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Updated: Dec 27, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Large-scale single-molecule imaging aided by artificial intelligence.

Michio Hiroshima¹, Masato Yasui², Masahiro Ueda^1,3

¹Laboratory for Cell Signaling Dynamics, RIKEN BDR, Suita 565-0874, Japan.

Microscopy (Oxford, England)

|February 25, 2020

Summary

This summary is machine-generated.

We developed an automated single-molecule imaging system using artificial intelligence (AI) to analyze molecular behaviors in cells. This AI-aided system significantly increases throughput, enabling large-scale analysis and revealing previously unrecognized cellular heterogeneity.

Keywords:

deep learning drug screening high throughput large-scale analysis single-molecule imaging

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Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

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Published on: December 1, 2016

Area of Science:

Cell Biology
Biophysics
Pharmacology

Background:

Single-molecule imaging is crucial for understanding molecular dynamics in cells.
Current limitations include low throughput and high expertise requirements.
Scaling single-molecule analysis is essential for advancing cell biology.

Purpose of the Study:

To automate single-molecule imaging analysis for high-throughput cellular studies.
To overcome limitations of manual analysis in throughput and expertise.
To enable large-scale investigation of molecular behaviors and signaling.

Main Methods:

Development of an automated in-cell single-molecule imaging system (AiSIS) integrating AI, robotics, and computer operations.
AI was employed to ensure high-quality image acquisition for quantitative analysis.
System designed for high-throughput analysis of molecular dynamics in living cells.

Main Results:

The AiSIS system achieved ~100-fold higher efficiency, analyzing 1600 cells daily compared to manual methods.
Large-scale analysis revealed significant cell-to-cell heterogeneity in molecular behavior.
Detailed receptor activation schemes and signal transduction pathways were elucidated rapidly.

Conclusions:

AI-aided automation dramatically enhances the efficiency and scale of single-molecule imaging analysis.
The system uncovers previously hidden cellular heterogeneity and provides insights into signaling mechanisms.
AiSIS is applicable to drug screening and comprehensive pharmacological analysis, broadening single-molecule analysis applications.