Deciphering the Finger Prints of Brain Cancer Glioblastoma Multiforme from Four Different Patients by Using Near Infrared Raman Spectroscopy

Monitoring alterations of all-trans-retinal in human brain cancer cells by label-free confocal Raman imaging: regulation of the redox status of cytochrome c

This article has shown the impact of all-trans-retinal on human brain cancer, which is apparent in the shifts in the redox status of cytochrome c in a single cell. The connection between cytochrome c expression and its role in cancer development remains relatively unexplored. To assess this, we employed Raman spectroscopy and imaging to determine the redox state of the iron ion in cytochrome c across different cellular locations, including mitochondria, cytoplasm, lipid droplets, and the endoplasmic reticulum within human brain cancer cells. We have analyzed normal human astrocytes (NHA) and two brain cancer cell lines (astrocytoma - CRL-1718 and glioblastoma - U-87 MG) without and supplemented with all-trans-retinal. Our results confirmed that human brain cancer cells demonstrate varying redox status compared to normal cells based on the established correlation between the intensity of the cytochrome c Raman band at 1583 cm-1 and the malignancy grade of brain cancer cells. Our research unveiled that all-trans-retinal induces remarkable changes in the mitochondrial functional activity (redox status) of cancer cells, which were measured by confocal Raman spectroscopy and imaging.

Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy

BACKGROUND

Gliomas are the most common brain tumours with the high-grade glioblastoma representing the most aggressive and lethal form. Currently, there is a lack of specific glioma biomarkers that would aid tumour subtyping and minimally invasive early diagnosis. Aberrant glycosylation is an important post-translational modification in cancer and is implicated in glioma progression. Raman spectroscopy (RS), a vibrational spectroscopic label-free technique, has already shown promise in cancer diagnostics.

METHODS

RS was combined with machine learning to discriminate glioma grades. Raman spectral signatures of glycosylation patterns were used in serum samples and fixed tissue biopsy samples, as well as in single cells and spheroids.

RESULTS

Glioma grades in fixed tissue patient samples and serum were discriminated with high accuracy. Discrimination between higher malignant glioma grades (III and IV) was achieved with high accuracy in tissue, serum, and cellular models using single cells and spheroids. Biomolecular changes were assigned to alterations in glycosylation corroborated by analysing glycan standards and other changes such as carotenoid antioxidant content.

CONCLUSION

RS combined with machine learning could pave the way for more objective and less invasive grading of glioma patients, serving as a useful tool to facilitate glioma diagnosis and delineate biomolecular glioma progression changes.

Lipid Alterations in Glioma: A Systematic Review

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Discrimination of glioma patient-derived cells from healthy astrocytes by exploiting Raman spectroscopy

Glioblastoma multiforme (GBM) is one of the most common and aggressive brain tumors. It presents a very bad prognosis with a patients' overall survival of 12-15 months; treatment failure is mainly ascribable to tumor recurrence. The development of new tools, that could help the precise detection of the tumor border, is thus an urgent need. During the last decades, different vibrational spectroscopy techniques have been developed to distinguish cancer tissue from heathy tissue; in the present work, we compared GBM cells deriving from four patients with healthy human astrocytes using Raman spectroscopy. We have shown that the region between 1000 and 1300 cm^-1 is enough informative for this discrimination, indeed highlighting that peaks related to DNA/RNA and cytochrome c are increased in cancer cells. Finally, our model has been able to discriminate cancer cells from healthy cells with an average accuracy of 92.5%. We believe that this study might help to further understand which are the essential Raman peaks exploitable in the detection of cancer cells, with important perspectives under a diagnostic point of view.

Raman spectroscopy and neuroscience: from fundamental understanding to disease diagnostics and imaging

Neuroscience would directly benefit from more effective detection techniques, leading to earlier diagnosis of disease. The specificity of Raman spectroscopy is unparalleled, given that a molecular fingerprint is attained for each species. It also allows for label-free detection with relatively inexpensive instrumentation, minimal sample preparation, and rapid sample analysis. This review summarizes Raman spectroscopy-based techniques that have been used to advance the field of neuroscience in recent years.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

Glioblastoma single-cell microRaman analysis under stress treatments

Glioblastoma multiforme (GBM) is the most frequent malignant brain tumor characterized by highly heterogeneous subpopulations. In order to reveal the heterogeneous cell response, single cell analysis is an essential requirement. In this study, optical microscopy and Raman microspectroscopy were used to follow the stress response of U251 single cells adherent on a silicon substrate. Cultured cells on silicon substrate were treated with hydrogen peroxide to promote apoptosis. Under these conditions expected changes occurred after a few hours and were revealed by the reduction of cytochrome c, lipid, nucleic acid and protein Raman signals: this ensured the possibility to analyse U251 cell line as grown on Si substrate, and to monitor the response of single cells to stress conditions. As a consequence, we used microRaman to monitor the effects induced by nutrient depletion: a fast change of Raman spectra showed two different sub-populations of sensible and resistant U251 cells. Furthermore, spectral variations after DMSO addition were associated to volume changes and confirmed by morphological analysis. Thus, our results highlight the sensitivity of Raman microspectroscopy to detect rapid variations of macromolecule concentration due to oxidative stress and/or cell volume changes at the single cell level.