Blood-based molecular signature of Alzheimer's disease via spectroscopy and metabolomics

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

Comprehensive spectroscopic, metabolomic, and proteomic liquid biopsy in the diagnostics of hepatocellular carcinoma

Liquid biopsy is a very topical issue in clinical diagnostics research nowadays. In this study, we explored and compared various analytical approaches to blood plasma analysis. Finally, we proposed a comprehensive procedure, which, thanks to the utilization of multiple analytical techniques, allowed the targeting of various biomolecules in blood plasma reflecting diverse biological processes underlying disease development. The potential of such an approach, combining proteomics, metabolomics, and vibrational spectroscopy along with preceding blood plasma fractionation, was demonstrated on blood plasma samples of patients suffering from hepatocellular carcinoma in cirrhotic terrain (n = 20) and control subjects with liver cirrhosis (n = 20) as well as healthy subjects (n = 20). Most of the applied methods allowed the classification of the samples with an accuracy exceeding 80.0 % and therefore have the potential to be used as a stand-alone method in clinical diagnostics. Moreover, a final panel of 48 variables obtained by a combination of the utilized analytical methods enabled the discrimination of the hepatocellular carcinoma samples from cirrhosis with 94.3 % cross-validated accuracy. Thus, this study, although limited by the cohort size, clearly demonstrated the benefit of the multimethod approach in clinical diagnosis.

Raman spectroscopy and Raman optical activity of blood plasma for differential diagnosis of gastrointestinal cancers

Improving the early diagnosis of gastrointestinal cancers is a crucial step in reducing their mortality. Given the non-specificity of the initial symptoms, the ability of any diagnostic method to differentiate between various types of gastrointestinal cancers also needs to be addressed. To detect disease-specific alterations in biomolecular structure and composition of the blood plasma, we have implemented an approach combining Raman spectroscopy and its conformation-sensitive polarized version, Raman optical activity, to analyze blood plasma samples of patients suffering from three different types of gastrointestinal cancer - hepatocellular, colorectal and pancreatic. First, we aimed to discriminate any type of gastrointestinal cancer from healthy control individuals; inthenext step, the focus was on differentiating among the three cancer types studied. The more straightforward of the two statistical approaches tested, the combination of linear discriminant analysis and principal component analysis applied to the entire spectral dataset, allowed the discrimination of cancer and control samples with 87% accuracy. The three gastrointestinal cancers were classified with an overall accuracy of 76%. The second method, the linear discriminant analysis applied to a selection of spectral bands, yielded even higher values. Cancer and control samples were distinguished with 89% accuracy and hepatocellular, colorectal and pancreatic cancer with an overall accuracy of 87%. The results obtained in our study suggest that the proposed approach may become a disease-specific diagnostic tool in daily clinical practice.

Comparative study of Raman spectroscopy techniques in blood plasma-based clinical diagnostics: A demonstration on Alzheimer's disease

Nowadays, there are still many diseases with limited or no reliable methods of early diagnosis. A popular approach in clinical diagnostic research is Raman spectroscopy, as a relatively simple, cost-effective, and high-throughput method for searching for disease-specific alterations in the composition of blood plasma. However, the high variability of the experimental designs, targeted diseases, or statistical processing in the individual studies makes it challenging to compare and compile the results to critically assess the applicability of Raman spectroscopy in real clinical practice. This study aimed to compare data from a single series of blood plasma samples of patients with Alzheimer's disease and non-demented elderly controls obtained by four different techniques/experimental setups - Raman spectroscopy with excitation at 532 and 785 nm, Raman optical activity, and surface-enhanced Raman scattering spectroscopy. The obtained results showed that the spectra from each Raman spectroscopy technique contain different information about biomolecules of blood plasma or their conformation and may, therefore, offer diverse points of view on underlying biochemical processes of the disease. The classification models based on the datasets generated by the three non-chiroptical variants of Raman spectroscopy exhibited comparable diagnostic performance, all reaching an accuracy close to or equal to 80%. Raman optical activity achieved only 60% classification accuracy, suggesting its limited applicability in the specific case of Alzheimer's disease diagnostics. The described differences in the outputs of the four utilized techniques/setups of Raman spectroscopy imply that their choice may crucially affect the acquired results and thus should be approached carefully concerning the specific purpose.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease

Objective

To systematically evaluate the accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease.

Methods

Databases including Web of Science, PubMed, The Cochrane Library, EMbase, CBM, CNKI, Wan Fang Data, and VIP were electronically searched for studies on Raman spectroscopy in diagnosis of Alzheimer's disease from inception to November 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias in the included studies. Then, meta-analysis was performed using Meta-Disc1.4 and Stata 16.0 software.

Results

A total of eight studies were finally included. The pooled sensitivity of Raman spectroscopy was 0.86 [95% CI (0.80–0.91)], specificity was 0.87 [95% CI (0.79–0.92)], positive likelihood ratio was 5.50 [95% CI (3.55–8.51)], negative likelihood ratio was 0.17 [95% CI (0.09–0.34)], diagnosis odds ratio and area under the curve of SROC were 42.44 [95% CI (19.80–90.97)] and 0.931, respectively. Sensitivity analysis was carried out after each study was excluded one by one, and the results showed that pooled sensitivity and specificity had no significant change, indicating that the stability of the meta-analysis results was great.

Conclusions

Our findings indicated that Raman spectroscopy had high accuracy in the diagnosis of AD, though it still did not rule out the possibility of misdiagnosis and missed diagnosis. Limited by the quantity and quality of the included studies, the above conclusions need to be verified by more high-quality studies.

Review: Emerging Eye-Based Diagnostic Technologies for Traumatic Brain Injury

The study of ocular manifestations of neurodegenerative disorders, Oculomics, is a growing field of investigation for early diagnostics, enabling structural and chemical biomarkers to be monitored overtime to predict prognosis. Traumatic brain injury (TBI) triggers a cascade of events harmful to the brain, which can lead to neurodegeneration. TBI, termed the "silent epidemic" is becoming a leading cause of death and disability worldwide. There is currently no effective diagnostic tool for TBI, and yet, early-intervention is known to considerably shorten hospital stays, improve outcomes, fasten neurological recovery and lower mortality rates, highlighting the unmet need for techniques capable of rapid and accurate point-of-care diagnostics, implemented in the earliest stages. This review focuses on the latest advances in the main neuropathophysiological responses and the achievements and shortfalls of TBI diagnostic methods. Validated and emerging TBI-indicative biomarkers are outlined and linked to ocular neuro-disorders. Methods detecting structural and chemical ocular responses to TBI are categorised along with prospective chemical and physical sensing techniques. Particular attention is drawn to the potential of Raman spectroscopy as a non-invasive sensing of neurological molecular signatures in the ocular projections of the brain, laying the platform for the first tangible path towards alternative point-of-care diagnostic technologies for TBI.

Toward Reagent-Free Discrimination of Alzheimer's Disease Using Blood Plasma Spectral Digital Biomarkers and Machine Learning

BACKGROUND

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. The detection of early-stage AD is particularly desirable because it would allow early intervention. However, a minimally invasive, low-cost, and accurate discrimination or diagnostic method for AD is especially difficult in the earliest stage of AD.

OBJECTIVE

The aim of this research is to discover blood plasma spectral digital biomarkers of AD, develop a novel intelligent method for the discrimination of AD and accelerate the translation of Fourier transform infrared (FTIR) spectral-based disease discrimination methods from the laboratory to clinical practice.

METHODS

Since vibration spectroscopy can provide the structure and chemical composition information of biological samples at the molecular level, we investigated the potential of FTIR spectral biomarkers of blood plasma to differentiate between AD patients and healthy controls. Combined with machine learning technology, we designed a hierarchical discrimination system that provides reagent-free and accurate AD discrimination based on blood plasma spectral digital biomarkers of AD.

RESULTS

Accurate segregation between AD patients and healthy controls was achieved with 89.3% sensitivity and 85.7% specificity for early-stage AD patients, 92.8% sensitivity and 87.5% specificity for middle-stage AD patients, and 100% sensitivity and 100% specificity for late-stage AD patients.

CONCLUSIONS

Our results show that blood plasma spectral digital biomarkers hold great promise as discrimination markers of AD, indicating the potential for the development of an inexpensive, reagent-free, and less laborious clinical test. As a result, our research outcome will accelerate the clinical application of spectral digital biomarkers and machine learning.

Linking the Amyloid, Tau, and Mitochondrial Hypotheses of Alzheimer's Disease and Identifying Promising Drug Targets

Damage or loss of brain cells and impaired neurochemistry, neurogenesis, and synaptic and nonsynaptic plasticity of the brain lead to dementia in neurodegenerative diseases, such as Alzheimer's disease (AD). Injury to synapses and neurons and accumulation of extracellular amyloid plaques and intracellular neurofibrillary tangles are considered the main morphological and neuropathological features of AD. Age, genetic and epigenetic factors, environmental stressors, and lifestyle contribute to the risk of AD onset and progression. These risk factors are associated with structural and functional changes in the brain, leading to cognitive decline. Biomarkers of AD reflect or cause specific changes in brain function, especially changes in pathways associated with neurotransmission, neuroinflammation, bioenergetics, apoptosis, and oxidative and nitrosative stress. Even in the initial stages, AD is associated with Aβ neurotoxicity, mitochondrial dysfunction, and tau neurotoxicity. The integrative amyloid-tau-mitochondrial hypothesis assumes that the primary cause of AD is the neurotoxicity of Aβ oligomers and tau oligomers, mitochondrial dysfunction, and their mutual synergy. For the development of new efficient AD drugs, targeting the elimination of neurotoxicity, mutual potentiation of effects, and unwanted protein interactions of risk factors and biomarkers (mainly Aβ oligomers, tau oligomers, and mitochondrial dysfunction) in the early stage of the disease seems promising.

Recent Advances in Understanding of Alzheimer's Disease Progression through Mass Spectrometry-Based Metabolomics

Alzheimer's disease (AD) is the leading cause of dementia in the aging population, but despite extensive research, there is no consensus on the biological cause of AD. While AD research is dominated by protein/peptide-centric research based on the amyloid hypothesis, a theory that designates dysfunction in beta-amyloid production, accumulation, or disposal as the primary cause of AD, many studies focus on metabolomics as a means of understanding the biological processes behind AD progression. In this review, we discuss mass spectrometry (MS)-based AD metabolomics studies, including sample type and preparation, mass spectrometry specifications, and data analysis, as well as biological insights gleaned from these studies, with the hope of informing future AD metabolomic studies.

Tackling Stereochemistry in Drug Molecules with Vibrational Optical Activity

Chirality plays a crucial role in drug discovery and development. As a result, a significant number of commercially available drugs are structurally dissymmetric and enantiomerically pure. The determination of the exact 3D structure of drug candidates is, consequently, of paramount importance for the pharmaceutical industry in different stages of the discovery pipeline. Traditionally the assignment of the absolute configuration of druggable molecules has been carried out by means of X-ray crystallography. Nevertheless, not all molecules are suitable for single-crystal growing. Additionally, valuable information about the conformational dynamics of drug candidates is lost in the solid state. As an alternative, vibrational optical activity (VOA) methods have emerged as powerful tools to assess the stereochemistry of drug molecules directly in solution. These methods include vibrational circular dichroism (VCD) and Raman optical activity (ROA). Despite their potential, VCD and ROA are still unheard of to many organic and medicinal chemists. Therefore, the present review aims at highlighting the recent use of VOA methods for the assignment of the absolute configuration of chiral small-molecule drugs, as well as for the structural analysis of biologics of pharmaceutical interest. A brief introduction on VCD and ROA theory and the best experimental practices for using these methods will be provided along with selected representative examples over the last five years. As VCD and ROA are commonly used in combination with quantum calculations, some guidelines will also be presented for the reliable simulation of chiroptical spectra. Special attention will be paid to the complementarity of VCD and ROA to unambiguously assess the stereochemical properties of pharmaceuticals.

Multimodal, label-free fluorescence and Raman imaging of amyloid deposits in snap-frozen Alzheimer's disease human brain tissue

Alzheimer's disease (AD) neuropathology is characterized by hyperphosphorylated tau containing neurofibrillary tangles and amyloid-beta (Aβ) plaques. Normally these hallmarks are studied by (immuno-) histological techniques requiring chemical pretreatment and indirect labelling. Label-free imaging enables one to visualize normal tissue and pathology in its native form. Therefore, these techniques could contribute to a better understanding of the disease. Here, we present a comprehensive study of high-resolution fluorescence imaging (before and after staining) and spectroscopic modalities (Raman mapping under pre-resonance conditions and stimulated Raman scattering (SRS)) of amyloid deposits in snap-frozen AD human brain tissue. We performed fluorescence and spectroscopic imaging and subsequent thioflavin-S staining of the same tissue slices to provide direct confirmation of plaque location and correlation of spectroscopic biomarkers with plaque morphology; differences were observed between cored and fibrillar plaques. The SRS results showed a protein peak shift towards the β-sheet structure in cored amyloid deposits. In the Raman maps recorded with 532 nm excitation we identified the presence of carotenoids as a unique marker to differentiate between a cored amyloid plaque area versus a non-plaque area without prior knowledge of their location. The observed presence of carotenoids suggests a distinct neuroinflammatory response to misfolded protein accumulations.

Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

Vibrational spectroscopy (mid-infrared (IR) and Raman) and its fingerprinting capabilities offer rapid, high-throughput, and non-destructive analysis of a wide range of sample types producing a characteristic chemical "fingerprint" with a unique signature profile. Nuclear magnetic resonance (NMR) spectroscopy and an array of mass spectrometry (MS) techniques provide selectivity and specificity for screening metabolites, but demand costly instrumentation, complex sample pretreatment, are labor-intensive, require well-trained technicians to operate the instrumentation, and are less amenable for implementation in clinics. The potential for vibration spectroscopy techniques to be brought to the bedside gives hope for huge cost savings and potential revolutionary advances in diagnostics in the clinic. We discuss the utilization of current vibrational spectroscopy methodologies on biologic samples as an avenue towards rapid cost saving diagnostics.

Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning

Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signature-based diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic 'fingerprints' rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nano- technologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies.

High-resolution metabolomic profiling of Alzheimer's disease in plasma

Background

Alzheimer’s disease (AD) is a complex neurological disorder with contributions from genetic and environmental factors. High‐resolution metabolomics (HRM) has the potential to identify novel endogenous and environmental factors involved in AD. Previous metabolomics studies have identified circulating metabolites linked to AD, but lack of replication and inconsistent diagnostic algorithms have hindered the generalizability of these findings. Here we applied HRM to identify plasma metabolic and environmental factors associated with AD in two study samples, with cerebrospinal fluid (CSF) biomarkers of AD incorporated to achieve high diagnostic accuracy.

Methods

Liquid chromatography‐mass spectrometry (LC–MS)‐based HRM was used to identify plasma and CSF metabolites associated with AD diagnosis and CSF AD biomarkers in two studies of prevalent AD (Study 1: 43 AD cases, 45 mild cognitive impairment [MCI] cases, 41 controls; Study 2: 50 AD cases, 18 controls). AD‐associated metabolites were identified using a metabolome‐wide association study (MWAS) framework.

Results

An MWAS meta‐analysis identified three non‐medication AD‐associated metabolites in plasma, including elevated levels of glutamine and an unknown halogenated compound and lower levels of piperine, a dietary alkaloid. The non‐medication metabolites were correlated with CSF AD biomarkers, and glutamine and the unknown halogenated compound were also detected in CSF. Furthermore, in Study 1, the unknown compound and piperine were altered in MCI patients in the same direction as AD dementia.

Conclusions

In plasma, AD was reproducibly associated with elevated levels of glutamine and a halogen‐containing compound and reduced levels of piperine. These findings provide further evidence that exposures and behavior may modify AD risks.

Search for biomarkers of Alzheimer's disease: Recent insights, current challenges and future prospects

Due to the trend of prolonged lifespan leading to higher incidence of age-related diseases, the demand for reliable biomarkers of dementia rises. In this review, we present novel biomarkers of high potential, especially those found in blood, urine or saliva, which could lead to a more comfortable patient experience and better time- and cost-effectivity, compared to the currently used diagnostic methods. We focus on biomarkers that might allow for the detection of Alzheimer's disease before its clinical manifestations. Such biomarkers might be helpful for better understanding the etiology of the disease and identifying its risk factors. Moreover, it could be a base for developing new treatment or at least help to prolong the presymptomatic stage in patients suffering from Alzheimer's disease. As potential candidates, we present, for instance, neurofilament light in both cerebrospinal fluid and blood plasma or amyloid β in plasma. Above all, we provide an overview of different approaches to the diagnostics, analyzing patient's biofluids as a whole using molecular spectroscopy. Infrared and Raman spectroscopy and especially chiroptical methods provide information not only on the chemical composition, but also on molecular structure. Therefore, these techniques are promising for the diagnostics of Alzheimer's disease, as the accumulation of amyloid β in abnormal conformation is one of the hallmarks of this disease.