Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation.
Nat Cell Biol
; 26(1): 153-167, 2024 Jan.
Article
en En
| MEDLINE
| ID: mdl-38182825
ABSTRACT
In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
Multiómica
Tipo de estudio:
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
Nat Cell Biol
Año:
2024
Tipo del documento:
Article
País de afiliación:
Bélgica