Protocol for the detection of large dense-core vesicle exocytosis using an automated image-processing algorithm

Area of Science:

• Cell Biology
• Biophysics
• Microscopy Techniques

Background:

• Exocytosis in human pancreatic islet cells is difficult to study due to small vesicle size and inconsistent imaging.
• Existing methods lack efficiency for analyzing large datasets.

Purpose of the Study:

• To develop and validate a novel image-processing algorithm for detecting and analyzing exocytosis in human pancreatic islet cells.
• To provide a robust protocol for high-throughput microscopy research.

Main Methods:

• Sample preparation for pancreatic islet cells.
• Total Internal Reflection Fluorescence (TIRF) microscopy for imaging.
• Lagrangian particle tracking-based image-processing algorithm for computational analysis.

Main Results:

• The algorithm successfully detects and analyzes exocytosis, even with small vesicle sizes.
• Validation was achieved using mathematical models, TetraSpeck beads, and cell images.
• The protocol demonstrates applicability to other cellular processes and handles large datasets efficiently.

Conclusions:

• The developed algorithm and protocol offer a reliable method for studying exocytosis in pancreatic islet cells.
• This approach is valuable for advancing high-throughput microscopy in cellular research.
• The method provides a foundation for future investigations into vesicle dynamics.