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 (www.cellprofiler.org) 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 (www.cellprofiler.org), to accommodate the increasing scale and scope of modern microscopy experiments.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM089652-05A1
Application #
8761195
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Deatherage, James F
Project Start
2010-07-01
Project End
2018-07-31
Budget Start
2014-08-15
Budget End
2015-07-31
Support Year
5
Fiscal Year
2014
Total Cost
$522,488
Indirect Cost
$223,272
Name
Broad Institute, Inc.
Department
Type
DUNS #
623544785
City
Cambridge
State
MA
Country
United States
Zip Code
02142
Nieland, Thomas J F; Logan, David J; Saulnier, Jessica et al. (2014) High content image analysis identifies novel regulators of synaptogenesis in a high-throughput RNAi screen of primary neurons. PLoS One 9:e91744
Chudnovsky, Yakov; Kim, Dohoon; Zheng, Siyuan et al. (2014) ZFHX4 interacts with the NuRD core member CHD4 and regulates the glioblastoma tumor-initiating cell state. Cell Rep 6:313-24
Wählby, Carolina; Conery, Annie Lee; Bray, Mark-Anthony et al. (2014) High- and low-throughput scoring of fat mass and body fat distribution in C. elegans. Methods 68:492-9
Stanley, Sarah A; Barczak, Amy K; Silvis, Melanie R et al. (2014) Identification of host-targeted small molecules that restrict intracellular Mycobacterium tuberculosis growth. PLoS Pathog 10:e1003946
Majithia, Amit R; Flannick, Jason; Shahinian, Peter et al. (2014) Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc Natl Acad Sci U S A 111:13127-32
March, Sandra; Ng, Shengyong; Velmurugan, Soundarapandian et al. (2013) A microscale human liver platform that supports the hepatic stages of Plasmodium falciparum and vivax. Cell Host Microbe 14:104-15
Shan, Jing; Schwartz, Robert E; Ross, Nathan T et al. (2013) Identification of small molecules for human hepatocyte expansion and iPS differentiation. Nat Chem Biol 9:514-20
Kitami, Toshimori; Logan, David J; Negri, Joseph et al. (2012) A chemical screen probing the relationship between mitochondrial content and cell size. PLoS One 7:e33755
Wahlby, Carolina; Kamentsky, Lee; Liu, Zihan H et al. (2012) An image analysis toolbox for high-throughput C. elegans assays. Nat Methods 9:714-6
Bray, Mark-Anthony; Fraser, Adam N; Hasaka, Thomas P et al. (2012) Workflow and metrics for image quality control in large-scale high-content screens. J Biomol Screen 17:266-74

Showing the most recent 10 out of 15 publications