Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns
Abstract Recent barcoding technologies allow reconstructing lineage trees while capturing paired single-cell RNA-sequencing (scRNA-seq) data. Such datasets provide opportunities to compare gene expression memory maintenance through lineage branching and pinpoint critical genes in these processes. He...
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56388-7 |
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| author | Hannah M. Schlüter Caroline Uhler |
| author_facet | Hannah M. Schlüter Caroline Uhler |
| author_sort | Hannah M. Schlüter |
| collection | DOAJ |
| description | Abstract Recent barcoding technologies allow reconstructing lineage trees while capturing paired single-cell RNA-sequencing (scRNA-seq) data. Such datasets provide opportunities to compare gene expression memory maintenance through lineage branching and pinpoint critical genes in these processes. Here we develop Permutation, Optimization, and Representation learning based single Cell gene Expression and Lineage ANalysis (PORCELAN) to identify lineage-informative genes or subtrees where lineage and expression are tightly coupled. We validate our method using synthetic data and apply it to recent paired lineage and scRNA-seq data of lung cancer in a mouse model and embryogenesis of mouse and C. elegans. Our method pinpoints subtrees giving rise to metastases or new cell states, and genes identified as most informative about lineage overlap with known pathways involved in lung cancer progression. Furthermore, our method highlights differences in how gene expression memory is maintained through divisions in cancer and embryogenesis, thereby providing a tool for studying cell state memory through divisions across biological systems. |
| format | Article |
| id | doaj-art-a1a322974a69428dbe582894c2506c2e |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-a1a322974a69428dbe582894c2506c2e2025-02-02T12:31:38ZengNature PortfolioNature Communications2041-17232025-01-0116111510.1038/s41467-025-56388-7Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patternsHannah M. Schlüter0Caroline Uhler1Laboratory for Information and Decision Systems, Massachusetts Institute of TechnologyLaboratory for Information and Decision Systems, Massachusetts Institute of TechnologyAbstract Recent barcoding technologies allow reconstructing lineage trees while capturing paired single-cell RNA-sequencing (scRNA-seq) data. Such datasets provide opportunities to compare gene expression memory maintenance through lineage branching and pinpoint critical genes in these processes. Here we develop Permutation, Optimization, and Representation learning based single Cell gene Expression and Lineage ANalysis (PORCELAN) to identify lineage-informative genes or subtrees where lineage and expression are tightly coupled. We validate our method using synthetic data and apply it to recent paired lineage and scRNA-seq data of lung cancer in a mouse model and embryogenesis of mouse and C. elegans. Our method pinpoints subtrees giving rise to metastases or new cell states, and genes identified as most informative about lineage overlap with known pathways involved in lung cancer progression. Furthermore, our method highlights differences in how gene expression memory is maintained through divisions in cancer and embryogenesis, thereby providing a tool for studying cell state memory through divisions across biological systems.https://doi.org/10.1038/s41467-025-56388-7 |
| spellingShingle | Hannah M. Schlüter Caroline Uhler Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns Nature Communications |
| title | Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns |
| title_full | Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns |
| title_fullStr | Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns |
| title_full_unstemmed | Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns |
| title_short | Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns |
| title_sort | integrating representation learning permutation and optimization to detect lineage related gene expression patterns |
| url | https://doi.org/10.1038/s41467-025-56388-7 |
| work_keys_str_mv | AT hannahmschluter integratingrepresentationlearningpermutationandoptimizationtodetectlineagerelatedgeneexpressionpatterns AT carolineuhler integratingrepresentationlearningpermutationandoptimizationtodetectlineagerelatedgeneexpressionpatterns |