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Raman Spectroscopy: Overview

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
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Related Experiment Video

Updated: Jun 4, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified

Daniel West¹, Susan Stepney¹, Y Hancock^2,3

¹Department of Computer Science, University of York, Heslington, York, YO10 5DD, UK.

Scientific Reports

|January 4, 2025

Summary

New research uses machine learning on Raman spectroscopy data to classify prostate cancer cells. This approach identifies distinct protein-rich and lipid-rich cell groups, aiding in more accurate cancer characterization and treatment.

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Published on: May 29, 2012

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Last Updated: Jun 4, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

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Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

Area of Science:

Biomedical Engineering
Computational Biology
Oncology

Background:

Prostate cancer diagnosis faces challenges in risk-stratifying patients.
Differentiating aggressive from indolent forms is crucial for effective treatment.
Current diagnostics struggle to accurately sub-classify the heterogeneous disease at a molecular level.

Purpose of the Study:

To develop and exemplify an unsupervised machine learning method for biomolecular sub-classification of prostate cancer.
To stratify prostate cancer at the single-cell level using high-dimensional data.
To identify distinct disease-state features for improved characterization and treatment.

Main Methods:

Analysis of live-cell Raman spectroscopy data from prostate cell lines.
Application of an unsupervised self-organizing map (SOM) approach.
Minimal preprocessing of high-dimensional datasets for sub-clustering.

Main Results:

Successful sub-clustering of prostate cancer cell lines into two distinct groups: protein-rich and lipid-rich.
Identification of mechanistically linked sub-cellular components.
Demonstration of unsupervised machine learning's potential in discovering novel disease features.

Conclusions:

Unsupervised machine learning, specifically SOM, can effectively sub-classify prostate cancer at the single-cell level.
The identified protein-rich and lipid-rich clusters offer new insights into prostate cancer heterogeneity.
This approach holds promise for more targeted diagnoses, prognoses, and the development of novel, broad-spectrum treatments.