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

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

Association of protein tyrosine phosphatase non receptor type 22 (PTPN22) C1858T gene polymorphism with type 1 diabetes mellitus in Egyptian children cohort

Background: The protein tyrosine phosphatase non receptor 22 gene (PTPN22) is an important negative regulator of signal transduction through the T-cell receptors. Recently a single-nucleotide polymorphism (SNP) 1858 C/T within this gene was shown to be a risk factor for several autoimmune diseases. Aim: To analyze a possible association between 1858 C/T SNP and T1DM in Egyptian cohort. Patients and methods: Patients with T1DM and healthy controls were genotyped for the 1858 C/T SNP in PTPN22 gene. Results: A non-significant association between PTPN22 1858 C/T SNP and T1DM was found. 1858T/T genotype was not observed more frequently in T1DM patients compared to control subjects. Conclusion: In concordance with previous data establishing PTPN22 1858 C/T SNP association with several autoimmune diseases, our findings deny further evidence that the PTPN22 gene may play an important role in the susceptibility to T1DM