Multidimensional Drug Profiling By Automated Microscopy

Institute of Chemistry and Cell Biology, Harvard Medical School, Boston, MA 02115, USA.
Science (Impact Factor: 33.61). 11/2004; 306(5699):1194-8. 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
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    • "Furthermore, integration of basic cell viability endpoints with gene expression profiling provide a useful source of biomarkers that predict sensitivity to cell-cycle arrest but poorly inform on optimal combination strategies or markers for other important cancer phenotypes such as apoptosis and invasion. Recent advances in fully automated brightfield and fluorescent microscopic acquisition platforms and associated image analysis algorithms have facilitated the integration of quantitative microscopic imaging of multiple endpoints upon both fixed and live-cells assays (Perlman et al., 2004; Yarrow et al., 2004; Tanaka et al., 2005; Caie et al., 2010). Screening beyond simplistic 2D monoculture assays is a necessary aim to target more relevant pathophysiological mechanisms and discover novel synergistic drug combination activity. "
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    ABSTRACT: Advances in target-based drug discovery strategies have enabled drug discovery groups in academia and industry to become very effective at generating molecules that are potent and selective against single targets. However, it has become apparent from disappointing results in recent clinical trials that a major challenge to the development of successful targeted therapies for treating complex multifactorial diseases is overcoming heterogeneity in target mechanism among patients and inherent or acquired drug resistance. Consequently, reductionist target directed drug-discovery approaches are not appropriately tailored toward identifying and optimizing multi-targeted therapeutics or rational drug combinations for complex disease. In this article, we describe the application of emerging high-content phenotypic profiling and analysis tools to support robust evaluation of drug combination performance following dose-ratio matrix screening. We further describe how the incorporation of high-throughput reverse phase protein microarrays with phenotypic screening can provide rational drug combination hypotheses but also confirm the mechanism-of-action of novel drug combinations, to facilitate future preclinical and clinical development strategies.
    Frontiers in Pharmacology 05/2014; 5:118. DOI:10.3389/fphar.2014.00118 · 3.80 Impact Factor
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    • "plasma membrane permeability, cell morphology ). Similarly, automated fluorescence microscopy was used by Perlman et al. (2004) for multivariate single-cell analysis of HeLa cells to identify dose dependency and discriminate mechanisms of cytotoxicity for 100 drugs. They multiplexed a DNA stain with two of the following antibodyprobes per well: SC35, anillin; alpha-tubulin, actin, p38, ERK; p53, c-Fos, CREB and calmodulin. "
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    ABSTRACT: High-content analysis (HCA) of in vitro, biochemical and morphological effects of classic (small molecule) drugs and chemicals is concordant with potential for human toxicity. For hepatotoxicity, concordance is greater for cytotoxic effects assessed by HCA than for conventional cytotoxicity tests and for regulatory animal toxicity studies. Additionally, HCA identifies chronic toxicity potential, and drugs producing idiosyncratic adverse reactions and / or toxic metabolites are also identified by HCA. Mechanistic information on the subcellular basis for the toxicity is frequently identified, including various mitochondrial effects, oxidative stress, calcium dyshomeostasis, phospholipidosis, apoptosis and antiproliferative effects, and a fingerprinting of the sequence and pattern of subcellular events. As these effects are frequently non-specific and affect many cell types, some toxicities may be detected and monitored by HCA of peripheral blood cells, such as for anticancer and anti-infective drugs. Critical methodological and interpretive features are identified that are critical to the effectiveness of the HCA cytotoxicity assessment, including, the need for multiple days of exposure of cells to drug, use of a human hepatocyte cell line with metabolic competence, assessment of multiple pre-lethal effects in individual live cells, consideration of hormesis, the need for interpretation of relevance of cytotoxicity concentration compared to efficacy concentration, and quality management. Limitations of the HCA include assessment of drugs that act on receptors, transporters or processes not found in hepatocytes. HCA may be used in a) screening lead candidates for potential human toxicity in drug discovery alongside of in vitro assessment of efficacy and pharmacokinetics, b) elucidating mechanisms of toxicity, and c) monitoring in vivo toxicity of drugs with known toxicity of known mechanism.This article is protected by copyright. All rights reserved.
    Basic & Clinical Pharmacology & Toxicology 03/2014; 115(1). DOI:10.1111/bcpt.12227 · 2.38 Impact Factor
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    • "The goal of 'unsupervised' machine learning is to group data points into clusters on the basis of a similarity measure or to facilitate data mining by reducing the complexity of the data (Hastie et al., 2005; Bishop, 2006; Tarca et al., 2007; de Ridder et al., 2013). Unlike supervised approaches, unsupervised methods enable the exploration of unknown phenotypes (Wang et al., 2008; Lin et al., 2010) and have been successfully used for phenotypic profiling of drug effects (Perlman et al., 2004). A number of recent reviews and textbooks provide extensive theoretical background on different machine-learning algorithms (Hastie et al., 2005; Bishop, 2006; Larrañaga et al., 2006; Tarca et al., 2007; Danuser, 2011; de Ridder et al., 2013). "
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    ABSTRACT: Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.
    Journal of Cell Science 11/2013; 126(24). DOI:10.1242/jcs.123604 · 5.43 Impact Factor
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