Single-cell inference of class-switch recombination (sciCSR) is a computational method that analyzes single-cell RNA sequencing data to deduce the temporal trajectory of how B cells develop antibody response.
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References
Xu, Z. et al. Immunoglobulin class-switch DNA recombination: induction, targeting and beyond. Nat. Rev. Immunol. 12, 517–531 (2012). This review article summarizes the genetic mechanisms of CSR.
Horton, M. B. et al. Lineage tracing reveals B cell antibody class switching is stochastic, cell-autonomous, and tuneable. Immunity 55, 1843–1855.e6 (2022). This paper describes a tailored lineage tracing system to explore the molecular determinants of CSR.
Lange, M. et al. CellRank for directed single-cell fate mapping. Nat. Methods 19, 159–170 (2022). This paper presents CellRank, a computational method that builds Markov chains to describe cellular dynamics in scRNA-seq data.
Stewart, A. et al. Single-cell transcriptomic analyses define distinct peripheral B cell subsets and discrete development pathways. Front. Immunol. 12, 602539 (2021). This paper presents a scRNA-seq atlas of circulating B cells from healthy individuals.
Hoffmann, M. et al. Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn. Sci. Technol. 3, 015009 (2022). This paper describes a software package to build dynamical models in physical chemistry and molecular dynamics.
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This is a summary of: Ng, J. C. F. et al. sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data. Nat. Methods https://doi.org/10.1038/s41592-023-02060-1 (2023).
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‘Snipping’ the transcriptome unravels the dynamics of antibody response. Nat Methods (2023). https://doi.org/10.1038/s41592-023-02061-0
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DOI: https://doi.org/10.1038/s41592-023-02061-0
