Najafi, A and Jolly, MK and George, JT (2023) Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity. In: iScience, 26 (7).
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Abstract
The Epithelial-to-Mesenchymal Transition (EMT) is a hallmark of cancer metastasis and morbidity. EMT is a non-binary process, and cells can be stably arrested en route to EMT in an intermediate hybrid state associated with enhanced tumor aggressiveness and worse patient outcomes. Understanding EMT progression in detail will provide fundamental insights into the mechanisms underlying metastasis. Despite increasingly available single-cell RNA sequencing (scRNA-seq) data that enable in-depth analyses of EMT at the single-cell resolution, current inferential approaches are limited to bulk microarray data. There is thus a great need for computational frameworks to systematically infer and predict the timing and distribution of EMT-related states at single-cell resolution. Here, we develop a computational framework for reliable inference and prediction of EMT-related trajectories from scRNA-seq data. Our model can be utilized across a variety of applications to predict the timing and distribution of EMT from single-cell sequencing data.
| Item Type: | Journal Article |
|---|---|
| Publication: | iScience |
| Publisher: | Elsevier Inc. |
| Additional Information: | The copyright for this article belongs to the Author. |
| Keywords: | Biological sciences; Cancer; Cell biology; Integrative aspects of cell biology |
| Department/Centre: | Division of Interdisciplinary Sciences > Centre for Biosystems Science and Engineering |
| Date Deposited: | 20 Jul 2023 09:01 |
| Last Modified: | 20 Jul 2023 09:01 |
| URI: | https://eprints.iisc.ac.in/id/eprint/82488 |
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