CellProfiler: image analysis software for identifying and quantifying cell phenotypes

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Abstract

CellProfiler, the first free, open-source system for flexible and high-throughput cell image analysis is described.

Abstract

Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

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Most cited references 59

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Causal protein-signaling networks derived from multiparameter single-cell data.

Karen Sachs, Omar Perez, Dana Pe'er … (2005)

Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.

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Multidimensional drug profiling by automated microscopy.

Zachary Perlman, Michael Slack, Yan Feng … (2004)

We present a method for high-throughput cytological profiling by microscopy. Our system provides quantitative multidimensional measures of individual cell states over wide ranges of perturbations. We profile dose-dependent phenotypic effects of drugs in human cell culture with a titration-invariant similarity score (TISS). This method successfully categorized blinded drugs and suggested targets for drugs of uncertain mechanism. Multivariate single-cell analysis is a starting point for identifying relationships among drug effects at a systems level and a step toward phenotypic profiling at the single-cell level. Our methods will be useful for discovering the mechanism and predicting the toxicity of new drugs.

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A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells.

M. Boland, R Murphy (2001)

Assessment of protein subcellular location is crucial to proteomics efforts since localization information provides a context for a protein's sequence, structure, and function. The work described below is the first to address the subcellular localization of proteins in a quantitative, comprehensive manner. Images for ten different subcellular patterns (including all major organelles) were collected using fluorescence microscopy. The patterns were described using a variety of numeric features, including Zernike moments, Haralick texture features, and a set of new features developed specifically for this purpose. To test the usefulness of these features, they were used to train a neural network classifier. The classifier was able to correctly recognize an average of 83% of previously unseen cells showing one of the ten patterns. The same classifier was then used to recognize previously unseen sets of homogeneously prepared cells with 98% accuracy. Algorithms were implemented using the commercial products Matlab, S-Plus, and SAS, as well as some functions written in C. The scripts and source code generated for this work are available at http://murphylab.web.cmu.edu/software. murphy@cmu.edu

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Author and article information

Journal

Journal ID (nlm-ta): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1465-6906

ISSN (Electronic): 1465-6914

Publication date (Print): 2006

Publication date (Electronic): 31 October 2006

Volume: 7

Issue: 10

Page: R100

Affiliations

[1 ]Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA

[2 ]Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA

[3 ]Department of Radiology, Brigham and Women's Hospital, Boston, MA 02115, USA

[4 ]Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA

Article

Publisher ID: gb-2006-7-10-r100

DOI: 10.1186/gb-2006-7-10-r100

PMC ID: 1794559

PubMed ID: 17076895

SO-VID: 48b108a4-cdfc-4442-810c-d4a0d35ab68f

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 15 September 2006

Date accepted : 31 October 2006

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CellProfiler: image analysis software for identifying and quantifying cell phenotypes

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Evolutionary Cell Biology

Most cited references 59

Causal protein-signaling networks derived from multiparameter single-cell data.

Multidimensional drug profiling by automated microscopy.

A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells.

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Cited by 1,711

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