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

Abstract

We investigated the use of narrowband Raman spectra for rapid label-free molecular imaging aimed at cell classification using principal component regression and linear discriminant analysis. In the classification of breast nontumorigenic epithelial and cancer cell lines, the classification accuracies using a spectral range of 100 cm-1 were equivalent to or better than that with using the fingerprint and high-wavenumber regions. Narrowing the Raman spectral range for analysis allows reduction of the charge-coupled device (CCD) pixels required for spectrum detection, resulting in the improvement of image acquisition speed with adequate classification accuracy. Our measurements revealed that the wavenumber region at 1397-1501 cm-1 can provide molecular information sufficient for cell classification without causing notable errors in the baseline-correction. A spectral resolution of ∼9 cm-1 was found to be sufficient to provide high accuracy in cell classification, which allowed us to apply pixel binning at the CCD readout for further acceleration of the imaging speed. As a result, the acquisition time for a 1200 × 1500 pixels Raman hyperspectral image at 1397-1501 cm-1 was reduced to 21 min. Under this condition, different cell lines were classified at accuracies higher than 90%. The presented approach will improve throughput of cell and tissue analysis and classification using Raman spectroscopy and extend practical uses of Raman imaging in biology and medicine.