T-bet and RORα control lymph node formation by regulating embryonic innate lymphoid cell differentiation

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Abstract

The generation of lymphoid tissues during embryogenesis relies on group 3 innate lymphoid cells (ILC3) displaying lymphoid tissue inducer (LTi) activity and expressing the master transcription factor RORγt. Accordingly, RORγt-deficient mice lack ILC3 and lymphoid structures, including lymph nodes (LN). Whereas T-bet affects differentiation and functions of ILC3 postnatally, the role of T-bet in regulating fetal ILC3 and LN formation remains completely unknown. Using multiple mouse models and single-cell analyses of fetal ILCs and ILC progenitors (ILCP), here we identify a key role for T-bet during embryogenesis and show that its deficiency rescues LN formation in RORγt-deficient mice. Mechanistically, T-bet deletion skews the differentiation fate of fetal ILCs and promotes the accumulation of PLZF ^hi ILCP expressing central LTi molecules in a RORα-dependent fashion. Our data unveil an unexpected role for T-bet and RORα during embryonic ILC function and highlight that RORγt is crucial in counteracting the suppressive effects of T-bet.

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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Massively parallel digital transcriptional profiling of single cells

Grace Zheng, Jessica M. Terry, Phillip Belgrader … (2017)

Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.

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UMAP: Uniform Manifold Approximation and Projection

Leland McInnes, John Healy, Nathaniel Saul … (2018)

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Author and article information

Journal

Journal ID (nlm-journal-id): 100941354

Journal ID (nlm-ta): Nat Immunol

Journal ID (iso-abbrev): Nat Immunol

Title: Nature immunology

ISSN (Print): 1529-2908

ISSN (Electronic): 1529-2916

Publication date (Print): 01 October 2021

Publication date (Electronic): 23 September 2021

Publication date Nihms-submitted: 13 June 2023

Publication date PMC-release: 14 August 2023

Volume: 22

Issue: 10

Pages: 1231-1244

Affiliations

[1 ]Innate Immunity, German Rheumatism Research Centre – a Leibniz Institute, Berlin, Germany

[2 ]Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2TT, UK

[3 ]Immune Dynamics, German Rheumatism Research Centre – a Leibniz Institute, Berlin, Germany

[4 ]Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Rheumatology and Clinical Immunology, Berlin, Germany

[5 ]Cell Biology, German Rheumatism Research Centre – a Leibniz Institute, Berlin, Germany

[6 ]Therapeutic Gene Regulation, German Rheumatism Research Centre – a Leibniz Institute, Berlin, Germany

[7 ]Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Charitéplatz 1, 10117 Berlin, Germany

[8 ]Department of Biomedicine and University Children's Hospital of Basel, University of Basel, Basel, Switzerland

[9 ]Schwiete Laboratory for Microbiota and Inflammation, German Rheumatism Research Centre – a Leibniz Institute, Berlin, Germany

[10 ]Department of Cytometry, Institute of Biotechnology, Technische Universität Berlin, Germany

[11 ]Maurice Müller Laboratories (DBMR), Universitätsklinik für Viszerale Chirurgie und Medizin Inselspital, University of Bern, Bern, Switzerland

[12 ]Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Gastroenterology, Infectious Diseases, Rheumatology, Berlin, Germany

[13 ]Leibniz-Science Campus Chronic Inflammation