CellProfiler: image analysis software for identifying and quantifying cell phenotypes

Abstract

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. Cell image analysis software <p>CellProfiler, the first free, open-source system for flexible and high-throughput cell image analysis is described.</p> AbstractBiologists 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). RationaleExamining cells by microscopy has long been a primary method for studying cellular function. When cells are stained appropriately, visual analysis can reveal biological mechanisms. Advanced microscopes can now, in a single day, easily collect thousands of high resolution images of cells from timelapse experiments and from large-scale screens using chemical compounds, RNA interference (RNAi) reagents, or expression plasmids [1][2][3][4][5]. However, a bottleneck exists at the image analysis stage. Several pioneering large screens have been scored through visual inspection by expert biologists [6,7], whose interpretive ability will not soon be replicated by a computer. Still, for most applications, image cytometry (automated cell image analysis) is strongly preferable to analysis by eye. In fact, in some cases image cytometry is absolutely required to extract the full spectrum of information present in biological images, for reasons we discuss here.First, while human observers typically score one or at most a few cellular features, image cytometry simultaneously yields many informative measures of cells, including the intensity and localization of each fluorescently labeled cellular component (for example, DNA or protein) within each subcellular compartment, as well as the number, size, and shape of those subcellular compartments. Image-based analysis is thus versatile, inherently multiplexed, and high in information content. Like flow cytometry, image cytometry measures the percell amount of protein and DNA, but can more conveniently handle hundreds of thousands of distinct samples and is also compatible with adherent cell types, time-lapse samples, and intact tissues. In addition, image cytometry can accurately

SNX 18 regulates ATG 9A trafficking from recycling endosomes by recruiting Dynamin‐2

“…GFP‐LC3, ATG16L1 and YFP puncta experiments were imaged using a Zeiss Axio Observer widefield microscope. Images were subsequently quantified for punctate structures using CellProfiler analysis software 44.…”

Landscape of nuclear transport receptor cargo specificity

“…Image analysis was performed using CellProfiler (Carpenter et al , 2006). Nuclei were segmented using CellProfiler's automated maximum correlation thresholding (MCT) algorithm (Padmanabhan et al , 2010).…”

Tuberous sclerosis complex is required for tumor maintenance in MYC‐driven Burkitt's lymphoma

“…For the quantification of TSC1 and MYC staining in CD20‐positive B cells, images (200× magnified) of seven follicles of the superficial cortex of three control lymph nodes each (21 images) were compared with seven images taken from eight BL samples derived from different sites, lymph node (4×), cerebellum (1×), bone marrow (1×), oropharynx (1×), nasopharynx (1×). Analysis was performed using CellProfiler (Carpenter et al , 2006). Nuclei were visualized by the DAPI staining, and the cell outline was identified from the TSC1 staining.…”