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Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Updated: Dec 11, 2025

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry

Published on: May 13, 2019

Pollen analysis using multispectral imaging flow cytometry and deep learning.

Susanne Dunker^1,2, Elena Motivans^2,3,4, Demetra Rakosy^1,2

¹Helmholtz-Centre for Environmental Research - UFZ, Permoserstraße 15, Leipzig, 04318, Germany.

The New Phytologist

|August 18, 2020

Summary

This summary is machine-generated.

This study introduces a novel method combining multispectral imaging flow cytometry and deep learning for rapid and accurate pollen identification and quantification. This automated approach achieves 96% accuracy, surpassing manual microscopy for diverse research applications.

Keywords:

convolutional neural networks deep learning multispectral imaging flow cytometry pollen pollinator species identification

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Last Updated: Dec 11, 2025

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Published on: May 13, 2019

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

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Published on: January 27, 2023

Area of Science:

Botany
Ecology
Evolutionary Biology
Bioinformatics

Background:

Pollen analysis is vital for ecological, evolutionary, and applied research but manual microscopy is time-consuming and challenging.
Automated methods are sought to improve efficiency and accuracy in pollen identification and quantification.
Manual microscopy remains the standard despite ongoing research into alternative techniques.

Purpose of the Study:

To develop and validate a novel method for automated pollen analysis using multispectral imaging flow cytometry and deep learning.
To achieve high-accuracy, rapid identification and quantification of pollen grains from various plant species.
To enable detailed extraction of pollen morphological traits for further analysis.

Main Methods:

Utilized multispectral imaging flow cytometry for high-throughput pollen data acquisition.
Developed and trained a convolutional neural network classifier on a dataset of 426,876 pollen images from 35 plant species.
Integrated deep learning for automated species identification, pollen quantification, and morphological trait analysis.

Main Results:

Achieved a species-averaged accuracy of 96% in pollen identification using the deep learning classifier.
Demonstrated successful differentiation of even morphologically similar pollen species.
Enabled accurate quantification of pollen grains and detailed extraction of traits like size, symmetry, and structure.
Phylogenetic analyses indicated potential phylogenetic conservatism in certain pollen traits.

Conclusions:

The developed method offers a powerful, rapid, and accurate tool for pollen analysis, suitable for diverse research needs.
This approach significantly enhances pollen identification, quantification, and trait extraction compared to traditional methods.
A comprehensive pollen reference database can further optimize the application of this technology in various scientific fields.