METHODS AND RESOURCES CellProfiler 3.0: Next-generation image processing for biology Claire McQuin1☯, Allen Goodman1☯, Vasiliy Chernyshev2,3☯, Lee Kamentsky1☯, Beth A. Cimini1☯, Kyle W. Karhohs1☯, Minh Doan1, Liya Ding4, Susanne M. Rafelski4, Derek Thirstrup4, Winfried Wiegraebe4, Shantanu Singh1, Tim Becker1, Juan C. Caicedo1, Anne E. Carpenter1* 1 Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America, 2 Skolkovo Institute of Science and Technology, Skolkovo, Moscow Region, Russia, 3 Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia, 4 Allen Institute for Cell Science, a1111111111 Seattle, Washington, United States of America a1111111111 a1111111111 ☯ These authors contributed equally to this work. a1111111111 *
[email protected] a1111111111 Abstract CellProfiler has enabled the scientific research community to create flexible, modular image OPEN ACCESS analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new ver- Citation: McQuin C, Goodman A, Chernyshev V, sion of the software supporting both whole-volume and plane-wise analysis of three-dimen- Kamentsky L, Cimini BA, Karhohs KW, et al. (2018) sional (3D) image stacks, increasingly common in biomedical research. CellProfiler's CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol 16(7): e2005970. https://doi. infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale org/10.1371/journal.pbio.2005970 image processing. New plugins enable running pretrained deep learning models on images. Academic Editor: Tom Misteli, National Cancer Designed by and for biologists, CellProfiler equips researchers with powerful computational Institute, United States of America tools via a well-documented user interface, empowering biologists in all fields to create Received: March 9, 2018 quantitative, reproducible image analysis workflows.