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

Single-cell RNA sequencing (RNA-Seq) provides rich information about cell types and states. However, it is difficult to capture rare dynamic processes, such as adult neurogenesis, because isolation of rare neurons from adult tissue is challenging and markers for each phase are limited. Here, we develop Div-Seq, which combines scalable single-nucleus RNA-Seq (sNuc-Seq) with pulse labeling of proliferating cells by 5-ethynyl-2′-deoxyuridine (EdU) to profile individual dividing cells. sNuc-Seq and Div-Seq can sensitively identify closely related hippocampal cell types and track transcriptional dynamics of newborn neurons within the adult hippocampal neurogenic niche, respectively. We also apply Div-Seq to identify and profile rare newborn neurons in the adult spinal cord, a noncanonical neurogenic region. sNuc-Seq and Div-Seq open the way for unbiased analysis of diverse complex tissues.

until nearly all variable combinations are considered (figs. S15 to S20). Moreover, alternative criteria for selecting candidate views may be desirable to address specific objectives. For example, a more robust but less specifically predictive model could be constructed by selecting variable combinations that are maximally distinct.
With enough data, it should even be possible to identify optimal weightings of the different views or have such weightings be state-dependent (e.g., to correct for the state-dependent biases of individual views). Regardless of details, the implementation of MVE demonstrated here is intended to be as simple as possible.
The main innovation of MVE is to leverage the interconnectedness (the shared information) of complex systems. As seen in Fig. 3, improvements in forecast skill can be especially evident for short time series (~25 time points). This result is especially promising given that many current ecological data sets are wide in scope, with many different variables being tracked, but shallow in terms of time series length. Furthermore, the noise-mitigating aspects of MVE are potentially useful for many other applications such as reconstructing historical behavior, signal processing (31), or nonlinear system control (32). Although the high-dimensionality of complex systems is typically perceived as an obstacle, such complexity is actually an advantage, leading to better clarity and prediction. Single-cell RNA sequencing (RNA-Seq) provides rich information about cell types and states. However, it is difficult to capture rare dynamic processes, such as adult neurogenesis, because isolation of rare neurons from adult tissue is challenging and markers for each phase are limited. Here, we develop Div-Seq, which combines scalable single-nucleus RNA-Seq (sNuc-Seq) with pulse labeling of proliferating cells by 5-ethynyl-2′-deoxyuridine (EdU) to profile individual dividing cells. sNuc-Seq and Div-Seq can sensitively identify closely related hippocampal cell types and track transcriptional dynamics of newborn neurons within the adult hippocampal neurogenic niche, respectively. We also apply Div-Seq to identify and profile rare newborn neurons in the adult spinal cord, a noncanonical neurogenic region. sNuc-Seq and Div-Seq open the way for unbiased analysis of diverse complex tissues. S ingle-cell RNA sequencing (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 by scRNA-Seq. First, scRNA-Seq requires enzymatic tissue dissociation (Fig.  1A), which may compromise the integrity of neurons and their RNA content, skew data toward 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.
Analysis of sNuc-Seq data revealed distinct nuclei clusters (Fig. 1, B to D; figs. S4, S5, and S6, A to C; and table S1) corresponding to known cell types and anatomical distinctions in the hippocampus. Analysis was consistent with microdissections, in situ hybridization [Allen Brain Atlas 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

Immature neuron Sox11
GABAergic Gad1   Fig. 3C) colored by a glia-neuron RNA expression score. (F) Regionspecific gene expression in immature neurons (6 to 7 days after EdU labeling). A total of 236 genes differentially expressed between SC and DG (t-test false discovery rate <0.05, log-ratio >1) in olfactory bulb (OB), SC, and DG. Box: average expression of example genes up-regulated in OB and SC compared to DG. neighbor embedding) ( fig. S7), which partitioned the GABAergic neurons into subclusters ( fig. S8 and table S2) and associated each subcluster 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 subregions with divergent transcriptional profiles. BiSNE partitioned glutamatergic cells into subclusters ( Fig. 2A and fig. S10), which were further mapped to hippocampal subregions, with ISH of spatial landmark genes (10) (Fig. 2B and figs. S11 to S13). We validated our mapping by confirming expression patterns using the Allen ISH data set (10) (Fig. 2C and fig. S14). Although some subregions were assigned to a single subcluster (e.g., dorsal lateral CA1, Fig. 2B), most subregions were assigned partially overlapping subclusters, suggesting a gradual transition of transcriptional profiles between neighboring subregions. This extends current anatomical resolutions of the hippocampus (fig. S15) and supports the notion that cellular diversity does not always partition into discrete subtypes (12).
We identified genes that are indicative of specific cell type or position (tables S1 to S3). For example, Penk, encoding an opioid neuropeptide (enkephalin), and its receptor Oprd1 (13) were expressed in mutually exclusive subclusters of cells (Fig. 2D), which we validated by FISH and the Allen ISH data set (10) (Fig. 2E and 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 to 14 days after cell division; Fig. 3B, fig. S17, and table S4). Div-Seq enriched for diverse newborn cell types and neurogenic stages (fig. S17, F and 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 ex-pression in specific stages of neurogenesis (figs. S18 and 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 noncanonical 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 to 7 days) in the SC ( fig.  S20). SC nuclei 6 to 7 days after labeling (Fig. 4,  A Fig. 4D and  fig. S20C) but also revealed notable distinctions in the expression dynamics and branching along the DG and SC trajectories (Fig. 4, D and E) (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 composed 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 (figs. S22 to 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.