Annotated high-throughput microscopy image sets for validation

Ljosa, Vebjorn; Sokolnicki, Katherine L; Carpenter, Anne E.

doi:10.1038/nmeth.2083

Cited by 473 publications

(373 citation statements)

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“…We have published a comparison of several such methods for creating morphological profiles 47 . Here, we describe the approach of population-averaging profiles, which has been shown to be effective.…”

Section: Methodsmentioning

confidence: 99%

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

et al. 2016

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In morphological profiling, quantitative data are extracted from microscopy images of cells to identify biologically relevant similarities and differences among samples based on these profiles. This protocol describes the design and execution of experiments using Cell Painting, a morphological profiling assay multiplexing six fluorescent dyes imaged in five channels, to reveal eight broadly relevant cellular components or organelles. Cells are plated in multi-well plates, perturbed with the treatments to be tested, stained, fixed, and imaged on a high-throughput microscope. Then, automated image analysis software identifies individual cells and measures ~1,500 morphological features (various measures of size, shape, texture, intensity, etc.) to produce a rich profile suitable for detecting subtle phenotypes. Profiles of cell populations treated with different experimental perturbations can be compared to suit many goals, such as identifying the phenotypic impact of chemical or genetic perturbations, grouping compounds and/or genes into functional pathways, and identifying signatures of disease. Cell culture and image acquisition takes two weeks; feature extraction and data analysis take an additional 1-2 weeks.

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Section: Methodsmentioning

confidence: 99%

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

et al. 2016

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“…Images of Drosophila melanogaster Kc167 cells [BBBC007_v1 (A9)] and whole organism Caenorhabditis elegans (BBBC012v1) were obtained from the Broad Bioimage Benchmark Collection (Ljosa et al, 2012). Hand drawn boundaries of D. melanogaster cells were downloaded from the same collection and inverted to select cells (Jones et al, 2005).…”

Section: Methodsmentioning

confidence: 99%

Systematic and general method for quantifying localization in microscopy images

2016

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Quantifying the localization of molecules with respect to other molecules, cell structures and intracellular regions is essential to understanding their regulation and actions. However, measuring localization from microscopy images is often difficult with existing metrics. Here, we evaluate a metric for quantifying localization termed the threshold overlap score (TOS), and show it is simple to calculate, easy to interpret, able to be used to systematically characterize localization patterns, and generally applicable. TOS is calculated by: (i) measuring the overlap of pixels that are above the intensity thresholds for two signals; (ii) determining whether the overlap is more, less, or the same as expected by chance, i.e. colocalization, anti-colocalization, or non-colocalization; and (iii) rescaling to allow comparison at different thresholds. The above is repeated at multiple threshold combinations to generate a TOS matrix to systematically characterize the relationship between localization and signal intensities. TOS matrices were used to identify and distinguish localization patterns of different proteins in various simulations, cell types and organisms with greater specificity and sensitivity than common metrics. For all the above reasons, TOS is an excellent first line metric, particularly for cells with mixed localization patterns.

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“…The performance of these new methods can then be evaluated by comparing their output with the data stored in SSBD. The Broad Bioimage Benchmark Collection (BBBC; Ljosa et al , 2012) is a database for storing annotated microscopy image sets for testing image-processing methods. Each image set is provided with the corresponding quantitative data such as contours of biological objects extracted by image analysis.…”

Section: Discussionmentioning

confidence: 99%

SSBD: a database of quantitative data of spatiotemporal dynamics of biological phenomena

Tohsato

Kyoda

et al. 2016

Bioinformatics

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Motivation: Rapid advances in live-cell imaging analysis and mathematical modeling have produced a large amount of quantitative data on spatiotemporal dynamics of biological objects ranging from molecules to organisms. There is now a crucial need to bring these large amounts of quantitative biological dynamics data together centrally in a coherent and systematic manner. This will facilitate the reuse of this data for further analysis.Results: We have developed the Systems Science of Biological Dynamics database (SSBD) to store and share quantitative biological dynamics data. SSBD currently provides 311 sets of quantitative data for single molecules, nuclei and whole organisms in a wide variety of model organisms from Escherichia coli to Mus musculus. The data are provided in Biological Dynamics Markup Language format and also through a REST API. In addition, SSBD provides 188 sets of time-lapse microscopy images from which the quantitative data were obtained and software tools for data visualization and analysis.Availability and Implementation: SSBD is accessible at http://ssbd.qbic.riken.jp.Contact: sonami@riken.jp

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Annotated high-throughput microscopy image sets for validation

Cited by 473 publications

References 3 publications

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

Systematic and general method for quantifying localization in microscopy images

SSBD: a database of quantitative data of spatiotemporal dynamics of biological phenomena

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