CSF biomarker discovery using label-free nano-LC-MS based proteomic profiling: technical aspects

Proteomic Biomarker Identification in Cerebrospinal Fluid for Leptomeningeal Metastases with Neurological Complications

Leptomeningeal metastases (LM) from solid tumours, lymphoma and leukaemia are characterized by multifocal neurological deficits with a high mortality rate. Early diagnosis and initiation of treatment are essential to kerb neurological deterioration. However, this is not always possible as 25% of cerebrospinal fluid samples produce false-negative results at first cytological examination. The identification of biomarkers that allow stratification of individuals according to risk for developing LM would be a major benefit. Proteomic-based approaches are now in increasing use for this purpose, and these are reviewed in this chapter with a focus on cerebrospinal fluid (CSF) analyses. The construction of a CSF proteome disease database would also facilitate analysis of other neurological disorders.

Comparative Proteomics for the Evaluation of Protein Expression and Modifications in Neurodegenerative Diseases

Together with hypothesis-driven approaches, high-throughput differential proteomic analysis performed primarily not only in human cerebrospinal fluid and serum but also on protein content of other tissues (blood cells, muscles, peripheral nerves, etc.) has been used in the last years to investigate neurodegenerative diseases. Even if the goal for these analyses was mainly the discovery of neurodegenerative disorders biomarkers, the characterization of specific posttranslational modifications (PTMs) and the differential protein expression resulted in being very informative to better define the pathological mechanisms. In this chapter are presented and discussed the positive aspects and challenges of the outcomes of some of our investigations on neurological and neurodegenerative disease, in order to highlight the important role of protein PTMs studies in proteomics-based approaches.

[Proteomics in infectious diseases]

Infectious diseases have a high incidence in the population, causing a major impact on global health. In vitro culture of microorganisms is the first technique applied for infection diagnosis which is laborious and time consuming. In recent decades, efforts have been focused on the applicability of "Omics" sciences, highlighting the progress provided by proteomic techniques in the field of infectious diseases. This review describes the management, processing and analysis of biological samples for proteomic research.

Classical MALDI-MS versus CE-based ESI-MS proteomic profiling in urine for clinical applications

Human urine is an attractive and informative biofluid for medical diagnosis, which has been shown to reflect the (patho)-physiology of not only the urogenital system, but also others such as the cardiovascular system. For this reason, many studies have concentrated on the study of the urine proteome, aiming to find relevant biomarkers that could be applied in a clinical setting. However, this goal can only be achieved after reliable quantitative and qualitative analysis of the urinary proteome. In the last two decades, MS-based platforms have evolved to become indispensable tools for biomarker research. In this review, we will present and compare two of the most clinically relevant analytical platforms that have been used for the study of the urinary proteome, namely CE-based ESI-MS and classical MALDI-MS. These platforms, although not directly comparable, have been extensively used in proteomic profiling and therefore their comparison is fundamentally relevant to this field.

A targeted in vivo SILAC approach for quantification of drug metabolism enzymes: regulation by the constitutive androstane receptor

The modulation of drug metabolism enzyme (DME) expression by therapeutic agents is a central mechanism of drug-drug interaction and should be assessed as early as possible in preclinical drug development. Direct measurement of DME levels is typically achieved by Western blotting, qPCR, or microarray, but these techniques have their limitations; antibody cross-reactivity among highly homologous subfamilies creates ambiguity, while discordance between mRNA and protein expression undermines observations. The aim of this study was to design a simple targeted workflow by combining in vivo SILAC and label-free proteomics approaches for quantification of DMEs in mouse liver, facilitating a rapid and comprehensive evaluation of metabolic potential at the protein level. A total of 197 peptides, representing 51 Phase I and Phase II DMEs, were quantified by LC-MS/MS using targeted high resolution single ion monitoring (tHR/SIM) with a defined mass-to-charge and retention time window for each peptide. In a constitutive androstane receptor (Car) activated mouse model, comparison of tHR/SIM-in vivo SILAC with Western blotting for analysis of the expression of cytochromes P450 was favorable, with agreement in fold-change values between methods. The tHR/SIM-in vivo SILAC approach therefore permits the robust analysis of multiple DME in a single protein sample, with clear utility for the assessment of the drug-drug interaction potential of candidate therapeutic compounds.

A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS proteomics experiments

A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.

General overview: biomarkers in neuroscience research

Biomarkers are in demand for disease diagnosis, treatment response monitoring, and development of novel therapeutics. Biomarker discovery in neuroscience is challenging due to absence of robust molecular correlates and the interpatient heterogeneity that characterizes neuropsychiatric disorders. Because of the complexity of these disorders, a panel of biomarkers derived from different platforms will be required to precisely reflect disease-related alterations. Animal models of psychiatric phenotypes as well as -omics and imaging methodologies are important tools for biomarker discovery. However, the limitations of current research concerning sample handling and collection, candidate biomarker validation, and a lack of interdisciplinary approaches need to be addressed. Ultimately, the coordinated effort of relevant stakeholders including researchers, physicians, and funding organizations together with standardization initiatives will be vital to overcome the present challenges and to advance personalized health care using sensitive and specific biomarkers.

Mass spectrometry-based label-free quantitative proteomics

In order to study the differential protein expression in complex biological samples, strategies for rapid, highly reproducible and accurate quantification are necessary. Isotope labeling and fluorescent labeling techniques have been widely used in quantitative proteomics research. However, researchers are increasingly turning to label-free shotgun proteomics techniques for faster, cleaner, and simpler results. Mass spectrometry-based label-free quantitative proteomics falls into two general categories. In the first are the measurements of changes in chromatographic ion intensity such as peptide peak areas or peak heights. The second is based on the spectral counting of identified proteins. In this paper, we will discuss the technologies of these label-free quantitative methods, statistics, available computational software, and their applications in complex proteomics studies.

Schizophrenia genomics and proteomics: are we any closer to biomarker discovery?

The field of proteomics has made leaps and bounds in the last 10 years particularly in the fields of oncology and cardiovascular medicine. In comparison, neuroproteomics is still playing catch up mainly due to the relative complexity of neurological disorders. Schizophrenia is one such disorder, believed to be the results of multiple factors both genetic and environmental. Affecting over 2 million people in the US alone, it has become a major clinical and public health concern worldwide. This paper gives an update of schizophrenia biomarker research as reviewed by Lakhan in 2006 and gives us a rundown of the progress made during the last two years. Several studies demonstrate the potential of cerebrospinal fluid as a source of neuro-specific biomarkers. Genetic association studies are making headway in identifying candidate genes for schizophrenia. In addition, metabonomics, bioinformatics, and neuroimaging techniques are aiming to complete the picture by filling in knowledge gaps. International cooperation in the form of genomics and protein databases and brain banks is facilitating research efforts. While none of the recent developments described here in qualifies as biomarker discovery, many are likely to be stepping stones towards that goal.

Shotgun proteomic analysis of cerebrospinal fluid using off-gel electrophoresis as the first-dimension separation

Shotgun proteomic analysis usually employs multidimensional separations with the first dimension most commonly being strong cation exchange (SCX) liquid chromatography (LC). SCX-LC is necessarily a serial process for preparation of multiple samples. Here, we apply a newly available tool, off-gel electrophoresis (OGE), for first-dimension separation of peptide mixtures from digests of cerebrospinal fluid (CSF), a complex and low total protein-containing sample. OGE first-dimension fractionation enabled identification of a total of 156 unique proteins compared to 115 identified in previous work using first-dimension SCX fractionation. OGE can be used to process multiple samples unattended with easy retrieval of the separated fractions. Thus, shotgun analysis using OGE as the first-dimension separation offers a significant advantage both in terms of sample throughput as well as increased numbers of identified proteins.

Psychiatric research: psychoproteomics, degradomics and systems biology

While proteomics has excelled in several disciplines in biology (cancer, injury and aging), neuroscience and psychiatryproteomic studies are still in their infancy. Several proteomic studies have been conducted in different areas of psychiatric disorders, including drug abuse (morphine, alcohol and methamphetamine) and other psychiatric disorders (depression, schizophrenia and psychosis). However, the exact cellular and molecular mechanisms underlying these conditions have not been fully investigated. Thus, one of the primary objectives of this review is to discuss psychoproteomic application in the area of psychiatric disorders, with special focus on substance- and drug-abuse research. In addition, we illustrate the potential role of degradomic utility in the area of psychiatric research and its application in establishing and identifying biomarkers relevant to neurotoxicity as a consequence of drug abuse. Finally, we will discuss the emerging role of systems biology and its current use in the field of neuroscience and its integral role in establishing a comprehensive understanding of specific brain disorders and brain function in general.

Cerebrospinal fluid: identification of diagnostic markers for schizophrenia

Schizophrenia is a complex neuropsychiatric disease but, despite extensive research efforts over the last 100 years, the etiology of this disorder remains elusive. Diagnosis is still based on a subjective, interview-based process, which may not align with the biological underpinnings of the symptoms. This old-fashioned descriptive approach contributes to the low treatment success and impedes early intervention, which is thought to be crucial for successful therapy. Therefore, there is an urgent need to discover biochemical analytes that facilitate an objective and reliable diagnosis. Disease markers might also have utility for tracking treatment success and compliance, as well as the discovery of novel drug targets. For schizophrenia and psychiatric disorders at large, analyzing cerebrospinal fluid (CSF) is an intuitive choice due to its close proximity to the brain and its clinical accessibility in the living patient. Although numerous studies have aimed to identify potential diagnostic markers in the CSF of schizophrenia patients, as yet not one has found its way to clinical application. Here, we review molecular alterations of proteins and metabolites that have been identified in schizophrenia CSF and discuss their potential applicability as diagnostic markers.