Characterizing heterogeneous cellular responses to perturbations

Slack, Michael D.; Martínez, Elisabeth D.; Wu, Lani F.; Altschuler, Steven J.

doi:10.1073/pnas.0807038105

Cited by 152 publications

(181 citation statements)

References 39 publications

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“…Initial analysis was commonly performed by averaging single-cell results to derive mean scores or by clustering such results (Gil et al 2002;Piano et al 2002;Neumann et al 2006;Bakal et al 2007). Recently, researchers have quantified morphological variability on the single-cell level in response to various stimuli, e.g., genetic or chemical perturbations (Levy and Siegal 2008;Slack et al 2008). Classification of cells toward particular phenotypes of interest has been successfully accomplished in multiple cases (Boland et al 1998;Boland and Murphy 2001;Tanaka et al 2005;Adams et al 2006;Chen and Murphy 2006;Loo et al 2007;Wang et al 2008;Young et al 2008;Jones et al 2009).…”

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confidence: 99%

Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen

Nir

Bakal²,

Perrimon³

et al. 2010

Genome Res.

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Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/ inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations.

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confidence: 99%

Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen

Nir

Bakal²,

Perrimon³

et al. 2010

Genome Res.

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“…Remarkably, a higher number of features does not necessarily generate more significant cellular profiles. 44,45 It is more relevant to select features that reasonably represent the cellular reaction in response to a given compound. For instance, in a setup that aims at probing cytostatic agents, a feature like "shape of tubulin-staining" will add more informative content to a cellular profile than a feature like "average intensity of nuclear staining".…”

Section: From Image Data To Numeric Datamentioning

confidence: 99%

Target identification by image analysis

et al. 2016

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Each biologically active compound induces phenotypic changes in target cells that are characteristic for its mode of action. These phenotypic alterations can be directly observed under the microscope or made visible by labelling structural elements or selected proteins of the cells with dyes. A comparison of the cellular phenotype induced by a compound of interest with the phenotypes of reference compounds with known cellular targets allows predicting its mode of action. While this approach has been successfully applied to the characterization of natural products based on a visual inspection of images, recent studies used automated microscopy and analysis software to increase speed and to reduce subjective interpretation. In this review, we give a general outline of the workflow for manual and automated image analysis, and we highlight natural products whose bacterial and eucaryotic targets could be identified through such approaches.

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“…In conclusion, this first issue of the year shows the broad range of developments in cytometry providing to improve drug discovery and to get a better hold on the heterogeneity of responses that unknown drugs may evoke in respective cellular test systems (12).…”

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confidence: 99%

Cytomics for discovering drugs

Tárnok

2009

Cytometry Pt A

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THERE are two major fields of applications for the cytomics approach: Predictive medicine for preventative medicine and drug discovery. Drug discovery demands that thousands of compounds are rapidly tested for their biological activity in order to detect those rare events that may bear the potential of becoming useful pharmaceutics somewhere in the future (1,2). This requires the combination of appropriate cell systems for testing, robust high-throughput technological platforms allowing for high-content screening (mostly imaging systems) as well as fast and stable automated image and data analysis systems enabling unbiased data scrutiny. Ideally, such cytomic oriented detection work flow should help to reduce the frequency of false positive hits (requiring substantial effort and costs for further testing) and false negative hits (leading to loss of potentially precious compounds).Evensen et al. (3) established a unique co-culture assay of endothelial cells and vascular smooth muscle cells for screening of anti-angiogenic drugs for cancer therapy. Their system enables investigation of drug efficacy on immature, VEGFdependent, and growing endothelium as well as on mature, nonproliferating endothelium. The authors tested in a pilot study several anti-angiogenic drugs using a microtiter-plate imaging system. They found that tube total length is the most informative parameter derived from the images and they discovered by their quantitative assay some unexpected compound activity.Automated high-throughput imaging combined with quantitative analysis of the actin cytoskeleton opens the opportunity for rapid standardized determination of the effects of treatment or infection on cell structure. An image analysis tool for automated measurement of cytoskeleton modifications in a high-throughput imaging system is demonstrated by Weichsel et al. (4). They introduce the parameter image coherency and show that it is suitable to detect accurately global alterations in the cytoskeleton organization.De Vos et al. (5) focus their interest on the fully automated multivariate phenotypic classification of individual cell nuclei and subnuclear spots by automated classification and supervised machine learning. These authors recently developed controlled light exposure microscopy, a novel technology that strongly reduces photodamage by limiting excitation in parts of the image where full exposure is not needed (6). In their present publication, they applied fluorescence mosaic microscopy for image acquisition and ImageJ for image analysis of DAPI and histone gamma-H2AX (7) labeled cells. The authors developed a comprehensive work flow for highcontent screening of the nuclear architecture of cell lines treated with a genotoxic agent without or in combination with ionizing or UV irradiation. This approach is suitable for highthroughput screening in drug discovery.Image quality is crucial in image-based screening and quantitation. Low quality images may render unreliable results and need to be further processed or discarded. Now Zeder et ...

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Characterizing heterogeneous cellular responses to perturbations

Cited by 152 publications

References 39 publications

Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen

Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen

Target identification by image analysis

Cytomics for discovering drugs

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