Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells
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...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-82406-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862119157727232 |
|---|---|
| author | Tian Xu Qin Xu Ran Lu David N. Oakland Song Li Liwu Li Christopher M. Reilly Xin M. Luo |
| author_facet | Tian Xu Qin Xu Ran Lu David N. Oakland Song Li Liwu Li Christopher M. Reilly Xin M. Luo |
| author_sort | Tian Xu |
| collection | DOAJ |
| description | 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 Klra7 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. |
| format | Article |
| id | doaj-art-0f96e2033dc84cbfb37dfd49129f8e0e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0f96e2033dc84cbfb37dfd49129f8e0e2025-02-09T12:38:02ZengNature PortfolioScientific Reports2045-23222024-12-0114111210.1038/s41598-024-82406-7Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cellsTian Xu0Qin Xu1Ran Lu2David N. Oakland3Song Li4Liwu Li5Christopher M. Reilly6Xin M. Luo7Department of Biomedical Sciences and Pathobiology, Virginia TechDepartment of Mathematics, The University of ArizonaDepartment of Biomedical Sciences and Pathobiology, Virginia TechGraduate Program in Translational Biology, Medicine, and Health, Virginia TechSchool of Plant and Environmental Sciences, Virginia TechDepartment of Biological Science, Virginia TechDepartment of Biomedical Sciences, Edward Via College of Osteopathic MedicineDepartment of Biomedical Sciences and Pathobiology, Virginia TechAbstract 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 Klra7 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.https://doi.org/10.1038/s41598-024-82406-7 |
| spellingShingle | Tian Xu Qin Xu Ran Lu David N. Oakland Song Li Liwu Li Christopher M. Reilly Xin M. Luo Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells Scientific Reports |
| title | Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells |
| title_full | Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells |
| title_fullStr | Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells |
| title_full_unstemmed | Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells |
| title_short | Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells |
| title_sort | application of deep learning models on single cell rna sequencing analysis uncovers novel markers of double negative t cells |
| url | https://doi.org/10.1038/s41598-024-82406-7 |
| work_keys_str_mv | AT tianxu applicationofdeeplearningmodelsonsinglecellrnasequencinganalysisuncoversnovelmarkersofdoublenegativetcells AT qinxu applicationofdeeplearningmodelsonsinglecellrnasequencinganalysisuncoversnovelmarkersofdoublenegativetcells AT ranlu applicationofdeeplearningmodelsonsinglecellrnasequencinganalysisuncoversnovelmarkersofdoublenegativetcells AT davidnoakland applicationofdeeplearningmodelsonsinglecellrnasequencinganalysisuncoversnovelmarkersofdoublenegativetcells AT songli applicationofdeeplearningmodelsonsinglecellrnasequencinganalysisuncoversnovelmarkersofdoublenegativetcells AT liwuli applicationofdeeplearningmodelsonsinglecellrnasequencinganalysisuncoversnovelmarkersofdoublenegativetcells AT christophermreilly applicationofdeeplearningmodelsonsinglecellrnasequencinganalysisuncoversnovelmarkersofdoublenegativetcells AT xinmluo applicationofdeeplearningmodelsonsinglecellrnasequencinganalysisuncoversnovelmarkersofdoublenegativetcells |