Evidence map›Paper›PMID 38076860›Full record

ArticlebioRxiv : the preprint server for biology2025

T cell transcriptional landscapes are shaped by TCR sequence similarity.

Hao Wang, Zhicheng Ji

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 1 institution in 1 country.

Hao WangDepartment of Statistical Science, Duke University, Durham, NC, USA.
Zhicheng JiDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Duke University · US

Funding

The Duke Senescent Cell Evaluations in Normal Tissues (SCENT) Mapping CenterU54AG075936 · NIA · DUKE UNIVERSITY · PI NIXON, ANDREW B. · 2021 to 2025
$12.7M
Computational methods for in situ spatial transcriptomicsR35GM154865 · NIGMS · DUKE UNIVERSITY · PI Zhicheng Ji · 2024 to 2026
$1.2M
NIA NIH HHS U54 AG075936NIGMS NIH HHS R35 GM154865
6 · The paper itself

Abstract

T cells exhibit high heterogeneity in both their gene expression profiles and antigen specificities, but the precise relationship between these two features remains largely unknown. Utilizing a large-scale single-cell immune-profiling dataset that includes both T cell receptor (TCR) sequences and gene expression profiles of individual cells, we systematically investigated how the transcriptional profiles of T cells are linked to their TCR sequence similarity. We observed that T cells sharing the same clonotype often belong to the same cell type, and that gene expression similarity among T cells correlates with TCR sequence similarity. Furthermore, we identified features such as clonality, VJ gene usage, sequence motifs, and amino acid preferences that are distinctly associated with specific T cell subtypes. These findings suggest that T cell transcriptional landscapes are broadly shared and influenced by TCR sequence similarity.

Identifiers

PMID38076860
PMCPMC10705237
OpenAlexW4389109604

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.