The analysis of protein dynamics or turnover in patients has the potential to reveal altered protein recycling, such as in Alzheimer\'s disease, and to provide informative data regarding drug efficacy or certain biological processes. The observed protein dynamics in a solid tissue or a fluid is the net result of not only protein synthesis and degradation but also transport across biological compartments. We report an accurate 3-biological compartment model able to simultaneously account for the protein dynamics observed in blood plasma and the cerebrospinal fluid (CSF) including a hidden central nervous system (CNS) compartment. We successfully applied this model to 69 proteins of a single individual displaying similar or very different dynamics in plasma and CSF. This study puts a strong emphasis on the methods and tools needed to develop this type of model. We believe that it will be useful to any researcher dealing with protein dynamics data modeling.

Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer\'s disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.

Intracranial infection is a common complication after neurosurgery and can increase the length of hospital stay, affect patient prognosis, and increase mortality. We aimed to investigate the value of the combined detection of cerebrospinal fluid (CSF) heparin-binding protein (HBP), interleukin-6 (IL-6), interleukin-10 (IL-10), and procalcitonin (PCT) for post-neurosurgical intracranial infection.
This study assessed the diagnostic values of CSF HBP, IL-6, IL-10, PCT levels, and combined assays for post-neurosurgical intracranial infection with the area under the receiver operating characteristic (ROC) curve by retrospectively analysing biomarkers of post-neurosurgical patients.
The CSF HBP, IL-6, IL-10, and PCT levels were significantly higher in the infected group than the uninfected group and the control group (P < 0.001). The indicators in the groups with severe intracranial infections were significantly higher than those in the groups with mild intracranial infections (P < 0.001), and the groups with poor prognoses had significantly higher indexes than the groups with good prognoses. According to the ROC curve display, the AUC values of CSF HBP, IL-6, IL-10, and PCT were 0.977 (95 % CI 0.952-1.000), 0.973 (95 % CI 0.949-0.998), 0.884 (95 % CI 0.823-0.946), and 0.819 (95 % CI 0.733-0.904), respectively. The AUC of the combined test was 0.996 (95 % CI 0.989-1.000), which was higher than those of the four indicators alone.
The combined detection can be an important indicator for the diagnosis and disease monitoring of post-neurosurgical intracranial infection.

This article presents a practical guide to mass spectrometry-based data-independent acquisition and label-free quantification for proteomics analysis applied to cerebrospinal fluid, offering a robust and scalable approach to probing the proteomic composition of the central nervous system. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Cerebrospinal fluid sample collection and preparation for mass spectrometry analysis Basic Protocol 2: Mass spectrometry sample analysis with data-independent acquisition Support Protocol: Data-dependent mass spectrometry and spectral library construction Basic Protocol 3: Analysis of mass spectrometry data.

Proteomic profiling is an effective way to identify biomarkers for Parkinson\'s disease (PD). Cerebrospinal fluid (CSF) has direct connectivity with the brain and could be a source of finding biomarkers and their clinical implications. Comparative proteomic profiling has shown that a group of differentially displayed proteins exist. The studies performed using conventional and classical tools also supported the occurrence of these proteins. Many studies have highlighted the potential of CSF proteomic profiling for biomarker identification and their clinical applications. Some of these proteins are useful for disease diagnosis and prediction. Proteomic profiling of CSF also has immense potential to distinguish PD from similar neurodegenerative disorders. A few protein biomarkers help in fundamental knowledge generation and clinical interpretation. However, the specific biomarker of PD is not yet known. The use of proteomic approaches in clinical settings is also rare. A large-scale, multi-centric, multi-population and multi-continental study using multiple proteomic tools is warranted. Such a study can provide valuable, comprehensive and reliable information for a better understanding of PD and the development of specific biomarkers. The current article sheds light on the role of CSF proteomic profiling in identifying biomarkers of PD and their clinical implications. The article also explains the achievements, obstacles and hopes for future directions of this approach.

Neuropsychiatric disorders present a global health challenge, necessitating an understanding of their molecular mechanisms for therapeutic development. Using Mendelian randomization (MR) analysis, this study explored associations between genetically predicted levels of 173 proteins in cerebrospinal fluid (CSF) and 25 in the brain with 14 neuropsychiatric disorders and risk factors. Follow-up analyses assessed consistency across plasma protein levels and gene expression in various brain regions. Proteins were instrumented using tissue-specific genetic variants, and colocalization analysis confirmed unbiased gene variants. Consistent MR and colocalization evidence revealed that lower cortical expression of low-density lipoprotein receptor-related protein 8, coupled higher abundance in the CSF and plasma, associated with lower fluid intelligence scores and decreased bipolar disorder risk. Additionally, elevated apolipoprotein-E2 and hepatocyte growth factor-like protein in the CSF and brain were related to reduced leisure screen time and lower odds of physical activity, respectively. Furthermore, elevated CSF soluble tyrosine-protein kinase receptor 1 level increased liability to attention deficit hyperactivity disorder and schizophrenia alongside lower fluid intelligence scores. This research provides genetic evidence supporting novel tissue-specific proteomic targets for neuropsychiatric disorders and their risk factors. Further exploration is necessary to understand the underlying biological mechanisms and assess their potential for therapeutic intervention.

Mutations in the GBA1 gene increase the risk of developing Parkinson\'s disease (PD). However, most carriers of GBA1 mutations do not develop PD throughout their lives. The mechanisms of how GBA1 mutations contribute to PD pathogenesis remain unclear. Cerebrospinal fluid (CSF) is used for detecting pathological conditions of diseases, providing insights into the molecular mechanisms underlying neurodegenerative disorders. In this study, we utilized the proximity extension assay to examine the levels of metabolism-linked protein in the CSF from 17 PD patients carrying GBA1 mutations (GBA1-PD) and 17 idiopathic PD (iPD). The analysis of CSF secretome in GBA1-PD identified 11 significantly altered proteins, namely FKBP4, THOP1, GLRX, TXNDC5, GAL, SEMA3F, CRKL, APLP1, LRP11, CD164, and NPTXR. To investigate GBA1-associated CSF changes attributed to specific neuronal subtypes responsible for PD, we analyzed the cell culture supernatant from GBA1-PD-induced pluripotent stem cell (iPSC)-derived midbrain dopaminergic (mDA) neurons. The secretome analysis of GBA1-PD iPSC-derived mDA neurons revealed that five differently regulated proteins overlapped with those identified in the CSF analysis: FKBP4, THOP1, GLRX, GAL, and CRKL. Reduced intracellular level of the top hit, FKPB4, was confirmed via Western Blot. In conclusion, our findings identify significantly altered CSF GBA1-PD-associated proteins with FKPB4 being firmly attributed to mDA neurons.

BACKGROUND: Although cerebrospinal fluid (CSF) amyloid-β42 peptide (Aβ42) and phosphorylated tau (p-tau) and blood p-tau are valuable for differential diagnosis of Alzheimer\'s disease (AD) from cognitively normal (CN) there is a lack of validated biomarkers for mild cognitive impairment (MCI).
OBJECTIVE: This study sought to determine how plasma and CSF protein markers compared in the characterization of MCI and AD status.
METHODS: This cohort study included Alzheimer\'s Disease Neuroimaging Initiative (ADNI) participants who had baseline levels of 75 proteins measured commonly in plasma and CSF (257 total, 46 CN, 143 MCI, and 68 AD). Logistic regression, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF) methods were used to identify the protein candidates for the disease classification.
RESULTS: We observed that six plasma proteins panel (APOE, AMBP, C3, IL16, IGFBP2, APOD) outperformed the seven CSF proteins panel (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) as well as AD markers (CSF p-tau and Aβ42) to distinguish the MCI from AD [area under the curve (AUC) = 0.75 (plasma proteins), AUC = 0.60 (CSF proteins) and AUC = 0.56 (CSF p-tau and Aβ42)]. Also, these six plasma proteins performed better than the CSF proteins and were in line with CSF p-tau and Aβ42 in differentiating CN versus MCI subjects [AUC = 0.89 (plasma proteins), AUC = 0.85 (CSF proteins) and AUC = 0.89 (CSF p-tau and Aβ42)]. These results were adjusted for age, sex, education, and APOEϵ4 genotype.
CONCLUSIONS: This study suggests that the combination of 6 plasma proteins can serve as an effective marker for differentiating MCI from AD and CN.

Chronic inflammatory demyelinating polyneuropathy (CIDP) is an uncommon immune-mediated neuropathy with an often unknown etiology. Patients typically present with gradual muscle weakness, sensory loss, and reduced deep tendon reflexes. Diagnostic challenges persist due to the absence of specific lab findings and definitive criteria. Treatment commonly involves glucocorticoids, IVIG, or plasma exchange, with varied long-term outcomes. We aim to elucidate the diagnostic complexities and treatment modalities associated with chronic CIDP through a comprehensive review of a patient\'s clinical presentation, diagnostic work-up, and therapeutic interventions. A 70-year-old female with a complex medical history, including dermatomyositis and IgG subclass deficiency, presented with progressive lower extremity weakness and numbness. Initial workup including MRI and CT scans were inconclusive. She was diagnosed with CIDP based on electromyography (EMG)/nerve conduction studies and cerebrospinal fluid (CSF) analysis. Plasma exchange (PLEX) treatment was initiated but led to multifocal cerebral infarcts, complicating her course. Subsequent rounds of PLEX alongside dual antiplatelet therapy showed no adverse neurological events and yielded minimal to moderate improvement in her mobility. The patient was discharged to an inpatient rehabilitation center for continued care. Elevated WBCs and other abnormal lab results were monitored throughout, underscoring the need for a multidisciplinary approach in complex cases like this one. Our comprehensive overview of CIDP and its diagnostic and treatment complexities underscores the challenges clinicians face in both accurate diagnosis and effective management. The multifaceted approach - spanning history-taking, electrodiagnostic studies, and advanced imaging - highlights the necessity for a nuanced, evidence-based practice. The variability in treatment outcomes emphasizes the need for personalized medicine and continuous research to optimize therapeutic strategies. Given the inconclusive nature of some diagnostic tools and the variable treatment responses, there remains a clear need for ongoing study and long-term follow-up to further refine our understanding and management of CIDP.

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