AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity.

ABSTRACT:

Background

Immunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. This study aimed to streamline the identification of ICD inducers by leveraging cellular morphological correlates of ICD, specifically the condensation of nucleoli (CON).

Methods

We applied artificial intelligence (AI)-based imaging analyses to Cell Paint-stained cells exposed to drug libraries, identifying CON as a marker for ICD. CON was characterized using SYTO 14 fluorescent staining and holotomographic microscopy, and visualized by AI-deconvoluted transmitted light microscopy. A neural network-based quantitative structure-activity relationship (QSAR) model

SUBMITTER: Cerrato G 

PROVIDER: S-EPMC11660623 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature