CellProfiler: image analysis software for identifying and quantifying cell phenotypes

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).

Figure 1

CellProfiler overview and features. (a) Main CellProfiler interface, with an analysis pipeline displayed. (b) Schematic of a typical CellProfiler pipeline. (c) Image processing example: uneven illumination from the left to the right within each field of view is noticeable in this three row by five column tiled image (left). CellProfiler's illumination correction modules correct these anomalies (right). Images were contrast-enhanced to display this effect. (d) These corrections reduce noise in quantitative measurements, demonstrated here in DNA content measures (middle) from images of Drosophila Kc167 cells that are improved over the raw images (left). The results are comparable to those produced by white referenced images (right), but they do not require the error prone and often omitted step of collecting a white reference image immediately before image acquisition. (e) Outlines show the identification of nuclei and identification of cell edges made by CellProfiler in human HT29 (left) and Drosophila Kc167 (right) cells. Cells touching the border are intentionally excluded from analysis and images were contrast stretched for display. Scale bars = 15 μm.

Figure 2

Validation of CellProfiler for many cellular phenotypes. (a) Cell count: for a set of 6 images of wild-type human HT29 cells (left), two researchers' counts varied by 11%, and CellProfiler's counts were within 6% of their average. For images of Drosophila Kc167 cells with various genes knocked down by RNAi (right), the two researchers' counts varied by 16% and CellProfiler's counts were within 17% of their average. Example images and CellProfiler outlines for these cell types are shown in Figure 1e. (b) Cell size: CellProfiler's cell area measurements are comparable to those of a Coulter particle counter for Drosophila Kc167 cells, for wild-type (no dsRNA) and RNA-interference induced samples. The SEM is too small to show error bars. (c) DNA content in cell populations: measurements are shown for human HT29 cell populations (1 image for each RNAi condition, left) and for Drosophila Kc167 cell populations (1,750 images for each RNAi condition were combined, right). The cell cycle distributions are as expected, with the 2N peak being predominant in the wild-type human sample, whereas most wild-type Drosophila nuclei are known to have 4N DNA content [62]. RNAi-targeted samples were also as expected for Aurora kinase B (polyploid), Mad2 (fairly normal cell cycle distribution), String (4N-enriched), and Anillin and Cyclin A (both polyploid). (d) Chromatin content in time lapse movies: GFP-histone H4 (S. cerevisiae) or GFP-histone H2B (HeLa and C. elegans) content is shown near each nucleus in arbitrary intensity units. The histone content is decreased by roughly half in each daughter nucleus after division. For C. elegans, only the boxed region of interest was analyzed. Scale bars: C. elegans, unknown; human HeLa = 20 μm; S. cerevisiae = 10 μm. (e) Phospho-protein content: human HT29 cells treated with RNAi reagent against Polo kinase have an increased percentage of nuclei with high phospho-H3 staining compared to wild-type cells, consistent with a mitosis-stalled phenotype (left). Wild-type human HT29 nuclei that stain positively for phospho-histone H3 tend to be smaller than phospho-H3-negative cells (right). (f) Protein localization: the mean intensity of NFκB staining in the cytoplasm and the nucleus is shown in response to TNFα in human MCF7 cells (top). Totals do not equal 100% due to slight overlap between compartments. (g) Speckles: fluorescent foci of phospho-Histone2AX induced by 2 Gy of irradiation in human U2OS cells disappear at timepoints as the cells recover. Scale bar = 10 μm. The SEM is too small to show error bars.

Figure 3

Identifying mutant shapes and textures. In each case, four images of each sample were quantitatively analyzed and images were adjusted using Adobe Photoshop auto levels for display only. Scale bars = 15 μm. (a) The unusual cell shape induced by an RNAi reagent against Myo3A in human HT29 cells is quantitatively distinguishable from wild-type control cells. (b) The unusual cell shape induced by an RNAi reagent against PTPN21 in human HT29 cells is quantitatively distinguishable from wild-type control cells. (c) The unusual actin texture induced by an RNAi reagent against DUSP19 in human HT29 cells is quantitatively distinguishable from wild-type control cells. The images are pseudocolored to show the actin staining texture. The biological basis of these morphological changes and the specificity of the RNAi reagents remain to be determined.

Figure 4

CellProfiler analysis of a Forkhead (FOXO1A) cytoplasm-nucleus translocation assay. (a) Example images from the high throughput image set in human U2OS osteosarcoma cells, showing no treatment (left) and 150 nM Wortmannin (right) after 1 hour treatment, scale unknown. (b) Translocation scored as the fraction of cells whose ratio of GFP in the cytoplasm versus the nucleus was above a threshold. Error bars = SEM. (c) Statistical analysis using Z' and non-logistic-fit V factors, which are standard measures of assay quality (>0.4 is considered screenable and 1 is an ideal assay) [63-65]. (d) Nuclei change shape in response to wortmannin but not LY294002, as judged by three shape features. Error bars = SEM; *p < 0.05.