Dissection and integration of bursty transcriptional dynamics for complex systems.
Proc Natl Acad Sci U S A
; 121(18): e2306901121, 2024 Apr 30.
Article
em En
| MEDLINE
| ID: mdl-38669186
ABSTRACT
RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transcrição Gênica
/
Diferenciação Celular
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Análise de Classes Latentes
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Análise da Expressão Gênica de Célula Única
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article