Evidence map›Paper›PMID 42737863›Full record

ArticleInternational journal of molecular sciences2026

Integrated Single Cell Analysis Reveals the Transcriptional Heterogeneity of Mouse Double Negative T Cells.

Jun Zhao, Jiang Zhu, Jian Zhang

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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

3 authors.

Jun ZhaoSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.
Jiang ZhuSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.
Jian ZhangSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China.

Funding

the Fundamental Research Funds for the Provincial Universities of Harbin Medical University JFQN202309
6 · The paper itself

Abstract

Double negative T cells (DNT cells) are a rare T cell population involved in immune regulation, inflammation, autoimmunity, transplantation, and tumor immunity. However, their low abundance and dispersed distribution across tissues have limited a systematic understanding of their cellular organization and transcriptional diversity. To address this problem, we developed an integrative single cell framework to reconstruct the mouse DNT cell landscape across tissues using multiple public single cell RNA sequencing datasets. Using transcriptomic criteria, we identified and integrated 7984 mouse RNA defined DNT cells. The integrated population was resolved into multiple transcriptionally distinct clusters. These clusters were organized into naive like, proinflammatory, cytotoxic, proliferative, and myeloid associated states, revealing that mouse RNA defined DNT cells constitute a highly heterogeneous yet structured transcriptional compartment. Integrated downstream analyses indicated transcriptional relationships among these subsets. Naive like populations showed inferred transcriptional relationships with inflammatory and cytotoxic programs, while virtual knockout and intercellular communication analyses identified distinct predicted regulatory and signaling features across states. To place these mouse DNT associated genes in a human disease context, selected genes were mapped to their corresponding human homologs and examined using TCGA pan cancer transcriptomic data. Together, these findings define a cross tissue transcriptional framework for mouse RNA defined DNT cells and provide a basis for further evaluating the conservation and relevance of these transcriptional programs in human DNT biology. More broadly, this study provides a generalizable integrative strategy for reconstructing and characterizing rare immune cell populations that are insufficiently represented in individual single cell datasets.

Indexed as

Single-Cell AnalysisT-LymphocytesTranscription, GeneticTranscriptomeAnimalsGene Expression ProfilingHumansMiceSequence Analysis, RNASingle-Cell Gene Expression Analysiscellular heterogeneitydouble negative T cellsimmune landscaperare immune cellssingle cell data integrationsingle cell RNA sequencing

Identifiers

PMID42737863
PMCPMC13566193

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