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SinCMat: A single-cell-based method for predicting functional maturation transcription factors
A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs...
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Published in: | Stem cell reports 2024-02, Vol.19 (2), p.270-284 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.
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•SinCMat is the first computational method for identifying functional maturation TFs•SinCMat predicted known and novel maturation TFs•SinCMat is applicable to any mouse or human cell type•SinCMat is embedded in a user-friendly web interface
In this article, Del Sol and colleagues developed SinCMat, a computational platform for systematically predicting functional maturation TFs. Based on a model of cell maturation that integrates identity and environment components, SinCMat can predict known and novel maturation TFs required for cell functionalization. Furthermore, Del Sol and colleagues introduce SinCMatDB, a manually curated database of experimentally validated maturation TFs. |
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ISSN: | 2213-6711 2213-6711 |
DOI: | 10.1016/j.stemcr.2023.12.006 |