild type (B, D, F, H) and Prdm1a-/- (C, E, G, I) embryos at 24 hr post fertilization (hpf). Hnk-1 staining marks RB cells in all images (note loss of HNK-1 in Prdm1a-/- embryos). Expression of all marker genes is lost from the dorsal neural tube (NT) in Prdm1a-/- embryos that show NCC and RB specification defects. Arrows point to expression of marker genes in dorsal neural tube NCCs (B, D) and RBs (F, H). Expression of genes in regions of the embryo other than the NC and RB is still present. For example, expression of cxcr4b in the ventral neural tube is not altered in the Prdm1a mutant (arrowheads). All images are 3D projections of lateral views of the neural tube taken over the yolk extension of 24 hpf embryos.

Discussion and conclusion

We found that tNCCs are a transcriptionally heterogeneous population during the early stages of migration and identify a number of previously uncharacterized genes for further study. Our data identify both presumptive multipotent tNCC progenitor cells and a subset of tNCCs already expressing genes associated with differentiated pigment cells prior to migration and at relatively early developmental stages (see also Ling and Sauka-Spengler, 2019; Soldatov et al., 2019). Cells specified towards the xanthophore lineage, in particular, may be one of the earliest tNCC derivatives to begin differentiating.

While we find NCCs expressing markers of differentiated pigment cells, we did not find tNCCs beginning to express genes associated with the PNS. This observation was supported in analyses that compare tNCCs from 24, 48, and 68 hpf embryos (Figure 2). Cells expressing genes associated with PNS are only observed at 48 and 68 hpf when these post-migratory NCCs are already differentiating/differentiated. Thus, while genes associated with pigment cell derivatives are expressed at pre-/early migratory stages, NCCs delay expression of genes associated with PNS cell types until post-migratory stages.

Identification of pre-migratory pigment NCCs is consistent with results from some cell labeling experiments in zebrafish, suggesting that NCCs are lineage restricted prior to migration (Raible and Eisen, 1994; Raible and Eisen, 1996; Schilling and Kimmel, 1994). In these experiments, pre-migratory NCCs labeled in the neural tube only make a single NCC derivative. While these classic cell labeling experiments could not identify whether pre-migratory NCCs were beginning to differentiate, our data extend these studies by showing that some pre-migratory NCCs are already expressing a pigment cell GRN. Whether presumptive pigment cells identified in our scRNA-seq data are truly fate restricted, however, is unknown and would require additional experimental manipulation.

Some scRNA-seq studies have observed multipotent cells transiently expressing genes associated with two or more differentiated fates (Farrell et al., 2018; Soldatov et al., 2019). These data challenge a classical bifurcating model of cell specification, suggesting that instead multipotent cells begin initiating GRNs characteristic of multiple fates before becoming committed to one lineage over others. We do not observe populations of cells expressing multiple fate derivatives in our data, though we do note that xanthoblasts have a more pan-NCC gene expression signature than differentiated xanthophores from 68 hpf embryos. However, we also note that our relatively small sample size for scRNA-seq datasets (607 cells) and lack of multiple sampled developmental stages may prohibit identifying multi-fate expressing cells. Future work sampling earlier stages is likely to provide further insight into how NCC early specification occurs in zebrafish and complement other studies in both mouse and chick (Ling and Sauka-Spengler, 2019; Soldatov et al., 2019; Zalc et al., 2021).

Other studies have used scRNA-seq to identify transcriptionally cryptic populations of cells associated with the leading edge of the cranial NCC migratory front in chick (Morrison et al., 2020; Morrison et al., 2017). In zebrafish, cell tracking studies in the trunk similarly suggest that a subset of migrating tNCCs act as leader cells forging a path along which follower NCCs migrate (Richardson et al., 2016). While we identify multiple transcriptionally unique populations of NCCs in our data, we do not identify a cluster of cells associated with this ‘leader’ cell type. Neither do we find any cluster in our data characterized by expression of orthologous genes that identify trailblazer NCCs in chick (Morrison et al., 2020; Morrison et al., 2017). Thus the identification of zebrafish trunk leader cell transcriptome remains open.

Though our experimental design targeted NCCs, we also sequenced a population of RB neurons and show that in zebrafish some RBs express sox10 and sox10 transgenes. While these data do not directly test for a shared progenitor, it is intriguing to consider these data in the context of other studies showing that NCCs and RBs share a similar developmental origin at the neural plate border (Artinger et al., 1999; Hernandez-Lagunas et al., 2005; Olesnicky et al., 2010). Cells at the neural plate border are thought to be an equivalence group, able to differentiate into both NCCs and RBs (Cornell and Eisen, 2002). Interactions among neural plate border cells, mediated in part by notch signaling, bias cells towards one fate over the other (Appel and Eisen, 1998; Cornell and Eisen, 2000; Cornell and Eisen, 2002). Our data suggesting that RBs express both sox10 and sox10 transgenes seems to be in line with this idea of a shared developmental potential.

To what extent NCCs and RBs share a common progenitor and whether the evolution of these two tissues is temporally coincident at the root of the vertebrate phylogeny are questions that have long captured the imagination of scholars of vertebrate biology (Artinger et al., 1999; Bone, 1960; Cornell and Eisen, 2000; Fritzsch and Northcutt, 1993). The neural crest is a novel migratory cell population unique to vertebrates with no known homology to tissues in non-vertebrate chordates (Gans and Northcutt, 1983), though see Jeffery et al., 2008; Jeffery et al., 2004. RBs are a non-migratory neuron with uncertain homology to morphologically similar motor neurons in non-vertebrate chordates (Baker and Bronner-Fraser, 1997; Bone, 1960; Gans and Northcutt, 1983). Here, we identify a zebrafish RB transcriptome providing unique insight into the RB GRN. Future work identifying whether orthologous genes are expressed by motor neurons in non-vertebrate chordates could provide further insight into whether these morphologically similar neurons are homologous or homoplastic to vertebrate RB neurons (McCune and Schimenti, 2012; Wagner, 2007). Whether emerging technologies like scRNA-seq will help illuminate the evolutionary origins of new tissue types such as RBs and NCCs only time will tell.

Ultimately, our data suggest that pre-migratory tNCCs are transcriptionally heterogenous and that some pre-migratory NCCs express differentiated pigment cell GRNs. Importantly, this finding is concordant with results from other recent studies in mouse and chick that similarly find NCCs expressing differentiation programs at pre-/early-migratory stages (Ling and Sauka-Spengler, 2019; Soldatov et al., 2019; Zalc et al., 2021). Taken together, these single-cell genomic studies are challenging some classic models of NCC development that emphasize the role of environmental signals from the migratory environment in biasing NCCs towards one derivative state over another (Bronner-Fraser and Fraser, 1988; Bronner-Fraser and Fraser, 1989; Collazo et al., 1993; Trainor and Krumlauf, 2000). In contrast, this emerging work emphasizes interactions among NCC progenitors in the neural tube as critical for specifying at least some NCCs towards different fates prior to migration (Raible and Eisen, 1996).

What mechanisms may be driving these data are speculative and an area of active interest in the field. Notch signaling plays a role in early NCC specification at the neural plate border (Cornell and Eisen, 2000; Cornell and Eisen, 2002), but whether notch signaling continues to play a role in specifying pre-migratory NCCs to different fates at later time points is unknown. Furthermore, there is a temporal component to NCC development. In most taxa, PNS NCCs migrate first and there is a delay before pigment NCC migration occurs. Thus, differences in signals that pre-migratory NCCs receive in the neural tube over time may play a role in biasing cells towards different fates. Ultimately, our and others’ data suggest that unknown interactions in the neural tube are critical for understanding NCC development, and that this pre-migratory period represents an area that deserves continued research attention.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Gene (Danio rerio)Prdm1aGenBankZFIN:ZDB-GENE-030131-2193, Gene ID: 323473
Strain, strain background (D. rerio)ABZIRCZDB-GENO-960809-7, RRID:ZIRC_ZL1
Genetic reagent (D. rerio)tg(–4.9sox10:eGFP)ba2Carney et al., 2006RRID:ZFIN_ZDB-ALT-050913-4
Genetic reagent (D. rerio)Tg(sox10:TagRFP)co26Blasky et al., 2014RRID:ZFIN_ZDB-GENO-150316-2
Genetic reagent (D. rerio)Prdm1am805Artinger et al., 1999RRID:ZFIN_ZDB-ALT-980621-8
AntibodyAnti HNK-1/N-CAM(mouse, monoclonal)Sigma-AldrichCat#: C0678, RRID:AB_1078473IF:(1:20)
Sequence-based reagentAox5 RNA ProbeMolecular InstrumentsCustomWM ISH(2 µL/500 µL)
Sequence-based reagentSlc2a15b RNA ProbeMolecular InstrumentsCustomWM ISH(2 µL/500 µL)
Sequence-based reagentGjb8 RNA ProbeMolecular InstrumentsCustomWM ISH(2 µL/500 µL)
Sequence-based reagentElavl3 RNA ProbeMolecular InstrumentsCustomWM ISH(2 µL/500 µL)
Sequence-based reagentFgf13a RNA ProbeMolecular InstrumentsCustomWM ISH(2 µL/500 µL)
Sequence-based reagentCxcr4b RNA ProbeMolecular InstrumentsCustomWM ISH(2 µL/500 µL)
Chemical compound, drugAccumaxInnovative Cell TechnologiesCat# AM105-500
Software, algorithmVelocyto.R (v0.6)Bergen et al., 2020RRID:SCR_018167R implementation
Software, algorithmVelocyto.py (v0.17.17)Bergen et al., 2020RRID:SCR_018167Python implementation
Software, algorithmSeurat (v4.0.1)Butler et al., 2018RRID:SCR_007322R implementation
Software, algorithmCode for analysesCurrent studySource data file: Figure 1&2_RCode.txt
Software, algorithmR (v4.0.3)CRANRRID:SCR_003005
Software, algorithmRStudiohttp://www.rstudio.com/RRID:SCR_000432
Software, algorithmCellRanger (v3.2 & v5.0.1)10X GenomicsRRID:SCR_017344
Software, algorithmFijihttp://fiji.scRRID:SCR_002285
Software, algorithmNaparihttps://napari.org/

Zebrafish lines and husbandry

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All zebrafish lines were maintained at the University of Colorado Denver | Anschutz medical school under common zebrafish husbandry conditions. Lines have been previously described and include the tg(sox10:eGFP)ba2 (Carney et al., 2006), tg(sox10:TagRFP)co26TG (Blasky et al., 2014), and NRDm805/prdm1a mutant line (Hernandez-Lagunas et al., 2005).

Single-cell RNA sequencing
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Fertilized zebrafish eggs were collected in the morning, stage matched, and reared at 30°C overnight until embryos were at approximately the 25 segment stage corresponding to embryos of ~20–24 hpf at 28.5°C. GFP-positive embryos were dissected posterior to the otic organ using forceps in cold 1x PBS. Trunks from 80 dissected embryos were pooled for dissociation and FAC sorting.

Dissected trunks were dissociated in Accumax (Innovative Cell Technologies) plus DNaseI (1 µL/100 µL). Cells were triturated every 15 min using decreasing-sized pipette tips until a homogenous solution was observed. Cells were filtered through a 40 µM mesh, spun, and resuspended in Sorting buffer (1% FBS/1 mM EDTA/25 mM HEPES in 1× Dullbecco’s PBS; Sigma D8537;). GFP-positive cells were FAC sorted on a MoFlow XDP100 cell sorter into sorting buffer and diluted to approximately 700–1100 cells/µL.

Cells were captured and prepared for sequencing using the 10X Genomics platform by the University of Colorado Denver | Anschutz Genomics Core by loading approximately 10,000 cells. Libraries were sequenced on two lanes of an Illumina NovaSEQ 6000 to an average depth of 159,520 reads per cell and a median of 666 genes per cell. After filtering and quality control, we obtained cells expressing a median of 4255 UMI and 32,425 genes.

Single-cell analysis
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Reads were mapped to the Ensembl build of the zebrafish genome (GRCz11) and UMI barcodes were assigned to unique identifiers using CellRanger (v3.2). Primary analyses were conducted using the Seurat package v4.0.1 as implemented in R v4.0.3 (Butler et al., 2018). Cells were filtered for quality based on mitochondrial gene expression (≤6% and ≥0.5%) and number of unique transcripts expressed (≥5000 and ≤16,000) in order to remove low-quality cells as well as any possible doublets. Data was normalized using the sctransform function in Seurat (variable features = 3000). Cells were clustered (resolution = 0.6) and projected with the Uniform Manifold Approximation and Projection (UMAP) method using the first 15 principal component analysis (PCA) axes as implemented in Seurat. To find marker genes associated with cell clusters, we performed differential expression using the Wilcoxon rank-sum test as implemented by the FindAllMarkers command in Seurat (logfc.threshold = 0.5, min.pct = 0.25). Changing cell filtering, clustering, and UMAP projection parameters had minor affects and does not change major results of the study (Figure 1—figure supplement 1).

To further explore mitotic cells in cluster 5, we reanalyzed our data regressing out variation due to cell cycle, mitochondrial expression, and ribosomal expression either in combination (Figure 1—figure supplement 1) or individually (not shown). For these analyses, we first calculated an estimate of cell cycle for each cell with the CellCycleScoring function in Seurat using zebrafish orthologs of cell cycle genes available from the Seurat website. We then regressed out the effects of these variables using the SCTransform function prior to clustering and analysis. The primary difference across different analyses is that most mitotic cells in cluster 5 (Figure 1) group with other posterior arch cells from cluster 4 when cell cycle is removed (Figure 1—figure supplement 1). A smaller number of mitotic cells cluster with tNCC progenitors from cluster 1. Importantly, in all analyses we recover similar clusters of cells and marker genes, indicating that the major conclusions from these data are robust to differences in analysis (Figure 1—figure supplement 1).

We used the package Velocyto to infer developmental pseudotime from our data (Bergen et al., 2020). This method uses estimates of RNA splicing from scRNA-seq data to infer how cells may be transitioning across different transcriptional states; the analysis assumes that the ratio of spliced to unspliced transcripts should provide an estimate of the time since a cell has started expressing a new set of genes associated with a different transcriptional state. We used the Python implementation of Velocyto to infer RNA splicing with default parameters, and the R implementation of Velocyto to infer RNA velocity (e.g., pseudo-time) with default parameters and plot velocities onto UMAP space.

To quantify GFP expression in RB cells, reads were aligned to the Aequorea victoria green fluorescence protein sequence (EBI accession #AAA27722.1) using cell ranger (v5.0.1). The same analysis parameters as above were used to generate plots.

Dataset integration
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We integrated our scRNA-seq data with complementary scRNA-seq atlases from Howard et al., 2021 and Wagner et al., 2018. For Howard et al., 2021, we remapped sequences downloaded from SRA (BioProject PRJNA640816) to the Ensembl build of the zebrafish genome (GRCz11) using CellRanger v5.0.1. Cell annotations were downloaded from UCSC Cell browser (cells.ucsc.edu). Cells were filtered to only include those used in the original Howard et al., 2021 study. For Wagner et al., 2018, data and annotations were downloaded directly from NCBI GEO (GSE112294). We only analyze data obtained from 14, 18, and 24 hpf embryos from Wagner et al., 2018.

Datasets were integrated using Seurat IntegrateData function on anchors identified from 2000 genes using log normalized data. We present data integrated with Howard et al., 2021 using the first 30 PCAs for analyses. We present data integrated with Wagner et al., 2018 using the first 100 PCAs for analyses. We get similar results with different parameters when integrating and analyzing the data (not shown). For all analyses, we annotate cells with the original annotations given in Howard et al., 2021 and Wagner et al., 2018, respectively. Differential expression was performed using the Wilcoxon rank-sum test in Seurat on log normalized data.

Code for all analyses is available as source data file: Figure 1—source code 1.

In situ hybridizations and immunofluorescence
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To confirm expression of genes, we used fluorescent qHCR in situ hybridization (Choi et al., 2018). Custom RNA probes were purchased from Molecular Instruments. Probes were designed using either the B2 or B3 hairpin loop sequences with adaptor fluorophores of either Alexa Fluor 488 (B3) or Alexa Fluor 647 (B2). In situ hybridizations were conducted following Molecular Instruments protocol for whole-mount zebrafish. Samples from 25 somite (24 hpf), 8 somite, 15 somite, and 20 somite embryos were fixed in 4% paraformaldehyde and stored in methanol at –20°C. Samples were rehydrated in phosphate buffered solution (PBS) and permeabilized for 5 min in 1 µg/mL proteinase K at room temperature. Hybridization was conducted using RNA probes diluted to 4 picamols/mL at 37°C overnight. Amplification was performed diluting hairpins to 60 picamols/mL at room temperature overnight.

Following in situ hybridization, Rohon–Beard cells were identified by HNK-1 immunofluorescence. Samples were blocked in IF blocking buffer (2% normal goat serum, 2% BSA, 1× PBS) at room temperature for a minimum of 1 hr. Primary mouse anti-HNK-1 antibody (Sigma) was diluted 1:20 in blocking buffer, and samples were incubated in this primary antibody at 4°C for 24–48 hr (Hernandez-Lagunas et al., 2011), and visualized using secondary goat anti-mouse IgM conjugated to Alexa Fluor 594.

Samples were imaged on a Leica SP8 confocal microscope. For whole-mount images, samples were mounted in 0.2% agarose and imaged at either 10× or 40× magnification. For co-expression of xanthophore genes, samples were cryosectioned every 15 µM and imaged at 63× magnification. For both whole-mount and sectioned samples, we imaged over the yolk extension in order to make images comparable.

Unless noted, all images are 3D projections from either whole-mount or sectioned material produced using either ImageJ or Napari. Image quantification was performed using ImageJ. To calculate co-expression of aox5 with slc2a15b and gjb8, we calculated corrected total cell fluorescence (CTCF) from 15 µM sections. Briefly, RNA expression by a cell is proportional to relative fluorescence intensity of the HCR in situ signal within a cell (Choi et al., 2018). NCCs were manually segmented in three dimensions using the segmentation editor in ImageJ based on the expression of the tg(sox10:TagRFP) transgene. Average cell fluorescence intensity of aox5/slc2a15b/gjb8, and cell volume, was calculated using the ImageJ 3D suite (Ollion et al., 2013). Average background fluorescence intensity was estimated using regions of the embryo where no signal was observed. CTCF was calculated as

CTCF=(Avg. Signal IntensityCell Volume)(Avg. background IntensityCell Volume)

We noticed that expression of fgf13a and cxcr4b in the dorsal neural tube was more binary than quantitative. Thus we simply counted the number of HNK-1-positive cells in the dorsal neural tube that were also fgf13a/cxcr4b positive.

All plots, and the calculation of Pearson’s correlation coefficient, were produced in R (v.4.0.3). A minimum of three embryos were quantified for all analyses. Sample sizes are reported in relevant figure legends and text. Data is available in source data files: Figure 3—source data 1 and Figure 3—source data 2.

Data availability

Sequencing data have been deposited in GEO under accession code GSE163907.

The following data sets were generated
    1. Lencer E
    2. Prekeris R
    3. Artinger KB
    (2020) NCBI Gene Expression Omnibus
    ID GSE163907. Single cell RNA analysis of trunk neural crest cells in zebrafish.

References

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    (1999)
    Zebrafish narrowminded suggests a genetic link between formation of neural crest and primary sensory neurons
    Development 126:3969–3979.
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    2. Bronner-Fraser M
    3. Fraser SE
    (1993)
    Vital dye labellin of Xenopus laevis trunk neural crest reveals multipotency and novel pathways of migration
    Development 118:363–376.
    1. Ornitz DM
    2. Itoh N
    (2015) The Fibroblast Growth Factor signaling pathway
    Wiley Interdisciplinary Reviews. Developmental Biology 4:215–266.
    https://doi.org/10.1002/wdev.176
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Decision letter

  1. Lilianna Solnica-Krezel
    Reviewing Editor; Washington University School of Medicine, United States
  2. Richard M White
    Senior Editor; Memorial Sloan Kettering Cancer Center, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Acceptance summary:

This manuscript investigates specification and differentiation of the neural crest, a migratory population of stem-like cells that contribute to multiple tissues, including the bones of the skull, pigment, and peripheral nervous system. This work presents a single-cell RNAseq dataset from zebrafish trunk neural crest cells during the early stages of migration that identifies the subpopulations of trunk neural crest cells, new genetic markers and a subset of Rohon-Beard neurons. The paper generates a dataset for further investigations and reports expression of differentiated pigment cell markers in the pre-migratory neural crest populations.

Decision letter after peer review:

Thank you for submitting your article "Single cell RNA analysis identifies pre-migratory neural crest cells expressing markers of differentiated derivatives" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Richard White as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor who also read the manuscript, has drafted this to help you prepare a revised submission.

Essential revisions:

1. The authors should more clearly state how this study advances their past work that demonstrated the presence of HNK1+ (NV migratory marker) RB neurons in a well-characterized pdrm1 mutant (Hernandez-Lagunas et al., 2014).

2. Reviewers were also concerned that such an analysis performed at a single stage and at a relatively late time point cannot be used to infer common developmental origin or path. This raises questions about the identification of both sox10+ and sox10- RB neurons – both of which are lost in pdrm1 mutant – this finding should be more fully addressed appropriately.

3. Reviewers felt that there is a disconnect between the title and what forms the bulk of the discussion and figures in this paper. The authors should either change the title or put more emphasis on the findings that premigratory NCCs express markers of differentiated derivatives.

4. A stronger attempt should be made to compare/integrate their limited dataset with other more extensive existing datasets (Wagner et al.,2018) to investigate whether markers (fgf13a, cxcr4b) are expressed at earlier time points to help support the authors' hypothesis? Similarly, the authors do not seek to utilize single-cell datasets at later time points obtained using the same transgenic line to verify whether these markers are expressed (Aubrey et al., 2021). Further mining and integration with available datasets would help strengthen the authors' point.

5. When discussing their finding that some premigratory cells already express differentiated genes as a novelty but, the authors should cite studies that have shown this in the neural crest in zebrafish and other models (Soldatov et al., 2019, Ling et al., 2019).

6. The "unknown" cluster 7 described by the authors as a potential new NCC lineage cluster is most likely (authors should verify this) a previously reported mesenchymal cluster expressing a wealth of collagen genesl; this should be verified and rectified.

7. The claim 'Some of 156 cells (Cluster 5) are presumably neural tube tissue' is unclear, as sox10 found in Cluster 5 does not label neural tube. What are the Cluster 5 marker genes; they should be shown as Supp. Figure 1. Cluster 5 also seems to be split into two subclusters. Do different genes mark these regions? The authors should elaborate on the differential split of sox10-expressing and non-expressing cells within the cluster (feature plots in 1F indicate that sox10 is downregulated in the top left portion of this cluster and the RB cluster).

8. One of the primary novelty points emphasized by the authors is the subset of RB neurons. If this is to be confirmed, the authors should perform KO of some of the known marker genes specific to this cell population to show their relevance to RB cell development? What role do these subsets of NCC-RB cells play? While this experimental work is not essential, in the time of CRISPR/Cas9, querying such candidates in F0 generation would significantly strengthen the manuscript.

9. Important technical points: The study lacks sufficient information to verify the data quality and level of rigour in the analysis. For instance, information such as the number of embryos used to get 607 cells should be provided (this is important for defining genetic heterogeneity). What proportion of embryos were 20hpf and 24hpf? How many cells were loaded into the channel? What was the mean number of genes and UMI's were found per cell? How was the analysis performed, what were the parameters/cut-offs used? How many cells are found within each cluster? Furthermore, the data is not always of the highest quality, and figure annotations should be improved. In general, figures need better annotation and precision – indicating developmental stages on HCR images, the section's location, and clarifying the number of sections used for quantification. Also, figure legends should clearly describe annotation detail.

10. Figure 1 Supplement 1 while very visually appealing, it could have a stronger information content. The authors might consider presenting these results in the form of a dot plot (see Seurat function DotPlot), which in addition to conveying the 10 strongest markers of each of these clusters, would also provide information about (a) their expression level and (b) how specific they are to a particular population that might be helpful to readers. Additionally, the authors might consider adding something that presentation about which of those markers are novel, versus which are well established markers of these cell types. This could even be suitable for promotion into main figure 1, as it would add a lot of information content for readers interested in the neural crest and would clarify the novelty of the authors' findings.

11. The presentation and validation of the pair of novel markers for both the xanthophores and Rohon-Beard neurons are very compelling. However, it seems like there is a missed opportunity here to present more of the novel expression findings in a way that is accessible to a broader audience. While it would certainly be unrealistic to validate them all, it seems like it would be very valuable to provide a broader presentation of the convincing novel markers identified in these populations via differential expression as a figure (or figure panel) – perhaps dot plots or some other format.

12. Lines 217-218 "While we cannot rule out that RB neurons are represented in our dataset due to the proximity of RBs and NCCs to each other in the neural tube". Whereas the presentation in Figure 3g, h that sox10::RFP labels some Rohon Beard neurons is convincing, the authors could go one step further – as they point out, the most likely other potential explanation would be that some of these represent doublets, where an RB neuron and another cell ended up in a droplet together perhaps due to imperfect dissociation. A fairly simple analysis that would help further exclude this possibility would be to simply check whether any of the cells identified as RB neurons express markers that are exclusive markers of another cell type in the dataset that is expected to be sox10+. If they do not, then they are unlikely to represent a combination of two cells. (This could be done in fancy ways, but also simply by checking in scatter plots or some other format the expression of the best markers of other clusters in the RB neurons.) It would provide an additional piece of strong evidence that some RB neurons must have expressed sox10 at some point in their developmental history.

13. That some cells prior to migration express markers of pigment cells is convincingly demonstrated. One is left wondering – do the authors think that ALL pigment cells begin to express their markers prior to migration? Are all of the migrating tNCCs that are not expressing cell-type specific markers bound to become PNS derivatives? If there are data from sox10 lines that perhaps estimate the percentage of cells that give rise to these different cell types. The authors could make statements in the Discussion section to help clear this up.

14. The authors note expression of fgf13a in the RB neuron cluster and comment that "FGF and chemokine signaling are critical for proper morphogenesis.." and later "The RBs are apparently sources of and responsive to morphogens important for development. The authors need to reconsider this interpretation of fgf13 expression in RBs, as Fgf13 belongs to the class of intracellular FGF, iFGFs, which are not secreted and have no identified interaction with signaling FGFRs (Ornitz DM, Wires Dev Bio, 2015).

15. Discussion could have more explanation or speculation about why only NCCs expressing pigment cell markers are observed prior to migration.

https://doi.org/10.7554/eLife.66078.sa1

Author response

Essential revisions:

1. The authors should more clearly state how this study advances their past work that demonstrated the presence of HNK1+ (NV migratory marker) RB neurons in a well-characterized pdrm1 mutant (Hernandez-Lagunas et al., 2014).

We present a greater discussion of the reasons for studying the expression of select marker genes in the prdm1a mutant zebrafish line. At the stage studied in zebrafish, HNK-1 marks Rohon-Beard neurons, and unlike chick, does not label migratory NCCs.

Both NCCs and RBs require prdm1a for proper specification. We present a new panel to Figure 4 that summarizes the GRNs for both NCCs and RBs. Characterizing the expression of these genes in the prdm1a mutant is important because it both (1) confirms expression of these genes to the NCC/RB lineages respectively, and (2) puts these genes within known NCC/RB GRNs. We discuss how these data affect our previous and ongoing work in the results (lines: 301-317). These data identify an RB transcriptome and we discuss relevance of this for past and future work in the discussion (lines:362-387).

2. Reviewers were also concerned that such an analysis performed at a single stage and at a relatively late time point cannot be used to infer common developmental origin or path. This raises questions about the identification of both sox10+ and sox10- RB neurons – both of which are lost in pdrm1 mutant – this finding should be more fully addressed appropriately.

We absolutely agree! We do not think that these data directly support a common developmental origin. However, we do not think it is appropriate to ignore the decades of literature hypothesizing a common developmental origin. Finding RBs in our dataset was unexpected result. We were also shocked to observe low level expression of sox10 in RBs. We further note that crestin, another gene that labels NCCs in zebrafish, is expressed by RBs in an independent dataset (Wagner et al. 2018). We have gone through our manuscript to be sure that we are careful in how we address this issue. We feel that it is important to consider these data within the context of other similarities among these two cell populations without making a direct claim of shared developmental origin. We agree that future work is needed to tease this apart. We are currently studying the function and expression of RB GRNs identified from these data, and future scRNAseq analyses at earlier developmental stages may help to further illuminate this long-standing hypothesis.

3. Reviewers felt that there is a disconnect between the title and what forms the bulk of the discussion and figures in this paper. The authors should either change the title or put more emphasis on the findings that premigratory NCCs express markers of differentiated derivatives.

We feel that the additional presentation of data, and inclusion of new analyses comparing our dataset to other scRNA-seq datasets in zebrafish, adds emphasis that makes our title appropriate. In particular, analyses integrating our dataset with those of Howard et al. 2021 and Wagner et al. 2018 support our finding that pre-migratory tNCCs express markers of differentiated derivatives (see below). Thus we feel that our manuscript stresses a finding that pre/early migratory tNCCs are already expressing markers of differentiated derivatives. This is shown by both scRNA-seq analyses and confirmation of some xanthophore marker genes by ISH. While we are willing to change our title if requested, we would rather not. In our opinion the current title communicates a major finding from the data that will be interest to other researchers in the field.

4. A stronger attempt should be made to compare/integrate their limited dataset with other more extensive existing datasets (Wagner et al., 2018) to investigate whether markers (fgf13a, cxcr4b) are expressed at earlier time points to help support the authors' hypothesis? Similarly, the authors do not seek to utilize single-cell datasets at later time points obtained using the same transgenic line to verify whether these markers are expressed (Aubrey et al., 2021). Further mining and integration with available datasets would help strengthen the authors' point.

We think this is a great idea and thus we have integrated our data with both Howard et al. 2021 and Wagner et al. 2018, and we add a detailed discussion of these analyses to the manuscript. This work has led to a new section in the main text, and we have modified our figures to produce a new figure 2 that covers this integrated data analysis.

Importantly, these analyses of the integrated data further support the main findings and arguments that we made in the original manuscript. NCCs cluster by cell identity when integrating our data with Howard et al. 2021 or Wanger et al. 2018. As noted in the comment, Aubrey Howard et al. 2021 samples tNCCs using the same transgene as our own study, but at older developmental stages. We find that cells from our dataset cluster with similar cell types from these older embryos. Pigment cells that we identify as expressing markers of differentiated derivatives group with pigment cells at these later stages. Further, pre-migratory xanthoblasts that we characterize from 24 hpf embryos express the same genes as differentiated xanthophores sampled from 68hpf embryos. Thus, this integrated analysis supports our hypothesis that pre-migratory NCCs are already expressing markers of differentiated derivatives. We do observe some pigment cell maturation and comment on these findings in the manuscript. We note that tNCCs at 24hpf do not express markers of differentiated neurons supporting the hypothesis that NCCs delay expressing PNS associated genes until post-migratory stages.

Integrating our data with Wagner et al. 2018 produces a similar result in that cells in our dataset cluster with similarly annotated cells in the Wagner et al. dataset. Importantly, Wagner et al. sampled whole embryos and annotated a small number of cells as RBs. RBs from our dataset cluster with RBs from Wagner et al. and these cells, derived from these independent studies, express the same genes. These data confirm our own analyses and further extend support for identification of genes previously unknown to be expressed by RBs.

In addition to these integrated analyses, we directly looked at how gene expression changes across developmental time by using HCR to examine expression of xanthoblast and RB marker genes at earlier developmental stages. These data are included as Figure 3—supplement1, support our previous findings, and are discussed in lines: 253-258 and 282-284.

5. When discussing their finding that some premigratory cells already express differentiated genes as a novelty but, the authors should cite studies that have shown this in the neural crest in zebrafish and other models (Soldatov et al., 2019, Ling et al., 2019).

We apologize for this oversight and have rectified discussion of these missing citations. We thank the reviewers for bringing our attention to this oversight on our part.

6. The "unknown" cluster 7 described by the authors as a potential new NCC lineage cluster is most likely (authors should verify this) a previously reported mesenchymal cluster expressing a wealth of collagen genesl; this should be verified and rectified.

This is a great hypothesis and supported by integrating our data with other datasets that fully sample a ‘mesenchymal’ NCC cell type (see above). In these integrated analyses cells in this cluster group with mesenchymal NCCs from these other datasets. We now label these cells as “mesenchymal”.

7. The claim 'Some of 156 cells (Cluster 5) are presumably neural tube tissue' is unclear, as sox10 found in Cluster 5 does not label neural tube. What are the Cluster 5 marker genes; they should be shown as Supp. Figure 1. Cluster 5 also seems to be split into two subclusters. Do different genes mark these regions? The authors should elaborate on the differential split of sox10-expressing and non-expressing cells within the cluster (feature plots in 1F indicate that sox10 is downregulated in the top left portion of this cluster and the RB cluster).

This is a good point. These cells express many neural markers that are also in the neural plate and neural tube (as well as many neurons). We provide a greater detailed discussion of these cells as a new Figure1—supplement 3. We note that these cells are characterized by the expression of many neural genes from the neural plate and a number of zic transcription factors, as well as lower (but not absent) sox10. Thus these cells may represent ‘early’ NCCs still expressing a number of NPB genes. Some of these marker genes are also expressed by a limited number of NCC progenitors. We refer to these cells as ‘neural’ and note in the main text that they are characterized by expression of multiple neural plate genes (lines: 145-152).

The comment that the cluster is split is in reference to when the data are analyzed by regressing out biologic variation due to cell cycle etc. In this case, some cells that otherwise group with NCCs now cluster with neural cells (see Figure 1—supplement1). These neural/NCC cells are almost certainly NCCs and express NCC marker genes (e.g. sox10 etc) but not neural plate genes (olig3, zic2a, etc). This is a rather minor observation in the data, and does not affect any major conclusions, and thus we do not feel a lengthy discussion is necessary on this topic. Instead, a broader discussion of the analysis is provided in the manuscript where appropriate that shows how the same conclusions are reached regardless of different analysis parameters (Figure 1—supplement 1, lines:107-115, 449-460).

8. One of the primary novelty points emphasized by the authors is the subset of RB neurons. If this is to be confirmed, the authors should perform KO of some of the known marker genes specific to this cell population to show their relevance to RB cell development? What role do these subsets of NCC-RB cells play? While this experimental work is not essential, in the time of CRISPR/Cas9, querying such candidates in F0 generation would significantly strengthen the manuscript.

This is a great idea, and one we are pursuing. However, we agree that this work is not essential for the current manuscript and feel that making CRISPR/Cas9 KOs is outside of the scope of the current study. In our experience, phenotypes from mosaic CRISPR F0s are often not seen in the full knockout F1s. We are thus hesitant to do a study of F0 mosaic embryos. To do these KO studies, we would want to make F1 heterozygous mutants carrying loss of function alleles, and study the function of these genes in the F2+ generations. This would take substantial time to generate these mutants and characterize them sufficiently. So while we absolutely love this idea, and it is a focus of ongoing research, we feel that this is future work and not necessary for the conclusions drawn in the current manuscript.

9. Important technical points: The study lacks sufficient information to verify the data quality and level of rigour in the analysis. For instance, information such as the number of embryos used to get 607 cells should be provided (this is important for defining genetic heterogeneity).

We have provided this information in the methods, results, and figure legends where appropriate. We used the trunks from 80 embryos for cell dissociation (lines:91, 418-420).

What proportion of embryos were 20hpf and 24hpf?

All embryos are of the same stage. We age them as 20-24hpf to account for uncertainty in fertilization time. This is fairly standard practice in zebrafish (see Howard et al. 2021). Further, we note that time is a poor substitute for developmental stage as slight differences in temperature can mean that 24 hpf embryos of very different stages. This is why we also have staged these embryos to the 25 segment stage. We have moved this information into the results from the methods (line 91).

How many cells were loaded into the channel? What was the mean number of genes and UMI's were found per cell? How was the analysis performed, what were the parameters/cut-offs used? How many cells are found within each cluster? Furthermore, the data is not always of the highest quality, and figure annotations should be improved.

These are now provided in the methods where appropriate. We loaded approximated 10000 cells. Median number of UMI = 4255 and genes/features = 32425. Analysis parameters and cutoffs are indicated in methods (lines 436-448). Number of cells per cluster is now indicated in parentheses within the results.

In general, figures need better annotation and precision – indicating developmental stages on HCR images, the section's location, and clarifying the number of sections used for quantification. Also, figure legends should clearly describe annotation detail.

We have updated our figure legends to provide more information. Developmental stage and section location is indicated. Samples sizes for quantification are included in appropriate legends.

10. Figure 1 Supplement 1 while very visually appealing, it could have a stronger information content. The authors might consider presenting these results in the form of a dot plot (see Seurat function DotPlot), which in addition to conveying the 10 strongest markers of each of these clusters, would also provide information about (a) their expression level and (b) how specific they are to a particular population that might be helpful to readers. Additionally, the authors might consider adding something that presentation about which of those markers are novel, versus which are well established markers of these cell types. This could even be suitable for promotion into main figure 1, as it would add a lot of information content for readers interested in the neural crest and would clarify the novelty of the authors' findings.

11. The presentation and validation of the pair of novel markers for both the xanthophores and Rohon-Beard neurons are very compelling. However, it seems like there is a missed opportunity here to present more of the novel expression findings in a way that is accessible to a broader audience. While it would certainly be unrealistic to validate them all, it seems like it would be very valuable to provide a broader presentation of the convincing novel markers identified in these populations via differential expression as a figure (or figure panel) – perhaps dot plots or some other format.

These are great ideas. We have updated figure 1 to provide more information on marker genes associated with different cell clusters. We also show additional feature plots for each cluster to visualize gene expression patterns. This new figure 1 provides much more data for readers interested in what genes mark different cell clusters and how restricted different genes are to these different clusters.

We further provide information in supplements to figure 1 that provide further insight into the genes that mark the xanthoblast, RB, and neural cell clusters. We show the top 80 genes (filtering out SI:#### genes for clarity) that mark each of these clusters. These new supplementary figures provide much more information to the reader on known and novel genes that mark these clusters of cells.

12. Lines 217-218 "While we cannot rule out that RB neurons are represented in our dataset due to the proximity of RBs and NCCs to each other in the neural tube". Whereas the presentation in Figure 3g, h that sox10::RFP labels some Rohon Beard neurons is convincing, the authors could go one step further – as they point out, the most likely other potential explanation would be that some of these represent doublets, where an RB neuron and another cell ended up in a droplet together perhaps due to imperfect dissociation. A fairly simple analysis that would help further exclude this possibility would be to simply check whether any of the cells identified as RB neurons express markers that are exclusive markers of another cell type in the dataset that is expected to be sox10+. If they do not, then they are unlikely to represent a combination of two cells. (This could be done in fancy ways, but also simply by checking in scatter plots or some other format the expression of the best markers of other clusters in the RB neurons.) It would provide an additional piece of strong evidence that some RB neurons must have expressed sox10 at some point in their developmental history.

We like this idea a lot and generated a new supplementary Figure that shows this (Figure 1— supplement 4). We note that (1) FAC sorting gated for single cells, (2) our bioinformatic pipeline filtered cells for quality and doublets (e.g. very high number of genes), and (3) scatterplots showing that RBs do not express genes that mark other NCC populations. The one caveat here is that we see three RB cells that do express mitfa. However, these data overwhelming indicate that RBs are not expressing other NCC genes and thus unlikely to be doublets. Interestingly, we do find low sox10 expression in these RB cells. We also note that independent data from Wagner et al. 2018 observe crestin expression in RBs. These data do not rule out the possibility that RBs are doublets, but we find this explanation unlikely.

13. That some cells prior to migration express markers of pigment cells is convincingly demonstrated. One is left wondering – do the authors think that ALL pigment cells begin to express their markers prior to migration? Are all of the migrating tNCCs that are not expressing cell-type specific markers bound to become PNS derivatives? If there are data from sox10 lines that perhaps estimate the percentage of cells that give rise to these different cell types. The authors could make statements in the Discussion section to help clear this up.

15. Discussion could have more explanation or speculation about why only NCCs expressing pigment cell markers are observed prior to migration.

This is a great point. No, we do not find NCCs at 24hpf expressing markers of the peripheral nervous system. This is further supported by integrating our data with Howard et al. 2021 where differentiated peripheral nervous system neurons are present in 48 and 68 hpf embryos. In these integrated analyses NCCs from 24hpf embryos group with pigment cells from older embryos reflecting expression of pigment lineage genes in these pre-migratory NCCs. In contrast, NCCs from 24hpf embryos do not group with neural derivative cells from these 48 and 68 hpf embryos. This suggests that NCCs do not express genes marking PNS derivatives until post-migratory stages. We have included this as discussion in the relevant locations of the results (lines 177-186) and discussion (lines 326-332).

To our knowledge there is not a clear estimate of the percentage of cells that give rise to these different cell types. However our qualitative observations are that fewer NCC cells migrate along the medial path during the 24 hpf time point than are pre-migratory pigment cells in the neural tube.

14. The authors note expression of fgf13a in the RB neuron cluster and comment that "FGF and chemokine signaling are critical for proper morphogenesis.." and later "The RBs are apparently sources of and responsive to morphogens important for development. The authors need to reconsider this interpretation of fgf13 expression in RBs, as Fgf13 belongs to the class of intracellular FGF, iFGFs, which are not secreted and have no identified interaction with signaling FGFRs (Ornitz DM, Wires Dev Bio, 2015).

This was an oversight on our part and one that we also discovered with further reading while our manuscript was in review. We apologize, and have updated our discussion of these data to account for what is known about these non-canonical fgfs. They seem to play a role in solute carrier binding and critical for neuron function. It is intriguing to us that these fgf13 paralogs are not expressed in all neurons, suggesting some specific role that is required for some neuronal function but not critical for pan-neuronal function.

https://doi.org/10.7554/eLife.66078.sa2

Article and author information

Author details

  1. Ezra Lencer

    1. Department of Craniofacial Biology, University of Colorado, Denver, United States
    2. Department of Cell and Developmental Biology, University of Colorado, Denver, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing - original draft, Writing – review and editing
    For correspondence
    ezra.lencer@cuanschutz.edu
    Competing interests
    none
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6795-0260
  2. Rytis Prekeris

    Department of Cell and Developmental Biology, University of Colorado, Denver, United States
    Contribution
    Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review and editing
    Competing interests
    none
  3. Kristin Bruk Artinger

    Department of Craniofacial Biology, University of Colorado, Denver, United States
    Contribution
    Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review and editing
    Competing interests
    None

Funding

National Institute of General Medical Sciences (GM122768)

  • Rytis Prekeris

Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD103406)

  • Ezra Lencer

National Cancer Institute (T32CA17468)

  • Ezra Lencer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank members of the Prekeris and Artinger laboratories for their thoughtful comments on this project and help throughout. Special thanks to Dr. Lomeli Shull for help with cell dissociation and Dr. Kenneth Jones for help discussing bioinformatics analysis. This work was funded by R01 GM122768 to RP. EL was supported by National Institutes of Health Ruth L. Kirschstein National Research Service Awards (T32CA17468 and F32HD103406). EL, RP, and KA conceived of the study and wrote the manuscript. EL performed research and analyses.

Ethics

This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All procedures were performed according to approved institutional animal care and use committee (IACUC) protocols (#00147) of the University of Colorado Anschutz Medical Campus, and every effort was made to minimize suffering.

Senior Editor

  1. Richard M White, Memorial Sloan Kettering Cancer Center, United States

Reviewing Editor

  1. Lilianna Solnica-Krezel, Washington University School of Medicine, United States

Publication history

  1. Preprint posted: December 20, 2020 (view preprint)
  2. Received: December 29, 2020
  3. Accepted: August 3, 2021
  4. Version of Record published: August 16, 2021 (version 1)

Copyright

© 2021, Lencer et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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