Dataset Information

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).

SUBMITTER: Carpenter AE

PROVIDER: S-EPMC1794559 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

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CellProfiler: image analysis software for identifying and quantifying cell phenotypes.

Carpenter Anne E AE Jones Thouis R TR Lamprecht Michael R MR Clarke Colin C Kang In Han IH Friman Ola O Guertin David A DA Chang Joo Han JH Lindquist Robert A RA Moffat Jason J Golland Polina P Sabatini David M DM

Genome biology 20061031 10

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 examp ...[more]

PMID: 17076895

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Project description:This study evaluated in vitro cell viability by the colorimetric MTT stands for 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay compared to image analysis by CellProfiler® software.Hepatoma (Hepa-1c1c7) and fibroblast (L929) cells were exposed to isolated substances, camptothecin, lycorine, tazettine, albomaculine, 3-epimacronine, trispheridine, galanthine and Padina gymnospora, Sargassum sp. methanolic extract, and Habranthus itaobinus Ravenna ethyl acetate in different concentrations. After MTT assay, cells were stained with Panotic dye kit. Cell images were obtained with an inverted microscope equipped with a digital camera. The images were analyzed by CellProfiler®.No cytotoxicity at the highest concentration analyzed for 3-epimacronine, albomaculine, galanthine, trispheridine, P. gymnospora extract and Sargassum sp. extract where detected. Tazettine offered cytotoxicity only against the Hepa1c1c7 cell line. Lycorine, camptothecin, and H. itaobinus extract exhibited cytotoxic effects in both cell lines. The viability methods tested were correlated demonstrated by Bland-Atman test with normal distribution with mean difference between the two methods close to zero, bias value 3.0263. The error was within the limits of the confidence intervals and these values had a narrow difference. The correlation between the two methods was demonstrated by the linear regression plotted as R2.CellProfiler® image analysis presented similar results to the MTT assay in the identification of viable cells, and image analysis may assist part of biological analysis procedures. The presented methodology is inexpensive and reproducible.In vitro cell viability assessment with MTT (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay may be replaced by image analysis by CellProfiler®. The viability methods tested were correlated demonstrated by Bland-Atman test with normal distribution with mean difference between the two methods close to zero, bias value 3.0263. The correlation between the two methods was demonstrated by the linear regression plotted as R2. Abbreviations: HPLC: High pressure liquid chromatography MTT: (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide).

Project description:BackgroundThe technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language-quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data.ResultsQuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.ConclusionsThe presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.

Project description:BackgroundHistological assessment of skeletal muscle sections is important for the research of muscle physiology and diseases. Quantifiable measures of skeletal muscle often include mean fiber diameter, fiber size distribution, and centrally nucleated muscle fibers. These parameters offer insights into the dynamic adaptation of skeletal muscle cells during repeated cycles of degeneration and regeneration associated with many muscle diseases and injuries. Computational programs designed to obtain these parameters would greatly facilitate such efforts and offer significant advantage over manual image analysis, which is very labor-intensive and often subjective. Here, we describe a customized pipeline termed MuscleAnalyzer for muscle histology analysis based upon CellProfiler, a free, open-source software for measuring and analyzing cell images.ResultsThe MuscleAnalyzer pipeline consists of loading, adjusting, and running a series of image-processing modules provided by CellProfiler. This pipeline was evaluated using wild-type and mdx muscle sections co-stained with laminin (to demarcate the muscle fiber boundaries) and 4',6-diamidino-2-phenylindole (DAPI, to label the nuclei). The immunofluorescence images analyzed using the MuscleAnalyzer pipeline or manually yielded similar results in the number of muscle fibers per image (p = 0.42) and central nucleated fiber (CNF) percentage (p = 0.29) in mdx mice. However, for a total of 67 images, CellProfiler completed the analysis in ~ 10 min on a regular PC while it took an investigator ~ 3 h using the manual approach in order to quantify the number of muscle fibers and CNF. Moreover, the MuscleAnalyzer pipeline also provided the measurement of the cross-sectional area (CSA) and minimal Feret's diameter (MFD) of muscle fibers, and thus fiber size distribution can be plotted.ConclusionsOur data indicate that the MuscleAnalyzer pipeline can efficiently and accurately analyze laminin and DAPI co-stained muscle images in a batch format and provide quantitative measurements for muscle histological properties such as muscle fiber diameters, fiber size distribution, and CNF percentage.

Dataset Information

CellProfiler: image analysis software for identifying and quantifying cell phenotypes.

Publications

CellProfiler: image analysis software for identifying and quantifying cell phenotypes.

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