Neural circuits in the spinal cord are composed of diverse populations of interneurons that play crucial roles in shaping motor output. However, the extent of interneuron heterogeneity and how this diversity relates to functional aspects of movement remain unclear. Here, through a focus on mouse spinal V1 interneurons, we show that loss of the V1 transcription factor En1 selectively disrupts the frequency of rhythmic locomotor output but does not disrupt flexion/extension limb movement, thereby decoupling two key functional roles ascribed to this neuronal population. To investigate the cellular basis of these deficits, we generated a single-nucleus transcriptomic atlas of V1 interneurons across postnatal development. Our analysis reveals age-dependent transcriptional changes while also demonstrating that their core molecular taxonomy perdures into adulthood. Notably, En1 deficiency selectively perturbed a single subset of V1
Pou6f2
interneurons, thereby identifying a possible cellular substrate for influencing locomotor speed. Beyond serving as a molecular resource, our study highlights how deep neuronal profiling provides an entry point for understanding the multifunctional nature of heterogeneous interneuron populations.
Alexandra J. Trevisan, Katie Han, Phillip Chapman et al.· Nature Communications· 0 citations
Single-cell chromatin accessibility (scATAC-seq) profiles genome-wide regulatory elements that shape immune cell identity and function, but its interpretation is currently limited by low cell type resolution and small reference datasets. Existing datasets annotate fewer than 20 immune cell types and are too coarse to resolve heterogeneity and characterize cell type-specific gene regulatory programs and functions. Here, we present a large-scale scATAC-seq resource that substantially improves immune cell annotation and regulatory inference.
By integrating matched-donor scRNA-seq and scATAC-seq data from human peripheral blood mononuclear cells (PBMCs) with trimodal TEA-seq (single-cell ATAC, RNA, and surface protein), we classified 36 immune cell types, including 4 myeloid, 6 B cell, 5 NK cell, 6 CD4 T cell, and 15 CD8 T cell subtypes. Cell frequencies from published scRNA-seq and new scATAC-seq labels were highly correlated (median ρ = 0.84). Labels were applied to our longitudinal multi-modal dataset of 206 samples spanning over 3 million PBMCs from 78 healthy human donors.
We used these annotations to define baseline epigenetic states, age-associated differences, and epigenetic changes following influenza vaccination. Our analysis revealed extensive sets of differentially accessible tiles and enriched transcription factor motifs that define cell type-specific regulatory identities. Linking these chromatin regions and transcription factors to differentially expressed target genes enabled the construction of gene regulatory circuits associated with cell type, aging, and vaccination. Additionally, we trained a classification model for high resolution cell type labeling and doublet detection in new scATAC-seq datasets.
Together, this multi-modal atlas and associated cell type-labeling model provide an unprecedented reference for immune cell gene regulatory circuits and a valuable resource for exploring the epigenome of human immune cells.
n/a
Computational and Systems Immunology (COMP)
Sydney Kuhl, Upaasana Krishnan, A. Tjaernberg et al.· Journal of Immunology· 0 citations
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