Serrano Erik, Peters John, Wagner Jesko, Graham Rebecca E, Chen Zhenghao, Feng Brian, Miranda Gisele, Kalinin Alexandr A, Vulliard Loan, Tomkinson Jenna, Mattson Cameron, Lippincott Michael J, Kang Ziqi, Sitani Divya, Bunten Dave, Seal Srijit, Carragher Neil O, Carpenter Anne E, Singh Shantanu, Marin Zapata Paula A, Caicedo Juan C, Way Gregory P
Department of Biomedical Informatics, University of Colorado School of Medicine.
Morgridge Institute for Research, University of Wisconsin-Madison.
ArXiv. 2025 Aug 7:arXiv:2508.05800v1.
For over two decades, image-based profiling has revolutionized cellular phenotype analysis. Image-based profiling processes rich, high-throughput, microscopy data into unbiased measurements that reveal phenotypic patterns powerful for drug discovery, functional genomics, and cell state classification. Here, we review the evolving computational landscape of image-based profiling, detailing current procedures, discussing limitations, and highlighting future development directions. Deep learning has fundamentally reshaped image-based profiling, improving feature extraction, scalability, and multimodal data integration. Methodological advancements such as single-cell analysis and batch effect correction, drawing inspiration from single-cell transcriptomics, have enhanced analytical precision. The growth of open-source software ecosystems and the development of community-driven standards have further democratized access to image-based profiling, fostering reproducibility and collaboration across research groups. Despite these advancements, the field still faces significant challenges requiring innovative solutions. By focusing on the technical evolution of image-based profiling rather than the wide-ranging biological applications, our aim with this review is to provide researchers with a roadmap for navigating the progress and new challenges in this rapidly advancing domain.
二十多年来，基于图像的分析方法彻底改变了细胞表型分析。基于图像的分析方法将丰富的、高通量的显微镜数据处理为无偏差的测量结果，揭示出对药物发现、功能基因组学和细胞状态分类具有强大作用的表型模式。在此，我们回顾基于图像的分析方法不断发展的计算领域，详细介绍当前的程序，讨论局限性，并突出未来的发展方向。深度学习从根本上重塑了基于图像的分析方法，改进了特征提取、可扩展性和多模态数据整合。诸如单细胞分析和批次效应校正等方法学进展，借鉴了单细胞转录组学的经验，提高了分析精度。开源软件生态系统的发展以及社区驱动标准的制定，进一步使基于图像的分析方法的使用更加普及，促进了研究团队之间的可重复性和协作。尽管取得了这些进展，但该领域仍面临重大挑战，需要创新解决方案。通过专注于基于图像的分析方法的技术发展而非广泛的生物学应用，我们撰写本综述的目的是为研究人员提供一份路线图，以应对这一快速发展领域中的进展和新挑战。
