Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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

Lukyanov, Daniil K., Egorov, Evgeniy S., Kriukova, Valeriia V., Syrko, Denis, Kotliar, Victor V., Ladell, Kristin ORCID: https://orcid.org/0000-0002-9856-2938, Price, David A. ORCID: https://orcid.org/0000-0001-9416-2737, Franke, Andre and Chudakov, Dmitry M. 2025. Splicing-aware scRNA-Seq resolution reveals execution-ready programs in effector Tregs. PLoS Computational Biology 21 (11) , e1013682. 10.1371/journal.pcbi.1013682

[thumbnail of journal.pcbi.1013682.pdf] PDF - Published Version
Available under License Creative Commons Attribution.

Download (1MB)

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 paradigms suggest 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 in processing bodies (P-bodies) and/or unspliced mRNA reservoirs for prompt initiation of transcription-independent expression. 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 upregulation of functionally relevant IL10RA, LGALS3, FCRL3, CD38, ITGAL, and LEF1 spliced gene forms 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Schools > Medicine
Publisher: Public Library of Science
ISSN: 1553-734X
Date of First Compliant Deposit: 27 November 2025
Date of Acceptance: 28 October 2025
Last Modified: 27 Nov 2025 14:30
URI: https://orca.cardiff.ac.uk/id/eprint/182723

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics