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 (MOA), 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.

翻译：形态学分析是表型药物发现中的重要工具。高通量自动化成像技术的出现，使得在单细胞分辨率下捕获细胞或生物体在扰动后的广泛形态特征成为可能。与此同时，机器学习和深度学习（尤其是计算机视觉领域）的重大进展，显著提升了大规模、高通量高内涵图像的分析能力。这些研究有助于理解化合物的作用机制、推动药物再利用、表征扰动下的细胞形态动力学，并最终助力新疗法的开发。本综述全面概述了形态学分析领域的最新进展。我们总结了图像分析工作流程，调研了涵盖基于特征工程和基于深度学习的多种分析策略，并介绍了公开可用的基准数据集。我们特别强调了深度学习在该流程中的应用，包括细胞分割、图像表示学习和多模态学习。此外，我们阐明了形态学分析在表型药物发现中的应用，并指出了该领域潜在的挑战与机遇。