Dataset Information

High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease.

ABSTRACT: High-throughput imaging (HTI) is a powerful tool in the discovery of cellular disease mechanisms. While traditional approaches to identify disease pathways often rely on knowledge of the causative genetic defect, HTI-based screens offer an unbiased discovery approach based on any morphological or functional defects of disease cells or tissues. In this review, we provide an overview of the use of HTI for the study of human disease mechanisms. We discuss key technical aspects of HTI and highlight representative examples of its practical applications for the discovery of molecular mechanisms of disease, focusing on infectious diseases and host-pathogen interactions, cancer, and rare genetic diseases. We also present some of the current challenges and possible solutions offered by novel cell culture systems and genome engineering approaches.

SUBMITTER: Pegoraro G

PROVIDER: S-EPMC5562530 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease.

Pegoraro Gianluca G Misteli Tom T

Trends in genetics : TIG 20170718 9

High-throughput imaging (HTI) is a powerful tool in the discovery of cellular disease mechanisms. While traditional approaches to identify disease pathways often rely on knowledge of the causative genetic defect, HTI-based screens offer an unbiased discovery approach based on any morphological or functional defects of disease cells or tissues. In this review, we provide an overview of the use of HTI for the study of human disease mechanisms. We discuss key technical aspects of HTI and highlight ...[more]

PMID: 28732598

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Project description:BackgroundThe expansion of automatic imaging technologies has created a need to be able to efficiently compare and review large sets of image data. To enable comparisons of image data between samples we need to define the normal variation within distinct images of the same sample. Even with tightly controlled experimental conditions, protein expression can vary widely between cells, and because of the difficulty in viewing and comparing large image sets this might not be observed. Here we introduce a novel methodology, iCluster, for visualizing, clustering and comparing large sub-cellular localization image sets. For each member of an image set, iCluster generates statistics that have been found to be useful in distinguishing sub-cellular localization. The statistics are mapped into two or three dimensions such as to preserve distances between the statistics vectors. The complete image set is then visualized in two or three dimensions using the coordinates so determined. The result is images that are statistically similar are spatially close in the visualization allowing for easy comparison of images that are similar and distinguishment of dissimilar images into distinct clusters.ResultsThe methodology was tested on a set of 502 previously published images containing 10 known sub-cellular localizations. The clustering of images of like type was evaluated both by examining the classes of nearest neighbors to each image and by visual inspection. In three dimensions, 3-neighbor classification accuracy was 83.2%. Visually, each class clustered well with the majority of classes localizing to distinct regions of the space. In two dimensions, 3-neighbor classification accuracy was 68.9%, though visually clustering into classes could be readily discerned. Computational expense was found to be relatively low, and sets of up to 1400 images visualized and interacted with in real time.ConclusionThe feasibility of automated spatial layout to allow comparison and discrimination of high throughput sub-cellular imaging has been demonstrated. There are many potential applications such as image database curation, semi-automated interactive classification, outlier detection and reference image comparison. By allowing the observation of the full range of imaging data available using modern microscopes these methods will provide an invaluable tool for cell biologists.

Project description:Micro- and nanoscale technologies have radically transformed biological research from genomics to tissue engineering, with the relative exception of microbial cell culture, which is still largely performed in microtiter plates and petri dishes. Here, we present nanoscale culture of the opportunistic fungal pathogen Candida albicans on a microarray platform. The microarray consists of 1,200 individual cultures of 30 nl of C. albicans biofilms ("nano-biofilms") encapsulated in an inert alginate matrix. We demonstrate that these nano-biofilms are similar to conventional macroscopic biofilms in their morphological, architectural, growth, and phenotypic characteristics. We also demonstrate that the nano-biofilm microarray is a robust and efficient tool for accelerating the drug discovery process: (i) combinatorial screening against a collection of 28 antifungal compounds in the presence of immunosuppressant FK506 (tacrolimus) identified six drugs that showed synergistic antifungal activity, and (ii) screening against the NCI challenge set small-molecule library identified three heretofore-unknown hits. This cell-based microarray platform allows for miniaturization of microbial cell culture and is fully compatible with other high-throughput screening technologies.Microorganisms are typically still grown in petri dishes, test tubes, and Erlenmeyer flasks in spite of the latest advances in miniaturization that have benefitted other allied research fields, including genomics and proteomics. Culturing microorganisms in small scale can be particularly valuable in cutting down time, cost, and reagent usage. This paper describes the development, characterization, and application of nanoscale culture of an opportunistic fungal pathogen, Candida albicans. Despite a more than 2,000-fold reduction in volume, the growth characteristics and drug response profiles obtained from the nanoscale cultures were comparable to the industry standards. The platform also enabled rapid identification of new drug candidates that were effective against C. albicans biofilms, which are a major cause of mortality in hospital-acquired infections.

Dataset Information

High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease.

Publications

High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease.

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