Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.
Science
; 365(6455): 786-793, 2019 08 23.
Article
en En
| MEDLINE
| ID: mdl-31395745
ABSTRACT
How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Análisis de Secuencia de ARN
/
Epistasis Genética
/
Análisis de la Célula Individual
Límite:
Female
/
Humans
Idioma:
En
Revista:
Science
Año:
2019
Tipo del documento:
Article