Deep Learning in Label-free Cell Classification
Abstract
Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressionsmore »
- Authors:
- Publication Date:
- Research Org.:
- Univ. of California, Los Angeles, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1310540
- Grant/Contract Number:
- FC02-02ER63421
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Volume: 6; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES
Citation Formats
Chen, Claire Lifan, Mahjoubfar, Ata, Tai, Li-Chia, Blaby, Ian K., Huang, Allen, Niazi, Kayvan Reza, and Jalali, Bahram. Deep Learning in Label-free Cell Classification. United States: N. p., 2016.
Web. doi:10.1038/srep21471.
Chen, Claire Lifan, Mahjoubfar, Ata, Tai, Li-Chia, Blaby, Ian K., Huang, Allen, Niazi, Kayvan Reza, & Jalali, Bahram. Deep Learning in Label-free Cell Classification. United States. https://doi.org/10.1038/srep21471
Chen, Claire Lifan, Mahjoubfar, Ata, Tai, Li-Chia, Blaby, Ian K., Huang, Allen, Niazi, Kayvan Reza, and Jalali, Bahram. 2016.
"Deep Learning in Label-free Cell Classification". United States. https://doi.org/10.1038/srep21471. https://www.osti.gov/servlets/purl/1310540.
@article{osti_1310540,
title = {Deep Learning in Label-free Cell Classification},
author = {Chen, Claire Lifan and Mahjoubfar, Ata and Tai, Li-Chia and Blaby, Ian K. and Huang, Allen and Niazi, Kayvan Reza and Jalali, Bahram},
abstractNote = {Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.},
doi = {10.1038/srep21471},
url = {https://www.osti.gov/biblio/1310540},
journal = {Scientific Reports},
issn = {2045-2322},
number = ,
volume = 6,
place = {United States},
year = {Tue Mar 15 00:00:00 EDT 2016},
month = {Tue Mar 15 00:00:00 EDT 2016}
}
Web of Science
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Direct Observation of Spatiotemporal Dynamics of Short Electron Bunches in Storage Rings
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Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry
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In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning
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A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions
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Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research
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Plasmonic nanostructure design and characterization via Deep Learning
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PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
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Reconstructing cell cycle and disease progression using deep learning
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Machine learning analysis of extreme events in optical fibre modulation instability
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Matrix Analysis of Warped Stretch Imaging
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Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry
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Assessing microscope image focus quality with deep learning
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A novel machine learning based approach for iPS progenitor cell identification
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The Temporal Gearbox
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ParasNet: Fast Parasites Detection with Neural Networks
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Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research
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Sensitivity and specificity evaluation of multiple neurodegenerative proteins for Creutzfeldt-Jakob disease diagnosis using a deep-learning approach
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