Multidimensional Drug Profiling By Automated Microscopy

By Z. Perlman, M. Slack, Yan Feng, T. Mitchison, Lani F. Wu, Steven J. Altschuler. Science, 2004

642 citations recorded by the source corpus.

Subfields: Biophysics, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8b73514fd708f6bdd3786d47a8673a97fdce4dfe

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Citations in the corpus

Applications in image-based profiling of perturbations. (2016, background): “In image-based profiling (also known as morphological profiling or cytological profiling [1]), large amounts of quantitative morphological data are extracted from microscopy images of cells to generate a profile comprised of various measures of the shape and size of various cellular compartments and”
Cellular heterogeneity: when do differences make a difference? (2010, background): “Though such approaches simplify analysis (Perlman et al., 2004), and may uncover heterogeneity one readout at a time, important relationships among the readouts within individual cells may be missed.”
High-Content Screening for Quantitative Cell Biology. (2016, methodology): “For example, in one of the earliest screens, the nonparametric Kolmogorov-Smirnov [34] statistic was used to broadly profile 100 chemicals over a range of concentrations, analyzing 11 different markers and a large set of measurements, to assess multidimensional single cell phenotypes [35].”
Large‐scale image‐based screening and profiling of cellular phenotypes (2017, methodology): “They include univariate methods, such as the fold-change filter, variants of t test, and Kolmogorov-Smirnov test (37); and multivariate methods, such as discriminant function analysis (88,97) and the Relief algorithm (98).”
Computer vision for high content screening (2016, methodology): “Examples of phenotypes that have been labeled and trained using supervised learning algorithms include changes in cell morphology (Bakal et al ., 2007; Yin et al ., 2013), drug profiling (Ljosa et al ., 2013; Perlman et al ., 2004), and protein localization (Chen et al ., 2007; Chong et al ., 2015).”
Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes (2016, methodology): “7| Dilute A549 cells to 50,000 live cells per ml in media, and dispense 40 µ l (2,000 live A549 cells) into each well of the 384-well plates.”
Image-based multivariate profiling of drug responses from single cells (2007, background): “Several recent pioneering studies have developed high-throughput image-based assay approaches to build profiles to characterize drug effect”
CellProfiler: image analysis software for identifying and quantifying cell phenotypes (2006, methodology): “Cell image analysis has been described as one of the greatest remaining challenges in screening [5,29], and as a field is "very much in its infancy" [30] and "lag [s] behind the adoption of high-throughput imaging technologies" [10].”
Biological Imaging Software Tools (2012, methodology): “For example, clustering has been used to group drugs by their effects 48 and proteins by their subcellular patterns 49 .”
Machine learning and computer vision approaches for phenotypic profiling (2017, background): “For example, in a high-throughput chemical screening project in which cell samples are exposed to an array of small molecules, changes in cell morphology can be quantified and used to infer a mechanism of drug action in downstream experiments (Perlman et al., 2004; Ljosa et al., 2013).”
Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning (2009, methodology): “First, we automatically identify and measure every cell in every image in the experiment by using the cell-image analysis software CellProfiler (13), which generates a cytological profile (27), or cytoprofile, for each cell.”