Most laboratories studying biological processes and human disease use microscopes to image cells or other biological samples. Even for small-scale experiments, the information sought from images is increasingly quantitative and complex, and automated microscopes collect images faster than can be examined by eye. We will continue our development of CellProfiler ( to meet this strong and growing demand for software to analyze biological images. CellProfiler is a versatile, open-source toolbox. Using its point-and-click interface, researchers build a customized workflow of image-analysis modules to identify and measure biological objects in images. It can extract valuable biological information from images quickly, even for high-throughput experiments, while increasing objectivity and statistical power in microscopy experiments. Published only seven years ago, CellProfiler is already an important and widely used tool: it is launched 100,000+ times per year by users around the world and has been cited in more than 800 papers from 600 distinct laboratories. The software evolves in an intensely collaborative and interdisciplinary research environment with dozens of ongoing projects and has been successfully applied to a wide range of biological samples and assays, from counting cells to scoring complex phenotypes by machine learning. We propose to improve CellProfiler's capabilities in order to enable further biological imaging research, meet the needs of the growing user base, and expand the community that benefits from CellProfiler: First, microscopy experiments are rapidly expanding in both scale and scope, and are beginning to push CellProfiler's limits, particularly when they involve larger images (e.g., for tissue samples, cellular microarrays, large field-of-view cameras), multi-dimensional images (time-lapse and three-dimensional imaging), images for morphological profiling, and novel microscopy types (super-resolution and single-molecule imaging). We will upgrade CellProfiler's capabilities to serve these needs and add proven, state-of-the-art algorithms for image processing (especially segmentation and filtering for non-fluorescence images), time-lapse and 3D analysis, and novel microscopy types. We will also add features and usability improvements requested by the large CellProfiler community. Second, we will enable researchers to create sophisticated bioimaging analysis workflows by expanding CellProfiler's interoperability with complementary software (e.g., MATLAB, ImageJ, MicroManager, KNIME). Third, we will disseminate CellProfiler and provide user, educator, and developer support. There is great demand for our online forum, downloadable materials, and in-person tutorials (1,000+ attendees so far). These improvements to the first, and still preeminent, open-source software for modular, high- throughput biological image analysis will enable hundreds of NIH-funded laboratories to make high-impact biological discoveries from microscopy images across all disciplines within biology.

Public Health Relevance

Most laboratories studying biological processes and human disease use microscopy to analyze cells and other samples. We will enable these researchers to rapidly and accurately extract numerical data from microscopy images by continuing to develop and support our popular, user-friendly, open-source image analysis software, CellProfiler (, to accommodate the increasing scale and scope of modern microscopy experiments.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Deatherage, James F
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Broad Institute, Inc.
United States
Zip Code
Bray, Mark-Anthony; Carpenter, Anne E (2018) Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler. Methods Mol Biol 1683:89-112
Bray, Mark-Anthony; Singh, Shantanu; Han, Han et al. (2016) Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc 11:1757-74
Dao, David; Fraser, Adam N; Hung, Jane et al. (2016) CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets. Bioinformatics 32:3210-3212
Dordea, Ana C; Bray, Mark-Anthony; Allen, Kaitlin et al. (2016) An open-source computational tool to automatically quantify immunolabeled retinal ganglion cells. Exp Eye Res 147:50-56
Jolly, Amber L; Luan, Chi-Hao; Dusel, Brendon E et al. (2016) A Genome-wide RNAi Screen for Microtubule Bundle Formation and Lysosome Motility Regulation in Drosophila S2 Cells. Cell Rep 14:611-620
Goodman, Allen; Carpenter, Anne E (2016) High-Throughput, Automated Image Processing for Large-Scale Fluorescence Microscopy Experiments. Microsc Microanal 22:538-539
Uhlmann, Virginie; Singh, Shantanu; Carpenter, Anne E (2016) CP-CHARM: segmentation-free image classification made accessible. BMC Bioinformatics 17:51
Bray, Mark-Anthony; Vokes, Martha S; Carpenter, Anne E (2015) Using CellProfiler for Automatic Identification and Measurement of Biological Objects in Images. Curr Protoc Mol Biol 109:14.17.1-13
Du, Ziming; Abedalthagafi, Malak; Aizer, Ayal A et al. (2015) Increased expression of the immune modulatory molecule PD-L1 (CD274) in anaplastic meningioma. Oncotarget 6:4704-16
Bray, Mark-Anthony; Carpenter, Anne E (2015) CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data. BMC Bioinformatics 16:368

Showing the most recent 10 out of 36 publications