Journal
NAR GENOMICS AND BIOINFORMATICS
Volume 4, Issue 1, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nargab/lqac023
Keywords
-
Funding
- Australian Research Council [DP210100521]
- National Health and Medical Research Council Investigator Grant [1173469]
- Australian Research Council Discovery Project grant [DP210100521]
- Research Training Program Tuition Fee Offset to AT
- National Health and Medical Research Council of Australia [1173469] Funding Source: NHMRC
Ask authors/readers for more resources
Cell reprogramming offers a potential treatment for many diseases, but identifying the transcription factors that promote cell reprogramming has been a time-consuming and costly process. In this study, a computational model called scREMOTE is presented, which utilizes single cell multiomics data to provide a more comprehensive view and accurate predictions for cell reprogramming.
Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available