New software for automated cilia detection in cells (ACDC).

Area of Science:

• Cell Biology
• Microscopy
• Bioinformatics

Background:

• Primary cilia are crucial for understanding ciliogenesis and ciliopathies.
• Manual analysis of cilia length and frequency is time-consuming and prone to bias.
• Existing software often requires extensive parameter optimization and lacks rigorous quality assessment.

Purpose of the Study:

• To develop an automated software solution for accurate and efficient primary cilia analysis.
• To overcome limitations of manual analysis and existing automated tools.
• To provide a robust and adaptable method for quantitative cilia measurements.

Main Methods:

• Developed Automated Cilia Detection in Cells (ACDC) software.
• Implemented a four-step process: image importation, pre-processing, detection auto-optimization, and analysis.
• Utilized Ground Truth data for auto-optimization of parameters (SNR, length, directional score, intensity SD) based on F1 score.

Main Results:

• ACDC demonstrated accurate and adaptable detection of cilia across various cell types, reporters, and magnifications.
• Achieved low false-positive/negative rates (1-6%) for Arl13b-stained cilia with high F1 scores (0.96-0.97).
• Showcased significant speed enhancement (up to 96-fold) compared to manual analysis, analyzing ~18,000 cilia/hour.

Conclusions:

• ACDC provides a robust automated solution for analyzing microscopic images of ciliated cells.
• The software is highly adaptable, accurate, and offers substantial time savings.
• ACDC facilitates efficient research in ciliogenesis and ciliopathies.