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Generalizable, High-throughput Image Analysis Of Subcellular Structures Using Dispersion Indices.

Home
Generalizable, High-throughput Image Analysis Of Subcellular Structures Using Dispersion Indices.

Related Experiment Video

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Generalizable, high-throughput image analysis of subcellular structures using dispersion indices.

Andrew Martin¹, Sue Zhang¹, Amanda Williamson²

¹Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.

|April 17, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

A new algorithm called GRID quantifies subcellular changes using image analysis, enabling efficient high-throughput studies of cell dynamics and disease mechanisms.

Keywords:

biocomputational method biological sciences methodology in biological sciences optical imaging

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Published on: August 16, 2021

Area of Science:

Cell Biology
Bioimaging
Computational Biology

Background:

Subcellular structural dynamics are crucial for cell function.
Current methods for quantifying these changes are labor-intensive and difficult to scale.
A need exists for efficient and generalizable tools to analyze subcellular organization.

Purpose of the Study:

To introduce GRID (generalized readout of image dispersion), an open-source algorithm for quantifying subcellular structural dynamics.
To demonstrate the algorithm's generalizability across various cellular structures and model systems.
To enable high-throughput analysis for disease research and drug discovery.

Main Methods:

Developed GRID, an algorithm applying dispersion indices to quantify diffusiveness and aggregation of subcellular structures from image data.

Applied GRID to analyze autophagic puncta, mitochondrial clustering, and microtubule dynamics.

Validated GRID in 2D and 3D cell cultures, including multicellular human midbrain organoids.

Main Results:

GRID efficiently quantifies subcellular structural dynamics and spatial locations.
Demonstrated successful detection of changes in autophagy, mitochondria, and microtubules.
Identified cell-type specific autophagy responses in human organoids.
Enabled estimation of half-maximal effective concentration (EC50) values.

Conclusions:

GRID offers a computationally efficient and generalizable approach for high-throughput subcellular analysis.
The algorithm facilitates understanding of disease mechanisms in areas like metabolic disorders, neurodegeneration, and cancer.
GRID serves as a valuable tool for initial screening in drug discovery efforts.