Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affec...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2024-01-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/84613 |
| _version_ | 1827367659723816960 |
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| author | Idan Hekselman Assaf Vital Maya Ziv-Agam Lior Kerber Ido Yairi Esti Yeger-Lotem |
| author_facet | Idan Hekselman Assaf Vital Maya Ziv-Agam Lior Kerber Ido Yairi Esti Yeger-Lotem |
| author_sort | Idan Hekselman |
| collection | DOAJ |
| description | Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability. |
| first_indexed | 2024-03-08T09:12:36Z |
| format | Article |
| id | doaj.art-d00a8e20aff247e0ae80199a85eda7c7 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-03-08T09:12:36Z |
| publishDate | 2024-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-d00a8e20aff247e0ae80199a85eda7c72024-01-31T19:12:28ZengeLife Sciences Publications LtdeLife2050-084X2024-01-011310.7554/eLife.84613Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell dataIdan Hekselman0https://orcid.org/0000-0003-0529-7838Assaf Vital1Maya Ziv-Agam2Lior Kerber3https://orcid.org/0009-0005-0583-5618Ido Yairi4Esti Yeger-Lotem5https://orcid.org/0000-0002-8279-7898Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be’er Sheva, IsraelDepartment of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be’er Sheva, IsraelDepartment of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be’er Sheva, IsraelDepartment of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be’er Sheva, IsraelDepartment of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be’er Sheva, IsraelDepartment of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be’er Sheva, Israel; The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Be’er Sheva, IsraelMendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.https://elifesciences.org/articles/84613single-cell transcriptomicsMendelian diseasescell-type selectivitydata integrationtissue selectivity |
| spellingShingle | Idan Hekselman Assaf Vital Maya Ziv-Agam Lior Kerber Ido Yairi Esti Yeger-Lotem Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data eLife single-cell transcriptomics Mendelian diseases cell-type selectivity data integration tissue selectivity |
| title | Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data |
| title_full | Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data |
| title_fullStr | Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data |
| title_full_unstemmed | Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data |
| title_short | Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data |
| title_sort | affected cell types for hundreds of mendelian diseases revealed by analysis of human and mouse single cell data |
| topic | single-cell transcriptomics Mendelian diseases cell-type selectivity data integration tissue selectivity |
| url | https://elifesciences.org/articles/84613 |
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