Clinical neuroproteomics and biomarkers: from basic research to clinical decision making

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

Proteomics reveals protein phosphatase 1γ as a biomarker associated with Hippo signal pathway in glioma

Hub proteins related with Hippo signal pathway in glioma were investigated using proteomics methods (Tandem Mass Tag, TMT) to determine the differentially expressed proteins in glioblastoma (GBM). Ingenuity Pathway Analysis (IPA) was performed to complement proteomic findings by identifying the top canonical pathways as well as to suggest novel proteins for the targeted therapy of glioma. A total of 222 formalin-fixed paraffin-embedded (FFPE) glioma tissue samples were used to verify the expression of protein phosphatase 1γ (PP1γ), Yes-associated protein 1 (YAP1), and SOX2 via immunohistochemistry. Bioinformatics analysis revealed these proteins as crucial in the Hippo signaling pathway in GBM. Spearman correlation was performed to analyze the relationship of these three proteins, and survival analysis was conducted to investigate their effects on prognosis. Among the 5808 proteins identified by TMT with the standard of P-value < 0.05 and fold change (FC) of>1.2 or <0.83, 1398 upregulated and 1060 downregulated differentially expressed proteins were found. IPA revealed that the Hippo signaling was activated in the top 10 canonical pathways, and PP1γ was activated in the Hippo signaling. Immunohistochemistry analysis indicated that PP1γ, YAP1, and SOX2 were highly and positively expressed in glioma. PP1γ expression was related to WHO grade (p = 0.003) and ki-67 expression (p = 0.012). Low PP1γ expression was associated with IDH1-mut in low-grade glioma (LGG; WHO grades II and III) (p = 0.037). PP1γ was positively correlated with YAP1 (p < 0.001; r = 0.259) and SOX2 (p = 0.009; r = 0.175). In survival analysis, age, WHO grade, ki-67 expression, and PP1γ expression independently predicted a short OS in total cohort (p < 0.05). Therefore, PP1γ is a hub protein associated with Hippo signal pathway in glioma, and its expression indicates poor prognosis in patients with glioma. Therefore, PP1γ may be a promising prognostic biomarker and a therapeutic target in glioma.

Proteomic Advances in Glial Tumors through Mass Spectrometry Approaches

Being the fourth leading cause of cancer-related death, glial tumors are highly diverse tumor entities characterized by important heterogeneity regarding tumor malignancy and prognosis. However, despite the identification of important alterations in the genome of the glial tumors, there remains a gap in understanding the mechanisms involved in glioma malignancy. Previous research focused on decoding the genomic alterations in these tumors, but due to intricate cellular mechanisms, the genomic findings do not correlate with the functional proteins expressed at the cellular level. The development of mass spectrometry (MS) based proteomics allowed researchers to study proteins expressed at the cellular level or in serum that may provide new insights on the proteins involved in the proliferation, invasiveness, metastasis and resistance to therapy in glial tumors. The integration of data provided by genomic and proteomic approaches into clinical practice could allow for the identification of new predictive, diagnostic and prognostic biomarkers that will improve the clinical management of patients with glial tumors. This paper aims to provide an updated review of the recent proteomic findings, possible clinical applications, and future research perspectives in diffuse astrocytic and oligodendroglial tumors, pilocytic astrocytomas, and ependymomas.

The Human Brain Proteome Project: Biological and Technological Challenges

Brain proteomics has become a method of choice that allows zooming-in where neuropathophysiological alterations are taking place, detecting protein mediators that might eventually be measured in cerebrospinal fluid (CSF) as potential neuropathologically derived biomarkers. Following this hypothesis, mass spectrometry-based neuroproteomics has emerged as a powerful approach to profile neural proteomes derived from brain structures and CSF in order to map the extensive protein catalog of the human brain. This chapter provides a historical perspective on the Human Brain Proteome Project (HBPP), some recommendation to the experimental design in neuroproteomic projects, and a brief description of relevant technological and computational innovations that are emerging in the neurobiology field thanks to the proteomics community. Importantly, this chapter highlights recent discoveries from the biology- and disease-oriented branch of the HBPP (B/D-HBPP) focused on spatiotemporal proteomic characterizations of mouse models of neurodegenerative diseases, elucidation of proteostatic networks in different types of dementia, the characterization of unresolved clinical phenotypes, and the discovery of novel biomarker candidates in CSF.

SWATH Mass Spectrometry Applied to Cerebrospinal Fluid Differential Proteomics: Establishment of a Sample-Specific Method

Mass spectrometry (MS) has become the gold standard method for proteomics by allowing the simultaneous identification and/or quantification of thousands of proteins of a given sample. Over time, mass spectrometry has evolved into newer quantitative approaches with increased sensitivity and accuracy, such as the sequential windows acquisition of all theoretical fragment-ion spectra (SWATH)-MS approach. Moreover, in the past few years, some improvements were made in the SWATH-acquisition algorithm, allowing the design of sample-customized acquisition methods by adjusting the Q1 windows' width in order to reduce it in the most populated m/z regions. This customization results in an increase in the specificity and a reduction in the interferences, ultimately leading to an improvement in the amount of quantitative data extracted to eventually increase the proteome coverage. These improvements are especially relevant for clinical neuroproteomics, which is mainly based on the analysis of circulatory biofluids, in particular the cerebrospinal fluid (CSF) due to its close connection with the brain.In the present chapter, a detailed description of the methodologies necessary to perform a whole-proteome relative quantification of CSF samples by SWATH-MS is presented, starting with the isolation of the protein fraction, its preparation for MS analysis, with all the necessary information for the design of a SWATH-MS method specific for each sample batch, and finally providing different methodologies for the analysis of the quantitative data obtained.

Glial Fibrillary Acidic Protein and Ubiquitin C-Terminal Hydrolase-L1 as Outcome Predictors in Traumatic Brain Injury

OBJECTIVE

Biomarkers ubiquitin C-terminal hydrolase-L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) may help detect brain injury, assess its severity, and improve outcome prediction. This study aimed to evaluate the prognostic value of these biomarkers during the first days after brain injury.

METHODS

Serum UCH-L1 and GFAP were measured in 324 patients with traumatic brain injury (TBI) enrolled in a prospective study. The outcome was assessed using the Glasgow Outcome Scale (GOS) or the extended version, Glasgow Outcome Scale-Extended (GOSE).

RESULTS

Patients with full recovery had lower UCH-L1 concentrations on the second day and patients with favorable outcome had lower UCH-L1 concentrations during the first 2 days compared with patients with incomplete recovery and unfavorable outcome. Patients with full recovery and favorable outcome had significantly lower GFAP concentrations in the first 2 days than patients with incomplete recovery or unfavorable outcome. There was a strong negative correlation between outcome and UCH-L1 in the first 3 days and GFAP levels in the first 2 days. On arrival, both UCH-L1 and GFAP distinguished patients with GOS score 1-3 from patients with GOS score 4-5, but not patients with GOSE score 8 from patients with GOSE score 1-7. For UCH-L1 and GFAP to predict unfavorable outcome (GOS score ≤ 3), the area under the receiver operating characteristic curve was 0.727, and 0.723, respectively. Neither UCHL-1 nor GFAP was independently able to predict the outcome when age, worst Glasgow Coma Scale score, pupil reactivity, Injury Severity Score, and Marshall score were added into the multivariate logistic regression model.

CONCLUSIONS

GFAP and UCH-L1 are significantly associated with outcome, but they do not add predictive power to commonly used prognostic variables in a population of patients with TBI of varying severities.

Development and validation of a microfluidic immunoassay capable of multiplexing parallel samples in microliter volumes

Immunoassays are widely utilized due to their ability to quantify a vast assortment of biomolecules relevant to biological research and clinical diagnostics. Recently, immunoassay capabilities have been improved by the development of multiplex assays that simultaneously measure multiple analytes in a single sample. However, these assays are hindered by high costs of reagents and relatively large sample requirements. For example, in vitro screening systems currently dedicate individual wells to each time point of interest and this limitation is amplified in screening studies when the investigation of many experimental conditions is necessary; resulting in large volumes for analysis, a correspondingly high cost and a limited temporal experimental design. Microfluidics based immunoassays have been developed in order to overcome these drawbacks. Together, previous studies have demonstrated on-chip assays with either a large dynamic range, high performance sensitivity, and/or the ability to process samples in parallel on a single chip. In this report, we develop a multiplex immunoassay possessing all of these parallel characteristics using commercially available reagents, which allows the analytes of interest to be easily changed. The device presented can measure 6 proteins in 32 samples simultaneously using only 4.2 μL of sample volume. High quality standard curves are generated for all 6 analytes included in the analysis, and spiked samples are quantified throughout the working range of the assay. In addition, we demonstrate a strong correlation (R(2) = 0.8999) between in vitro supernatant measurements using our device and those obtained from a bench-top multiplex immunoassay. Finally, we describe cytokine secretion in an in vitro inflammatory hippocampus culture system, establishing proof-of-concept of the ability to use this platform as an in vitro screening tool. The low-volume, multiplexing abilities of the microdevice described in this report could be broadly applied to numerous situations where sample volumes and costs are limiting.

Integrative biomarker discovery in neurodegenerative diseases

Data mining has been widely applied in biomarker discovery resulting in significant findings of different clinical and biological biomarkers. With developments in technology, from genomics to proteomics analysis, a deluge of data has become available, as well as standardized data repositories. Nonetheless, researchers are still facing important challenges in analyzing the data, especially when considering the complexity of pathways involved in biological processes and diseases. Data from single sources appear unable to explain complex processes, such as those involved in brain-related disorders, including Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis, thus raising the need for a more comprehensive perspective. A possible solution relies on data and model integration, where several data types are combined to provide complementary views. This in turn can result in the discovery of previously unknown biomarkers by unraveling otherwise hidden relationships between data from different sources, and/or validate such composite biomarkers in more powerful predictive models.

Proteomics analysis of human brain glial cell proteome by 2D gel

INTRODUCTION

Proteomics is increasingly employed in both neurological and oncological research, and applied widely in every area of neuroscience research including brain cancer. Astrocytomas are the most common glioma and can occur in most parts of the brain and occasionally in the spinal cord. Patients with high-grade astrocytomas have a life expectancy of <1 year even after surgery, chemotherapy, and radiotherapy.

MATERIALS AND METHODS

We extracted proteins from tumors and normal brain tissues and then evaluated the protein purity by Bradford test and spectrophotometry method. In this study, we separated proteins by the two-dimensional (2DG) gel electrophoresis method, and the spots were analyzed and compared using statistical data.

RESULTS

On each analytical 2D gel, an average of 800 spots was observed. In this study, 164 spots exhibited up-regulation of expression level, whereas the remaining 179 spots decreased in astrocytoma tumor relative to normal tissue. RESULTS demonstrate that functional clustering and principal component analysis (PCA) has considerable merits in aiding the interpretation of proteomic data. Proteomics is a powerful tool in identifying multiple proteins that are altered following a neuropharmacological intervention in a disease of the central nervous system (CNS).

CONCLUSION

2-D gel and cluster analysis have important roles in the diagnostic management of astrocytoma patients, providing insight into tumor biology. The application of proteomics to CNS research has invariably been very successful in yielding large amounts of data.

Development of a low-volume, highly sensitive microimmunoassay using computational fluid dynamics-driven multiobjective optimization

Immunoassays are one of the most versatile and widely performed biochemical assays and, given their selectivity and specificity, are used in both clinical and research settings. However, the high cost of reagents and relatively large sample volumes constrain the integration of immunoassays into many applications. Scaling the assay down within microfluidic devices can alleviate issues associated with reagent and sample consumption. However, in many cases a new device is designed and empirically optimized for each specific analyte, a costly and time consuming approach. In this paper, we report the development of a microfluidic bead-based immunoassay which, using antibody coated microbeads, can potentially detect any analyte or combination of analytes for which antibody coated microbeads can be generated. We also developed a computational reaction model and optimization algorithm that can be used to optimize the device for any analyte. We applied this technique to develop a low volume IL-6 immunoassay with high sensitivity (358 fM, 10 pg/mL) and a large dynamic range (4 orders of magnitude). This device design and optimization technique can be used to design assays for any protein with an available antibody and can be used with a large number of applications including biomarker discovery, temporal in vitro studies using a reduced number of cells and reagents, and analysis of scarce biological samples in animal studies and clinical research settings.

The role of protein and peptide separation before mass spectrometry analysis in clinical proteomics

The purpose of clinical proteomics is to characterise protein profiles of a plethora of diseases with the aim of finding specific biomarkers. These are particularly valuable for early diagnosis, and represent key molecules suitable to elucidate pathogenic mechanisms. Samples deriving from patients (i.e. blood, urine, cerebrospinal fluid, biopsies) are the sources for clinical proteomics. Due to the complexity of the extracted samples their direct analysis is unachievable. Any analytical clinical proteomics study should start with the choice of the optimal combination of strategies with respect to both sample preparations and MS approaches. Protein or peptide fractionation (off-line or on-line) is essential to reduce complexity of biological samples and to achieve the most complete and reproducible analysis. The aim of this review is to introduce the readers to a functional range of strategies to help scientists in their proteomics set up. In particular, the separation approaches of proteins or peptides (both gel-based and gel-free) are reviewed with special attention paid to their advantages and limitations, and to the different liquid chromatography techniques used to peptide fractionation after protein enzymatic digestion and before their detection. Finally, the role of mass spectrometry (MS) for protein identification and quantification is discussed including emerging MS data acquisition strategies.

Senescence-associated-gene signature identifies genes linked to age, prognosis, and progression of human gliomas

BACKGROUND

Senescence-associated genes (SAGs) are responsible for the senescence-associated secretory phenotype, linked in turn to cellular aging, the aging brain, and the pathogenesis of cancer.

OBJECTIVE

We hypothesized that senescence-associated genes are overexpressed in older patients, in higher grades of glioma, and portend a poor prognosis.

METHODS

Forty-seven gliomas were arrayed on a custom version of the Affymetrix HG-U133+2.0 GeneChip, for expression of fourteen senescence-associated genes: CCL2, CCL7, CDKN1A, COPG, CSF2RB, CXCL1, ICAM-1, IGFBP-3, IL-6, IL-8, SAA4, TNFRSF-11B, TNFSF-11 and TP53. A combined "senescence score" was generated using principal component analysis to measure the combined effect of the senescence-associated gene signature.

RESULTS

An elevated senescence score correlated with older age (r=0.37; P=.01) as well as a higher degree of malignancy, as determined by WHO, histological grade (r=0.49; P<.001). There was a mild association with poor prognosis (P=.06). Gliosarcomas showed the highest scores. Six genes independently correlated with either age (IL-6, TNFRSF-11B, IGFBP-3, SAA4, and COPG), prognosis (IL-6, SAA4), or the grade of the glioma (IL-6, IL-8, ICAM-1, IGFBP-3, and COPG).

CONCLUSION

We report: 1) a novel molecular signature in human gliomas, based on cellular senescence, translating the concept of SAG to human cancer; 2) the senescence signature is composed of genes central to the pathogenesis of gliomas, defining a novel, aggressive subtype of glioma; and 3) these genes provide prognostic biomarkers, as well as targets, for drug discovery and immunotherapy.

Integration of proteomics, bioinformatics, and systems biology in traumatic brain injury biomarker discovery

Traumatic brain injury (TBI) is a major medical crisis without any FDA-approved pharmacological therapies that have been demonstrated to improve functional outcomes. It has been argued that discovery of disease-relevant biomarkers might help to guide successful clinical trials for TBI. Major advances in mass spectrometry (MS) have revolutionized the field of proteomic biomarker discovery and facilitated the identification of several candidate markers that are being further evaluated for their efficacy as TBI biomarkers. However, several hurdles have to be overcome even during the discovery phase which is only the first step in the long process of biomarker development. The high-throughput nature of MS-based proteomic experiments generates a massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. A strategy that has promise in advancing biomarker development involves the triad of proteomics, bioinformatics, and systems biology. In this review, a brief overview of how bioinformatics and systems biology tools analyze, transform, and interpret complex MS datasets into biologically relevant results is discussed. In addition, challenges and limitations of proteomics, bioinformatics, and systems biology in TBI biomarker discovery are presented. A brief survey of researches that utilized these three overlapping disciplines in TBI biomarker discovery is also presented. Finally, examples of TBI biomarkers and their applications are discussed.