High-content image-based screening and deep learning for the detection of anti-inflammatory drug leads.

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Abstract

We developed a high-content image-based screen that utilizes the pro-inflammatory stimulus lipopolysaccharide (LPS) and murine macrophages (RAW264.7) with the goals of identifying anti-inflammatory drug leads. We screened 2,259 bioactive compounds with annotated mechanisms of action (MOA) to identify compounds that reverse the LPS-induced phenotype in macrophages. We utilized a set of seven fluorescence microscopy probes to generate images that were used to train and optimize a deep neural network classifier to distinguish between unstimulated and LPS-stimulated macrophages. The top hits from the deep learning classifier were validated using a linear classifier trained on individual cells and subsequently investigated in a multiplexed cytokine secretion assay. All 12 hits significantly modulated the expression of at least one cytokine upon LPS stimulation. Seven of these were allosteric inhibitors of the mitogen-activated protein kinase kinase (MEK1/2) and showed distinct effects on cytokine expression, consistent with the complex pharmacology of MEK1/2 inhibition. This deep learning morphological assay identified compounds that modulate the innate immune response to LPS and may aid in identifying new drug leads against sepsis.© 2023 Wiley-VCH GmbH.