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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: Jul 30, 2025

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

Optical time-stretch imaging flow cytometry in the compressed domain.

Siyuan Lin¹, Rubing Li¹, Yueyun Weng^1,2

¹The Institute of Technological Sciences, Wuhan University, Wuhan, China.

Journal of Biophotonics

|May 12, 2023

Summary

This study introduces a new method for optical time-stretch (OTS) imaging flow cytometry that analyzes cell data in its compressed form, bypassing image decompression. This approach achieves over 99% accuracy in cell classification, addressing big data challenges in single-cell analysis.

Keywords:

cell classification compressive sensing image decompression imaging flow cytometry machine learning microfluidics optical time-stretch imaging

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Area of Science:

Biophotonics
Single-cell analysis
Machine learning applications

Background:

Optical time-stretch (OTS) imaging flow cytometry offers high throughput, precision, and label-free analysis for large-scale single-cell studies.
Compressive sensing reduces data load in OTS imaging but requires computationally intensive decompression.
Image decompression in OTS imaging systems adds computational overhead without generating new information.

Purpose of the Study:

To develop and demonstrate an OTS imaging flow cytometry system that operates in the compressed domain.
To eliminate the need for image decompression in OTS imaging flow cytometry.
To provide a solution for managing the massive data generated in OTS imaging flow cytometry.

Main Methods:

Integration of a machine-learning network for direct analysis of compressed OTS imaging data.
Development of a novel OTS imaging flow cytometry system capable of compressed domain analysis.
Validation of the system's performance using cell classification tasks.

Main Results:

The proposed system achieves high-quality imaging and accurate cell classification.
Cell classification accuracy exceeds 99% at a 10% compression ratio.
Demonstrated viability of analyzing OTS imaging data in the compressed domain.

Conclusions:

OTS imaging flow cytometry in the compressed domain offers a viable solution to big data challenges.
The developed machine-learning approach enhances the practical application of OTS imaging flow cytometry.
This method significantly boosts the efficiency and applicability of large-scale single-cell analysis.