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Quantification of Cell-Substrate Adhesion Area and Cell Shape Distributions in MCF7 Cell Monolayers

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High-Content Microscopy Analysis of Subcellular Structures: Assay Development and Application to Focal Adhesion

Torsten Kroll^1,2, David Schmidt^1,3,2, Georg Schwanitz¹

¹Leibniz Institute on Aging-Fritz Lipmann Institute, Jena, Germany.

Current Protocols in Cytometry

|July 2, 2016

Summary

High-content analysis (HCA) provides quantitative cell data from microscopy. This study details a protocol and bioinformatics pipeline for high-content screening (HCS) to analyze subcellular features like focal adhesions.

Keywords:

focal adhesions high-content analysis high-content screening microscopy automation quantitative microscopy subcellular organelles

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Published on: September 27, 2021

Area of Science:

Cell Biology
Bioinformatics
Microscopy

Background:

High-content analysis (HCA) transforms microscopy images into quantitative data.
High-content screening (HCS) combines HCA with high-throughput imaging for chemical or RNAi screening.
Developing robust HCA and bioinformatics pipelines for HCS is complex.

Purpose of the Study:

To describe the step-by-step development of an HCA assay protocol.
To present an HCS bioinformatics analysis pipeline.
To demonstrate the utility of the developed methods for analyzing subcellular features.

Main Methods:

Development of a novel HCA assay protocol.
Creation of a dedicated HCS bioinformatics analysis pipeline.
Application of the protocol and pipeline to focal adhesion (FA) detection and analysis.

Main Results:

Successful quantitative analysis of multiple FA features.
Functional annotation of signaling pathways regulating FA size.
Demonstration of the protocol's effectiveness on published RNAi screen data.

Conclusions:

The developed HCA assay protocol and HCS bioinformatics pipeline facilitate quantitative analysis of subcellular features.
This strategy is valuable for both small-scale microscopy and large HCS experiments.
Enables researchers to extract biologically meaningful data from microscopy-based studies.