Journal Article (Review Article)

DZNE-2026-00473

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Progress and new challenges in image-based profiling.

Serrano, E. ; Peters, J. ; Wagner, J. ; Graham, R. E. ; Chen, Z. ; Feng, B. Y. ; Miranda, G. ; Kalinin, A. A. ; Vulliard, L. ; Tomkinson, J. ; Mattson, C. ; Lippincott, M. J. ; Kang, Z. ; Divya, S.DZNE* ; Bunten, D. ; Seal, S. ; Carragher, N. O. ; Carpenter, A. E. ; Singh, S. ; Marin Zapata, P. A. ; Caicedo, J. C. ; Way, G. P.

2026
Nature Publishing Group UK [London]

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Please use a persistent id in citations: doi:10.1038/s44320-026-00197-7

Abstract: For over two decades, image-based profiling has revolutionized cell phenotype analysis. Image-based profiling processes rich, high-throughput, microscopy data into thousands of 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 the bioinformatics processes involved from feature extraction to normalization and batch correction. We discuss how deep learning has fundamentally reshaped the field. We examine key methodological advancements, such as single-cell analysis, the development of robust similarity metrics, and the expansion into new modalities like optical pooled screening, temporal imaging, and 3D organoid profiling. We also highlight the growth of public benchmarks and open-source software ecosystems as a key driver for fostering reproducibility and collaboration. Despite these advances, the field still faces substantial challenges, particularly in developing methods for emerging temporal and 3D data modalities, establishing robust quality control standards and workflows, and interpreting the processed features. 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.

Keyword(s): Humans (MeSH) ; Single-Cell Analysis: methods (MeSH) ; Computational Biology: methods (MeSH) ; Image Processing, Computer-Assisted: methods (MeSH) ; Deep Learning (MeSH) ; Software (MeSH) ; Animals (MeSH) ; Cell Profiling ; Deep Learning ; Feature Extraction ; Image-Based Profiling ; Phenotypic Screening

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Contributing Institute(s):

1. Clinical Single Cell Omics (CSCO) / Systems Medicine (AG Schultze)

Research Program(s):

1. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)

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Database coverage:
; ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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