Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/13/12/2264 |
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| author | Mario A. Flores Karla Paniagua Wenjian Huang Ricardo Ramirez Leonardo Falcon Andy Liu Yidong Chen Yufei Huang Yufang Jin |
| author_facet | Mario A. Flores Karla Paniagua Wenjian Huang Ricardo Ramirez Leonardo Falcon Andy Liu Yidong Chen Yufei Huang Yufang Jin |
| author_sort | Mario A. Flores |
| collection | DOAJ |
| description | The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients. |
| first_indexed | 2024-03-09T16:29:47Z |
| format | Article |
| id | doaj.art-cde90624852345c0bdaa2d37717a1b71 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-09T16:29:47Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-cde90624852345c0bdaa2d37717a1b712023-11-24T15:03:50ZengMDPI AGGenes2073-44252022-12-011312226410.3390/genes13122264Characterizing Macrophages Diversity in COVID-19 Patients Using Deep LearningMario A. Flores0Karla Paniagua1Wenjian Huang2Ricardo Ramirez3Leonardo Falcon4Andy Liu5Yidong Chen6Yufei Huang7Yufang Jin8Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USADepartment of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USADepartment of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USADepartment of Electrical Engineering and Cyber Engineering, Houston Baptist University, Houston, TX 77074, USADepartment of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USAMcKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX 78712, USAGreehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USADepartment of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15232, USADepartment of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USAThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent responsible for coronavirus disease 2019 (COVID-19), has affected the lives of billions and killed millions of infected people. This virus has been demonstrated to have different outcomes among individuals, with some of them presenting a mild infection, while others present severe symptoms or even death. The identification of the molecular states related to the severity of a COVID-19 infection has become of the utmost importance to understanding the differences in critical immune response. In this study, we computationally processed a set of publicly available single-cell RNA-Seq (scRNA-Seq) data of 12 Bronchoalveolar Lavage Fluid (BALF) samples diagnosed as having a mild, severe, or no infection, and generated a high-quality dataset that consists of 63,734 cells, each with 23,916 genes. We extended the cell-type and sub-type composition identification and our analysis showed significant differences in cell-type composition in mild and severe groups compared to the normal. Importantly, inflammatory responses were dramatically elevated in the severe group, which was evidenced by the significant increase in macrophages, from 10.56% in the normal group to 20.97% in the mild group and 34.15% in the severe group. As an indicator of immune defense, populations of T cells accounted for 24.76% in the mild group and decreased to 7.35% in the severe group. To verify these findings, we developed several artificial neural networks (ANNs) and graph convolutional neural network (GCNN) models. We showed that the GCNN models reach a prediction accuracy of the infection of 91.16% using data from subtypes of macrophages. Overall, our study indicates significant differences in the gene expression profiles of inflammatory response and immune cells of severely infected patients.https://www.mdpi.com/2073-4425/13/12/2264deep learningsingle-cell RNA-SeqSARS-CoV-2cell type identificationinfection severity |
| spellingShingle | Mario A. Flores Karla Paniagua Wenjian Huang Ricardo Ramirez Leonardo Falcon Andy Liu Yidong Chen Yufei Huang Yufang Jin Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning Genes deep learning single-cell RNA-Seq SARS-CoV-2 cell type identification infection severity |
| title | Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning |
| title_full | Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning |
| title_fullStr | Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning |
| title_full_unstemmed | Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning |
| title_short | Characterizing Macrophages Diversity in COVID-19 Patients Using Deep Learning |
| title_sort | characterizing macrophages diversity in covid 19 patients using deep learning |
| topic | deep learning single-cell RNA-Seq SARS-CoV-2 cell type identification infection severity |
| url | https://www.mdpi.com/2073-4425/13/12/2264 |
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