Subtyping on Live Lymphoma Cell Lines by Raman Spectroscopy

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.

Validation of optimised intracranial spectroscopic probe for instantaneous in-situ monitoring and classification of traumatic brain injury

The development of an optical interface to directly distinguish the brain tissue's biochemistry is the next step in understanding traumatic brain injury (TBI) pathophysiology and the best and most appropriate treatment in cases where in-hospital intracranial access is required. Despite TBI being a globally leading cause of morbidity and mortality in patients under 40, there is still a lack of objective diagnostical tools. Further, given its pathophysiological complexity the majority of treatments provided are purely symptomatic without standardized therapeutic targets. Our tailor-engineered prototype of the intracranial Raman spectroscopy probe (Intra-RSP) is designed to bridge the gap and provide real-time spectroscopic insights to monitor TBI and its evolution as well as identify patient-specific molecular targets for timely intervention. Raman spectroscopy being rapid, label-free and non-destructive, renders it an ideal portable diagnostics tool. In combination with our in-house developed software, using machine learning algorithms for multivariate analysis, the Intra-RSP is shown to accurately differentiate simulated TBI conditions in rat brains from the healthy controls, directly from the brain surface as well as through the rat's skull. Using clinically pre-established methods of cranial entry, the Intra-RSP can be inserted into a 2-piece optimised cranial bolt with integrated focussing and correctly identify a sample in real-life conditions with an accuracy >80 %. To further validate the Intra-RSP's efficiency as a TBI monitoring device, rat brains mildly damaged from inflicted spinal cord injury were found to be correctly classified with 94.5 % accuracy. Through optimization and rigorous in-vivo validation, the Intra-RSP prototype is envisioned to seamlessly integrate into existing standards of neurological care, serving as a minimally invasive, in-situ neuromonitoring tool. This transformative approach has the potential to revolutionize the landscape of neurological care by providing clinicians with unprecedented insights into the nature of brain injuries and fostering targeted, timely and effective therapeutic interventions.

Automatic subtyping of Diffuse Large B-cell Lymphomas (DLBCL): Raman-based genetic and metabolic classification

Diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin's lymphoma in adults, is a genetically and metabolically heterogeneous group of aggressive malignancies. The complexity of their molecular composition and the variability in clinical presentation make clinical diagnosis and treatment selection a serious challenge. The challenge is therefore to quickly and correctly classify DLBCL cells. In this work, we show that Raman imaging is a tool with high diagnostic potential, providing unique information about the biochemical components of tumor cells and their metabolism. We present models of classification of lymphoma cells based on their Raman spectra. The models automatically and efficiently identify DLBCL cells and assign them to a given cell-of-origin (COO) subtype (activated B cell-like (ABC) or germinal center B cell-like (GCB)) or, respectively, to a comprehensive cluster classification (CCC) subtype (OxPhos/non-OxPhos). In addition, we describe each lymphoma subtype by its unique spectral profile, linking it to biochemical, genetic, or metabolic features.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

Raman Spectroscopy Characterization of Multi-Functionalized Liposomes as Drug-Delivery Systems for Neurological Disorders

The characterization of nanoparticle-based drug-delivery systems represents a crucial step in achieving a comprehensive overview of their physical, chemical, and biological features and evaluating their efficacy and safety in biological systems. We propose Raman Spectroscopy (RS) for the characterization of liposomes (LPs) to be tested for the control of neuroinflammation and microglial dysfunctions in Glioblastoma multiforme and Alzheimer's disease. Drug-loaded LPs were functionalized to cross the blood-brain barrier and to guarantee localized and controlled drug release. The Raman spectra of each LP component were used to evaluate their contribution in the LP Raman fingerprint. Raman data analysis made it possible to statistically discriminate LPs with different functionalization patterns, showing that each molecular component has an influence in the Raman spectrum of the final LP formulation. Moreover, CLS analysis on Raman data revealed a good level of synthetic reproducibility of the formulations and confirmed their stability within one month from their synthesis, demonstrating the ability of the technique to evaluate the efficacy of LP synthesis using small amount of sample. RS represents a valuable tool for a fast, sensitive and label free biochemical characterization of LPs that could be used for quality control of nanoparticle-based therapeutics.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.