A Shared Transcriptional Identity for Forebrain and Dentate Gyrus Neural Stem Cells from Embryogenesis to Adulthood

A V-SVZ NSC core transcriptional signature is conserved in developing and adult SGZ NSCs

To compare V-SVZ and SGZ NSCs, we used two recently-published single-cell transcriptome datasets, one including forebrain V-SVZ cells from E14 to P61 (Borrett et al., 2020) and a second including dentate gyrus cells from E16.5 to P132 (Hochgerner et al., 2018). Since these datasets were generated using two different protocols, we ensured that they were comparable by analyzing both of them using a slightly modified version of a previously described scRNA-Seq computational pipeline (see Materials and Methods for details; Yuzwa et al., 2017; Carr et al., 2019; Borrett et al., 2020; Storer et al., 2020). This pipeline was originally described in Yuzwa et al. (2017) and incorporates extensive low level data quality analysis and evidence-based parameter selection to visualize and cluster transcriptomes from scRNA-Seq datasets. For the hippocampus, we used this pipeline to analyze the 24,185 dentate gyrus transcriptomes of all ages from Hochgerner et al. (2018; termed Dataset C in Hochgerner et al., 2018; GSE 95 753). Following analysis, we used UMAPs to visualize clustering and were able to identify transcriptome clusters corresponding to both neural and nonneural cell types (Figs. 1A, 2A), as previously described (Hochgerner et al., 2018). Of particular relevance, we found that neonatal NSCs (P0 and P5) and nonproliferative E16.5 RPs (together labeled developing NSCs) were co-clustered and were distinct from clusters containing the P18 and older NSCs (P18, P19, P23, P120, and P132; labeled adult NSCs). There was also a population of proliferative E16.5 RPs that were co-clustered with P0 and P5 cells that were previously-defined as transit-amplifying IPs (Figs. 1A, 2B; labeled IPs + E16 RPs). All of the precursor clusters were segregated from two additional distinct clusters containing perinatal astrocytes (P0 and P5) and juvenile/adult astrocytes (P18 and older; labeled Astrocytes; Fig. 1A,B). This clustering analysis therefore suggests that juvenile and adult SGZ NSCs are very similar to each other but are quite distinct from embryonic and perinatal SGZ NSCs, a finding previously reported in Hochgerner et al. (2018).

To start to ask about potential transcriptional similarities between SGZ and V-SVZ NSCs, we examined 79 genes that were first identified as highly enriched in embryonic cortical RPs relative to all other embryonic cortex cell types (Yuzwa et al., 2017) and then were shown to also be enriched in postnatal V-SVZ NSCs (Borrett et al., 2020; see Table 1 for a list of the differentially-expressed genes). We used these 79 genes to compute a single-cell gene expression score and applied this to all of the cells in the dentate gyrus dataset (Fig. 1C,D). The gene signature was enriched in developing and adult SGZ NSCs, and in all E16.5 hippocampal RPs. To confirm this result, we also analyzed average expression levels for these 79 genes. This analysis showed that 63 of the 79 genes were enriched in nonproliferative SGZ NSCs (cells highlighted in red in Fig. 1B; Table 1) relative to the collection of the remaining dentate gyrus cells (as shown in Fig. 1A; adjusted p value < 0.05; FWER). The genes that were not enriched were NdeI, Rgcc, Ednrb, Metrn, Kbtbd11, Gm11627, Acadl, Aldhl11, Bcan, Vit, Acss1, Acsbg1, Atp1a2, Clu, Pnp, and Rcn3.

These data suggest that a similar core gene signature is enriched in V-SVZ and SGZ precursors from embryogenesis through to adulthood. We validated expression of a subset of these genes in SGZ NSCs by performing FISH for Ptprz1, Ttyh1, Aldoc, and Mt3 on the neonatal P5 dentate gyrus. To identify NPCs, we combined the FISH with immunostaining for the precursor protein Sox2. As predicted by the scRNA-Seq analysis, there were Sox2-positive cells within the developing SGZ that co-expressed these different mRNAs (Fig. 1E).

Defining a gene signature that distinguishes niche astrocytes from NSCs

One limitation of this analysis is that the V-SVZ RP/NSC gene signature, as well as many of the individual genes, were also enriched in SGZ niche astrocytes (Fig. 1C,D; Table 1), as previously observed in the V-SVZ (Borrett et al., 2020). For example, Tnc, Gas1, and Ddah1 mRNAs were enriched in both astrocytes and NSCs, although some mRNAs, such as Tfap2c, Vimentin (Vim), and Nestin (Nes) were more specific to the NSCs (Fig. 2C; Table 1). We therefore asked whether we could identify genes that more definitively distinguished NSCs from niche astrocytes in the SGZ by focusing on a gene set recently shown to be differentially expressed in these two cell types in the P20–P61 V-SVZ. This gene set included 537 mRNAs that were significantly higher in their expression in V-SVZ astrocytes versus NSCs, and 498 genes that were significantly lower (Borrett et al., 2020). Analysis of these same genes in the dentate gyrus dataset (Fig. 1F) showed that 64% of the genes that were expressed more highly in V-SVZ NSCs were also expressed at higher levels in SGZ NSCs than in SGZ astrocytes, while 56% of genes that were higher in V-SVZ astrocytes were also higher in SGZ astrocytes (Fig. 1F; Table 2).

This analysis suggests that the same genes that distinguish NSCs from astrocytes in the V-SVZ distinguish these two cell types in the dentate gyrus. To test this idea, we selected 26 of the genes in this dataset that were most highly enriched in astrocytes versus NSCs in both the V-SVZ and SGZ (Table 2, asterisks), as exemplified by the patterns of expression of Aqp4 and Agt (Fig. 2D). A gene signature score computed using these 26 genes was specifically enriched in niche astrocytes relative to all other cells in the dentate gyrus dataset (Fig. 1G). This gene signature was similarly enriched in V-SVZ niche astrocytes, as shown by computing a similar signature score for the P20, P34, and P61 V-SVZ transcriptomes (Borrett et al., 2020) that had been put through the same computational pipeline (Fig. 2E). Thus, while niche astrocytes share many transcriptional commonalities with SGZ and V-SVZ NSCs, astrocytes and NSCs can be readily distinguished at the transcriptional level.

V-SVZ and SGZ precursors share transcriptional similarities as they progress from active embryonic to dormant adult NSCs

It was previously reported that the transition from an embryonic to adult V-SVZ NSC reflects a switch from an active to a dormant stem cell state, involving a broad dampening of cell biological processes associated with an active state including cell division, transcription, RNA metabolism and protein translation, processing and trafficking (Borrett et al., 2020). The finding that a V-SVZ RP/NSC gene signature is also enriched in SGZ RP/NSCs suggests that these two populations might be more transcriptionally similar than previously appreciated and thus might share similar transcriptional trajectories to a dormant state. To test this idea further, we extracted all nonproliferative SGZ RP and NSC transcriptomes (the red cells in Fig. 1B; 885 total cells) and combined them with the V-SVZ RP/NSC transcriptomes (as identified in Borrett et al., 2020), including P2, P6/7, P20, P34, and P61 dormant NSCs and E14 and E17 cortical and GE-derived RPs. This combined dataset was put through the computational pipeline and included V-SVZ and SGZ precursors of similar developmental stages from embryogenesis to adulthood (shown in Fig. 3A).

One potential caveat of combining the two datasets is that the SGZ and V-SVZ cells were prepared and sequenced in two different laboratories using different protocols, and thus apparent differences might derive from batch effects as opposed to biological heterogeneity. To correct for this possibility, we also included endothelial cells (P19 SGZ and P20 V-SVZ) and microglial cells (P23 SGZ and P20 V-SVZ) from both datasets with the assumption that V-SVZ endothelial cells and microglia should be similar enough to co-cluster with the same cell types from the SGZ. However, when the combined dataset was visualized on a two-dimensional UMAP plot, the endothelial cells and microglia from the two different regions/datasets were partially segregated from each other (Fig. 4A), indicating variability because of batch effects. We therefore corrected for these batch effects using Harmony, a computational method for data integration that iteratively removes batch-mediated technical variation within principal component space of high dimensional data (Korsunsky et al., 2019; Tran et al., 2020). With the lowest level of Harmony correction, one iteration, there was complete integration of V-SVZ and SGZ endothelial and immune cells (Fig. 4A,B; see Materials and Methods).

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Figure 4.

Batch correction and cell cycle regression for the combined V-SVZ and SGZ transcriptome analyses. A, As a control to optimize the batch correction between V-SVZ and SGZ NSCs, raw transcriptomes from the dataset shown in Figure 3B were merged with endothelial cells from the P19 dentate gyrus (SGZ endo), endothelial cells from the P20 V-SVZ (V-SVZ endo), microglia from the P23 dentate gyrus (SGZ Imm), and microglia from the P20 V-SVZ (V-SVZ Imm). PCA visualization (left) and UMAP visualization (right) of the total dataset without batch correction showed that endothelial and immune cells from the two regions did not co-cluster well. Cells are colored based on cell type and region of origin. B, The same dataset shown in A was batch corrected with one iteration of Harmony. The harmonized PCA visualization (left) and UMAP visualization (right) of the merged cells show that endothelial cells and immune cells were now well-clustered. Cells are colored based on cell type and region of origin. C, Batch-corrected UMAP visualization of the merged V-SVZ NSC and SGZ NSC dataset shown in Figure 3B, where the cell cycle genes were regressed out as previously described (see Materials and Methods). Cells were grouped into color coded and numbered clusters based on gene expression profiles, and the NSCs of different ages are shown for direct comparison to Figure 3B. D, Correlation heatmap showing Pearson correlation coefficients between averaged expression profiles of total NSCs (including all ages) and P19/20 V-SVZ and SGZ endothelial cells. Correlation coefficients are color coded as per the adjacent color key. Gene expression values were not batch-corrected.

Having established this protocol, we removed the endothelial and immune cells and analyzed only the RP/NSC transcriptomes, using one iteration of Harmony batch correction. UMAP visualization of these data (Fig. 3B) defined three groups of clusters, one including the juvenile and adult V-SVZ and SGZ NSCs, a second including the perinatal and postnatal NSCs of both origins and a third including the embryonic hippocampal, cortical and GE RPs. At any given developmental stage (adult, postnatal, or embryonic) there was some segregation between SGZ and V-SVZ NSCs suggesting that these two NSC populations were very similar but not identical (Fig. 3B–D).

One explanation for the differential clustering of developing and adult NSCs is that cell cycle genes associated with proliferation are partially responsible for driving this segregation. To test this idea, we removed 678 cell cycle-related genes (see Materials and Methods) and redid the analysis. UMAP visualization (Fig. 4C) showed that results were similar with and without removal of these cell cycle genes. There were three groups of clusters containing embryonic, perinatal/postnatal, or juvenile/adult NSCs, and there was some segregation of V-SVZ and SGZ NSCs of the same age within these clusters. Thus, cell cycle genes are not major drivers of the differential clustering seen for NSCs of different ages.

The strong age-dependent segregation of NSCs in the cluster plot (Fig. 3B) suggests that there may be greater transcriptional differences between NSCs at different developmental stages than there are between V-SVZ and SGZ precursors at the same time point. This conclusion was confirmed by performing two types of correlation analysis that do not involve any batch correction. The first was Pearson correlation analysis of average gene expression for V-SVZ and SGZ precursors at different timepoints (Fig. 3E). This analysis showed that at many timepoints, V-SVZ and SGZ precursors were more similar to each other than they were to any of the other precursor groups at different ages. For example, E14 V-SVZ and E16.5 SGZ RPs were correlated with a high value of r = 0.94, while E14 V-SVZ RPs and P20 V-SVZ NSCs were only correlated with r = 0.78. As predicted, all NSC populations were more similar to each other than they were to endothelial cells (Fig. 4D).

As a second approach, we performed a correlation analysis that compares single-cell transcriptomes rather than averaged gene expression (see Materials and Methods). To perform this single-cell correlation analysis, we first defined gene expression profiles for comparison to each individual cell transcriptome. As a first comparator, we determined average gene expression for E14 V-SVZ RPs versus juvenile/adult V-SVZ NSCs (P20/34/61; Fig. 3F, x-axis) and as a second comparator we determined average gene expression for E16.5 nonproliferative SGZ RPs versus juvenile/adult SGZ NSCs (P18–P132; Fig. 3F, y-axis). We then correlated all V-SVZ and SGZ NSC single-cell transcriptomes from all timepoints with these averaged datasets and used these correlations to assign a two-dimensional coordinate for each cell. This analysis, which uses gene expression values that are not batch corrected, showed that during embryogenesis and the first postnatal week, the V-SVZ and SGZ precursors were very similar, with the E16.5–P5 SGZ precursors closely mingled with the E17–P6/7 V-SVZ precursors of the same approximate age (Fig. 3F). By contrast, the juvenile/adult V-SVZ and SGZ NSCs were more similar to each other than they were to the developing precursors of the same origin (Fig. 3F). Thus, SGZ and V-SVZ precursors follow similar transcriptional trajectories from active embryonic RPs to dormant adult NSCs.

Embryonic dentate gyrus and cortex RPs but not GE RPs express genes associated with excitatory neurogenesis and a common pallial origin

One explanation for the high similarity between SGZ and V-SVZ precursors is that they derive from RPs in adjacent lateral ventricle neuroepithelial regions during embryogenesis; dentate gyrus and cortical RPs are beside each other in the pallial region while GE RPs are immediately adjacent to cortical RPs in the subpallial region. We therefore directly compared E16.5 dentate gyrus RPs, E14 cortex RPs and E14 GE RPs, taking advantage of the fact that the V-SVZ cells were lineage traced so that cortex and GE-derived cells could be distinguished (see Borrett et al., 2020). We combined these different transcriptomes, put them through the pipeline together and used one round of Harmony batch correction. UMAP visualization of this combined dataset showed that the cortex, GE and dentate gyrus RP transcriptomes were largely but not completely segregated from each other (Fig. 5A), in good correspondence with the correlation analyses showing that these RPs were very similar to each other but not identical.

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Figure 5.

Embryonic dentate gyrus and cortex RPs express genes associated with excitatory neurogenesis and a common pallial origin. A, Batch-corrected UMAP visualization of the transcriptomes of E16.5 dentate neuroepithelium RPs, E14 cortical RPs and E14 GE RPs, where transcriptomes are colored to indicate cell type. B, UMAP marker gene expression overlays of the dataset in A. Cells are color-coded for levels of gene expression as per the adjacent color keys. C, D, Heatmap illustrating common genes upregulated (C) or downregulated (D) in E14 cortical RPs and E16.5 SGZ RPs relative to E14 GE RPs. Genes are color-coded for levels of expression as per the adjacent color keys. Gene expression values are not batch-corrected. E, Batch-corrected UMAP visualization of transcriptomes of P5 SGZ NSCs, P6/7 cortically derived V-SVZ NSCs and P6/7 GE-derived V-SVZ NSCs, colored to indicate cell type. F, UMAP visualizations as in E, overlaid for expression of genes defined in Borrett et al. (2020) as being enriched in cortical versus GE-derived V-SVZ NSCs. Cells are color-coded for levels of expression as per the adjacent color keys.

To more specifically identify differences between these RP populations, we focused on 117 genes that were previously-shown (Borrett et al., 2020) to be differentially expressed between cortical and GE RPs (average expression difference of ≥0.5; adj. p value < 0.05; FWER). Fifty-four of these genes were expressed at higher levels in cortical than GE RPs, and of these about half (26) were also significantly enriched in dentate gyrus versus GE RPs (Table 3), as shown by UMAP gene expression overlays (Fig. 5B) and by a heatmap indicating mRNA expression levels in single cells (Fig. 5C). These included genes like Emx1, Tfap2c, Pax6, Fezf2, Neurog2, and Eomes. Notably, some of these shared enriched genes are associated with glutamatergic neurogenesis (Fezf2, Neurog2, and Eomes), while others are associated with a pallial origin (Emx1, Pax6, and Tfap2c). We also asked about the other 63 genes, which were expressed at higher levels in GE versus cortical RPs (average expression difference of ≥0.5; adj. p value < 0.05; FWER). Of these, 49% were also higher in GE versus dentate gyrus RPs, as exemplified by Dlx2, Six3, and Gsx2, genes that are associated with GABAergic neurogenesis or GE identity (Fig. 5B,D; Table 3). Thus, the embryonic RP parents of V-SVZ and SGZ NSCs are all very similar to each, but are distinguished by expression of small cohorts of genes that are known to play important roles in determining regional identity and/or glutamatergic versus GABAergic neurogenesis.

Postnatal SGZ NSCs also express genes that may be associated with a pallial origin

We asked whether postnatal SGZ NSCs might continue to express genes reflective of their embryonic origin, as was previously seen for postnatal V-SVZ NSCs (Borrett et al., 2020). To ask this, we compared P5 SGZ NSCs to lineage-traced P6/7 V-SVZ NSCs deriving from the cortex and GE. We put all the transcriptomes through the batch-corrected pipeline together and visualized clustering on a UMAP (Fig. 5E). This analysis showed that as seen for the embryonic cells, NSCs from the cortex, GE, and dentate gyrus were largely segregated from one another. Together with the Pearson correlation analysis (Fig. 3E), these results indicate that these different postnatal NSC populations are very similar but not identical. We then asked about genes previously-defined as differentially expressed in cortically-derived versus GE-derived postnatal V-SVZ NSCs (Borrett et al., 2020). UMAP gene expression overlays showed that genes that were enriched in cortical NSCs such as Hopx, Tfap2c, and Rgs5 were also enriched in dentate gyrus NSCs (Fig. 5F) and thus represented potential markers of their shared pallial origin. By contrast, genes that were enriched in GE NSCs and might be indicative of a subpallial origin, were largely not detectable in the SGZ NSCs, as exemplified by Lmo1, Six3, and Crym mRNAs (Fig. 5F). We validated one of the potential pallial NSC marker genes, Rgs5, by performing FISH on the P5 dentate gyrus. Rgs5 mRNA was expressed in Sox2-positive SGZ cells that also expressed the precursor gene Aldoc (Fig. 1E), likely NSCs. Thus, as seen during embryogenesis, cortically-derived and dentate neuroepithelium-derived NSCs, but not GE-derived NSCs, express potential marker genes for a pallial origin.

The developmental transition to a dormant adult NSC occurs over a prolonged postnatal period in the SGZ as it does in the V-SVZ

In the V-SVZ, the transition from an active embryonic RP to a dormant postnatal NSC occurs over a prolonged, largely postnatal timeframe (Borrett et al., 2020). We asked whether this was also true for the SGZ using trajectory analysis, an approach that orders cells based on changes in their transcriptomes over pseudo-time. To do this, we combined transcriptomes of all dentate gyrus nonproliferative RP/NSCs from E16.5 to adulthood and performed a trajectory analysis using Monocle (Fig. 6A,B). We did not use batch correction for this analysis and, to ensure that the trajectory was not driven by precursor proliferative status, we removed the aforementioned 678 cell cycle-related genes. We also excluded a small number of cells (31 of 885 total) that expressed genes consistent with activated NSCs. This analysis resulted in a trajectory that correctly reflected the developmental progression. The E16.5 RPs were ordered at one end, and the adult dormant NSCs were at the other end. Some of the P0 and P5 NSCs were mingled with the E16.5 RPs, but most perinatal cells extended to eventually meet the juvenile NSCs, which then extended further along the trajectory to meet and mingle with the adult dormant NSCs at the other end. This trajectory was very similar to an analogous Monocle trajectory analysis of the V-SVZ RP/NSCs (Borrett et al., 2020), with the transition to an adult NSC state occurring gradually from birth until the third postnatal week.

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Figure 6.

Mapping the trajectory from embryonic to adult SGZ NSCs with trajectory and GSEA analyses. A, To understand the progression of SGZ NSCs from E16.5 to adulthood (P120/P132), SGZ NSCs at all ages (E16.5, P0, P5, P18, P19, P23, P120, P132) were ordered along a pseudotime trajectory using Monocle 2. To ensure cell cycle effects would not affect the ordering of the trajectory, we regressed out cell cycle genes (see Materials and Methods). SGZ NSCs along the trajectory are colored by age category (as defined in Fig. 3A, left) or by pseudotime ordering scores (right). These data are not batch corrected. B, Density plot showing the relative distribution of pseudotime ordering scores of SGZ NSCs in the trajectory depicted in A from each age category. C, D, GSEA analysis of the combined SGZ NSC dataset from E16.5 to P132, performed without batch correction. Pie chart shows broad categories of genes sets negatively correlated (C) or positively correlated (D) with time that fell into a number of broad categories (FDR cutoffs are indicated). Categories negatively correlated with time in C include DNA replication, DNA repair, chromosome stability and segregation and the cell cycle (DNA + cell division), transcription, epigenetics, and chromatin regulation (Transcription), RNA homeostasis, translation and tRNA and ribosome biogenesis (RNA biology + translation), general protein processing and trafficking including ubiquitination and sumoylation (Protein turnover), signaling pathways (Signaling), and metabolism, oxidative phosphorylation and mitochondrial activity (Metabolism + mitochondria). Other categories are termed as miscellaneous (Misc.). The detailed categorization is shown in Table 4. Categories positively correlated with time (D) include neurotransmitter transport and synaptic regulation (Neurotransmitter + synapse regulation), Ion regulation and membrane transport (Ion + membrane transport), signaling pathways (Signaling), gliogenesis and metabolism and lipid oxidation (Metabolism). Other categories are termed as miscellaneous (Misc.). The detailed categorization is shown in Table 5. E, Histogram of Spearman rank correlation coefficients of the combined SGZ NSC dataset for a signature of 49 quiescence genes described in Cheung and Rando (2013; red) versus all genes (gray). Correlations of >0 or <0 indicate expression increases or decreases over time; *p = 0.024, Wilcoxon rank-sum test.

These findings suggest that the transition from an active embryonic RP to a dormant adult NSC might be similar for the two major forebrain NSC populations. To further examine this idea and to determine what types of genes and/or cellular pathways are changed in SGZ NSCs at different ages, we performed a GSEA over SGZ developmental time from E16.5 to P132. We compared this GSEA to a previously-published (Borrett et al., 2020) analogous GSEA analysis for V-SVZ NSCs from E14 to P61. Notably, the SGZ analysis (Fig. 6C,D; Table 4) showed that 115 gene sets decreased significantly (adj. p value < 0.01; FDR) as the E16.5 SGZ RPs transitioned to dormant adult SGZ NSCs. Most of these gene sets involved basic cellular processes required to maintain an active, proliferative stem cell, including transcriptional programs required for cell division, DNA and chromosome replication, RNA biology, transcription, and protein synthesis and turnover, indicating that the predominant change that occurs over this timeframe is a transition to cellular dormancy. The developing NSCs were also enriched for gene sets involved with oxidative phosphorylation. Conversely, 63 gene sets were significantly enriched in dormant adult NSCs relative to their developing NSC counterparts (adj. p value < 0.05; FDR; Fig. 6D; Table 5). Notably, 65% of these were involved in regulating and/or sensing the niche environment, with a particular enrichment for sensing/handling neurotransmitters and ions like sodium and potassium. They also included gene sets involved in lipid metabolism and, of particular note, a quiescence gene set (Fig. 6E) that was shown to be significantly enriched as V-SVZ NSCs transitioned to dormancy (Borrett et al., 2020). Thus, as previously shown for adult V-SVZ NSCs, adult SGZ NSCs are transcriptionally quiet with regard to genes involved in maintaining an active state and instead selectively express gene sets that allow them to sense and maintain themselves in a dynamic neuronal environment and to perform lipid metabolism.

Upon activation, adult SGZ NSCs reacquire a development-like state that includes re-expression of proneurogenic genes

Previous work (Borrett et al., 2020) showed that adult V-SVZ transit-amplifying cells (TAPs) exhibited an embryonic RP-like transcriptional program, implying that adult dormant NSCs reverted to an earlier developmental state when activated for cell genesis. To ask whether this was also true for adult SGZ NSCs, we performed a single-cell correlation analysis comparing dormant NSCs and their downstream activated NSC and TAP/IP progeny from the V-SVZ and SGZ (Fig. 7A). To perform this analysis, we determined average gene expression for E16.5 nonproliferative dentate gyrus RPs and juvenile/adult SGZ NSCs (P18–P132) as a first comparator (Fig. 7A, y-axis). As a second comparator, we determined average gene expression for E14 V-SVZ RPs and juvenile/adult V-SVZ dormant NSCs (P20/34/61; Fig. 7A, x-axis). We then correlated these average transcriptomes with single-cell transcriptomes from the E16.5 dentate gyrus RPs, adult SGZ NSCs and adult SGZ IPs. To enable a direct comparison, we also correlated single-cell transcriptomes from the V-SVZ dataset, including E14 cortical and GE RPs, adult dormant NSCs, adult activated NSCs and adult TAPs (all as defined in Borrett et al., 2020). This analysis showed that the various RP populations were largely but not completely intermingled, confirming that they were very similar to each other. Moreover, as previously published (Borrett et al., 2020), the adult V-SVZ TAPs were closely intermingled with the cortical and GE RPs. Notably, the adult SGZ IPs were closely mingled with the adult V-SVZ activated NSCs and were more highly correlated to embryonic RPs than to adult SGZ or V-SVZ dormant NSCs.

These data suggest that dormant SGZ and V-SVZ adult NSCs reacquire a development-like state when activated. We asked whether this was also true with regard to neurogenesis by examining genes associated with GABAergic (Dlx1, Dlx2, Dlx5, Sp9) and glutamatergic (Neurog2, Neurod1, Eomes) neurogenesis. We analyzed expression of these proneurogenic mRNAs in adult dormant SGZ and V-SVZ NSCs and in their TAP progeny, TAPs and IPs (the same adult transcriptomes included in Fig. 7A; V-SVZ activated NSCs at juvenile and adult ages were not included in this analysis). This analysis, shown as a single-cell heatmap (Fig. 7B) demonstrated that the proneurogenic mRNAs were detectably expressed in few of the dormant NSCs. However, many of the V-SVZ TAPs detectably expressed the GABAergic but not glutamatergic mRNAs, while many of the SGZ IPs expressed the glutamatergic but not GABAergic mRNAs. Thus, dormant postnatal V-SVZ and SGZ NSCs are not apparently transcriptionally primed for generating specific types of neurons. Instead, this proneurogenic priming, which is also observed in embryonic RPs (Fig. 5B–D), apparently only occurs in their downstream activated progeny.

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Figure 7.

Upon activation, adult SGZ NSCs reacquire a development-like state that includes re-expression of proneurogenic genes. A, Raw transcriptomes from the V-SVZ and SGZ NSC dataset shown in Figure 3B were merged with V-SVZ activated NSCs (actNSCs) and V-SVZ TAPs from juvenile and adult ages (P20/P34/P61; as defined in Borrett et al., 2020) as well as SGZ IPs from juvenile and adult ages (P18/P19/P23/P120/P132; as shown in Fig. 1A). Scatterplot shows single-cell correlation analysis of different V-SVZ and SGZ populations (color coded by cell type and age as defined in Fig. 3A), where individual transcriptomes were each correlated with averaged gene expression for E14 V-SVZ RPs versus juvenile/adult V-SVZ dormant NSCs (P20/P34/P61; x-axis), and with averaged gene expression for E16.5 SGZ RPs versus juvenile/adult SGZ NSCs (P18/P19/P23/P120/P132; y-axis). Gene expression values are not batch-corrected. B, Single-cell heatmap illustrating expression of genes involved in GABAergic and glutamatergic neurogenesis in the juvenile and adult SGZ and V-SVZ precursor populations shown in panel A. Genes are color-coded for levels of expression as per the adjacent color keys. Gene expression values are not batch-corrected.

Identification of shared genes that are selectively increased in dormant adult NSCs

These findings support a model where V-SVZ and SGZ precursors share many commonalities with regard to their transcriptional identity, developmental progression to dormancy and subsequent activation to make adult-born progeny. To further define their shared adult transcriptional state, we analyzed SGZ NSCs for mRNAs that were upregulated developmentally from embryogenesis to adulthood but then downregulated in activated adult IPs (Table 6). Notably, of 105 SGZ NSC mRNAs that fulfilled these criteria, 94 (90%) were also identified in a previous similar analysis of V-SVZ NSCs (Borrett et al., 2020). A single-cell heatmap confirmed that all 94 mRNAs were upregulated in V-SVZ and SGZ NSCs as they transitioned to dormancy postnatally and were then downregulated in the activated TAPs/IPs (Fig. 8A). Many of these genes were involved in sensing and responding to the adult niche environment, including genes for transport and buffering of neurotransmitters and ions, and for cell:cell and cell:extracellular matrix interactions (see Table 7 for functional annotations). They also included genes important for protecting these long-lived cells from adverse environmental events, such as genes involved in detoxification and lysosome function, as well as many genes involved in lipid metabolism. Examples include mRNAs encoding the sodium-potassium ATPase subunit Atp1a2 and the secreted inhibitor of cysteine proteases Cst3 (Fig. 8B). Notably, three of the 94 mRNAs encode proteins that functionally interact with the GABA neurotransmitter. These include the two GABA transporter mRNAs Slc6a11 and Slc6a1 and the GABA-A receptor subunit mRNA Gabrb1 (Fig. 8C). These findings reinforce the idea that adult dormant NSCs are specialized for sensing and regulating their niche environments, and suggest that NSCs of both origins may alter their responses to GABA as they progress to a dormant state.

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Figure 8.

Identification of shared genes selectively enriched in dormant adult NSCs. A, Single-cell heatmap showing the expression profiles of 94 genes selectively enriched in juvenile/adult V-SVZ and SGZ NSCs relative to embryonic NSCs and juvenile/adult V-SVZ transit-amplifying cells (TAPs) and SGZ IPs (same dataset as in Fig. 7A without the activated juvenile/adult V-SVZ NSCs). Each column line represents the level of expression in a single cell. Gene expression represents scaled expression and is color-coded as per the adjacent color key, where pink/purple represents no or low expression, and yellow the highest expression. Gene expression values are not batch-corrected. B, Violin plots showing gene expression profiles of two selected mRNAs from A, Atp1a2 and Cst3, in the same populations as shown in A. SGZ expression profiles are shown in red and V-SVZ profiles in blue. Gene expression values are not batch-corrected. C, Violin plots showing gene expression profiles of three selected mRNAs from A, Gabrb1, Slc6a1, and Slc6a11, all of which are involved with NSC responsiveness to GABA. Gene expression values are not batch-corrected.

We asked whether this group of differentially-enriched genes would specifically identify adult dormant NSCs. To do this, we combined transcriptomes for all V-SVZ and SGZ NSC populations with those of the adult IPs/TAPs, removed the cell cycle genes, and then ran them through the Harmony batch-corrected pipeline together. UMAP visualization identified four main groups of transcriptomes that were segregated by developmental stage and/or activation state; embryonic RPs, perinatal NSCs, juvenile/adult NSCs, and IPs/TAPs (Fig. 9A–D). Each group included cells of both V-SVZ and SGZ origin that were closely-associated, but only partially intermingled, consistent with the conclusion that they were very similar but not identical. We then used the 94 enriched dormant NSC genes (Table 7) to compute single-cell gene signature scores for these transcriptomes. This gene signature was very specific to the dormant NSCs from P6/7 through to adulthood (Fig. 9E,F).

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Figure 9.

Identification of a shared adult dormant NSC gene signature. A, Transcriptomes of embryonic, perinatal, early postnatal, juvenile and adult V-SVZ and SGZ NSCs were combined with those of juvenile and adult V-SVZ and SGZ TAPs/IPs (same dataset as shown in Fig. 7A), cell cycle genes were regressed, and the dataset was run through the batch-corrected pipeline. Shown is a UMAP visualization where cells are color-coded and labeled based on cell type. Note here that the Juv + Adult TAPs/IPs group shown in purple include V-SVZ activated NSCs at juvenile and adult ages. B, UMAP plot as in A with cells color coded based on region of origin (V-SVZ or SGZ). C, UMAP plot as in A annotated to depict two distinct trajectories. The first trajectory describes the progression from embryonic RP to adult NSC in the V-SVZ and SGZ (pink). The second trajectory describes the progression from dormant juvenile/adult NSCs to activated, differentiating TAPs/IPs (light blue). D, UMAP visualizations as in A overlaid with V-SVZ and SGZ cell types from different ages, as defined in Figure 3A. V-SVZ cells are shown in red and SGZ cells in blue. E, UMAP as in A overlaid with gene expression scores for a shared adult dormant NSC signature consisting of the 94 genes shown in the heatmap in Figure 8A and in Table 7. Red denotes cells with scores >1.5. F, Density plot showing the distribution of the shared adult dormant NSC signature scores in V-SVZ juvenile/adult TAPs (P20/P34/P61; blue), SGZ juvenile/adult IPs (P18/P19/P23/P120/P132; orange), V-SVZ E14 RPs (pink), E16.5 SGZ RPs (green), V-SVZ juvenile/adult dormant NSCs (dNSCs; turquoise), and juvenile/adult SGZ NSCs (yellow).

These gene enrichment studies provide insights into the common transcriptional ground-state of dormant postnatal NSCs. However, further analysis showed that, with the exception of Riiad1, all of the mRNAs in this enriched dataset were also expressed by niche astrocytes (Table 7). We therefore asked whether we could combine the dormant NSC signature with the astrocyte gene signature (Fig. 1G) to specifically identify dormant adult NSCs in the V-SVZ and SGZ. To do this, we overlaid both gene signatures on the complete dentate gyrus dataset (shown in Fig. 1A) and on the juvenile/adult V-SVZ neural cell dataset (shown in Fig. 2E), as visualized by UMAPs. As predicted, in both the SGZ and V-SVZ datasets the adult dormant NSCs were identified by the NSC but not the astrocyte gene signature, while the niche astrocytes were positive for both (Fig. 10A–D). These findings provide a way to definitively identify dormant adult NSCs in the V-SVZ and SGZ from other niche cell types and reinforce the conclusion that while adult dormant NSCs and niche astrocytes are very similar they can be distinguished transcriptionally.

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Figure 10.

Analysis of the shared adult dormant NSC gene signature in all V-SVZ and SGZ cells. A, B, UMAP visualization of dentate gyrus cells as in Figure 1A overlaid with expression scores for two different gene signatures, the shared 94 gene dormant adult NSC signature (panel B, top) and the shared 26 gene niche astrocyte signature (panel B, bottom). The region shown in the hatched boxes includes juvenile/adult astrocytes and NSCs as identified in Figure 1B and is shown at a larger size to the right in each case. Red denotes cells with scores >1.5 (top) or 0.75 (bottom). Data are not batch-corrected. C, D, Annotated UMAP visualization of juvenile/adult V-SVZ neural cells (P20, P34, P61 combined) as shown in Figure 2E, overlaid with expression scores for two different gene signatures, the shared 94 gene dormant NSC signature (panel D, top) and the shared 26 gene niche astrocyte signature (panel D, bottom). Red denotes cells with scores >1.5 (top) or 0.75 (bottom). Data are not batch-corrected.