sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.02.526789v1?rss=1

Authors: Ng, J. C., Montamat Garcia, G., Stewart, A., Blair, P., Dunn-Walters, D. K., Mauri, C., Fraternali, F.

Abstract: Class-switch recombination (CSR) is an integral part of B cell maturation. Steady-state analyses of isotype distribution (e.g. B cell receptor [BCR] repertoire analysis of snapshots during an immune response) do not directly measure CSR dynamics, which is crucial in understanding how B cell maturation is regulated across time. We present sciCSR (pronounced 'scissor', single-cell inference of class switch recombination), a computational pipeline which analyses CSR events and dynamics of B cells from single-cell RNA-sequencing (scRNA-seq) experiments. sciCSR re-analyses transcriptomic sequence 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 by the pipeline to infer the dynamics and direction of CSR. Applying sciCSR on SARS-CoV-2 vaccination time-course scRNA-seq data, we observe that sciCSR predicts, using data from an earlier timepoint in the collected time-course, the isotype distribution of BCR repertoires of subsequent timepoints with high accuracy (cosine similarity approximately 0.9). sciCSR also recapitulates CSR patterns in mouse models where B cell maturation was perturbed using gene knockouts. sciCSR infers cell state transitions using processes specific to B cells, identifies transitions which are often missed by conventional RNA velocity analyses, and can reveal insights into the regulation of CSR and the dynamics of B cell maturation during an immune response.

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