Single‐cell analysis reveals dynamic changes of neural cells in developing human spinal cord

Article4 October 2021free access Source DataTransparent process Single-cell analysis reveals dynamic changes of neural cells in developing human spinal cord Qi Zhang Qi Zhang State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Xianming Wu Xianming Wu State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Yongheng Fan Yongheng Fan State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Peipei Jiang Peipei Jiang Department of Obstetrics and Gynecology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China Search for more papers by this author Yannan Zhao Yannan Zhao State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Yaming Yang Yaming Yang State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Sufang Han Sufang Han State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Bai Xu Bai Xu State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Bing Chen Bing Chen State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Jin Han Jin Han State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Minghan Sun Minghan Sun State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Guangfeng Zhao Guangfeng Zhao Department of Obstetrics and Gynecology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China Search for more papers by this author Zhifeng Xiao Corresponding Author Zhifeng Xiao [email protected] orcid.org/0000-0001-8970-5565 State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Yali Hu Corresponding Author Yali Hu [email protected] orcid.org/0000-0001-5475-7840 Department of Obstetrics and Gynecology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China Search for more papers by this author Jianwu Dai Corresponding Author Jianwu Dai [email protected] orcid.org/0000-0002-3379-9053 State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Qi Zhang Qi Zhang State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Xianming Wu Xianming Wu State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Yongheng Fan Yongheng Fan State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Peipei Jiang Peipei Jiang Department of Obstetrics and Gynecology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China Search for more papers by this author Yannan Zhao Yannan Zhao State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Yaming Yang Yaming Yang State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Sufang Han Sufang Han State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Bai Xu Bai Xu State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Bing Chen Bing Chen State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Jin Han Jin Han State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Minghan Sun Minghan Sun State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Guangfeng Zhao Guangfeng Zhao Department of Obstetrics and Gynecology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China Search for more papers by this author Zhifeng Xiao Corresponding Author Zhifeng Xiao [email protected] orcid.org/0000-0001-8970-5565 State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Yali Hu Corresponding Author Yali Hu [email protected] orcid.org/0000-0001-5475-7840 Department of Obstetrics and Gynecology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China Search for more papers by this author Jianwu Dai Corresponding Author Jianwu Dai [email protected] orcid.org/0000-0002-3379-9053 State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China Search for more papers by this author Author Information Qi Zhang1,†, Xianming Wu1,†, Yongheng Fan1,†, Peipei Jiang2,†, Yannan Zhao1, Yaming Yang1, Sufang Han1, Bai Xu1, Bing Chen1, Jin Han1, Minghan Sun1, Guangfeng Zhao2, Zhifeng Xiao *,1, Yali Hu *,2 and Jianwu Dai *,1 1State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China 2Department of Obstetrics and Gynecology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China † These authors contributed equally to this work *Corresponding author. Tel: +86 010 82614420; E-mail: [email protected] *Corresponding author. Tel: +86 025 83106666; E-mail: [email protected] *Corresponding author (lead contact). Tel: +86 010 82614426; E-mail: [email protected] EMBO Reports (2021)22:e52728https://doi.org/10.15252/embr.202152728 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract During central nervous system development, neurogenesis and gliogenesis occur in an orderly manner to create precise neural circuitry. However, no systematic dataset of neural lineage development that covers both neurogenesis and gliogenesis for the human spinal cord is available. We here perform single-cell RNA sequencing of human spinal cord cells during embryonic and fetal stages that cover neuron generation as well as astrocytes and oligodendrocyte differentiation. We also map the timeline of sensory neurogenesis and gliogenesis in the spinal cord. We further identify a group of EGFR-expressing transitional glial cells with radial morphology at the onset of gliogenesis, which progressively acquires differentiated glial cell characteristics. These EGFR-expressing transitional glial cells exhibited a unique position-specific feature during spinal cord development. Cell crosstalk analysis using CellPhoneDB indicated that EGFR glial cells can persistently interact with other neural cells during development through Delta-Notch and EGFR signaling. Together, our results reveal stage-specific profiles and dynamics of neural cells during human spinal cord development. Synopsis Using single-cell nuclei and single-cell RNA-seq, this study reveals a developmental timeline and uncovers genetic heterogeneity in neural cells during embryonic and fetal stages of human spinal cord development. Single-cell RNA-seq of human spinal cord cells during embryonic and fetal stages. Timeline of sensory neurogenesis and gliogenesis in the human spinal cord EGFR-expressing transitional glial cells exhibit a unique regional-specific feature during spinal cord development. Introduction The spinal cord is a highly organized tissue, composed of three distinct neural cell types: neurons, astrocytes, and oligodendrocytes. The vertebrate spinal cord transmits signals between the periphery and brain and vice versa. The sensory dorsal horns mediate exteroceptive signals, while the ventral cord is critical for the execution of locomotor behaviors (Bican et al, 2013; Puelles & Martinez, 2013; Leung & Shimeld, 2019). Understanding spinal cord development will reveal fundamental principles of how the central nervous system is built. Although model systems have been studied to reveal this process, comparatively few investigations have examined the human spinal cord (Bradley et al, 2019). In rodent spinal cord, proliferative progenitor cells originated from the neuroepithelium of the ventricular zone during neural induction. These cells, which are maintained by SOX2, can give rise to all neurons and glial cells in the spinal cord (Graham et al, 2003; Ellis et al, 2004; Pevny & Nicolis, 2010). The antiparallel signaling gradients formed by sonic hedgehog (SHH) and bone morphogenic proteins regulate the transcriptional network in progenitor cells, ultimately determining neuronal subtype identity within a Cartesian-like coordinate system (Caspary & Anderson, 2003; Zagorski et al, 2017). Along with this spatial information, glial progenitors expressing the transcription factors SOX9 and NFIA are born in a precise order from the same progenitor cell zone following specification of neuronal progenitors (Miller & Gauthier, 2007; Kang et al, 2012). During spinal cord development, radial glial cells served as both progenitors and a migration guidance scaffold for neural cells. Radial glial cells are also important for regulating axon outgrowth and pathfinding processes during gliogenesis (Puche & Shipley, 2001; Brusco et al, 2009; Barry et al, 2013). The differentiation and lineage relationships of radial glial cells are regulated by genetic factors, cell–cell interaction, and microenvironmental factors (McDermott et al, 2005). Single-cell RNA sequencing has become a powerful tool to dissect tissue development at single-cell resolution. Several single-cell RNA sequencing studies of spinal cord development have been performed in model systems; however, as most of these studies focused on neurogenesis, there is still a lack of single-cell transcriptomics information for glial development, especially for astrocytes. Glia are a central component of the nervous system. Recent studies point to an increasingly broad spectrum of roles for glial cells both during development and in the mature central nervous system (Freeman & Rowitch, 2013). Single-cell analysis would provide more information on the diversity and development of human glial cells. Furthermore, compared with model vertebrates, the human embryo experiences a long gestation period and extended duration of neurogenesis and gliogenesis. This prolonged development is advantageous for analyzing neural cell subtypes and their transitional states during maturation. Accordingly, we performed both high-throughput single-cell nuclei RNA sequencing (snRNA-seq) and single-cell cellular RNA-seq (scRNA-seq) of the developing human spinal cord. Altogether, we analyzed 0.8 million cells from human embryonic spinal cords covering gestational week (GW) 7- GW 23. Cells were clustered according to their transcriptional state and subsequently visualized using Uniform Manifold Approximation and Projection (UMAP) plots, in which related cell types were placed in proximity to one another (Cao et al, 2019). Our findings describe development from an early stage of neuron generation to the differentiation of glial cells. Moreover, we identified a type of epidermal growth factor (EGFR)-expressing transitional glial cell that arises from neuronal progenitor cells during the late stage of neurogenesis and thereafter shifts progressively toward the astrocyte and oligodendrocyte lineages. Immunostaining showed that EGFR-expressing transitional glial cells adopted a markedly position-specific distribution during spinal cord development. Results snRNA-seq and scRNA-seq of the developing human spinal cord We obtained spinal cord samples from GW7 to GW23 (scRNA-seq: GW7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 19, 20, 22, 23; snRNA-seq: GW7, 8, 10, 11, 14, 18, 20, 21, and 23). GW was measured from the first day of the woman’s last menstrual period. Samples were processed for snRNA-seq and/or scRNA-seq (Fig 1A and Table EV1). According to the region from which cells were collected, data were designated as cervical (C), thoracic (T), lumbar (L), or whole spinal cord (S). After applying quality filters 827,265 cells were retained for further analysis (see Materials and Methods, Appendix Figs S1A, and S2A and B). Data were projected onto two dimensions via UMAP, and major cell types were identified based on differentially expressed genes (DEGs) (Dataset EV1) and gene ontology (GO) analyses. Eight major classes were identified neurons, astrocytes, oligodendrocytes, ependymal cells (including roof and floor plate cells), neural progenitor cells (NPCs), microglia, meningeal cells, and vascular cells (Fig 1B and Dataset EV2). A discrepancy in neuronal cell types was found between the two sets of sequencing results (Appendix Fig S1B, Dataset EV2). Neurons were undetectable after GW13 in the scRNA-seq dataset, whereas neuronal clusters were consistently detected in the snRNA-seq dataset. This may be because mature neurons were more vulnerable to the cell separation and enzyme digestion processes of scRNA-seq preparation. To rule out the possibility that cell preparation affected the transcriptional characteristics of cells, batch correction and integration analyses were performed using datasets from similar gestational stages (Appendix Fig S3A–D). Integration results showed no obvious differences in cluster distribution of the GW7 dataset. Although neuronal cell types were missing from scRNA-seq data with increasing GW, numbers of other neural cell types (astrocytes, oligodendrocytes, and NPCs) in datasets correlated well with gestational stages. These results indicate that the transcriptional profiles of cells were unaffected by scRNA-seq preparation. Hereafter, our analysis concentrated on snRNA-seq data, with scRNA-seq data used as a contrasting dataset for glial cell types. To assess for potential region-specific effects, snRNA-seq datasets of cervical, thoracic, and lumbar regions from the same spinal cord sample were compared at three gestational ages (GW10, GW14, and GW20). When data of different regions from matched samples were integrated, we found no significant difference in cluster distributions or proportions among segments (Appendix Fig S4A). Moreover, clusters from different segments shared similar DEGs (Dataset EV1), with the exception of HOX family genes, which are related to the polarization of axial patterning (Appendix Figs S4B and S5A–D). Figure 1. Integrated snRNA-seq data reveal transcriptomic features and changes in the proportions of neural cells in early and mid-gestation human spinal cord A schematic diagram of the single-cell sequencing process. Visualization of major classes of spinal cord cells by integrated UMAP plots of cell clusters. Cell types are labeled and circled with dotted lines (top). Violin plots show the most distinct and commonly used marker genes for each cell cluster (bottom). Cell-type annotations are listed on the right and shown in varying colors. Three-dimensional visualization of neural subtype developmental trajectories for individual samples from GW7, GW10, GW14, GW18, and GW23. Stage-dependent differentiation trends changed with development. The red arrow indicates the proliferative neural progenitor cell cluster (MKI67, PAX6, NES, and SOX2). Neural cell types are named and circled with dotted lines. Gray represents non-neural cell types (e.g., vascular and meningeal). Immunofluorescence staining of the spinal cord at GW7, GW8, GW10, GW11, and GW23 for RBFOX3 (red), OLIG2 (green), and GFAP (white). The appearance of astrocytes and oligodendrocytes was observed from GW8 onward. Scale bars: 100 μm. Download figure Download PowerPoint Glial differentiation commenced around GW8 and ventricular zone specification occurred around GW10 NPCs are capable of proliferating and differentiating into neuronal and glial cell types (Alaynick et al, 2011; Hagey et al, 2018; Ma et al, 2018). To investigate differentiation trends of NPCs during development, differentiation trajectories within individual samples were examined (at GW7, GW10, GW14, GW18, and GW23) (Fig 1C). Proliferative NPCs were defined by expression of the proliferation-related transcription factor MKI67 and neural progenitor markers NES and SOX2, as well as the absence of expression of genes characteristic of matured neural cells. At GW7, proliferative NPCs were associated with neuronal classes. Expression of glial progenitor-specification genes SOX9 and NFIA was observed in GW7 samples (Appendix Fig S6A–C). We did not observe expression of differentiation markers for astrocytes (GFAP and ALDH1L1) or oligodendrocytes (PDGFRA and SOX10) in GW7 spinal cord glial cell subtypes (Appendix Fig S7A–C). Glial differentiation emerged as early as GW8, when upregulated expression of GFAP, ALDH1L1, PDGFRA, and SOX10 was observed in glial cells (Appendix Fig S7D–F). Expression of HOPX and SLC1A3 (GLAST) was observed in GW8 progenitor cell clusters, which indicated the appearance of radial glia (Penisson et al, 2019) (Appendix Fig S7F). NPC clusters lost their connection with neuronal groups after GW14, while links with astrocyte and oligodendrocyte clusters remained. After GW22, most dividing NPCs tended to be linked with the oligodendrocyte group (Fig 1C). Expression of myelination genes also occurred at an earlier developmental stage (GW13) in human (Appendix Fig S8) compared with mouse (1 day prior to birth) (Foran & Peterson, 1992). Immunofluorescence analysis also showed that RBFOX3-positive neuronal cells were the primary cell type in GW7 spinal cord. GFAP-positive astrocytes and OLIG2/PDGFRA-positive oligodendrocytes were first observed at GW8. In GW8 samples, GFAP-positive astrocytes were localized in the roof plate area of the spinal cord, while OLIG2-positive oligodendrocyte progenitors (OPCs) were observed in the area ventral to the ventricular zone. After GW10, both GFAP- and OLIG2-positive glial cells were found in the ventral spinal cord. From GW10 onward, GFAP-positive astrocytes showed a radial glia morphology, which could assist glial cells migration. The spinal cord achieved its mature morphology around GW23 (Fig 1D). Collectively, these results reflect the dynamic production of the glial cell population during development. Proliferative NPCs reside in the ventricular zone (later becomes the ependymal zone) of the developing spinal cord—an area that changed dramatically during embryonic development (Appendix Fig S9A–C). After scanning a set of adult human ependymal-specific genes (Ghazale et al, 2019), we detected expression of primary cilia formation related genes, CCDC114 and NEK5 starting from GW10. Expression of the cilia-related gene DNAAF1 was also detected at this time. Moreover, the adult ependymal zone-specific gene, ODF3B, was observed at GW21 (Appendix Fig S9B). SHH is responsible for regulating neurogenesis, especially motor neurons specification, and is expressed by the floor plate at the beginning of spinal cord development (Ericson et al, 1996; Lee & Pfaff, 2001). Our data indicate that after initiation of cilia-related genes expression within the ependymal cluster at GW10, expression of SHH in this cluster sharply declined from GW10 to GW14. Meanwhile, motor neurons (expressing MNX1) began to express SHH for further maturation (Appendix Fig S9B). These results indicate that NPCs of the ventricular zone developed into ciliated ependymal cells during the fetal stage. Neuronal development and cross-species comparison To gain insight into the neuronal differentiation process, subcluster analysis of neuronal lineages based on NPCs and neuronal cells from GW7 to GW11 was performed (Appendix Fig S10A and B). Neuronal progenitors were identified by expression of basic helix–loop–helix and paired box transcription factors such as HES6, DLL1, and PAX3. Excitatory (Exc), inhibitory (Inh), and motor (MN) neuron populations were distinguished by expression of SLC17A6, GAD1, and MNX1, respectively. Neuronal classes could be further subdivided (e.g., DI3, DI4, DI5) (Appendix Fig S10A). Heatmap visualization revealed the top 10 DEGs among clusters (Fig 2A, Dataset EV2). Selected GO enrichment analyses of differential expression data indicated that neuronal progenitors were enriched for translation, initiation, and membrane-targeting proteins, whereas immature dorsal neurons were enriched for GO terms related to neuron differentiation and synaptic signaling (Fig 2B). We observed that mitotic neuronal precursor clusters strongly expressed the dorsal neural progenitor gene PAX3, while expression of the ventral gene NKX6-2 was less detectable in this population (Appendix Fig S10C and D). Immunostaining confirmed that the dorsal side of the ventricular zone was filled with neuronal progenitors after GW7 (Fig 2C). Across all integrating time points, there were 50,533 cells, including proliferative NPC (pro-NPC), glial progenitor (GPC), neuronal restricted progenitor (NRP), immature dorsal neuron (IDN), and differentiated neuronal classes. Next, we determined the relative proportion of each cell population at each developmental time points to estimate developmental trends (Fig 2D). The proportion of neuronal progenitors declined with increasing gestational age (21.5% at GW7 compared with 3.0% at GW11). Significant increases in the proportions of DI4 and DI5 cells were observed from GW7 to GW11 (0.4% at GW7 compared with 19.2% at GW11; GW7, 0.1% at GW7 compared with 24.2% at GW11, respectively). Although there was a suggestion of ventral neurons development at GW7, our data indicated that dorsal neuron development is the main event during this stage (GW7–GW11). Analysis of GW10 (Fig 2E) also showed that the neuronal differentiation path was enriched for dorsal neuron transcriptional regulators, such as POU4F1 and TLX3. Ventral neuron and MN clusters were located at considerable distances from the neuronal progenitor class in the trajectory plot, hence reflecting a more mature state. These data indicated that dorsal neurons generation peaks around GW10, while ventral neuronal generation occurs earlier. Figure 2. Integrated trajectory and expression feature analysis of neuronal cell developmental trajectory in GW7, GW8, GW10, and GW11 samples UMAP plot of cells integrated from datasets of GW7, GW8, GW10, and GW11 samples (GW7S-NC, GW8S-02NC, GW10C-01NC, GW10T-01NC, GW10L-01NC, GW10C-02NC, GW10T-02NC, GW10L-02NC, GW11C-NC, GW11T-NC, GW11L-NC) showing neuronal differentiation tendency. Cell types are labeled (left). Heatmap showing differentially expressed genes (DEGs) in neuronal restricted progenitors (NRP) and immature dorsal neurons (IDN) (right). Visualization of selected top gene ontology (GO) enrichment terms for NRP and IDN clusters based on DEG analysis. Immunofluorescence staining of the spinal cord from GW7, GW8, and GW10 for a dorsal marker (PAX7, green), neuronal precursor marker (ASCL1, red), and nuclei marker (DAPI, blue). Images show the neuronal differentiation process on the dorsal side of the spinal cord. Scale bar: 100 μm. Stacked proportional changes of neural progenitor cells (NPC) and neuronal cell classes from GW7–GW11. Cell types were identified as: proliferative neural progenitors (Pro-NPC), glial restricted progenitors (GRP), IDN, DI4 interneurons (DI4), DI5 interneurons (DI5), motor neuron (MN), DI1 and DI2 interneurons (DI1/2), DI3 interneurons (DI3), DI6 interneurons (DI6), and V0 and V1 interneurons (V0/1). Three-dimensional developmental trajectories for snRNA-seq samples from the GW10 cervical sample (top view in the upper right corner). Cells are classified by differentiation stage according to expression of specific markers (neuronal progenitor: PAX3 and PAX7; DI5 interneuron: POU4F1 and TLX3; V3 interneuron: SIM1; and motor neuron: MNX1). Cells in the neuronal development trajectory are colored by gene expression. Distances from progenitor cells to differentiated neurons represent the differing maturation status between dorsal neurons (DI4, DI5) and ventral neurons (MN, V3). Cells with no detectable expression for a given gene were omitted from the plot. Download figure Download PowerPoint Cross-species analysis can reveal the homology and heterogeneity of neural cells between species (Yu et al, 2014; Hodge et al, 2019). Thus, we next aligned our human neurogenesis trajectory results (GW7–GW11) with mouse spinal cord single-cell neurogenesis transcriptomics data (E9.5–E13.5) (Delile et al, 2019). Alignment based on expression covariation showed that the neural composition was largely conserved between species, confirming the reliability of our data (Fig EV1A, B and E). Our alignment results showed that human neuronal progenitor subtypes (NPC-Neuronal NOTCH1 and NPC-Neuronal HES6) were most similar to mouse PD4/5 subtypes (NPC-Neuronal ASCL1, NPC-Neuronal CENPA, and NPC-Neuronal TFAP2B) (Fig EV1C). Furthermore, GFAP expression was broadly detected in humans but not mouse (Fig EV1D), although we noticed the appearance of astrocyte progenitor cluster in mouse dataset from E10.5 (Fig EV2H). Gene families with the most divergent expression (> 5-fold change) showed a change in patterning across cell types. These gene families included extracellular matrix elements, neurotransmitter receptors, ion channels, and fatty acid transporters (Fig EV1F and G, and Table EV2). Our data indicated that between human and mouse, intermediary DI3 interneuron, and ipsilaterally projecting V2a interneuron classes exhibited more conserved cellular properties compared with most of the remaining classes (Fig EV2A–D and F). Furthermore, five human neuronal subclasses lacked homologous mouse equivalents: Exc-DI5 PIEZO2, Exc-DI5 ROBO, Inh-DI4 ROBO, Inh-VI ONECUT3, and MN COL6A2 (Fig EV2E). This may reflect either a difference among species or development phases. For example, because expression of PIEZO2 plays a role in the mechanotransduction channel for proprioception, the Exc-DI5 PIEZO2 subcluster in humans may represent a refined mechanosensation function (Florez-Paz et al, 2016) (Fig EV2G). Similarly, in early motor neuron development classes, a subcluster of human MN COL6A2 may indicate an MN subcluster with promoted axon growth as COL6A2 deficiency has been related to severe motor axon outgrowth defects (Ramanoudjame et al, 2015). Furthermore, we noticed several genes (such as CHD5, EGFR, GAD1, and NEUROG2) exhibited different expression patterns between human and mouse (Appendix Fig S11). These genes are related to several important aspects of spinal cord development, including neuronal gene activation, synaptic vesicle transporters, and extracellular matrix structure. Click here to expand this figure. Figure EV1. Alignment analysis of cell-type conservation and divergent expression between human and mouse UMAP visualization of human (n = 50,533 nuclei) and mouse (n = 26,039 cells) neuronal and neural progenitor cell (NPC) clusters after alignment. Radar chart showing similar distribution of neuronal subtype proportions among the two species during the corresponding developmental period. Human and mouse cell-type homologies for NPCs predicted from shared cluster membership. Gray shade corresponds to the minimum proportion of co-clustering between species. Columns show human clusters and rows show mouse clusters. Homologous clusters are labeled based on human and mouse cluster membership. Inferred cell-type homologies highlighted in red boxes. Orange boxes indicate subclasses of PD4/5-related neuronally restricted progenitors in human and mouse data. Violin plots showing GFAP expression in human and mous