Andy's Algorithms: new automated digital image analysis pipelines for FIJI.

Area of Science:

• Biotechnology
• Immunohistochemistry
• Cellular Biology

Background:

• Antibody-based detection methods are crucial for quantifying cellular antigens and interactions in research.
• Existing image analysis software presents challenges in high-throughput quantitation, especially for users with limited expertise.
• Need for accessible, automated solutions for complex biological image analysis.

Purpose of the Study:

• To develop automated image analysis pipelines for common biological assays.
• To simplify and standardize batch-processing of immunohistochemistry and proximity ligation assays.
• To provide an accessible tool for researchers lacking advanced image analysis skills.

Main Methods:

• Development of automated image analysis pipelines named Andy's Algorithms for FIJI.
• Implementation of batch-processing capabilities for 3,3'-diaminobenzidine (DAB) immunohistochemistry and proximity ligation assays (PLAs).
• Inclusion of a step-by-step tutorial and optimization pipeline for user-friendliness.

Main Results:

• Andy's Algorithms enable rapid, accurate, and reproducible batch-processing of biological assays.
• The pipelines offer a simpler, faster, and standardized workflow compared to existing methods.
• The tool provides equivalent performance with additional features in an open-source application.

Conclusions:

• Andy's Algorithms significantly improve the efficiency and accessibility of high-throughput image analysis for biological assays.
• The software empowers researchers with varying levels of expertise to perform complex quantitations.
• The open-source nature and availability on GitHub promote widespread adoption and collaboration.