High-Throughput Cell Imaging and Classification by Narrowband and Low-Spectral-Resolution Raman Microscopy

Area of Science:

• Biomedical Optics
• Spectroscopy
• Cell Biology

Background:

• Label-free molecular imaging offers a promising alternative to traditional cell classification methods.
• Raman spectroscopy provides rich molecular information but can be limited by acquisition speed.

Purpose of the Study:

• To investigate the efficacy of narrowband Raman spectra for rapid, label-free cell classification.
• To optimize Raman imaging parameters for enhanced throughput in biological and medical applications.

Main Methods:

• Utilized principal component regression and linear discriminant analysis for cell classification.
• Analyzed narrowband Raman spectra within the 1397-1501 cm^-1 region.
• Employed pixel binning on charge-coupled device (CCD) detectors to accelerate image acquisition.

Main Results:

• Achieved classification accuracies exceeding 90% for different cell lines.
• Demonstrated that a narrowed spectral range (100 cm^-1) yielded classification performance comparable or superior to broader ranges.
• Reduced acquisition time for hyperspectral images to 21 minutes for a 1200 × 1500 pixel dataset.

Conclusions:

• Narrowband Raman spectroscopy enables rapid and accurate label-free cell classification.
• Optimized spectral range and acquisition parameters significantly improve imaging throughput.
• This approach holds potential for advancing cell and tissue analysis in clinical and research settings.