Splicing-aware scRNA-Seq resolution reveals execution-ready programs in effector Tregs

Single-cell RNA sequencing (scRNA-Seq) provides valuable insights into cell biology. However, current scRNA-Seq analytic approaches do not distinguish between spliced and unspliced mRNA at the level of dimensionality reduction. RNA velocity paradigm suggests that the presence of unspliced mRNA reflects transitional cell states, informative for studies of dynamic processes such as embryogenesis or tissue regeneration. Alternatively, stable cell subsets may also maintain translationally repressed spliced mRNA (e.g., in P-bodies) and/or unspliced mRNA reservoirs for prompt initiation of transcription-independent expression. Thus, functional cell subsets may differ not only in the current levels of actively produced mRNAs, but also in which mRNAs and in what forms are stored in the nucleus and cytoplasm. To enable splicing-aware analysis of scRNA-Seq data, we developed a method called SANSARA (Splicing-Aware scrNa-Seq AppRoAch). We employed SANSARA to characterize peripheral blood regulatory T cell (Treg) subsets, revealing a complementary interplay between the FOXP3 and Helios master transcription factors and high levels of spliced IL10RA, LGALS3, FCRL3, CD38, ITGAL, and LEF1 mRNAs in effector Tregs. Among Th1 and cytotoxic CD4+ T cell subsets, SANSARA also revealed substantial splicing heterogeneity across subset-specific genes. SANSARA is straightforward to implement in current data analysis pipelines and opens new dimensions for scRNA-Seq-based discoveries.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-11-10
DOI
https://doi.org/10.1371/journal.pcbi.1013682
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Splicing-aware scRNA-Seq resolution reveals execution-ready programs in effector Tregs

Evgeniy S. Egorov, Daniil K. Lukyanov, Kristin Ladell, Dmitriy M. Chudakov et al.
PLoS Computational Biology
Single-cell and spatial transcriptomics
article

Splicing-aware scRNA-Seq resolution reveals execution-ready programs in effector Tregs

Evgeniy S. Egorov, Daniil K. Lukyanov, Kristin Ladell, Dmitriy M. Chudakov, V. Kotliar, Denis Syrko, André Franke, Valeriia Kriukova, David A. Price
article en

Abstract

Single-cell RNA sequencing (scRNA-Seq) provides valuable insights into cell biology. However, current scRNA-Seq analytic approaches do not distinguish between spliced and unspliced mRNA at the level of dimensionality reduction. RNA velocity paradigm suggests that the presence of unspliced mRNA reflects transitional cell states, informative for studies of dynamic processes such as embryogenesis or tissue regeneration. Alternatively, stable cell subsets may also maintain translationally repressed spliced mRNA (e.g., in P-bodies) and/or unspliced mRNA reservoirs for prompt initiation of transcription-independent expression. Thus, functional cell subsets may differ not only in the current levels of actively produced mRNAs, but also in which mRNAs and in what forms are stored in the nucleus and cytoplasm. To enable splicing-aware analysis of scRNA-Seq data, we developed a method called SANSARA (Splicing-Aware scrNa-Seq AppRoAch). We employed SANSARA to characterize peripheral blood regulatory T cell (Treg) subsets, revealing a complementary interplay between the FOXP3 and Helios master transcription factors and high levels of spliced IL10RA, LGALS3, FCRL3, CD38, ITGAL, and LEF1 mRNAs in effector Tregs. Among Th1 and cytotoxic CD4+ T cell subsets, SANSARA also revealed substantial splicing heterogeneity across subset-specific genes. SANSARA is straightforward to implement in current data analysis pipelines and opens new dimensions for scRNA-Seq-based discoveries.

PLoS Computational BiologyVol. 21(11)
Central European Institute of Technology (CZ), Lomonosov Moscow State University (RU), University Hospital of Wales (GB), Christian-Albrechts-Universität zu Kiel (DE), Pirogov Russian National Research Medical University (RU), Institute of Bioorganic Chemistry (RU), Institut de Biologie Moléculaire et Cellulaire (FR), Cardiff University (GB)
Openalex Percentile: Top 67%
Single-cell and spatial transcriptomics
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