Epigenetic small-molecule screen for inhibition and reversal of acinar ductal metaplasia in mouse pancreatic organoids

Atanasova, Kalina R.; Perkins, Corey M.; Ratnayake, Ranjala; Jiang, Jinmai; Chen, Qi-Yin; Schmittgen, Thomas D.; Luesch, Hendrik

doi:10.3389/fphar.2024.1335246

Cited by 4 publications

(1 citation statement)

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“…and Atanasova et al. demonstrated the effect of small molecules in mouse pancreatic acinar that causes inhibition or reversal of acinar-to-acinar ductal metaplasia (ADM) using high-content image-based screening in organoid culture [ 128 , 129 ]. Advances in technology in organoid culture and the remarkable self-organizing properties reflecting key structural and functional attributes of organs such as brain, kidney, lung, gut, or similar even hold promise to predict drug response in a personalized fashion.…”

Section: Novel Applications Of Morphological Profiling In Drug Discoverymentioning

confidence: 99%

Morphological profiling for drug discovery in the era of deep learning

Tang,

Ratnayake,

Seabra

et al. 2024

Briefings in Bioinformatics

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Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering– and deep learning–based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

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Section: Novel Applications Of Morphological Profiling In Drug Discoverymentioning

confidence: 99%