Conditional Generative Adversarial Network for Spectral Recovery to Accelerate Single-Cell Raman Spectroscopic Analysis

Raman spectroscopy is a powerful tool to investigate cellular heterogeneity. However, Raman spectra for single-cell analysis are hindered by a low signal-to-noise ratio (SNR). Here, we demonstrate a simple and reliable spectral recovery conditional generative adversarial network (SRGAN). SRGAN reduc...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2022-01, Vol.94 (2), p.577-582
Main Authors: Ma, Xiangyun, Wang, Kaidi, Chou, Keng C, Li, Qifeng, Lu, Xiaonan
Format: Article
Language:English
Subjects:
Chemistry
Data acquisition
Generative adversarial networks
Heterogeneity
Raman spectra
Raman spectroscopy
Signal to noise ratio
Single-Cell Analysis
Spectroscopy
Spectrum analysis
Spectrum Analysis, Raman - methods
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Summary:Raman spectroscopy is a powerful tool to investigate cellular heterogeneity. However, Raman spectra for single-cell analysis are hindered by a low signal-to-noise ratio (SNR). Here, we demonstrate a simple and reliable spectral recovery conditional generative adversarial network (SRGAN). SRGAN reduced the data acquisition time by 1 order of magnitude (i.e., 30 vs 3 s) by improving the SNR by a factor of ∼6. We classified five major foodborne bacteria based on single-cell Raman spectra to further evaluate the performance of SRGAN. Spectra processed using SRGAN achieved an identification accuracy of 94.9%, compared to 60.5% using unprocessed Raman spectra. SRGAN can accelerate spectral collection to improve the throughput of Raman spectroscopy and enable real-time monitoring of single living cells.
ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.1c04263