Evidence map›Paper›PMID 39732739›Full record

ArticleScientific reports2024

Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells.

Tian Xu, Qin Xu, Ran Lu, David N Oakland, Song Li, Liwu Li, Christopher M Reilly, Xin M Luo

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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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

8 authors.

Tian XuDepartment of Biomedical Sciences and Pathobiology, Virginia Tech, Blacksburg, VA, USA.
Qin XuDepartment of Mathematics, The University of Arizona, Tucson, AZ, USA.
Ran LuDepartment of Biomedical Sciences and Pathobiology, Virginia Tech, Blacksburg, VA, USA.
David N OaklandGraduate Program in Translational Biology, Medicine, and Health, Virginia Tech, Roanoke, VA, USA.
Song LiSchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA.
Liwu LiDepartment of Biological Science, Virginia Tech, Blacksburg, VA, USA.
Christopher M ReillyDepartment of Biomedical Sciences, Edward Via College of Osteopathic Medicine, Blacksburg, VA, USA.
Xin M LuoDepartment of Biomedical Sciences and Pathobiology, Virginia Tech, Blacksburg, VA, USA. xinluo@vt.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Double negative T (DNT) cells are a unique subset of CD3 + TCRαβ + T lymphocytes that lack CD4, CD8, or NK1.1 expression and constitute 3-5% of the total T cell population in C57BL/6 mice. They have increasingly gained recognition for their novel roles in the immune system, especially under autoimmune conditions. Conventional machine learning approaches such as principal component analysis have been employed in single-cell RNA sequencing (scRNA-seq) analysis to characterize DNT cells. However, advanced deep learning models such as Single Cell Variational Inference (scVI) have the capability to capture nonlinear gene expression patterns in the sequencing data. In this study, employing the deep learning methodology, we have revealed novel markers for splenic DNT cells in C57BL/6 mice which were validated with flow cytometry analysis. We classified DNT cells into two subgroups, naïve DNT (nDNT) cells differentiated by the expression of Ly6C and activated DNT (aDNT) cells differentiated by the expression of MHC-II. A prior study had predicted elevated expression of CD137/4-1BB encoded by Tnfrsf9 in nDNT cells; however, our analysis predicted and validated that CD137 was a marker for aDNT cells instead of nDNT cells. Innovatively, our data also identified CD30 encoded by Tnfrsf8 and CD153/CD30L encoded by Tnfsf8 as additional markers for aDNT cells. In addition, we classified three subgroups in nDNT cells and two subgroups in aDNT cells. Our scVI analysis suggested, and flow cytometry analysis confirmed, that Ly49G2 encoded by Slamf7 was a marker for the nDNT0 subgroup. Importantly, we validated that MHC-II was indeed expressed by a subset of human DNT cells suggesting the presence of a human aDNT population. Furthermore, we found increased expression of CD30, CD153, and CD137 on aDNT cells in MRL/lpr mice compared to those in C57BL/6 mice suggesting potential pathogenic roles of these molecules in autoimmunity. Together, our comprehensive analysis has uncovered and validated novel markers for different subpopulations of DNT cells that can be used in the phenotypic and/or functional characterization of these relatively rare cells in health and disease.

Indexed as

BiomarkersDeep LearningMice, Inbred C57BLSequence Analysis, RNASingle-Cell AnalysisAnimalsMiceBiomarkers

Identifiers

PMID39732739
PMCPMC11682054

What Socratic holds

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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 Socratic graph.