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Single-cell multiomic analysis of thymocyte development reveals drivers of CD4+ T cell and CD8+ T cell lineage commitment

ABSTRACT: Single-cell multiomic analysis of thymocyte development reveals drivers of CD4+ T cell and CD8+ T cell lineage commitment

PROVIDER: PRJNA772290 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR16474500_1.fastq.gz	Fastqsanger.gz
	SRR16474500_2.fastq.gz	Fastqsanger.gz
	SRR16474501_1.fastq.gz	Fastqsanger.gz
	SRR16474501_2.fastq.gz	Fastqsanger.gz
	SRR16474502_1.fastq.gz	Fastqsanger.gz

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Single-cell multiomic analysis of thymocyte development reveals drivers of CD4+ T cell and CD8+ T cell lineage commitment

Project description:The development of CD4+ T cells and CD8+ T cells in the thymus is critical to adaptive immunity and is widely studied as a model of lineage commitment. Recognition of self-MHCI or MHCII by the T cell antigen receptor (TCR) determines the CD8+ T cell or CD4+ T cell lineage choice, respectively, but how distinct TCR signals drive transcriptional programs of lineage commitment remains largely unknown. Here we applied CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and T cell lineage-restricted mice to generate a comprehensive timeline of cell state for each T cell lineage. These analyses identified a sequential process whereby all thymocytes initiate CD4+ T cell lineage differentiation during an initial wave of TCR signaling, followed by a second TCR signaling wave that coincides with CD8+ T cell lineage specification. CITE-seq and pharmaceutical inhibition experiments implicated a TCR-calcineurin-NFAT-GATA3 axis in driving the CD4+ T cell fate. Our data provide a resource for understanding cell fate decisions and implicate multiple redundant mechanisms in guiding lineage choice.

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Single-cell multiomic analysis of thymocyte development reveals drivers of CD4<sup>+</sup> T cell and CD8<sup>+</sup> T cell lineage commitment.

Project description:The development of CD4+ T cells and CD8+ T cells in the thymus is critical to adaptive immunity and is widely studied as a model of lineage commitment. Recognition of self-peptide major histocompatibility complex (MHC) class I or II by the T cell antigen receptor (TCR) determines the CD8+ or CD4+ T cell lineage choice, respectively, but how distinct TCR signals drive transcriptional programs of lineage commitment remains largely unknown. Here we applied CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and T cell lineage-restricted mice to generate a comprehensive timeline of cell states for each T cell lineage. These analyses identified a sequential process whereby all thymocytes initiate CD4+ T cell lineage differentiation during a first wave of TCR signaling, followed by a second TCR signaling wave that coincides with CD8+ T cell lineage specification. CITE-seq and pharmaceutical inhibition experiments implicated a TCR-calcineurin-NFAT-GATA3 axis in driving the CD4+ T cell fate. Our data provide a resource for understanding cell fate decisions and implicate a sequential selection process in guiding lineage choice.

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Predicting drivers of proximal tubule cell state through regularized regression analysis of single cell multiomic sequencing

Project description:In this study, we generated a single nucleus multiomic (snRNA-seq and snATAC-seq) dataset of adult human kidney. We developed a bioinformatic tool to analyze this dataset by identifying key cis-regulatory elements and transcription factors associated with specific cell types and states. We applied this tool to identify transcription factors implicated in proximal tubule cell injury and failed repair states. We demonstrate this tool can be applied to single cell multiomic datasets to derive insight into cell type- and state-specific gene regulatory networks.

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Project description:Boolean approaches and extensions thereof are becoming increasingly popular to model signaling and regulatory networks, including those controlling cell differentiation, pattern formation and embryonic development. Here, we describe a logical modeling framework relying on three steps: the delineation of a regulatory graph, the specification of multilevel components, and the encoding of Boolean rules specifying the behavior of model components depending on the levels or activities of their regulators. Referring to a non-deterministic, asynchronous updating scheme, we present several complementary methods and tools enabling the computation of stable activity patterns, the verification of the reachability of such patterns, as well as the generation of mean temporal evolution curves and the computation of the probabilities to reach distinct activity patterns. We apply this logical framework to the regulatory network controlling T lymphocyte specification. This process involves cross-regulations between specific T cell regulatory factors and factors driving alternative differentiation pathways, which remain accessible during the early steps of thymocyte development. Many transcription factors needed for T cell specification are required in other hematopoietic differentiation pathways and are combined in a fine-tuned, time-dependent fashion to achieve T cell commitment. Using the software GINsim, we integrated current knowledge into a dynamical model, which recapitulates the main developmental steps from early progenitors entering the thymus up to T cell commitment, as well as the impact of various documented environmental and genetic perturbations. Our model analysis further enabled the identification of several knowledge gaps. The model, software and whole analysis workflow are provided in computer-readable and executable form to ensure reproducibility and ease extensions.

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