ArticleNature methods2024
sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- B-Cell Aware Analysis of Single-Cell Transcriptomics Data.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Human primary antibody response to vaccination follows a partially sequential class-switching program with a checkpoint at IGHG2.Cell reports. Medicine · 2026Article
- Competing gene regulatory networks drive naive and memory B cell differentiation.Molecular systems biology · 2026Article
- Identification and characterization of bone marrow plasma cells producing IFN-γ-neutralizing autoantibodies in adult-onset immunodeficiency.Cell reports. Medicine · 2026Article
- Article
- Cell and tissue reprogramming: Unlocking a new era in medical drug discovery.Pharmacological reviews · 2025Review
- Reduced response to SARS-CoV-2 vaccination is associated with impaired immunoglobulin class switch recombination in SLE patients.Clinical and experimental immunology · 2025Article
- Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data.Cell genomics · 2024Article
- Single-cell immune repertoire analysis.Nature methods · 2024Review
- Year in review 2023.Nature methods · 2024Article
- B cell phylogenetics in the single cell era.Trends in immunology · 2024Review
- TRIBAL: Tree Inference of B cell Clonal Lineages.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
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.