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      Multiplex Cytological Profiling Assay to Measure Diverse Cellular States

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          Abstract

          Computational methods for image-based profiling are under active development, but their success hinges on assays that can capture a wide range of phenotypes. We have developed a multiplex cytological profiling assay that “paints the cell” with as many fluorescent markers as possible without compromising our ability to extract rich, quantitative profiles in high throughput. The assay detects seven major cellular components. In a pilot screen of bioactive compounds, the assay detected a range of cellular phenotypes and it clustered compounds with similar  annotated protein targets or chemical structure based on cytological profiles. The results demonstrate that the assay captures subtle patterns in the combination of morphological labels, thereby detecting the effects of chemical compounds even though their targets are not stained directly. This image-based assay provides an unbiased approach to characterize compound- and disease-associated cell states to support future probe discovery.

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          Most cited references16

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          Multidimensional drug profiling by automated microscopy.

          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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            Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.

            Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for every cell in every image. Next, the researcher generates a rule (i.e., classifier) to recognize cells with a phenotype of interest during a short, interactive training session using iterative feedback. Finally, all of the cells in the experiment are automatically classified and each sample is scored based on the presence of cells displaying the phenotype. By using this approach, we successfully scored images in RNA interference screens in 2 organisms for the prevalence of 15 diverse cellular morphologies, some of which were previously intractable.
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              Taxol suppresses dynamics of individual microtubules in living human tumor cells.

              Microtubules are intrinsically dynamic polymers, and their dynamics play a crucial role in mitotic spindle assembly, the mitotic checkpoint, and chromosome movement. We hypothesized that, in living cells, suppression of microtubule dynamics is responsible for the ability of taxol to inhibit mitotic progression and cell proliferation. Using quantitative fluorescence video microscopy, we examined the effects of taxol (30-100 nM) on the dynamics of individual microtubules in two living human tumor cell lines: Caov-3 ovarian adenocarcinoma cells and A-498 kidney carcinoma cells. Taxol accumulated more in Caov-3 cells than in A-498 cells. At equivalent intracellular taxol concentrations, dynamic instability was inhibited similarly in the two cell lines. Microtubule shortening rates were inhibited in Caov-3 cells and in A-498 cells by 32 and 26%, growing rates were inhibited by 24 and 18%, and dynamicity was inhibited by 31 and 63%, respectively. All mitotic spindles were abnormal, and many interphase cells became multinucleate (Caov-3, 30%; A-498, 58%). Taxol blocked cell cycle progress at the metaphase/anaphase transition and inhibited cell proliferation. The results indicate that suppression of microtubule dynamics by taxol deleteriously affects the ability of cancer cells to properly assemble a mitotic spindle, pass the metaphase/anaphase checkpoint, and produce progeny.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                2 December 2013
                : 8
                : 12
                : e80999
                Affiliations
                [1]Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America
                Baylor College of Medicine, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SLS TRG PAC AEC AFS SMG VL KLS. Performed the experiments: SMG MMK KLS. Analyzed the data: VL JAW HAC SMG PAC AEC AFS. Contributed reagents/materials/analysis tools: SMG KLS VL DW MMK KLS HAC KPS. Wrote the manuscript: SMG VL PAC AEC AFS.

                [¤a]

                Current address: inviCRO, Boston, Masachusetts, United States of America

                [¤b]

                Current address: Washington University School of Medicine, St. Louis, Missouri, United States of America

                [¤c]

                Current address: Division of Information Technology and Sciences, Champlain College, Burlington, Vermont, United States of America

                [¤d]

                Current address: moduleQ, San Francisco, California, United States of America

                ¶ These authors also contributed equally to this work.

                Article
                PONE-D-13-11135
                10.1371/journal.pone.0080999
                3847047
                24312513
                cdfb3fb8-e3d6-4bad-b15b-5e99cc1ef569
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 March 2013
                : 8 October 2013
                Funding
                This work was supported by National Science Foundation CAREER award DBI 1148823 (AEC), and National Institutes of Health grant U54 HG005032 (SLS). KPS and PAC were supported in part by US National Institutes of Health Genomics Based Drug Discovery–Target ID Project grant RL1HG004671, which is administratively linked to the US National Institutes of Health grants RL1CA133834, RL1GM084437, and UL1RR024924. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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