Automated Image Analysis for High-Content Screening and Analysis

Shariff, Aabid; Kangas, Joshua; Coelho, Luís Pedro; Quinn, Shannon; Murphy, Robert F.

doi:10.1177/1087057110370894

Cited by 125 publications

(83 citation statements)

References 53 publications

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“…With the advent of high-throughput acquisition technologies like tissue microarrays and automated slide scanners, computerized analysis of tissue images is highly desirable, and studies have shown that quantitative software can detect changes in disease states that are missed by visual inspection (7). Methods for analyzing changes in expression and pattern are well established in cultured cells (8), but histological images are typically more difficult to analyze because cellular heterogeneity and the closely packed organization of cells lead to significant cell segmentation challenges. Several projects have been initiated to build workflows that process IHC images (9,10).…”

mentioning

confidence: 99%

Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers

Kumar¹,

Rao²,

Bhavani³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Significance Changes in the expression of proteins are often associated with oncogenesis, and are frequently used as cancer biomarkers. Changes in the subcellular location of proteins have been less frequently investigated. In this paper, we describe a robust pipeline for identifying those proteins whose subcellular location undergoes statistically significant changes in cancers of four tissues, and also for identifying biochemical pathways that are enriched for proteins that translocate. Future investigation of these proteins and pathways may provide new insight into oncogenesis. Further, the analysis pipeline is expected to be useful for assessing disease type and severity in a clinical setting.

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mentioning

confidence: 99%

Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers

Kumar¹,

Rao²,

Bhavani³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…WCA is a critical in vitro anti-cancer screening tool with single cell resolution [12][13][14][15][16] , ideally utilized after traditional target screenings such as CDC and ADCC assays [9][10][11] , and before preclinical animal tests. Currently, primary target screening assays such as CDC or ADCC assays are all performed in a simplified media or a buffer system.…”

Section: Discussionmentioning

confidence: 99%

Real-time Cytotoxicity Assays in Human Whole Blood

Hsiao

et al. 2014

JoVE

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A live cell-based whole blood cytotoxicity assay (WCA) that allows access to temporal information of the overall cell cytotoxicity is developed with high-throughput cell positioning technology. The targeted tumor cell populations are first preprogrammed to immobilization into an array format, and labeled with green fluorescent cytosolic dyes. Following the cell array formation, antibody drugs are added in combination with human whole blood. Propidium iodide (PI) is then added to assess cell death. The cell array is analyzed with an automatic imaging system. While cytosolic dye labels the targeted tumor cell populations, PI labels the dead tumor cell populations. Thus, the percentage of target cancer cell killing can be quantified by calculating the number of surviving targeted cells to the number of dead targeted cells. With this method, researchers are able to access time-dependent and dose-dependent cell cytotoxicity information. Remarkably, no hazardous radiochemicals are used. The WCA presented here has been tested with lymphoma, leukemia, and solid tumor cell lines. Therefore, WCA allows researchers to assess drug efficacy in a highly relevant ex vivo condition. Video LinkThe video component of this article can be found at

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“…3). To avoid tedious manual adaptations of feature sets for each specific application, multi-purpose feature libraries have been developed, and these cover the needs for most cell biological assays (Jones et al, 2008;Held et al, 2010;Shariff et al, 2010).…”

Section: Feature Extractionmentioning

confidence: 99%

Machine learning in cell biology – teaching computers to recognize phenotypes

Sommer

Gerlich

2013

Journal of Cell Science

282

237

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SummaryRecent 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.

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Automated Image Analysis for High-Content Screening and Analysis

Cited by 125 publications

References 53 publications

Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers

Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers

Real-time Cytotoxicity Assays in Human Whole Blood

Machine learning in cell biology – teaching computers to recognize phenotypes

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