Revealing invisible cell phenotypes with conditional generative modeling.
Nat Commun
; 14(1): 6386, 2023 10 11.
Article
em En
| MEDLINE
| ID: mdl-37821450
ABSTRACT
Biological sciences, drug discovery and medicine rely heavily on cell phenotype perturbation and microscope observation. However, most cellular phenotypic changes are subtle and thus hidden from us by natural cell variability two cells in the same condition already look different. In this study, we show that conditional generative models can be used to transform an image of cells from any one condition to another, thus canceling cell variability. We visually and quantitatively validate that the principle of synthetic cell perturbation works on discernible cases. We then illustrate its effectiveness in displaying otherwise invisible cell phenotypes triggered by blood cells under parasite infection, or by the presence of a disease-causing pathological mutation in differentiated neurons derived from iPSCs, or by low concentration drug treatments. The proposed approach, easy to use and robust, opens the door to more accessible discovery of biological and disease biomarkers.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Células-Tronco Pluripotentes Induzidas
Idioma:
En
Revista:
Nat Commun
Assunto da revista:
BIOLOGIA
/
CIENCIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
França