Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes
Heterogeneity and limited comprehension of chronic autoimmune disease pathophysiology cause accurate diagnosis a challenging process. With the increasing resources of single-cell sequencing data, a reasonable way could be found to address this issue. In our study, with the use of large-scale public...
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| Format: | Article |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.870531/full |
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| author | Yuanchen Ma Jieying Chen Tao Wang Liting Zhang Xinhao Xu Yuxuan Qiu Andy Peng Xiang Weijun Huang |
| author_facet | Yuanchen Ma Jieying Chen Tao Wang Liting Zhang Xinhao Xu Yuxuan Qiu Andy Peng Xiang Weijun Huang |
| author_sort | Yuanchen Ma |
| collection | DOAJ |
| description | Heterogeneity and limited comprehension of chronic autoimmune disease pathophysiology cause accurate diagnosis a challenging process. With the increasing resources of single-cell sequencing data, a reasonable way could be found to address this issue. In our study, with the use of large-scale public single-cell RNA sequencing (scRNA-seq) data, analysis of dataset integration (3.1 × 105 PBMCs from fifteen SLE patients and eight healthy donors) and cellular cross talking (3.8 × 105 PBMCs from twenty-eight SLE patients and eight healthy donors) were performed to identify the most crucial information characterizing SLE. Our findings revealed that the interactions among the PBMC subpopulations of SLE patients may be weakened under the inflammatory microenvironment, which could result in abnormal emergences or variations in signaling patterns within PBMCs. In particular, the alterations of B cells and monocytes may be the most significant findings. Utilizing this powerful information, an efficient mathematical model of unbiased random forest machine learning was established to distinguish SLE patients from healthy donors via not only scRNA-seq data but also bulk RNA-seq data. Surprisingly, our mathematical model could also accurately identify patients with rheumatoid arthritis and multiple sclerosis, not just SLE, via bulk RNA-seq data (derived from 688 samples). Since the variations in PBMCs should predate the clinical manifestations of these diseases, our machine learning model may be feasible to develop into an efficient tool for accurate diagnosis of chronic autoimmune diseases. |
| first_indexed | 2024-04-14T07:43:57Z |
| format | Article |
| id | doaj.art-150d24207a9a4f0781a70ea04824728c |
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| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-14T07:43:57Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj.art-150d24207a9a4f0781a70ea04824728c2022-12-22T02:05:24ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-04-011310.3389/fimmu.2022.870531870531Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and MonocytesYuanchen Ma0Jieying Chen1Tao Wang2Liting Zhang3Xinhao Xu4Yuxuan Qiu5Andy Peng Xiang6Weijun Huang7Center for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, ChinaCenter for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, ChinaCenter for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, ChinaCenter for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, ChinaZhongshan School of Medicine, Sun Yat-sen University, Guangzhou, ChinaCenter for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, ChinaCenter for Stem Cell Biology and Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, ChinaHeterogeneity and limited comprehension of chronic autoimmune disease pathophysiology cause accurate diagnosis a challenging process. With the increasing resources of single-cell sequencing data, a reasonable way could be found to address this issue. In our study, with the use of large-scale public single-cell RNA sequencing (scRNA-seq) data, analysis of dataset integration (3.1 × 105 PBMCs from fifteen SLE patients and eight healthy donors) and cellular cross talking (3.8 × 105 PBMCs from twenty-eight SLE patients and eight healthy donors) were performed to identify the most crucial information characterizing SLE. Our findings revealed that the interactions among the PBMC subpopulations of SLE patients may be weakened under the inflammatory microenvironment, which could result in abnormal emergences or variations in signaling patterns within PBMCs. In particular, the alterations of B cells and monocytes may be the most significant findings. Utilizing this powerful information, an efficient mathematical model of unbiased random forest machine learning was established to distinguish SLE patients from healthy donors via not only scRNA-seq data but also bulk RNA-seq data. Surprisingly, our mathematical model could also accurately identify patients with rheumatoid arthritis and multiple sclerosis, not just SLE, via bulk RNA-seq data (derived from 688 samples). Since the variations in PBMCs should predate the clinical manifestations of these diseases, our machine learning model may be feasible to develop into an efficient tool for accurate diagnosis of chronic autoimmune diseases.https://www.frontiersin.org/articles/10.3389/fimmu.2022.870531/fullchronic autoimmune diseaseaccurate diagnosismachine learning (ML)scRNA-seqcellular cross talking |
| spellingShingle | Yuanchen Ma Jieying Chen Tao Wang Liting Zhang Xinhao Xu Yuxuan Qiu Andy Peng Xiang Weijun Huang Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes Frontiers in Immunology chronic autoimmune disease accurate diagnosis machine learning (ML) scRNA-seq cellular cross talking |
| title | Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes |
| title_full | Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes |
| title_fullStr | Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes |
| title_full_unstemmed | Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes |
| title_short | Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes |
| title_sort | accurate machine learning model to diagnose chronic autoimmune diseases utilizing information from b cells and monocytes |
| topic | chronic autoimmune disease accurate diagnosis machine learning (ML) scRNA-seq cellular cross talking |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.870531/full |
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