Lung cancer is difficult to detect using Raman spectroscopy, particularly due to tar fluorescence. We demonstrate improved performance for Raman classifiers by using fixed tissue sections and compare results with immunohistochemistry and hematoxylin and eosin (H&E) staining. In addition to eliminating fluorescence, fixed samples provide the flexibility of additional measurements and provide greater detail in borderline cases. Reliable classifiers based on Raman features would provide an additional tool to detect lung cancer during medical procedures would benefit patients and save medical resources.
KEYWORDS: Raman spectroscopy, Small targets, Biological samples, Systems modeling, Spectrometers, Time metrology, Support vector machines, Statistical modeling, Principal component analysis, Neural networks
Two main obstacles preventing widespread use of Raman spectroscopy in medical fields are slow acquisition times and poor classification model transferability. We present techniques for improving data acquisition using minimal sampling or other modalities for rapid pre-screening and an area based dimension reduction technique with improved transferability. We illustrate the strengths and weaknesses of these methods in comparison with common practices. Our investigation is based on microplastic samples. While these samples are not biological, they model problems Raman spectroscopy faces in the medical field on a stable time scale, allowing many measurements for detailed analysis of methods.
Cancer which has metastasized creates an issue in the medical field because the life expectancy of the patient doesn’t improve until the origin, type, and stage of metastatic cancer is determined. We present a system which uses Raman microspectroscopy to scan cancer cell cultures and determine the type of cancer present. A machine learning algorithm has been trained to distinguish between four cancer cell types. Known mixtures of cancer cells are mapped on a Raman hyperspectral image which displays where each cancer type is located.
Using integrated Raman and angular scattering microscopy (IRAM), we follow the response of EMT6 cancer cells
to photodynamic therapy (PDT) treatment. The study combines two non-labelling light scattering techniques
to extract chemical information and organelle sizes from single cells. Each cell is measured repeatedly over
several hours to follow changes in these parameters as the cell responds to the PDT treatment. An automated
algorithm identifies which parameters are changing in time. Size parameters extracted from angular scattering
measurements show a decrease in the size of 1-micron-diameter scatterers in treated cells. Treated cells also
exhibit trends in several Raman peaks, denoting changes in chemical concentrations of proteins, nucleic acids,
and lipids. Each of these parameters - acquired from both measurement modalities - can be monitored on a
cell-by-cell basis. The ability to track these chemical and structural changes over time allows access to greater
knowledge of biological processes.
Integrated Raman- and Angular-scatteringMicroscopy (IRAM) combines two light scattering techniques to make
chemical and morphological measurements of intact, single cells without the use of external labeling. IRAM has
previously demonstrated its ability to differentiate between activated and non-activated CD8+ T cells based on
both chemical and morphological differences. Activated cells showed an increase in protein and lipid content
as well as an increase in the size and number of 0.5-1.0 μm diameter scatterers (likely lysosomes). Recent
improvements to the IRAM system enable studies over an extended period of time. The applications of IRAM
to chemical and structural changes of single cells during biological processes and treatments will be discussed.
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