High-Content Analysis: The “Just Right” Solution

2014 ◽  
Vol 34 (3) ◽  
pp. 1, 16-18 ◽  
Author(s):  
Kathy Liszewski
Keyword(s):  
Content Analysis ◽  
High Content Analysis ◽  
Just Right

Development and Validation of High-Content Analysis for Screening HDAC6-Selective Inhibitors

2021 ◽  
pp. 247255522110024
Author(s):  
Yunhong Nong ◽  
Yanyan Hou ◽  
Yuting Pu ◽  
Si Li ◽  
Yan Lan
Keyword(s):  
Content Analysis ◽  
Adverse Effects ◽  
Hdac Inhibitors ◽  
Histone H3 ◽  
Class I ◽  
Cell Seeding ◽  
Clinical Evaluations ◽  
Structure Activity ◽  
High Content Analysis ◽  
Development And Validation

Throughout recent decades, histone deacetylase (HDAC) inhibitors have shown encouraging potential in cancer treatment, and several pan-HDAC inhibitors have been approved for treating malignant cancers. Numerous adverse effects of pan-HDAC inhibitors have been reported, however, during preclinical and clinical evaluations. To avoid undesirable responses, an increasing number of investigations are focusing on the development of isotype-selective HDAC inhibitors. In this study, we present an effective and quantitative cellular assay using high-content analysis (HCA) to determine compounds’ inhibition of the activity of HDAC6 and Class I HDAC isoforms, by detecting the acetylation of their corresponding substrates (i.e., α-tubulin and histone H3). Several conditions that are critical for HCA assays, such as cell seeding number, fixation and permeabilization reagent, and antibody dilution, have been fully validated in this study. We used selective HDAC6 inhibitors and inhibitors targeting different HDAC isoforms to optimize and validate the capability of the HCA assay. The results indicated that the HCA assay is a robust assay for quantifying compounds’ selectivity of HDAC6 and Class I HDAC isoforms in cells. Moreover, we screened a panel of compounds for HDAC6 selectivity using this HCA assay, which provided valuable information for the structure–activity relationship (SAR). In summary, our results suggest that the HCA assay is a powerful tool for screening selective HDAC6 inhibitors.

scholarly journals Machine learning-assisted high-content analysis of pluripotent stem cell-derived embryos in vitro

2021 ◽  
Author(s):  
Jianying Guo ◽  
Peizhe Wang ◽  
Berna Sozen ◽  
Hui Qiu ◽  
Yonglin Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Stem Cell ◽  
Pluripotent Stem Cell ◽  
High Content Analysis

scholarly journals Organizational metrics of interchromatin speckle factor domains: integrative classifier for stem cell adhesion & lineage signaling

2015 ◽  
Vol 7 (4) ◽  
pp. 435-446 ◽  
Author(s):  
Sebastián L. Vega ◽  
Anandika Dhaliwal ◽  
Varun Arvind ◽  
Parth J. Patel ◽  
Nick R. M. Beijer ◽  
...  
Keyword(s):  
Content Analysis ◽  
Stem Cell ◽  
Cell Adhesion ◽  
Nuclear Protein ◽  
Cell Lineage ◽  
Lineage Commitment ◽  
Cell Microenvironment ◽  
Stem Cell Lineage ◽  
High Content Analysis

Timely classification of stem cell lineage commitment in response to cell–microenvironment interactions using high content analysis of sub-nuclear protein organization.

scholarly journals High-Content Analysis to Leverage a Robust Phenotypic Profiling Approach to Vascular Modulation

2013 ◽  
Vol 18 (10) ◽  
pp. 1246-1259 ◽  
Author(s):  
Beverley J. Isherwood ◽  
Rebecca E. Walls ◽  
Mark E. Roberts ◽  
Thomas M. Houslay ◽  
Sandra R. Brave ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Content Analysis ◽  
Cell Biology ◽  
High Volume ◽  
Phenotypic Screening ◽  
Stromal Fibroblasts ◽  
Physiological Relevance ◽  
Automated Microscopy ◽  
High Content Analysis ◽  
Phenotypic Responses

Phenotypic screening seeks to identify substances that modulate phenotypes in a desired manner with the aim of progressing first-in-class agents. Successful campaigns require physiological relevance, robust screening, and an ability to deconvolute perturbed pathways. High-content analysis (HCA) is increasingly used in cell biology and offers one approach to prosecution of phenotypic screens, but challenges exist in exploitation where data generated are high volume and complex. We combine development of an organotypic model with novel HCA tools to map phenotypic responses to pharmacological perturbations. We describe implementation for angiogenesis, a process that has long been a focus for therapeutic intervention but has lacked robust models that recapitulate more completely mechanisms involved. The study used human primary endothelial cells in co-culture with stromal fibroblasts to model multiple aspects of angiogenic signaling: cell interactions, proliferation, migration, and differentiation. Multiple quantitative descriptors were derived from automated microscopy using custom-designed algorithms. Data were extracted using a bespoke informatics platform that integrates processing, statistics, and feature display into a streamlined workflow for building and interrogating fingerprints. Ninety compounds were characterized, defining mode of action by phenotype. Our approach for assessing phenotypic outcomes in complex assay models is robust and capable of supporting a range of phenotypic screens at scale.

scholarly journals A Novel High Throughput High Content Analysis Assay for Intermediate Filament Perturbing Drugs

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Bernard Michael Corfe ◽  
Joanna Chowdry ◽  
Gareth J. Griffiths ◽  
Rod P. Benson
Keyword(s):  
Content Analysis ◽  
High Throughput ◽  
Intermediate Filament ◽  
High Content Analysis

Automated multiwell fluorescence lifetime imaging for Förster resonance energy transfer assays and high content analysis

2015 ◽  
Vol 7 (10) ◽  
pp. 4071-4089 ◽  
Author(s):  
Douglas J. Kelly ◽  
Sean C. Warren ◽  
Dominic Alibhai ◽  
Sunil Kumar ◽  
Yuriy Alexandrov ◽  
...  
Keyword(s):  
Content Analysis ◽  
Energy Transfer ◽  
Fluorescence Lifetime ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Förster Resonance Energy Transfer ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging ◽  
High Content Analysis ◽  
Förster Resonance

An HCA-FLIM instrument is presented alongside exemplar oligomerisation, intermolecular and intramolecular FRET assays that require robust measurement of small lifetime changes.

High Content Analysis of an In Vitro Model for Metabolic Toxicity

2014 ◽  
Vol 19 (10) ◽  
pp. 1402-1408 ◽  
Author(s):  
Stephanie D. Cole ◽  
Janna S. Madren-Whalley ◽  
Albert P. Li ◽  
Russell Dorsey ◽  
Harry Salem
Keyword(s):  
Content Analysis ◽  
High Throughput ◽  
Metabolic Activation ◽  
Hepatic Metabolism ◽  
Cell Types ◽  
Multiple Organ ◽  
Compound Libraries ◽  
High Content Analysis ◽  
Calcein Am

In vitro models that accurately and rapidly assess hepatotoxicity and the effects of hepatic metabolism on nonliver cell types are needed by the U.S. Department of Defense and the pharmaceutical industry to screen compound libraries. Here, we report the first use of high content analysis on the Integrated Discrete Multiple Organ Co-Culture (IdMOC) system, a high-throughput method for such studies. We cultured 3T3-L1 cells in the presence and absence of primary human hepatocytes, and exposed the cultures to 4-aminophenol and cyclophosphamide, model toxicants that are respectively detoxified and activated by the liver. Following staining with calcein-AM, ethidium homodimer-1, and Hoechst 33342, high content analysis of the cultures revealed four cytotoxic endpoints: fluorescence intensities of calcein-AM and ethidium homodimer-1, nuclear area, and cell density. Using these endpoints, we observed that the cytotoxicity of 4-aminophenol in 3T3-L1 cells in co-culture was less than that observed for 3T3-L1 monocultures, consistent with the known detoxification of 4-aminophenol by hepatocytes. Conversely, cyclophosphamide cytotoxicity for 3T3-L1 cells was enhanced by co-culturing with hepatocytes, consistent with the known metabolic activation of this toxicant. The use of IdMOC plates combined with high content analysis is therefore a multi-endpoint, high-throughput capability for measuring the effects of metabolism on toxicity.

scholarly journals Novel antibody derivatives for proteome and high-content analysis

2010 ◽  
Vol 397 (8) ◽  
pp. 3203-3208 ◽  
Author(s):  
Katrin Schmidthals ◽  
Jonas Helma ◽  
Kourosh Zolghadr ◽  
Ulrich Rothbauer ◽  
Heinrich Leonhardt
Keyword(s):  
Content Analysis ◽  
High Content Analysis ◽  
Antibody Derivatives
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