Recent Advances in Raman Spectral Imaging in Cell Diagnosis and Gene Expression Prediction

Abstract

Normal and tumor regions within cancer tissue can be distinguished using various methods, such as histological analysis, tumor marker testing, X-ray imaging, or magnetic resonance imaging. Recently, new discrimination methods utilizing the Raman spectra of tissues have been developed and put into practical use. Because Raman spectral microscopy is a non-destructive and non-labeling method, it is potentially compatible for use in the operating room. In this review, we focus on the basics of Raman spectroscopy and Raman imaging in live cells and cell type discrimination, as these form the bases for current Raman scattering-based cancer diagnosis. We also review recent attempts to estimate the gene expression profile from the Raman spectrum of living cells using simple machine learning. Considering recent advances in machine learning techniques, we speculate that cancer type discrimination using Raman spectroscopy will be possible in the near future.

Recent advances in microfluidics-based bioNMR analysis

“…As summarized above, μNMR systems allow the detection of minimal samples in a non-invasive way at an atomic resolution, which is significantly advantageous over other analytical methods (such as microfluidics-based fluorescence systems, 100 Raman systems, 101 mass spectroscopy systems, 102 etc. ).…”

Establishing Monoclonal Gammopathy of Undetermined Significance as an Independent Pre-Disease State of Multiple Myeloma Using Raman Spectroscopy, Dynamical Network Biomarker Theory, and Energy Landscape Analysis

“…Raman spectroscopy is a label-free and non-invasive analytical method and is one of the vibrational spectroscopies providing information on the molecular composition, molecular structure, and chemical bonding in a sample [7][8][9][10]. Raman microscopic approaches for living cells have been established in combination with multivariate analytical methods such as principal component, classical least-squares, and partial least-squares analyses [11][12][13].…”