Integration of Raman spectra with transcriptome data in glioblastoma multiforme defines tumour subtypes and predicts patient outcome

Reste, Pierre-Jean Le; Pilalis, Eleftherios; Aubry, Marc; McMahon, Mari; Cano, Luis; Etcheverry, Amandine; Chatziioannou, Aristotelis; Chevet, Éric; Fautrel, Alain

doi:10.1111/jcmm.16902

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…To overcome these issues, Raman scattering spectroscopy is an alternative to the current method of pathology diagnosis. Recently, several groups reported the prediction of gene expression using Raman spectra in mammalian cells [ 54 , 55 ], which can be further expanded in the future. By prediction of gene expression, it may be possible to determine the type of disease, such as cancer, and select effective medication to treat it by measuring the Raman spectrum and predicting gene expression profiles of the tissue in situ in a clinical environment.…”

Section: Discussionmentioning

confidence: 99%

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

Watanabe

Sasaki

Fujita

2022

Genes

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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.

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Section: Discussionmentioning

confidence: 99%

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

Watanabe

Sasaki

Fujita

2022

Genes

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“…Le Reste et al applied RS to stratify different groups in glioblastoma multiforme (GBM), an aggressive form of primary brain tumor, and identified a RSbased signature that correlated well with transcriptomic profiles of GBM and predicted specific tumor features associated with aggressiveness (Figure 8c,d). 205 An interesting finding in their study was the occurrence of strong carotenoid peaks in Raman spectra which were correlated to two genes related to carotenoid metabolism (out of 36 genes signature). This study supports that RS-based transcriptomic profiles may enable intraoperative characteristics of tumors in real-time during surgeries and provide a measure of its aggressiveness allowing rapid clinical decisions with an affordable and timely tool.…”

Section: Integrating Raman With Multi-omics Techniquesmentioning

confidence: 97%

“…These examples show that each individual cell’s phenotypic characteristics can be captured by its metabolic transformation which ultimately modulates cellular signaling and disease progression. Le Reste et al applied RS to stratify different groups in glioblastoma multiforme (GBM), an aggressive form of primary brain tumor, and identified a RS-based signature that correlated well with transcriptomic profiles of GBM and predicted specific tumor features associated with aggressiveness (Figure c,d) . An interesting finding in their study was the occurrence of strong carotenoid peaks in Raman spectra which were correlated to two genes related to carotenoid metabolism (out of 36 genes signature).…”

Section: Label-free Raman Spectroscopy For Metabolic Profilingmentioning

confidence: 99%

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The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Cutshaw,

Uthaman,

Hassan

et al. 2023

Chem. Rev.

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Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

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“…However, although Raman spectra can separate cell states, the biological background detected in Raman measurements is not easily identified. Recently, transcriptome and Raman microscopy have been combined to investigate the correlation between Raman spectra and gene expression in cells, − which paves the way for the use of Raman microscopy as a reliable tool for understanding living systems and the omics-equivalent analysis of live cells and tissues. The combination with flow cytometry is one of the expected implementations of Raman microscopy for biological and medical applications, where cells can be sorted based on their characteristics and functions without labeling, providing various applications using live cells. − …”

Section: Label-free Raman Imaging and Analysis Of Biomaterialsmentioning

confidence: 99%

Raman Spectroscopy for Chemical Biology Research

2022

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In chemical biology research, various fluorescent probes have been developed and used to visualize target proteins or molecules in living cells and tissues, yet there are limitations to this technology, such as the limited number of colors that can be detected simultaneously. Recently, Raman spectroscopy has been applied in chemical biology to overcome such limitations. Raman spectroscopy detects the molecular vibrations reflecting the structures and chemical conditions of molecules in a sample and was originally used to directly visualize the chemical responses of endogenous molecules. However, our initial research to develop “Raman tags” opens a new avenue for the application of Raman spectroscopy in chemical biology. In this Perspective, we first introduce the label-free Raman imaging of biomolecules, illustrating the biological applications of Raman spectroscopy. Next, we highlight the application of Raman imaging of small molecules using Raman tags for chemical biology research. Finally, we discuss the development and potential of Raman probes, which represent the next-generation probes in chemical biology.

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Integration of Raman spectra with transcriptome data in glioblastoma multiforme defines tumour subtypes and predicts patient outcome

Cited by 7 publications

References 26 publications

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

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

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Raman Spectroscopy for Chemical Biology Research

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