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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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Label-free imaging flow cytometry for cell classification based directly on multiple off-axis holographic

Dana Aharoni¹, Matan Dudaie¹, Itay Barnea¹

¹Tel Aviv University, Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv, Israel.

Journal of Biomedical Optics

|January 24, 2025

Summary

This study introduces a novel, label-free imaging flow cytometry method for real-time cell classification. The technique uses deep learning on holographic projections, significantly improving throughput and accuracy for white blood cell analysis.

Keywords:

deep learning digital holography imaging flow cytometry

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Area of Science:

Biophotonics and Imaging
Cellular Biology
Machine Learning in Medicine

Background:

Imaging flow cytometry (IFC) enables detailed cell analysis but often requires staining and complex processing.
Off-axis holography offers label-free imaging but traditional methods involve computationally intensive preprocessing, limiting throughput.
Real-time cell classification is crucial for high-throughput biological assays and clinical diagnostics.

Purpose of the Study:

To develop an automatic cell classification scheme for stain-free IFC.
To directly utilize off-axis holographic projections for classification without preprocessing.
To enhance classification accuracy and throughput for white blood cells.

Main Methods:

A dedicated off-axis holographic microscopy system was built for acquiring white blood cells in flow.
Deep learning models were applied directly to off-axis holographic projections (hologram space).
Multiple-viewpoint holographic projections were utilized to capture comprehensive cellular information.

Main Results:

The proposed method achieved a 7.69% accuracy improvement using ten holographic projections compared to a single projection.
Direct holographic projection analysis outperformed methods requiring quantitative phase profile preprocessing by 17.95%.
The technique simplifies computational processes, enabling significant increases in cell classification throughput.

Conclusions:

This label-free IFC approach facilitates high-throughput, high-content analysis of cellular datasets.
The method shows great potential for real-time cell classification in clinical settings.
Direct analysis of holographic projections offers a more efficient and informative alternative to traditional IFC methods.