SinCMat: A single-cell-based method for predicting functional maturation transcription factors.
Stem Cell Reports
; 19(2): 270-284, 2024 Feb 13.
Article
en En
| MEDLINE
| ID: mdl-38215756
ABSTRACT
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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Factores de Transcripción
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
Idioma:
En
Año:
2024
Tipo del documento:
Article