Cerebrospinal Fluid Protein Biomarkers Research Articles

Tau, Glial Fibrillary Acidic Protein, and Neurofilament Light Chain as Brain Protein Biomarkers in Cerebrospinal Fluid and Blood for Diagnosis of Neurobiological Diseases.

Neurological damage is the pathological substrate of permanent disability in various neurodegenerative disorders. Early detection of this damage, including its identification and quantification, is critical to preventing the disease's progression in the brain. Tau, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), as brain protein biomarkers, have the potential to improve diagnostic accuracy, disease monitoring, prognostic assessment, and treatment efficacy. These biomarkers are released into the cerebrospinal fluid (CSF) and blood proportionally to the degree of neuron and astrocyte damage in different neurological disorders, including stroke, traumatic brain injury, multiple sclerosis, neurodegenerative dementia, and Parkinson's disease. Here, we review how Tau, GFAP, and NfL biomarkers are detected in CSF and blood as crucial diagnostic tools, as well as the levels of these biomarkers used for differentiating a range of neurological diseases and monitoring disease progression. We also discuss a biosensor approach that allows for the real-time detection of multiple biomarkers in various neurodegenerative diseases. This combined detection system of brain protein biomarkers holds significant promise for developing more specific and accurate clinical tools that can identify the type and stage of human neurological diseases with greater precision.

Cerebrospinal fluid synaptic biomarker changes in bipolar disorder – A longitudinal case-control study

BackgroundThis exploratory study investigated cerebrospinal fluid (CSF) synaptic protein biomarkers in bipolar disorder (BD), aiming to highlight the neurobiological basis of the disorder. With shared cognitive impairment features between BD and Alzheimer's disease, and considering increased dementia risk in BD patients, the study explores potential connections. MethodsFifty-nine well-characterized patients with BD and thirty-seven healthy control individuals were examined and followed for one year. Synaptic proteins encompassing neuronal pentraxins (NPTX)1, NPTX2, and NPTX-receptor, 14–3-3 protein family epsilon, and zeta/delta, activating protein-2 complex subunit beta, synucleins beta-synuclein and gamma-synuclein, complexin-2, phosphatidylethanolamine-binding protein 1, rab GDP dissociation inhibitor alpha, and syntaxins 1B and 7 were measured in CSF using a microflow liquid chromatography-mass spectrometric multiple reaction monitoring set-up. Biomarker levels were compared between BD and HC and in BD before, during, and after mood episodes. ResultsThe synaptic proteins revealed no statistically significant differences between BD and HC, neither at baseline, one-year follow-up, or in terms of changes from baseline to follow-up. Moreover, the CSF synaptic protein levels in patients with BD were unaltered compared to baseline when they stabilized in euthymia following an affective episode and at one-year follow-up. LimitationIt is uncertain what the CSF biomarker concentrations reflect since we yet do not know the mechanisms of release of these proteins, and we are uncertain of what increased or decreased levels reflect. ConclusionThis first-ever investigation of a panel of CSF protein biomarkers of synaptic dysfunction in patients with BD and HC individuals found no statistically significant differences cross-sectionally or longitudinally.

Utility of cerebrospinal fluid protein biomarkers in predicting the outcome of Guillain-Barre syndrome

Objectives The study aimed to investigate the potential of peripheral myelin protein 2 (P2) and Alpha B-crystallin (αBC) as predictive biomarkers in Guillain-Barré syndrome (GBS). Given the unpredictability of GBS prognosis, the need for specific and reliable biomarkers for disease development and intensity assessment is crucial. Material and Methods A prospective observational study was conducted on a cohort of 220 individuals diagnosed with GBS at a tertiary general hospital in South India. P2 and αBC levels in cerebrospinal fluid (CSF) were quantified using ELISA assay kits. The study spanned from March 2021 to April 2023, with participants aged 18–60 years. The study protocol adhered to ethical standards, and the Brighton criteria were employed for GBS diagnosis. CSF samples were collected at admission and two weeks post-onset. Data analysis utilised SPSS, and statistical significance was set at p < 0.05. Results Upon admission, mean P2 levels were 2.2 ± 0.5 ng/mL, and αBC levels were 9.8 ± 2.3 ng/mL. After two weeks, P2 increased to 4.8 ± 0.8 ng/mL, and αBC increased to 15.1 ± 2.3 ng/mL. A positive correlation was observed between the rise in P2 and αBC levels and enhanced muscle strength at 4 weeks and 6 months. Conclusion The study suggests a significant increase in P2 and αBC levels in GBS patients, correlating with improved muscle strength. P2 and αBC ratios in CSF between the second and first weeks may serve as prognostic markers for GBS. Limitations include a small sample size and the absence of a control group, necessitating caution in generalizability.

Aptamer-Based Proteomics Measuring Preoperative Cerebrospinal Fluid Protein Alterations Associated with Postoperative Delirium.

Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to a lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with the development of postoperative delirium in older surgical patients. We employed a nested case-control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected from the Healthier Postoperative Recovery (HiPOR) cohort, and the associations between preoperative protein levels and postoperative delirium were assessed using t-test statistics with further analysis by systems biology to elucidate delirium pathophysiology. Proteomics analysis identified 32 proteins in preoperative CSF that significantly associate with delirium (t-test p < 0.05). Due to the limited sample size, these proteins did not remain significant by multiple hypothesis testing using the Benjamini-Hochberg correction and q-value method. Three algorithms were applied to separate delirium cases from non-delirium controls. Hierarchical clustering classified 40/48 case-control samples correctly, and principal components analysis separated 43/48. The receiver operating characteristic curve yielded an area under the curve [95% confidence interval] of 0.91 [0.80-0.97]. Systems biology analysis identified several key pathways associated with risk of delirium: inflammation, immune cell migration, apoptosis, angiogenesis, synaptic depression and neuronal cell death. Proteomics analysis of preoperative CSF identified 32 proteins that might discriminate individuals who subsequently develop postoperative delirium from matched control samples. These proteins are potential candidate biomarkers for delirium and may play a role in its pathophysiology.

CSF metabolites associated with biomarkers of Alzheimer's disease pathology.

Metabolomics technology facilitates studying associations between small molecules and disease processes. Correlating metabolites in cerebrospinal fluid (CSF) with Alzheimer's disease (AD) CSF biomarkers may elucidate additional changes that are associated with early AD pathology and enhance our knowledge of the disease. The relative abundance of untargeted metabolites was assessed in 161 individuals from the Wisconsin Registry for Alzheimer's Prevention. A metabolome-wide association study (MWAS) was conducted between 269 CSF metabolites and protein biomarkers reflecting brain amyloidosis, tau pathology, neuronal and synaptic degeneration, and astrocyte or microglial activation and neuroinflammation. Linear mixed-effects regression analyses were performed with random intercepts for sample relatedness and repeated measurements and fixed effects for age, sex, and years of education. The metabolome-wide significance was determined by a false discovery rate threshold of 0.05. The significant metabolites were replicated in 154 independent individuals from then Wisconsin Alzheimer's Disease Research Center. Mendelian randomization was performed using genome-wide significant single nucleotide polymorphisms from a CSF metabolites genome-wide association study. Metabolome-wide association study results showed several significantly associated metabolites for all the biomarkers except Aβ42/40 and IL-6. Genetic variants associated with metabolites and Mendelian randomization analysis provided evidence for a causal association of metabolites for soluble triggering receptor expressed on myeloid cells 2 (sTREM2), amyloid β (Aβ40), α-synuclein, total tau, phosphorylated tau, and neurogranin, for example, palmitoyl sphingomyelin (d18:1/16:0) for sTREM2, and erythritol for Aβ40 and α-synuclein. This study provides evidence that CSF metabolites are associated with AD-related pathology, and many of these associations may be causal.

Quantitative Mass Spectrometry Analysis of Cerebrospinal Fluid Protein Biomarkers in Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia, with cerebrospinal fluid (CSF) β-amyloid (Aβ), total Tau, and phosphorylated Tau (pTau) providing the most sensitive and specific biomarkers for diagnosis. However, these diagnostic biomarkers do not reflect the complex changes in AD brain beyond amyloid (A) and Tau (T) pathologies. Here, we report a selected reaction monitoring mass spectrometry (SRM-MS) method with isotopically labeled standards for relative protein quantification in CSF. Biomarker positive (AT+) and negative (AT−) CSF pools were used as quality controls (QCs) to assess assay precision. We detected 62 peptides (51 proteins) with an average coefficient of variation (CV) of ~13% across 30 QCs and 133 controls (cognitively normal, AT−), 127 asymptomatic (cognitively normal, AT+) and 130 symptomatic AD (cognitively impaired, AT+). Proteins that could distinguish AT+ from AT− individuals included SMOC1, GDA, 14-3-3 proteins, and those involved in glycolysis. Proteins that could distinguish cognitive impairment were mainly neuronal proteins (VGF, NPTX2, NPTXR, and SCG2). This demonstrates the utility of SRM-MS to quantify CSF protein biomarkers across stages of AD.

Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study

Alzheimer’s Disease (AD) is a highly prevalent condition and most of the people suffering from it receive the diagnosis late in the process. The diagnosis is currently established following an evaluation of the protein biomarkers in cerebrospinal fluid (CSF), brain imaging, cognitive tests, and the medical history of the individuals. While diagnostic tools based on CSF collections are invasive, the tools used for acquiring brain scans are expensive. Taking these into account, an early predictive system, based on Artificial Intelligence (AI) approaches, targeting the diagnosis of this condition, as well as the identification of lead biomarkers becomes an important research direction. In this survey, we review the state-of-the-art research on machine learning (ML) techniques used for the detection of AD and Mild Cognitive Impairment (MCI). We attempt to identify the most accurate and efficient diagnostic approaches, which employ ML techniques and therefore, the ones most suitable to be used in practice. Research is still ongoing to determine the best biomarkers for the task of AD classification. At the beginning of this survey, after an introductory part, we enumerate several available resources, which can be used to build ML models targeting the diagnosis and classification of AD, as well as their main characteristics. After that, we discuss the candidate markers which were used to build AI models with the best results in terms of diagnostic accuracy, as well as their limitations.

Validation of host cerebrospinal fluid protein biomarkers for early diagnosis of tuberculous meningitis in children: a replication and new biosignature discovery study

Purpose: The diagnosis of tuberculous meningitis (TBM) in children is often delayed due to diagnostic difficulties. New tools are urgently needed to improve the diagnosis of the disease in this vulnerable group. The present study aimed to validate the accuracy of recently identified host cerebrospinal (CSF) biomarkers as candidates for the diagnosis of TBM in children. Materials and methods: We collected CSF samples from 87 children aged 3 months to 13 years, that were consecutively admitted at a tertiary hospital in Cape Town, South Africa, on suspicion of having TBM. We evaluated the concentrations of 67 selected host protein biomarkers using a multiplex platform. Results: Previously identified 3-marker (VEGF-A + IFN-γ + MPO) and 4-marker (IFN-γ + MPO + ICAM-1 + IL-8) signatures diagnosed TBM with AUCs of 0.89 (95% CI, 0.81–0.97) and 0.87 (95% CI, 0.79–0.95) respectively; sensitivities of 80.6% (95% CI, 62.5–92.5%) and 81.6% (95% CI, 65.7–92.3%), and specificities of 86.8% (71.9–95.6%) and 83.7% (70.4–92.7%) respectively. Furthermore, a new combination between the analytes (CC4b + CC4 + procalcitonin + CCL1) showed promise, with an AUC of 0.98 (95% CI, 0.94–1.00). Conclusions: We have shown that the accuracies of previously identified candidate CSF biomarkers for childhood TBM was reproducible. Our findings augur well for the future development of a simple bedside test for the rapid diagnosis of TBM in children.

Association of cerebrospinal fluid protein biomarkers with outcomes in patients with traumatic and non-traumatic acute brain injury: systematic review of the literature

BackgroundAcute brain injuries are associated with high mortality rates and poor long-term functional outcomes. Measurement of cerebrospinal fluid (CSF) biomarkers in patients with acute brain injuries may help elucidate some of the pathophysiological pathways involved in the prognosis of these patients.MethodsWe performed a systematic search and descriptive review using the MEDLINE database and the PubMed interface from inception up to June 29, 2021, to retrieve observational studies in which the relationship between CSF concentrations of protein biomarkers and neurological outcomes was reported in patients with acute brain injury [traumatic brain injury, subarachnoid hemorrhage, acute ischemic stroke, status epilepticus or post-cardiac arrest]. We classified the studies according to whether or not biomarker concentrations were associated with neurological outcomes. The methodological quality of the studies was evaluated using the Newcastle–Ottawa quality assessment scale.ResultsOf the 39 studies that met our criteria, 30 reported that the biomarker concentration was associated with neurological outcome and 9 reported no association. In TBI, increased extracellular concentrations of biomarkers related to neuronal cytoskeletal disruption, apoptosis and inflammation were associated with the severity of acute brain injury, early mortality and worse long-term functional outcome. Reduced concentrations of protein biomarkers related to impaired redox function were associated with increased risk of neurological deficit. In non-traumatic acute brain injury, concentrations of CSF protein biomarkers related to dysregulated inflammation and apoptosis were associated with a greater risk of vasospasm and a larger volume of brain ischemia. There was a high risk of bias across the studies.ConclusionIn patients with acute brain injury, altered CSF concentrations of protein biomarkers related to cytoskeletal damage, inflammation, apoptosis and oxidative stress may be predictive of worse neurological outcomes.

Autopsy-diagnosed neurodegenerative dementia cases support the use of cerebrospinal fluid protein biomarkers in the diagnostic work-up

Various proteins play a decisive role in the pathology of different neurodegenerative diseases. Nonetheless, most of these proteins can only be detected during a neuropathological assessment, although some non-specific biomarkers are routinely tested for in the cerebrospinal fluid (CSF) as a part of the differential diagnosis of dementia. In antemortem CSF samples from 117 patients with different types of neuropathologically confirmed neurodegenerative disease with dementia, we assessed total-tau (t-tau), phosphorylated-tau (181P) (p-tau), amyloid-beta (1–42) (Aβ42), TAR DNA binding protein (TDP)-43, progranulin (PGRN), and neurofilament light (NfL) chain levels, and positivity of protein 14-3-3. We found t-tau levels and the t-tau/p-tau ratios were significantly higher in prion diseases compared to the other neurodegenerative diseases. Statistically significant differences in the t-tau/Aβ42 ratio predominantly corresponded to t-tau levels in prion diseases and Aβ42 levels in AD. TDP-43 levels were significantly lower in prion diseases. Additionally, the TDP-43/Aβ42 ratio was better able to distinguish Alzheimer’s disease from other neurodegenerative diseases compared to using Aβ42 alone. In frontotemporal lobar degeneration, PRGN levels were significantly higher in comparison to other neurodegenerative diseases. There is an increasing need for biomarkers suitable for diagnostic workups for neurodegenerative diseases. It appears that adding TDP-43 and PGRN to the testing panel for neurodegenerative diseases could improve the resolution of differential diagnoses.

Inflammation-related plasma and CSF biomarkers for multiple sclerosis

Effective biomarkers for multiple sclerosis diagnosis, assessment of prognosis, and treatment responses, in particular those measurable in blood, are largely lacking. We have investigated a broad set of protein biomarkers in cerebrospinal fluid (CSF) and plasma using a highly sensitive proteomic immunoassay. Cases from two independent cohorts were compared with healthy controls and patients with other neurological diseases. We identified and replicated 10 cerebrospinal fluid proteins including IL-12B, CD5, MIP-1a, and CXCL9 which had a combined diagnostic efficacy similar to immunoglobulin G (IgG) index and neurofilament light chain (area under the curve [AUC] = 0.95). Two plasma proteins, OSM and HGF, were also associated with multiple sclerosis in comparison to healthy controls. Sensitivity and specificity of combined CSF and plasma markers for multiple sclerosis were 85.7% and 73.5%, respectively. In the discovery cohort, eotaxin-1 (CCL11) was associated with disease duration particularly in patients who had secondary progressive disease (P CSF < 4 × 10-5, P plasma < 4 × 10-5), and plasma CCL20 was associated with disease severity (P = 4 × 10-5), although both require further validation. Treatment with natalizumab and fingolimod showed different compartmental changes in protein levels of CSF and peripheral blood, respectively, including many disease-associated markers (e.g., IL12B, CD5) showing potential application for both diagnosing disease and monitoring treatment efficacy. We report a number of multiple sclerosis biomarkers in CSF and plasma for early disease detection and potential indicators for disease activity. Of particular importance is the set of markers discovered in blood, where validated biomarkers are lacking.

An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD)

The fact that ensemble methods enhance the prediction performance. Therefore, we focused on developing a weighted ensemble method using a novel combination of Cerebrospinal Fluid (CSF) protein biomarkers to predict AD's earlier stages with greater accuracy than the state-of-the-art CSF protein biomarkers. In this regard, two feature selection methods, namely the Recursive Feature Elimination (RFE) and L1 regularization method were used to screen the most important subset of features for building a classification model using the Mild Cognitive Impairment (MCI) dataset. A novel combination of three biomarkers, namely Cystatin C, Matrix metalloproteinases (MMP10), and tau protein, was screened using the linear Support Vector Machine (SVM) and Logistic Regression (LR) classifier based RFE method. Two-tailed unpaired t-test analysis at a 5% significance level showed a significant difference between the mean levels of Cystatin C, MMP10, and tau protein between cognitive normal and cognitively impaired groups. An ensemble model using a weighted average of two best performing classifiers (LR and Linear SVM) was created using a novel subset of three most informative features. Our ensemble model's weighted average results performed significantly better than LR and Linear SVM base classifiers' performance. The Receiver Operating Characteristic Curve (ROC_AUC) and Area under Precision-Recall values (AUPR) of our proposed model were observed to be 0.9799 ± 0.055 0.9108 ± 0.015, respectively. The performance of our proposed weighted averaged ensemble model built using a novel combination of CSF protein biomarkers was significantly better (p <; 0.001) than models generated using different combinations of CSF protein biomarkers obtained from recent studies. An ensemble-learning based application was implemented and deployed at Heroku at https://appsalzheimer.herokuapp.com.

Guidelines for CSF Processing and Biobanking: Impact on the Identification and Development of Optimal CSF Protein Biomarkers.

The field of neurological diseases strongly needs biomarkers for early diagnosis and optimal stratification of patients in clinical trials or to monitor disease progression. Cerebrospinal fluid (CSF) is one of the main sources for the identification of novel protein biomarkers for neurological diseases. Despite the enormous efforts employed to identify novel CSF biomarkers, the high variability observed across different studies has hampered their validation and implementation in clinical practice. Such variability is partly caused by the effect of different pre-analytical confounding factors on protein stability, highlighting the importance to develop and comply with standardized operating procedures. In this chapter, we describe the international consensus pre-analytical guidelines for CSF processing and biobanking that have been established during the last decade, with a special focus on the influence of pre-analytical confounders on the global CSF proteome. In addition, we provide novel results on the influence of different delayed storage and freeze/thaw conditions on the CSF proteome using two novel large multiplex protein arrays (SOMAscan and Olink). Compliance to consensus guidelines will likely facilitate the successful development and implementation of CSF protein biomarkers in both research and clinical settings, ultimately facilitating the successful development of disease-modifying therapies.

Protein variability in cerebrospinal fluid and its possible implications for neurological protein biomarker research.

Cerebrospinal fluid is investigated in biomarker studies for various neurological disorders of the central nervous system due to its proximity to the brain. Currently, only a limited number of biomarkers have been validated in independent studies. The high variability in the protein composition and protein abundance of cerebrospinal fluid between as well as within individuals might be an important reason for this phenomenon. To evaluate this possibility, we investigated the inter- and intraindividual variability in the cerebrospinal fluid proteome globally, with a specific focus on disease biomarkers described in the literature. Cerebrospinal fluid from a longitudinal study group including 12 healthy control subjects was analyzed by label-free quantification (LFQ) via LC-MS/MS. Data were quantified via MaxQuant. Then, the intra- and interindividual variability and the reference change value were calculated for every protein. We identified and quantified 791 proteins, and 216 of these proteins were abundant in all samples and were selected for further analysis. For these proteins, we found an interindividual coefficient of variation of up to 101.5% and an intraindividual coefficient of variation of up to 29.3%. Remarkably, these values were comparably high for both proteins that were published as disease biomarkers and other proteins. Our results support the hypothesis that natural variability greatly impacts cerebrospinal fluid protein biomarkers because high variability can lead to unreliable results. Thus, we suggest controlling the variability of each protein to distinguish between good and bad biomarker candidates, e.g., by utilizing reference change values to improve the process of evaluating potential biomarkers in future studies.

Event-related Potentials Improve the Efficiency of Cerebrospinal Fluid Biomarkers for Differential Diagnosis of Alzheimer's Disease.

The pathological process of Alzheimer's disease (AD) in the brain likely begins 20-30 years earlier than the emergence of its first clinical symptoms and symptoms of AD often overlap with the symptoms of other primary causes of dementia. Therefore, it is crucially important to improve early and differential diagnosis of the disease. Event-related potentials (ERP) measured non-invasively by electroencephalography have shown diagnostic potential in AD. The aim of this study was to compare the efficiency of P300 and N200 potentials and reaction time (RT) with commonly used protein biomarkers measured in the cerebrospinal fluid (CSF), including amyloid β peptide (β1-42), total tau (t-tau), tau protein phosphorylated at threonine 181 (p-tau181), tau protein phosphorylated at serine 199 (p-tau199), tau protein phosphorylated at threonine 231 (p-tau231), and visinin-like protein 1 (VILIP-1) in differential diagnosis of AD in mild cognitive impairment (MCI) and AD patients. The study involved 49 AD patients, 28 patients with MCI, 4 healthy control subjects and 16 patients with other primary causes of dementia. ERP (P300RT, N200RT, P300 counting and N200 counting) showed a moderate to strong correlation with protein CSF biomarkers. We confirmed previous observations of moderate to strong correlation between ERP and neuropsychological testing and showed that P300 latency and RT are shortened in AD patients on therapy with acetylcholinesterase inhibitors. Using ERP and RT, a predictive model for determination of AD likelihood in MCI patients was developed, detecting 56.3% of MCI patients with high risk for development of AD in our cohort. MCI patients with pathological levels of Aβ1-42 had prolonged P300 latency, indicating that a combination of ERP and CSF protein biomarkers could improve the differential diagnosis of AD in MCI patients. Additionally, the results suggested the potential of P300 latency in differentiating AD and FTD patients. Our data provide possible solutions for improvement of differential diagnosis of AD, and reveal that the diagnostic efficiency of CSF protein biomarkers t-tau, p-tau181, p-tau199, p-tau231 and VILIP-1 could be improved by adding ERP in clinical practice.

Cognitive Reserve Relates to Functional Network Efficiency in Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia, with no means of cure or prevention. The presence of abnormal disease-related proteins in the population is, in turn, much more common than the incidence of dementia. In this context, the cognitive reserve (CR) hypothesis has been proposed to explain the discontinuity between pathophysiological and clinical expression of AD, suggesting that CR mitigates the effects of pathology on clinical expression and cognition. fMRI studies of the human connectome have recently reported that AD patients present diminished functional efficiency in resting-state networks, leading to a loss in information flow and cognitive processing. No study has investigated, however, whether CR modifies the effects of the pathology in functional network efficiency in AD patients. We analyzed the relationship between CR, pathophysiology and network efficiency, and whether CR modifies the relationship between them. Fourteen mild AD, 28 amnestic mild cognitive impairment (aMCI) due to AD, and 28 controls were enrolled. We used education to measure CR, cerebrospinal fluid (CSF) biomarkers to evaluate pathophysiology, and graph metrics to measure network efficiency. We found no relationship between CR and CSF biomarkers; CR was related to higher network efficiency in all groups; and abnormal levels of CSF protein biomarkers were related to more efficient networks in the AD group. Education modified the effects of tau-related pathology in the aMCI and mild AD groups. Although higher CR might not protect individuals from developing AD pathophysiology, AD patients with higher CR are better able to cope with the effects of pathology—presenting more efficient networks despite pathology burden. The present study highlights that interventions focusing on cognitive stimulation might be useful to slow age-related cognitive decline or dementia and lengthen healthy aging.

Biomarkers in Cerebrospinal Fluid: Analysis of Cell-Free Circulating Mitochondrial DNA by Digital PCR.

Cerebrospinal fluid (CSF) contains molecules directly linked with brain function because it permeates brain tissue. The analysis of protein biomarkers in CSF is currently recommended for the diagnosis of neurodegenerative disorders, but the clinical sensitivity and specificity are still being investigated. A major drawback is that most of the currently used biomarkers of neurodegenerative diseases are proteins that are found at very low concentrations in CSF and need to be measured by immunoassays that provide relative values, which sometimes are difficult to reproduce between laboratories. In contrast, the recent availability of digital PCR platforms allows the absolute quantification of nucleic acids at single-molecule resolution, but their presence in CSF has not been characterized. CSF contains cell-free mitochondrial DNA (mtDNA) and changes in the concentration of this nucleic acid are linked to neurodegeneration. Here we describe a method to measure the concentration of cell-free circulating mtDNA directly in unpurified CSF using droplet digital PCR with either hydrolysis probes or fluorescent DNA-binding dye methods. This protocol allows the detection and absolute quantification of mtDNA content in the CSF with high analytical sensitivity, specificity, and accuracy.

A Parallel Reaction Monitoring Mass Spectrometric Method for Analysis of Potential CSF Biomarkers for Alzheimer's Disease.

The aim of this study was to develop and evaluate a parallel reaction monitoring mass spectrometry (PRM-MS) assay consisting of a panel of potential protein biomarkers in cerebrospinal fluid (CSF). Thirteen proteins were selected based on their association with neurodegenerative diseases and involvement in synaptic function, secretory vesicle function, or innate immune system. CSF samples were digested and two to three peptides per protein were quantified using stable isotope-labeled peptide standards. Coefficients of variation were generally below 15%. Clinical evaluation was performed on a cohort of 10 patients with Alzheimer's disease (AD) and 15 healthy subjects. Investigated proteins of the granin family exhibited the largest difference between the patient groups. Secretogranin-2 (p<0.005) and neurosecretory protein VGF (p<0.001) concentrations were lowered in AD. For chromogranin A, two of three peptides had significantly lowered AD concentrations (p<0.01). The concentrations of the synaptic proteins neurexin-1 and neuronal pentraxin-1, as well as neurofascin were also significantly lowered in AD (p<0.05). The other investigated proteins, β2-microglobulin, cystatin C, amyloid precursor protein, lysozyme C, neurexin-2, neurexin-3, and neurocan core protein, were not significantly altered. PRM-MS of protein panels is a valuable tool to evaluate biomarker candidates for neurodegenerative disorders.

ANALYSIS OF NEW POTENTIAL CSF BIOMARKERS FOR ALZHEIMER’S DISEASE BY PARALLEL REACTION MONITORING MASS SPECTROMETRY

Targeted mass spectrometric analysis has the advantage of high resolution and sensitivity without the use of antibodies, but requires instrumentation capable of parallel reaction monitoring (PRM). The aim for this study was the development of a parallel reaction monitoring mass spectrometry (PRM-MS) assay consisting of a panel of potential protein biomarkers in cerebrospinal fluid (CSF). The 13 proteins in the assay (Cystatin C, beta-2-microglobulin, neurosecretory protein VGF, Chromogranin A, Secretogranin-2, APP, Lysozyme c, Neurexin-1, -2, and -3, neuronal pentraxin-1, neurofascin, and neurocan CP) were selected based on their presumed role in processes regarding excretory vesicle function, synaptic function and innate immunity. 100-μL-CSF-samples were subjected to digestion with trypsin and 2–3 peptides of each protein were monitored. Stable-isotope-labeled peptides were used as standards in the quantification. The coefficient of variation was below 15% for the vast majority of peptides investigated. The assay was subjected to evaluation with a sample set of 10 patients with Alzheimer's disease (AD) and 15 healthy subjects. Secretogranin-2 (P<0.005) and neurosecretory protein VGF (P<0.001) levels were lowered in the AD patient group. For chromogranin A the levels of two peptides were significantly lowered in AD (P<0.01), while a third peptide was not. The synaptic proteins neurexin-1 and neuronal pentraxin-1, as well as neurofascin were also significantly lowered in AD but at a lower confidence level (P<0.05). PRM-MS seems to be a feasible technique to validate potential protein and peptide markers in CSF. The differentially expressed proteins must now be validated in a larger cohort of patients with AD and other neurodegenerative disorders, and healthy control subjects.

Targeted Metabolomic Analysis of Soluble Lysates from Platelets of Patients with Mild Cognitive Impairment and Alzheimer’s Disease Compared to Healthy Controls: Is PC aeC40:4 a Promising Diagnostic Tool?

Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the central nervous system. The use of biological fluids in AD diagnosis remains limited to the analysis of specific protein biomarkers in cerebrospinal fluid. However, metabolomic analysis has recently revealed several metabolites in plasma, especially phosphatidylcholines (PC), as putative biomarkers specific for AD. Following on previous reports of platelet abnormalities in AD, we hypothesized that platelets metabolites released in plasma may offer new biomarkers in AD. The aim of the present study was to apply targeted metabolomics to compare metabolites in soluble lysates of platelets from healthy controls (CO), patients with mild cognitive impairment (MCI), and patients with AD in a cohort of 90 subjects. We could target 163 metabolites and quantitative data were obtained for 91 metabolites. Among these, the lipid PC aeC40:4 significantly differentiated AD from CO (p = 0.0009), while four other lipids (PC aaC32:0, PC ae C32:2, PC aeC34:1, and SM(OH)C14:1) differentiated patients with MCI from CO. The combination of three phosphatidylcholines (PC aeC32:2, PC aeC34:1, PCaaC36:5), two lyso-phosphatidylcholines (lysoPC aC18:1, lysoPC aC16:0), and one sphingomyelin (SM(OH) C14:1) constructed a valuable prediction model using the C4.5 decision tree. The diagnosis accuracy for AD versus CO and MCI was 85%. In a blinded follow up conversion study (10 patients with a second blood collection after 9 months), we could verify the clinical diagnosis in 19 out of 20 cases. We propose that soluble platelet PCaeC40:4 is a promising marker to diagnose AD with a cut-off of <0.30μM and that platelets undergo metabolic processes during AD progression.