From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability.
Mol Biol Cell
; 35(3): mr2, 2024 Mar 01.
Article
en En
| MEDLINE
| ID: mdl-38170589
ABSTRACT
Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo drug effects. However, Cell Painting features extracted from classical software such as CellProfiler are based on statistical calculations and often not readily biologically interpretable. In this study, we propose a new feature space, which we call BioMorph, that maps these Cell Painting features with readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. Overall, BioMorph space offers a biologically relevant way to interpret the cell morphological features derived using software such as CellProfiler and to generate hypotheses for experimental validation.
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1
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Replicación del ADN
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Mol Biol Cell
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2024
Tipo del documento:
Article