Evidence map›Paper›PMID 34390166›Full record

ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2022

Single cell multiomic analysis of T cell exhaustion in vitro.

Mirko Corselli, Suraj Saksena, Margaret Nakamoto, Woodrow E Lomas, Ian Taylor, Pratip K Chattopadhyay

Open access · hybridAbstract read
In one paragraph

Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
1.1field-weighted citation impact, top 23% of its field
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

27 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Multi-omics and bulk sequencing analyses reveal the roles of prognosis and immune aspects of super-enhancer-induced ZBED2 in pan-cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  3. Review
  4. A Single-Cell Atlas of Uterine Carcinosarcoma from Diverse Ancestries.bioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Reversing T cell dysfunction in a novelFrontiers in immunology · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. CK2B Induces CD8Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Mirko CorselliMedical and Scientific Affairs, BD Biosciences, San Jose, California, USA.
Suraj SaksenaMedical and Scientific Affairs, BD Biosciences, San Jose, California, USA.
Margaret NakamotoMedical and Scientific Affairs, BD Biosciences, San Jose, California, USA.
Woodrow E LomasMedical and Scientific Affairs, BD Biosciences, San Jose, California, USA.
Ian TaylorMedical and Scientific Affairs, BD Biosciences, San Jose, California, USA.
Pratip K ChattopadhyayPrecision Immunology Laboratory, Perlmutter Cancer Center, NYU Langone Health, New York, New York, USA.
BD Biosciences (United States) · USNYU Langone Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

T-cell activation is a key step in the amplification of an immune response. Over the course of an immune response, cells may be chronically stimulated, with some proportion becoming exhausted; an enormous number of molecules are involved in this process. There remain a number of questions about the process, namely: (1) what degree of heterogeneity and plasticity do T-cells exhibit during stimulation? (2) how many unique cell states define chronic stimulation? and (3) what markers discriminate activated from exhausted cells? We addressed these questions by performing single-cell multiomic analysis to simultaneously measure expression of 38 proteins and 399 genes in human T cells expanded in vitro. This approach allowed us to study -with unprecedented depth-how T cells change over the course of chronic stimulation. Comprehensive immunophenotypic and transcriptomic analysis at day 0 enabled a refined characterization of T-cell maturational states and the identification of a donor-specific subset of terminally differentiated T-cells that would have been otherwise overlooked using canonical cell classification schema. As expected, activation downregulated naïve-cell markers and upregulated effector molecules, proliferation regulators, co-inhibitory and co-stimulatory receptors. Our deep kinetic analysis further revealed clusters of proteins and genes identifying unique states of activation, defined by markers temporarily expressed upon 3 days of stimulation (PD-1, CD69, LTA), markers constitutively expressed throughout chronic activation (CD25, GITR, LGALS1), and markers uniquely up-regulated upon 14 days of stimulation (CD39, ENTPD1, TNFDF10); expression of these markers could be associated with the emergence of short-lived cell types. Notably, different ratios of cells expressing activation or exhaustion markers were measured at each time point. These data reveal the high heterogeneity and plasticity of chronically stimulated T cells. Our study demonstrates the power of a single-cell multiomic approach to comprehensively characterize T-cells and to precisely monitor changes in differentiation, activation, and exhaustion signatures during cell stimulation.

Indexed as

CD8-Positive T-LymphocytesLymphocyte ActivationHumansImmunophenotypingKineticsSingle-Cell Analysismolecular cytometrymultiomicssingle-cell analysisT-cell exhaustion

Identifiers

PMID34390166
PMCPMC9293072
OpenAlexW3188249445

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.