Evidence map›Paper›PMID 41212920›Full record

ArticlePLoS computational biology2025

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

Daniil K Lukyanov, Evgeniy S Egorov, Valeriia V Kriukova, Denis Syrko, Victor V Kotliar, Kristin Ladell, David A Price, Andre Franke, Dmitry M Chudakov

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. T Cell Thoughts.Immunological reviews · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Daniil K LukyanovCenter for Molecular and Cellular Biology, Moscow, Russia.ORCID 0000-0002-8037-4752
Evgeniy S EgorovGenomics of Adaptive Immunity Department, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.
Valeriia V KriukovaInstitute of Clinical Molecular Biology, Kiel University, Kiel, Germany.
Denis SyrkoInstitute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia.ORCID 0000-0002-3263-4230
Victor V KotliarGenomics of Adaptive Immunity Department, Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.ORCID 0000-0002-7443-4042
Kristin LadellDivision of Infection and Immunity, Cardiff University School of Medicine, University Hospital of Wales, Cardiff, United Kingdom.
David A PriceDivision of Infection and Immunity, Cardiff University School of Medicine, University Hospital of Wales, Cardiff, United Kingdom.ORCID 0000-0001-9416-2737
Andre FrankeInstitute of Clinical Molecular Biology, Kiel University, Kiel, Germany.
Dmitry M ChudakovCenter for Molecular and Cellular Biology, Moscow, Russia.ORCID 0000-0003-0430-790X

Funding

PolyBio Research Foundation (Balvi B43)RSF
6 · The paper itself

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.

Indexed as

RNA-SeqRNA SplicingSequence Analysis, RNASingle-Cell AnalysisT-Lymphocytes, RegulatoryComputational BiologyForkhead Transcription FactorsHumansRNA, MessengerSingle-Cell Gene Expression AnalysisForkhead Transcription FactorsRNA, Messenger

Identifiers

PMID41212920
PMCPMC12646427

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Registered trials

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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.