Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS):Current Applications and Future Prospects

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer’s and Parkinson’s diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research

The diagnostic and prognostic potential of the EGFR/MUC4/MMP9 axis in glioma patients

Glioblastoma is the most aggressive form of brain cancer, presenting poor prognosis despite current advances in treatment. There is therefore an urgent need for novel biomarkers and therapeutic targets. Interactions between mucin 4 (MUC4) and the epidermal growth factor receptor (EGFR) are involved in carcinogenesis, and may lead to matrix metalloproteinase-9 (MMP9) overexpression, exacerbating cancer cell invasiveness. In this study, the role of MUC4, MMP9, and EGFR in the progression and clinical outcome of glioma patients was investigated. Immunohistochemistry (IHC) and immunofluorescence (IF) in fixed tissue samples of glioma patients were used to evaluate the expression and localization of EGFR, MMP9, and MUC4. Kaplan–Meier survival analysis was also performed to test the prognostic utility of the proteins for glioma patients. The protein levels were assessed with enzyme-linked immunosorbent assay (ELISA) in serum of glioma patients, to further investigate their potential as non-invasive serum biomarkers. We demonstrated that MUC4 and MMP9 are both significantly upregulated during glioma progression. Moreover, MUC4 is co-expressed with MMP9 and EGFR in the proliferative microvasculature of glioblastoma, suggesting a potential role for MUC4 in microvascular proliferation and angiogenesis. The combined high expression of MUC4/MMP9, and MUC4/MMP9/EGFR was associated with poor overall survival (OS). Finally, MMP9 mean protein level was significantly higher in the serum of glioblastoma compared with grade III glioma patients, whereas MUC4 mean protein level was minimally elevated in higher glioma grades (III and IV) compared with control. Our results suggest that MUC4, along with MMP9, might account for glioblastoma progression, representing potential therapeutic targets, and suggesting the ‘MUC4/MMP9/EGFR axis’ may play a vital role in glioblastoma diagnostics

Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy

Abstract 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

Featured Article Doublecortin expression in CD81 T-cells and microglia at sites of amyloid-b plaques: A potential role in shaping plaque pathology?

Introduction: One characteristic of Alzheimer’s disease is the formation of amyloid-b plaques, which are typically linked to neuroinflammation and surrounded by inflammatory cells such as microglia and infiltrating immune cells. Methods: Here, we describe nonneurogenic doublecortin (DCX) positive cells, DCX being generally used as a marker for young immature neurons, at sites of amyloid-b plaques in various transgenic amyloid mouse models and in human brains with plaque pathology. Results: The plaque-associated DCX1 cells were not of neurogenic identity, instead most of them showed coexpression with markers for microglia (ionized calcium-binding adapter molecule 1) and for phagocytosis (CD68 and TREM2). Another subpopulation of plaque-associated DCX1 cells was negative for ionized calcium-binding adapter molecule 1 but was highly positive for the panleukocyte marker CD45. These hematopoietic cells were identified as CD3-and CD8-positive and CD4-negative T-cells. Discussion: Peculiarly, the DCX1/ionized calcium-binding adapter molecule 11 microglia and DCX1/CD81 T-cells were closely attached, suggesting that these two cell types are tightly interacting and that this interaction might shape plaque pathology

Alpha-synuclein aggregation induces prominent cellular lipid changes as revealed by Raman spectroscopy and machine learning analysis

The aggregation of α-synuclein is a central neuropathological hallmark in neurodegenerative disorders known as Lewy body diseases, including Parkinson's disease and dementia with Lewy bodies. In the aggregation process, α-synuclein transitions from its native disordered/α-helical form to a β-sheet-rich structure, forming oligomers and protofibrils that accumulate into Lewy bodies, in a process that is thought to underlie neurodegeneration. Lipids are thought to play a critical role in this process by facilitating α-synuclein aggregation and contributing to cell toxicity, possibly through ceramide production. This study aimed to investigate biochemical changes associated with α-synuclein aggregation, focusing on lipid changes, using Raman spectroscopy coupled with machine learning. HEK293, Neuro2a and SH-SY5Y expressing increased levels of α-synuclein were treated with sonicated α-synuclein pre-formed fibrils, to model seeded aggregation. Raman spectroscopy, complemented by an in-house lipid spectral library, was used to monitor the aggregation process and its effects on cellular viability over 14 days. We detected α-synuclein aggregation by assessing β-sheet peaks at 1045 cm⁻1, in cells treated with α-synuclein pre-formed fibrils, using machine learning (principal component analysis and uniform manifold approximation and projection) analysis based on Raman spectral features. Changes in lipid profiles, and especially sphingolipids, including a decrease in sphingomyelin and increase in ceramides, were observed, consistent with oxidative stress and apoptosis. Altogether, our study informs on biochemical alterations that can be considered for the design of therapeutic strategies for Parkinson's disease and related synucleinopathies

Molecular Insights into α-Synuclein Fibrillation:A Raman Spectroscopy and Machine Learning Approach

The aggregation of α-synuclein is crucial to the development of Lewy body diseases, including Parkinson's disease and dementia with Lewy bodies. The aggregation pathway of α-synuclein typically involves a defined sequence of nucleation, elongation, and secondary nucleation, exhibiting prion-like spreading. This study employed Raman spectroscopy and machine learning analysis, alongside complementary techniques, to characterize the biomolecular changes during the fibrillation of purified recombinant wild-type α-synuclein protein. Monomeric α-synuclein was produced, purified, and subjected to a 7-day fibrillation assay to generate preformed fibrils. Stages of α-synuclein fibrillation were analyzed using Raman spectroscopy, with aggregation confirmed through negative staining transmission electron microscopy, mass spectrometry, and light scattering analyses. A machine learning pipeline incorporating principal component analysis and uniform manifold approximation and projection was used to analyze the Raman spectral data and identify significant peaks, resulting in differentiation between sample groups. Notable spectral shifts in α-synuclein were found in various stages of aggregation. Early changes (D1) included increases in α-helical structures (1303, 1330 cm-1) and β-sheet formation (1045 cm-1), with reductions in COO- and CH2 bond regions (1406, 1445 cm-1). By D4, these structural shifts persist with additional β-sheet features. At D7, a decrease in β-sheet H-bonding (1625 cm-1) and tyrosine ring breathing (830 cm-1) indicates further structural stabilization, suggesting a shift from initial helical structures to stabilized β-sheets and aggregated fibrils. Additionally, alterations in peaks related to tyrosine, alanine, proline, and glutamic acid were identified, emphasizing the role of these amino acids in intramolecular interactions during the transition from α-helical to β-sheet conformational states in α-synuclein fibrillation. This approach offers insight into α-synuclein aggregation, enhancing the understanding of its role in Lewy body disease pathophysiology and potential diagnostic relevance.</p