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Updated: Jan 8, 2026

Automated Detection and Analysis of Exocytosis

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

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

Aishwarya Makam¹, Vishnu Ramadas¹, Anly Tollan¹

¹Cell Metabolism Lab (GA-08), Department of Developmental Biology and Genetics (DBG), Indian Institute of Science (IISc), Bengaluru 560012, India.

|December 12, 2025

Summary

This study introduces a new image-processing algorithm for analyzing exocytosis in human pancreatic islet cells. The method enhances detection of small vesicles, overcoming imaging challenges for high-throughput research.

Keywords:

Cell biology Metabolism Microscopy

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Automated Detection and Analysis of Exocytosis

Automated Detection and Analysis of Exocytosis

Published on: September 11, 2021

Super-resolution Imaging of Neuronal Dense-core Vesicles

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

Extracellular Vesicle Uptake Assay via Confocal Microscope Imaging Analysis

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Published on: February 14, 2022

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.