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Classification of Leukocytes

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Deep Learning in Label-free Cell Classification.

Claire Lifan Chen^1,2, Ata Mahjoubfar^1,2, Li-Chia Tai²

¹Department of Electrical Engineering, University of California, Los Angeles, California 90095, USA.

Scientific Reports

|March 16, 2016

Summary

This summary is machine-generated.

This study introduces a high-throughput, label-free cell analysis system using photonic time stretch and deep learning for accurate cell classification. It overcomes limitations of current methods, enabling advanced applications in genomics and diagnostics.

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Area of Science:

Biophotonics
Computational Biology
Cellular Imaging

Background:

Label-free cell analysis is crucial for personalized genomics, cancer diagnostics, and drug development.
Existing methods lack differentiation and have low throughput, limiting sample size.
Staining reagents can negatively impact cell viability and signaling.

Purpose of the Study:

To develop a high-throughput, label-free cell classification system with enhanced accuracy.
To integrate feature extraction and deep learning with quantitative imaging.
To overcome limitations of single-feature assays and low throughput.

Main Methods:

Utilized photonic time stretch for high-throughput quantitative imaging of cells.
Extracted multiple biophysical features from optical phase and intensity images.
Employed supervised learning, including a novel deep learning pipeline, for cell classification.

Main Results:

Achieved record high accuracy in label-free cell classification.
Demonstrated successful classification of T-cells against colon cancer cells.
Showcased classification of lipid-accumulating algal strains for biofuel production.

Conclusions:

The developed system offers enhanced sensitivity and specificity for label-free cell analysis.
It enables data-driven phenotypic diagnosis and a better understanding of cellular heterogeneity.
Opens new avenues for applications in personalized medicine and biotechnology.