sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data.

Autor: Ng JCF; Department of Structural and Molecular Biology, Division of Biosciences and Institute of Structural and Molecular Biology, University College London, London, UK. joseph.ng@ucl.ac.uk., Montamat Garcia G; Division of Infection and Immunity and Institute of Immunity and Transplantation, Royal Free Hospital, University College London, London, UK., Stewart AT; School of Biosciences and Medicine, University of Surrey, Guildford, UK., Blair P; Division of Infection and Immunity and Institute of Immunity and Transplantation, Royal Free Hospital, University College London, London, UK., Mauri C; Division of Infection and Immunity and Institute of Immunity and Transplantation, Royal Free Hospital, University College London, London, UK., Dunn-Walters DK; School of Biosciences and Medicine, University of Surrey, Guildford, UK., Fraternali F; Department of Structural and Molecular Biology, Division of Biosciences and Institute of Structural and Molecular Biology, University College London, London, UK. f.fraternali@ucl.ac.uk.
Jazyk: angličtina
Zdroj: Nature methods [Nat Methods] 2024 May; Vol. 21 (5), pp. 823-834. Date of Electronic Publication: 2023 Nov 06.
DOI: 10.1038/s41592-023-02060-1
Abstrakt: Class-switch recombination (CSR) is an integral part of B cell maturation. Here we present sciCSR (pronounced 'scissor', single-cell inference of class-switch recombination), a computational pipeline that analyzes CSR events and dynamics of B cells from single-cell RNA sequencing (scRNA-seq) experiments. Validated on both simulated and real data, sciCSR re-analyzes scRNA-seq alignments to differentiate productive heavy-chain immunoglobulin transcripts from germline 'sterile' transcripts. From a snapshot of B cell scRNA-seq data, a Markov state model is built to infer the dynamics and direction of CSR. Applying sciCSR on severe acute respiratory syndrome coronavirus 2 vaccination time-course scRNA-seq data, we observe that sciCSR predicts, using data from an earlier time point in the collected time-course, the isotype distribution of B cell receptor repertoires of subsequent time points with high accuracy (cosine similarity ~0.9). Using processes specific to B cells, sciCSR identifies transitions that are often missed by conventional RNA velocity analyses and can reveal insights into the dynamics of B cell CSR during immune response.
(© 2023. The Author(s).)
Databáze: MEDLINE
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