Cell ReportsImmunologyPeer-reviewed journal article
Single-cell multiomics reveals archetypal regulatory programs shared across CD4 and CD8 T cell subsets in viral infection
Sarah K. WalkeriD, Joris van der VeekeniD, Alexander Yu RudenskyiD, Yuri PritykiniD
Abstract
T cells protect against pathogens and tumors and can differentiate into various functionally distinct subsets. While each subset exhibits a characteristic epigenomic and transcriptional profile, essential immune functions such as self-renewal, expansion, cytokine production, and cytotoxicity are common to multiple subsets and therefore must be programmed via shared regulatory mechanisms. To uncover these regulatory "archetypes", we integrated a new single-cell multiomic (scATAC+RNA-seq) dataset for CD4 and CD8 T cells responding to acute and chronic viral infection with a comprehensive compendium of bulk ATAC-seq data. Our analysis revealed that T cell identity is governed by a modular architecture, where distinct transcription factors drive the reuse of regulatory archetypes across T cell subsets and lineages. Notably, translationally important progenitor Tcf1+ CD8 T cells, critical for sustaining CD8 T cell responses and present in both acute and chronic infection, exhibited a composite regulatory state combining a CD8 T cell exhaustion program and, unexpectedly, a follicular helper CD4 T cell program. In sum, this resource will aid mechanistic dissection of adaptive immunity and immunotherapy design, and the framework will be broadly applicable across biological systems. Highlights- Single-cell multiomics and ATAC-seq atlas reveal T cell regulatory archetypes - A unified progenitor Tcf1+ CD8 T cell population is shared by acute and chronic infection - Progenitor CD8 T cell identity combines CD8 exhaustion and CD4 Tfh regulatory programs - A Tfh-like differentiation gradient underlies heterogeneity in progenitor CD8 T cells
- organism: human
- assay: scATAC-seq
- assay: bulk RNA-seq
- assay: ATAC-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: clustering
- research focus: trajectory inference
- approach: deep learning
- approach: statistical model
- Citations
- 1
- FWCI
- 2.64
- Field percentile
- top 18%
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