High-Throughput Image-Based Aggresome Quantification

Area of Science:

• Cell Biology
• Biochemistry
• Drug Discovery

Background:

• Aggresomes are cellular structures where misfolded proteins accumulate.
• Existing methods for aggresome detection are often inefficient and not quantitative.
• There is a need for robust, high-throughput assays to study aggresome formation.

Purpose of the Study:

• To develop and validate a miniaturized, high-throughput assay for quantifying aggresome formation.
• To evaluate image analysis methods, including machine learning, for aggresome quantification.
• To screen a chemical library for novel aggresome modulators.

Main Methods:

• Miniaturization of Proteostat dye assay in 384-well plates for high-throughput imaging.
• Evaluation of two image analysis methods (including machine learning) for data quantification.
• Screening of a 1280-compound chemical library to identify aggresome modulators.

Main Results:

• The developed assay provides robust and reproducible quantification of aggresome formation.
• Machine learning analysis yielded comparable results to traditional methods.
• The screen identified several hit compounds, some with known links to autophagy modulation.
• Hits shared similar structural and physicochemical properties.

Conclusions:

• The optimized Proteostat assay is a validated, miniaturized, automated, and quantitative high-content method for measuring aggresome formation.
• This assay facilitates the study of chemical compound activity in protein misfolding diseases and cancer.
• The assay is suitable for low, middle, and high-throughput applications.