Evidence map›Paper›PMID 40577285›Full record

ArticlePLoS computational biology2025

scRepertoire 2: Enhanced and efficient toolkit for single-cell immune profiling.

Qile Yang, Ksenia R Safina, Kieu Diem Quynh Nguyen, Zewen Kelvin Tuong, Nicholas Borcherding

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 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Human memory CD4bioRxiv : the preprint server for biology · 2026
    Article
  6. Article
  7. Article
  8. GPR15-guided CD8Nature · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. CNS1-dependent regulatory T cells shape recovery from acute lung injury.Journal of immunology (Baltimore, Md. : 1950) · 2026
    Article
  17. Article
  18. Article
  19. Article
  20. Article
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

5 authors.

Qile YangUniversity of California Berkeley, Berkeley, California, United States of America.ORCID 0009-0005-0148-2499
Ksenia R SafinaDivision of Hematology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
Kieu Diem Quynh NguyenIan Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Brisbane, Australia.
Zewen Kelvin TuongIan Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Brisbane, Australia.ORCID 0000-0002-6735-6808
Nicholas BorcherdingDepartment of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, United States of America.ORCID 0000-0003-1427-6342

Funding

Washington University Department of Pathology and Immunology
6 · The paper itself

Abstract

Single-cell adaptive immune receptor repertoire sequencing (scAIRR-seq) and single-cell RNA sequencing (scRNA-seq) provide a transformative approach to profiling immune responses at unprecedented resolution across diverse pathophysiologic contexts. This work presents scRepertoire 2, a substantial update to our R package for analyzing and visualizing single-cell immune receptor data. This new version introduces an array of features designed to enhance both the depth and breadth of immune receptor analysis, including improved workflows for clonotype tracking, repertoire diversity metrics, and novel visualization modules that facilitate longitudinal and comparative studies. Additionally, scRepertoire 2 offers seamless integration with contemporary single-cell analysis frameworks like Seurat and SingleCellExperiment, allowing users to conduct end-to-end single-cell immune profiling with transcriptomic data. Performance optimizations in scRepertoire 2 resulted in a 85.1% increase in speed and a 91.9% reduction in memory usage from the first version over the range repertoire size tested in benchmarking, addressing the demands of the ever-increasing size and scale of single-cell studies. This release marks an advancement in single cell immunogenomics, equipping researchers with a robust toolset to uncover immune dynamics in health and disease.

Indexed as

Single-Cell AnalysisSoftwareComputational BiologyGene Expression ProfilingHumansReceptors, ImmunologicRNA-SeqSequence Analysis, RNAReceptors, Immunologic

Identifiers

PMID40577285
PMCPMC12204475

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.