Multidimensional Drug Profiling By Automated Microscopy

Article (PDF Available)inScience 306(5699):1194-8 · November 2004with121 Reads
DOI: 10.1126/science.1100709 · Source: PubMed
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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Available from: Yan Feng, Jun 10, 2014
    • "phosphorylation ) could be simultaneously measured per protein species. In high-content imaging assays—the focus of our current study—hundreds of different features can be extracted for each biomarker per cell (Bakal et al., 2007; Boland and Murphy, 2001; Carpenter et al., 2006; Perlman et al., 2004). Such features could include standard biomarker measurements, such as intensity (reflecting biomarker abundance or activity level) or localization (reflecting properties such as cytosolic versus nuclear levels, or unpolarized versus polarized states), as well as other measurements, such as texture or statistical properties of the brightest pixels. "
    [Show abstract] [Hide abstract] ABSTRACT: Motivation: Quantification of cellular changes to perturbations can provide a powerful approach to infer crosstalk among molecular components in biological networks. Existing crosstalk inference methods conduct network-structure learning based on a single phenotypic feature (e.g. abundance) of a biomarker. These approaches are insufficient for analyzing perturbation data that can contain information about multiple features (e.g. abundance, activity or localization) of each biomarker. Results: We propose a computational framework for inferring phenotypic crosstalk (PHOCOS) that is suitable for high-content microscopy or other modalities that capture multiple phenotypes per biomarker. PHOCOS uses a robust graph-learning paradigm to predict direct effects from potential indirect effects and identify errors owing to noise or missing links. The result is a multi-feature, sparse network that parsimoniously captures direct and strong interactions across phenotypic attributes of multiple biomarkers. We use simulated and biological data to demonstrate the ability of PHOCOS to recover multi-attribute crosstalk networks from cellular perturbation assays. Availability and implementation: PHOCOS is available in open source at Contact: or
    Full-text · Article · Jun 2016
    • "Identifying mechanisms of action, targets, and toxicity for small molecules Small molecule perturbations can produce morphological changes detectable by microscopy, and these changes can reveal similarities among compounds in terms of their phenotypic impact in a cellular context. Many studies have demonstrated that morphological profiles can correctly predict the mechanism of action (plus toxicity in some cases) for blinded compounds, by grouping each unknown compound with already-annotated compounds, based on their phenotypic similarity [1,44,49,[55][56][57][58][59][60][61]; several have made novel predictions [62–65,66 ,67,68]. This builds on a foundation of earlier work that identified targets based on visual similarities, for example, the identification of the mitotic kinesin Eg5 as the target of the small molecule monastrol based on a distinctive monopolar spindle phe- notype [69] and the phenotypic matching of gene-compound pairs related to cytokinesis using parallel RNA interference (RNAi) and small molecule screens [70] or suppressor/enhancer screens for an RNAi-sensitized phe- notype [71]. "
    [Show abstract] [Hide abstract] ABSTRACT: A dramatic shift has occurred in how biologists use microscopy images. Whether experiments are small-scale or high-throughput, automatically quantifying biological properties in images is now widespread. We see yet another revolution under way: a transition towards using automated image analysis to not only identify phenotypes a biologist specifically seeks to measure ('screening') but also as an unbiased and sensitive tool to capture a wide variety of subtle features of cell (or organism) state ('profiling'). Mapping similarities among samples using image-based (morphological) profiling has tremendous potential to transform drug discovery, functional genomics, and basic biological research. Applications include target identification, lead hopping, library enrichment, functionally annotating genes/alleles, and identifying small molecule modulators of gene activity and disease-specific phenotypes.
    Full-text · Article · Apr 2016
    • "Because the phenotypic responses in this assay consist of changing cellular distributions at different concentration levels, which we call phenotypic trajectories, any similarity measure must capture the overlap of such trajectories in phenotypic space. We thus take the TISS approach of Perlman et al. [6] one step further with the maximum sequential weighted overlap (MSWO), defined as follows: suppose D 1 and D 2 are two distributions with means μ 1 and μ 2 and covariance matrices S 1 and S 2 ; then the overlap between these two distributions is defined as "
    [Show abstract] [Hide abstract] ABSTRACT: Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2'-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used.
    Full-text · Article · Feb 2016
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