Activity‐regulated gene expression across cell types of the mouse hippocampus

Abstract

Activity‐regulated gene (ARG) expression patterns in the hippocampus (HPC) regulate synaptic plasticity, learning, and memory, and are linked to both risk and treatment responses for many neuropsychiatric disorders. The HPC contains discrete classes of neurons with specialized functions, but cell type‐specific activity‐regulated transcriptional programs are not well characterized. Here, we used single‐nucleus RNA‐sequencing (snRNA‐seq) in a mouse model of acute electroconvulsive seizures (ECS) to identify cell type‐specific molecular signatures associated with induced activity in HPC neurons. We used unsupervised clustering and a priori marker genes to computationally annotate 15,990 high‐quality HPC neuronal nuclei from N = 4 mice across all major HPC subregions and neuron types. Activity‐induced transcriptomic responses were divergent across neuron populations, with dentate granule cells being particularly responsive to activity. Differential expression analysis identified both upregulated and downregulated cell type‐specific gene sets in neurons following ECS. Within these gene sets, we identified enrichment of pathways associated with varying biological processes such as synapse organization, cellular signaling, and transcriptional regulation. Finally, we used matrix factorization to reveal continuous gene expression patterns differentially associated with cell type, ECS, and biological processes. This work provides a rich resource for interrogating activity‐regulated transcriptional responses in HPC neurons at single‐nuclei resolution in the context of ECS, which can provide biological insight into the roles of defined neuronal subtypes in HPC function.

An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus

“…However, integrating data from model systems that incorporate experimental manipulations can aid functional interpretation. To demonstrate this approach, we leveraged snRNA-seq data generated in a mouse model of electroconvulsive seizures (ECS) to identify cell type-specific activity-regulated gene expression patterns in the HPC [44]. We asked if we could identify cells more likely to be activated by stimulation and find gene expression patterns predicting induction of neural activity.…”

“…However, integrating human transcriptomic data with functional genomics data in animal models has the potential to facilitate interpretation of cellular responses to various stimuli. To illustrate the feasibility of this approach, we mapped NMF patterns from the human HPC onto snRNA-seq data from a mouse model of induced electroconvulsive seizures (ECS) [44] to identify gene expression patterns that are putatively associated with neural activity in the human brain ( Fig. 7 ).…”

“…NMF patterns that differentially map to control versus activated neurons may represent the population of GCs that are more quiescent versus those that are either actively firing or intrinsically primed for activity [93]. Indeed, active and quiescent mouse GCs have substantially different transcriptomic profiles [44], and differences in GC activity levels may reflect differences in their ability to be recruited into neuronal ensembles in response to behavioral experiences [94–99]. These cross-species analyses provide insight about the putative spatial and molecular organization of cellular activity states in the human HPC, which is critical for attempting to design rational therapies for disorders involving dysregulated HPC neuron activity.…”

“…We used this approach to project NMF patterns defined in human HPC snRNA-seq data into three different datasets: human HPC SRT data, epi-retro-seq data in a mouse model linking single-nucleus methylation sequencing in neurons with their axon projection targets [43], and snRNA-seq data in a mouse model of induced electroconvulsive seizures (ECS) [44]. For pattern transfer to human HPC SRT data, we used library size-normalized, log 2 transformed counts as the A’ matrix.…”

TrkB-dependent regulation of molecular signaling across septal cell types

“…Tissue was pooled from both hemispheres of the LS from 2 mice of the same sex - thus, each individual sample consisted of 4 pooled LS dissections (e.g., N = 4 total samples, n = 8 mice; 4 female, 4 male). Nuclei were isolated using the “Frankenstein” nuclei isolation protocol for frozen tissues as previously described [ 32 , 33 ]. Tissue was homogenized in chilled nuclei EZ lysis buffer (Millipore Sigma #NUC101) using glass dounce with ~15 strokes per pestle.…”