Div-Seq: Single nucleus RNA-Seq reveals dynamics of rare adult newborn neurons

Single cell RNA-Seq (scRNA-Seq) has extended our understanding of heterogeneous tissues including the central nervous system (CNS) (1–3). However, dynamic processes, such as adult neurogenesis, remain challenging to study using scRNA-Seq. First, scRNA-Seq requires enzymatic tissue dissociation (Fig. 1A), which may compromise the integrity of neurons and their RNA content, skew data towards easily dissociated cell types, and is restricted to fetal or young animals (1). Second, it is difficult to capture rare cell types, such as adult newborn neurons (4), because of limitations in cell tagging and isolation at each phase of the dynamic process.

We therefore developed Div-Seq, a method for RNA-seq of individual, recently-divided cells. Div-Seq relies on sNuc-Seq, a single-nucleus isolation and RNA-Seq method compatible with frozen or fixed tissue (Fig. 1A), which enables enrichment of rare labeled cell populations by fluorescence-activated cell sorting (FACS) (fig. S1). Div-Seq combines sNuc-Seq with pulse labeling of dividing cells by 5-ethynyl-2′-deoxyuridine (EdU) (5, 6).

We validated that sNuc-Seq on population of nuclei faithfully represents tissue level RNA (7) (fig. S2A–B), in agreement with earlier studies on the feasibility of single nucleus RNA-seq (7, 8). Next, we analyzed 1,367 single nuclei from hippocampal anatomical sub-regions (DG, CA1, CA2, and CA3) from adult mice, including enrichment of genetically-tagged lowly abundant GABAergic neurons (9) (fig. S1). sNuc-Seq robustly generated high quality data across animal age groups (including 2 years old mice, fig. S2C–H, S3), detecting 5,100 expressed genes per nucleus on average, with comparable complexity to single neuron RNA-Seq from young mice (1–3) (fig. S3A–C).

Analysis of sNuc-Seq data revealed distinct nuclei clusters (Fig. 1B–D, fig. S4–S5, S6A–C and table S1) corresponding to known cell types and anatomical distinctions in the hippocampus. Analysis was consistent with microdissections, in situ hybridization (Allen ISH (10), fig. S5), and bulk RNA-Seq (11) (fig. S6D). We captured finer distinctions between closely related cells using a new clustering algorithm, biSNE (biclustering on Stochastic Neighbor Embedding) (fig. S7), which partitioned the GABAergic neurons into sub-clusters (fig. S8 and table S2), and associated each sub-cluster with combinations of canonical markers (fig. S8C). We validated selected markers using fluorescent RNA in situ hybridization (FISH) (fig. S9).

BiSNE also distinguished between spatial hippocampal sub-regions with divergent transcriptional profiles. BiSNE partitioned glutamatergic cells into sub-clusters (Fig. 2A and fig. S10), which were further mapped to hippocampal sub-regions, using ISH of spatial landmark genes (10) (Fig. 2B and fig. S11–S13). We validated our mapping by confirming expression patterns using the Allen ISH dataset (10) (Fig. 2C and fig. S14). While some sub-regions were assigned to a single sub-cluster (e.g., dorsal lateral CA1, Fig. 2B), most sub-regions were assigned partially overlapping sub-clusters, suggesting a gradual transition of transcriptional profiles between neighboring sub-regions. This extends current anatomical resolutions of the hippocampus (figure S15), and support the notion that cellular diversity does not always partition into discrete sub-types (12).

We identified genes that are indicative of specific cell type or position (tables S1–S3). For example, Penk, encoding an opioid neuropeptide (Enkephalin), and its receptor Oprd1 (13), were expressed in mutually exclusive sub-clusters of cells (Fig. 2D), which we validated by FISH and the Allen ISH dataset (10) (Fig 2E, fig. S16). These cell types and spatial positions may be involved in Enkephalin signaling within the hippocampus.

Next, to study transcriptional dynamics during adult neurogenesis we developed Div-Seq by combining sNuc-Seq with EdU labeling of dividing cells (Fig. 3A). Unlike genetic labeling (2, 14), EdU tags proliferating cells at any time window, marking stem cells and their progeny with high temporal resolution. We applied Div-Seq in the DG, a canonical neurogenic niche (4), over multiple time points (1–14 days after cell division, Fig. 3B, fig. S17, Table S4). Div-Seq enriched for diverse newborn cell types and neurogenic stages (fig. S17F–G), from proliferating stem cells to immature neurons (4).

BiSNE analysis of neuronal lineage nuclei placed the DG newborn neurons on a continuous trajectory. The order of nuclei along the trajectory matched the EdU labeling time (Fig. 3C), was independent of animal age (fig. S17H) and recapitulated known dynamics of neurogenesis markers (2, 3, 15) (fig S18A), indicating that the trajectory indeed captured the neuronal maturation process.

To characterize the transcriptional program of adult neurogenesis, we identified and clustered genes with dynamic expression patterns along the trajectory (Fig. 3D and table S5). We found major coordinated transcriptional waves, involving hundreds of genes, and aligned with known transitions between neurogenic stages, with expression shifts from proliferation to neuronal differentiation (consistent with (2)), and then to neuronal integration and maturation (Fig. 3D). We identified genes with restricted expression in specific stages of neurogenesis (fig. S18–S19), including transcription factors and chromatin regulators (fig. S18). We confirmed the early neurogenic stage-specific expression of the axon guidance molecule Draxin and the ribonucleotide reductase Rrm2 by FISH (fig. S19).

Accumulating evidence suggests that adult neurogliogenesis occurs in multiple non-canonical regions (16), but traditional methods are limited for the characterization of rare newborn cells, and can lead to less definitive findings, as in the spinal cord (SC) (17) (18). We applied Div-Seq over multiple time points (1–7 days) in the SC (fig. S20). SC nuclei 6–7 days post labeling (Fig. 4A–B) comprised a diverse population of newborn cells including oligodendrocyte precursor cells (OPCs, 44%) and immature neurons (19%), in contrast to 4% OPCs and no immature neurons in the non-EdU labeled population. The SC newborn neurons expressed the GABAergic markers Gad1 and Gad2, suggesting GABAergic neurogenesis (18) (Fig. 4B). Notably, we found a set of immature neuronal nuclei (10%) at 23–24 days post EdU labeling (fig. S21), suggesting survival of newborn neurons in the SC.

The full set of neuronal lineage nuclei (fig. S20A) map to a continuous trajectory (Fig. 4C), that matched labeling time and expression dynamics of known markers (fig. S20C). Comparison of dynamically expressed genes along the SC and DG trajectories (fig. S20B) identified 347 (28%) common neurogenesis genes (Fig. 4D, fig. S20C), and revealed notable distinctions in expression dynamics and branching in the DG and SC (Fig. 4D–E and (6)), which can result from differences in time scales, cell populations, or parallel gliogenesis and neurogenesis processes.

The immature neurons from SC and DG are comprised of different neuronal types (GABAergic in SC, granule cells in DG). To identify candidate genes driving neuronal lineage specification, we first identified differentially expressed genes between SC and DG (t-test), and then compared their expression patterns to those of newborn neurons in the olfactory bulb (OB), where GABAergic neurons are born (Fig. 4F). A set of SC-specific genes was also up-regulated in the OB relative to the DG, including the transcription factors Pbx3 and Meis2. This is consistent with previous reports (19, 20), and with immunohistochemistry of Pbx3 showing expression in newborn cells both in the OB and SC but not in the DG (fig. S22–S24).

Application of Div-Seq to the adult CNS highlighted potential regulators and the neurogenic potential of the SC, though the functional roles of these SC newborn neurons remained to be elucidated. Future technology developments may increase the sensitivity, throughput, and cell types amenable to these methods. sNuc-Seq and Div-Seq open new avenues in the study of neuronal diversity and dynamic processes in the CNS, and can be readily applied to diverse biological systems and human tissues.