Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis
Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demon...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-01-01
|
| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037023001344 |
| _version_ | 1797384145776672768 |
|---|---|
| author | Junha Cha Michael Lavi Junhan Kim Noam Shomron Insuk Lee |
| author_facet | Junha Cha Michael Lavi Junhan Kim Noam Shomron Insuk Lee |
| author_sort | Junha Cha |
| collection | DOAJ |
| description | Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis. |
| first_indexed | 2024-03-08T21:31:16Z |
| format | Article |
| id | doaj.art-70a9808aa15b4a69b107f1b8c780b555 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:31:16Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-70a9808aa15b4a69b107f1b8c780b5552023-12-21T07:31:18ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-012122962304Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasisJunha Cha0Michael Lavi1Junhan Kim2Noam Shomron3Insuk Lee4Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of KoreaFaculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, IsraelDepartment of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of KoreaFaculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel; Corresponding author.Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea; POSTECH Biotech Center, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea; Corresponding author at: Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.http://www.sciencedirect.com/science/article/pii/S2001037023001344Single-cell RNA sequencingImputationSingle cell network biologyCell-type-specific gene networkMetastasisBreast cancer |
| spellingShingle | Junha Cha Michael Lavi Junhan Kim Noam Shomron Insuk Lee Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis Computational and Structural Biotechnology Journal Single-cell RNA sequencing Imputation Single cell network biology Cell-type-specific gene network Metastasis Breast cancer |
| title | Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis |
| title_full | Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis |
| title_fullStr | Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis |
| title_full_unstemmed | Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis |
| title_short | Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis |
| title_sort | imputation of single cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis |
| topic | Single-cell RNA sequencing Imputation Single cell network biology Cell-type-specific gene network Metastasis Breast cancer |
| url | http://www.sciencedirect.com/science/article/pii/S2001037023001344 |
| work_keys_str_mv | AT junhacha imputationofsinglecelltranscriptomedataenablesthereconstructionofnetworkspredictiveofbreastcancermetastasis AT michaellavi imputationofsinglecelltranscriptomedataenablesthereconstructionofnetworkspredictiveofbreastcancermetastasis AT junhankim imputationofsinglecelltranscriptomedataenablesthereconstructionofnetworkspredictiveofbreastcancermetastasis AT noamshomron imputationofsinglecelltranscriptomedataenablesthereconstructionofnetworkspredictiveofbreastcancermetastasis AT insuklee imputationofsinglecelltranscriptomedataenablesthereconstructionofnetworkspredictiveofbreastcancermetastasis |