Raman Microscopy: Progress in Research on Cancer Cell Sensing.
Satheeshkumar ElumalaiStefano ManagóAnna Chiara De LucaPublished in: Sensors (Basel, Switzerland) (2020)
In the last decade, Raman Spectroscopy (RS) was demonstrated to be a label-free, non-invasive and non-destructive optical spectroscopy allowing the improvement in diagnostic accuracy in cancer and analytical assessment for cell sensing. This review discusses how Raman spectra can lead to a deeper molecular understanding of the biochemical changes in cancer cells in comparison to non-cancer cells, analyzing two key examples, leukemia and breast cancer. The reported Raman results provide information on cancer progression and allow the identification, classification, and follow-up after chemotherapy treatments of the cancer cells from the liquid biopsy. The key obstacles for RS applications in cancer cell diagnosis, including quality, objectivity, number of cells and velocity of the analysis, are considered. The use of multivariant analysis, such as principal component analysis (PCA) and linear discriminate analysis (LDA), for an automatic and objective assessment without any specialized knowledge of spectroscopy is presented. Raman imaging for cancer cell mapping is shown and its advantages for routine clinical pathology practice and live cell imaging, compared to single-point spectral analysis, are debated. Additionally, the combination of RS with microfluidic devices and high-throughput screening for improving the velocity and the number of cells analyzed are also discussed. Finally, the combination of the Raman microscopy (RM) with other imaging modalities, for complete visualization and characterization of the cells, is described.
Keyphrases
- high resolution
- label free
- raman spectroscopy
- induced apoptosis
- single molecule
- healthcare
- cell cycle arrest
- machine learning
- papillary thyroid
- primary care
- deep learning
- young adults
- computed tomography
- high throughput
- signaling pathway
- squamous cell
- single cell
- bone marrow
- blood flow
- cell death
- palliative care
- cell therapy
- locally advanced
- social media
- neural network
- health information
- density functional theory