B-Cell Aware Analysis of Single-Cell Transcriptomics Data.

Joseph C F Ng, Raul Ruiz-Hernandez, Isabella Withnell, Tooki Chu, Franca Fraternali

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Joseph C F NgResearch Department of Structural and Molecular Biology, Division of Biosciences, University College London, Gower Street, London, WC1E 6BT, UK. joseph.ng@ucl.ac.uk.

Raul Ruiz-HernandezInstitute of Structural and Molecular Biology, School of Natural Sciences, Birkbeck, University of London, London, WC1E 7HX, UK.

Isabella WithnellResearch Department of Structural and Molecular Biology, Division of Biosciences, University College London, Gower Street, London, WC1E 6BT, UK.

Tooki ChuResearch Department of Structural and Molecular Biology, Division of Biosciences, University College London, Gower Street, London, WC1E 6BT, UK.

Franca FraternaliResearch Department of Structural and Molecular Biology, Division of Biosciences, University College London, Gower Street, London, WC1E 6BT, UK. f.fraternali@ucl.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNA-seq) has facilitated the studies of cellular heterogeneity in many different biological contexts, including how B cells function and mediate immune responses to infection, malignancies, and autoimmunity. scRNA-seq as applied to B cells requires specific bioinformatics strategies, which consider the distinct biology of B cells and biological processes unique to these cells, such as somatic hypermutation (SHM) and class switch recombination (CSR). We present here a protocol that analyses scRNA-seq alignments and count matrices, as well as B-cell receptor sequencing data obtained in parallel to scRNA-seq. We highlight computational strategies to ensure cell clusters align with biologically relevant B-cell subsets and cell states. We provide practical guidance on how CSR and SHM information can be extracted from the data and utilized to build robust computational models of B-cell maturation.

Indexed as

B-LymphocytesComputational BiologyGene Expression ProfilingSingle-Cell AnalysisTranscriptomeAnimalsHumansImmunoglobulin Class SwitchingSequence Analysis, RNASingle-Cell Gene Expression AnalysisSoftwareSomatic Hypermutation, ImmunoglobulinB cellsClass switch recombinationHumoral immune responsescRNA-seqSingle-cell transcriptomics

Identifiers

PMID42734753

DOI10.1038/44565

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.