ArticleScientific reports2021
Raman spectroscopy and group and basis-restricted non negative matrix factorisation identifies radiation induced metabolic changes in human cancer cells.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Modelling of immune infiltration in prostate cancer treated with HDR-brachytherapy using Raman spectroscopy and machine learning.Scientific reports · 2025Article
- Demixing and Analysis of Complex Biological Raman Hyperspectra Based on Peak Fitting, Amplitude Trend Clustering, and Spectrum Reconstruction.Applied spectroscopy · 2025Article
- Metabolic Response to Small Molecule Therapy in Colorectal Cancer Tracked with Raman Spectroscopy and Metabolomics.Angewandte Chemie (International ed. in English) · 2024Article
- Metabolism changes caused by glucose in normal and cancer human brain cell lines by Raman imaging and chemometric methods.Scientific reports · 2024Article
- The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine.Chemical reviews · 2023Review
- Understanding radiation response and cell cycle variation in brain tumour cells using Raman spectroscopy.The Analyst · 2023Article
- Non-negative matrix factorisation of Raman spectra finds common patterns relating to neuromuscular disease across differing equipment configurations, preclinical models and human tissue.Journal of Raman spectroscopy : JRS · 2023Article
- Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts.Scientific reports · 2023Article
- Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study.Scientific reports · 2022Article
- A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering.Biosensors · 2022Article
- Nanotube abundance from non-negative matrix factorization of Raman spectra as an example of chemical purity from open source machine learning.Scientific reports · 2022Article
- Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification and Raman Biomarker Monitoring in Breast Cancer.Applied spectroscopy · 2022Article
- Raman Research on Bleomycin-Induced DNA Strand Breaks and Repair Processes in Living Cells.International journal of molecular sciences · 2022Article
- Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity.PloS one · 2022Article
- Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing.Cancers · 2021Review
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work combines single cell Raman spectroscopy (RS) with group and basis restricted non-negative matrix factorisation (GBR-NMF) to identify individual biochemical changes associated with radiation exposure in three human cancer cell lines. The cell lines analysed were derived from lung (H460), breast (MCF7) and prostate (LNCaP) tissue and are known to display varying degrees of radio sensitivity due to the inherent properties of each cell type. The GBR-NMF approach involves the deconstruction of Raman spectra into component biochemical bases using a library of Raman spectra of known biochemicals present in the cells. Subsequently, scores are obtained on each of these bases which can be directly correlated with the contribution of each chemical to the overall Raman spectrum. We validated GBR-NMF through the correlation of GBR-NMF-derived glycogen scores with scores that were previously observed using principal component analysis (PCA). Phosphatidylcholine, glucose, arginine and asparagine showed a distinct differential score pattern between radio-resistant and radio-sensitive cell types. In summary, the GBR-NMF approach allows for the monitoring of individual biochemical radiation-response dynamics previously unattainable with more traditional PCA-based approaches.
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