Towards a high resolution separation of human cerebrospinal fluid

Human cerebrospinal fluid is an ultrafiltrate of plasma that is largely produced by the choroid plexus. It consists of a mixture of anorganic salts, various sugars, lipids and proteins from the surrounding brain tissues. The predominant proteins in cerebrospinal fluid are isoforms of serum albumin, transferrin and immunoglobulins, representing more than 70% of the total protein amount. A rough overview of the protein compounds of human cerebrospinal fluid including their respective concentrations is given by Blennow et al. [Eur. Neurol. 33 (1993) 129]. In contrast, the aim of this work is to display the detailed protein composition of CSF by two-dimensional gel electrophoresis and to identify both high and low concentrated proteins using different mass spectrometry techniques. This extensive overview of proteins in human cerebrospinal fluid will be highly relevant for clinical research. Furthermore, the comparison of 2D gels will help to analyze the standard protein variability in CSF of healthy persons and detect specific protein variations of patients with various neurological diseases (e.g., Alzheimer's disease, Huntington's chorea). Sample preparation for two-dimensional gel electrophoresis must include concentration and desalting steps such as precipitation and ultrafiltration due to the high amount of salts, sugars and lipids and the low total amount of protein of 0.3-0.7 microg/microl present in human CSF. Up to now we were able to identify more than 480 spots from suchlike generated 2D gels using MALDI- and ESI-mass spectrometry.     

Characterization of tau in cerebrospinal fluid using mass spectrometry

The neurodegenerative disorder Alzheimer's disease (AD) is the most common cause of dementia in the elderly. The presence of neurofibrillary tangles, consisting of hyperphosphorylated tau protein, is one of the major neuropathologic characteristics of the disease, making this protein an attractive biomarker for AD and a possible target for therapy. Here, we describe an optimized immunoprecipitation mass spectrometry method that enables, for the first time, detailed characterization of tau in human cerebrospinal fluid. The identities of putative tau fragments were confirmed using nanoflow liquid chromatography and tandem mass spectrometry. Nineteen tryptic fragments of tau were detected, of which 16 are found in all tau isoforms while 3 represented unique tau isoforms. These results pave the way for clinical CSF studies on the tauopathies.     

Identification of neuropeptide FF-related peptides in human cerebrospinal fluid by mass spectrometry

Several neuropeptide FF (NPFF)-related peptides, known as modulators of the opioid system, have been previously characterized in bovine and rodent brain. Reverse-phase high pressure liquid chromatography (HPLC) fractions of a human with normal pressure hydrocephalus cerebrospinal fluid (CSF), co-migrating with NPFF-related synthetic peptides, were characterized by capillary HPLC coupled on-line to nanospray ion trap tandem mass spectrometry. Two peptides present in the pro-NPFF(A) precursor, NPAF (AGEGLNSQFWSLAAPQRF-NH2) and NPSF (SLAAPQRF-NH2), were identified. The monitoring of NPFF-related peptides in human CSF can be helpful to understand their roles in pain sensitivity.     

Peptide profiling of cerebrospinal fluid by mass spectrometry

The search for biomarkers is driven by the increasing clinical importance of early diagnosis. Reliable biomarkers can also assist in directing therapy, monitoring disease activity and the efficacy of treatment. In addition, the discovery of novel biomarkers might provide clues to the pathogenesis of a disease. The dynamic range of protein concentrations in body fluids exceeds 10 orders of magnitude. These huge differences in concentrations complicate the detection of proteins with low expression levels. Since all classical biomarkers have low expression levels (e.g., prostate-specific antigen: 2–4 µg/l; and CA125: 20–35 U/ml), new developments with respect to identification and validation techniques of the low-abundance proteins are required. This review will discuss the current status of profiling cerebrospinal fluid using mass spectrometry-based techniques, and new developments in this area.     

Peptide profiling of cerebrospinal fluid by mass spectrometry

The search for biomarkers is driven by the increasing clinical importance of early diagnosis. Reliable biomarkers can also assist in directing therapy, monitoring disease activity and the efficacy of treatment. In addition, the discovery of novel biomarkers might provide clues to the pathogenesis of a disease. The dynamic range of protein concentrations in body fluids exceeds 10 orders of magnitude. These huge differences in concentrations complicate the detection of proteins with low expression levels. Since all classical biomarkers have low expression levels (e.g., prostate-specific antigen: 2-4 microg/l; and CA125: 20-35 U/ml), new developments with respect to identification and validation techniques of the low-abundance proteins are required. This review will discuss the current status of profiling cerebrospinal fluid using mass spectrometry-based techniques, and new developments in this area.     

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Background  

Data generated from liquid chromatography coupled to high-resolution mass spectrometry (LC-MS)-based studies of a biological sample can contain large amounts of biologically significant information in the form of proteins, peptides, and metabolites. Interpreting this data involves inferring the masses and abundances of biomolecules injected into the instrument. Because of the inherent complexity of mass spectral patterns produced by these biomolecules, the analysis is significantly enhanced by using visualization capabilities to inspect and confirm results. In this paper we describe Decon2LS, an open-source software package for automated processing and visualization of high-resolution MS data. Drawing extensively on algorithms developed over the last ten years for ICR2LS, Decon2LS packages the algorithms as a rich set of modular, reusable processing classes for performing diverse functions such as reading raw data, routine peak finding, theoretical isotope distribution modelling, and deisotoping. Because the source code is openly available, these functionalities can now be used to build derivative applications in relatively fast manner. In addition, Decon2LS provides an extensive set of visualization tools, such as high performance chart controls.     

Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

Background

The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.

Results

In this work, new approaches to automatic classification of metabolomic data produced from sera of ovarian cancer patients and benign controls are investigated. The performance of support vector machines (SVM) for the classification of liquid chromatography/time-of-flight mass spectrometry (LC/TOF MS) metabolomic data focusing on recognizing combinations or "panels" of potential metabolic diagnostic biomarkers was evaluated. Utilizing LC/TOF MS, sera from 37 ovarian cancer patients and 35 benign controls were studied. Optimum panels of spectral features observed in positive or/and negative ion mode electrospray (ESI) MS with the ability to distinguish between control and ovarian cancer samples were selected using state-of-the-art feature selection methods such as recursive feature elimination and L1-norm SVM.

Conclusion

Three evaluation processes (leave-one-out-cross-validation, 12-fold-cross-validation, 52-20-split-validation) were used to examine the SVM models based on the selected panels in terms of their ability for differentiating control vs. disease serum samples. The statistical significance for these feature selection results were comprehensively investigated. Classification of the serum sample test set was over 90% accurate indicating promise that the above approach may lead to the development of an accurate and reliable metabolomic-based approach for detecting ovarian cancer.     

Multiscale processing of mass spectrometry data

This work addresses the problem of extracting signal content from protein mass spectrometry data. A multiscale decomposition of these spectra is used to focus on local scale-based structure by defining scale-specific features. Quantification of features is accompanied by an efficient method for calculating the location of features which avoids estimation of signal-to-noise ratios or bandwidths. Scale-based histograms serve as spectral-density-like functions indicating the regions of high density of features in the data. These regions provide bins within which features are quantified and compared across samples. As a preliminary step, the locations of prominent features within coarse-scale bins may be used for a crude registration of spectra. The multiscale decomposition, the scale-based feature definition, the calculation of feature locations, and subsequent quantification of features are carried out by way of a translation-invariant wavelet analysis.     

Porous graphitic carbon chromatography-tandem mass spectrometry for the study of isoprostanes in human cerebrospinal fluid

F2-isoprostanes are produced by the non-enzymatic peroxidation of arachidonic acid in membrane phospholipids. This paper describes a new method for the determination of all four classes of F2-isoprostanes in human cerebrospinal fluid (CSF) involving separation on a 1 mm x 150 mm porous graphitic carbon (PGC) column and detection by triple quadrupole mass spectrometry in negative-ion electrospray mode. The sample pre-treatment consisted of an ultrafiltration step, following which 300 microl of CSF sample could be injected directly onto a 1 mm x 10 mm PGC guard column functioning as a trap for the analytes. The loading solvent was Milli-Q water at 125 microl/min. After 3 min, the sample was switched into the separation column. The F2-isoprostanes were separated in 20 min using a linear solvent gradient comprising water, methanol, acetonitrile and ammonium hydroxide at a pH of 9.5 and a flow of 50 microl/min The limit of detection (calculated as 3S/N) was approximately 40 pM (14 pg/ml). The assay was linear within the examined range (18-450 pg/ml), using CSF spiked with iPF2alpha-III standard (r(2)>0.995). Repeatability data were calculated for CSF spiked to 90 pg/ml and the relative standard deviation (RSD) obtained was 3% (n=6).     

Quantitative analysis of amyloid-beta peptides in cerebrospinal fluid using immunoprecipitation and MALDI-Tof mass spectrometry.

Immunoprecipitation (IP) combined with matrix-assisted laser desorption ionization (MALDI) time of flight (Tof) mass spectrometry has been used to develop quantitative assays for amyloid-beta (Abeta) peptides in cerebrospinal fluid (CSF). Inclusion of (15)N labelled standard peptides allows for absolute quantification of multiple Abeta isoforms in individual samples. Characterization of variability associated with all steps of the assay indicated that the IP step is the single largest contributor to overall variability. Optimization of the assay resulted in overall coefficient of variation 相似文献   

Proteogenomic analysis of pathogenic yeast Cryptococcus neoformans using high resolution mass spectrometry

Background

Cryptococcus neoformans, a basidiomycetous fungus of universal occurrence, is a significant opportunistic human pathogen causing meningitis. Owing to an increase in the number of immunosuppressed individuals along with emergence of drug-resistant strains, C. neoformans is gaining importance as a pathogen. Although, whole genome sequencing of three varieties of C. neoformans has been completed recently, no global proteomic studies have yet been reported.

Results

We performed a comprehensive proteomic analysis of C. neoformans var. grubii (Serotype A), which is the most virulent variety, in order to provide protein-level evidence for computationally predicted gene models and to refine the existing annotations. We confirmed the protein-coding potential of 3,674 genes from a total of 6,980 predicted protein-coding genes. We also identified 4 novel genes and corrected 104 predicted gene models. In addition, our studies led to the correction of translational start site, splice junctions and reading frame used for translation in a number of proteins. Finally, we validated a subset of our novel findings by RT-PCR and sequencing.

Conclusions

Proteogenomic investigation described here facilitated the validation and refinement of computationally derived gene models in the intron-rich genome of C. neoformans, an important fungal pathogen in humans.     

Miniaturized proteomics and peptidomics using capillary liquid separation and high resolution mass spectrometry

Knowledge of the protein and peptide content in a tissue or a body fluid is vital in many areas of medical and biomedical sciences. Information from proteomic and peptidomic studies may reveal alterations in expression due to, e.g., a disease and facilitate the understanding of the pathophysiology and the identification of biological markers. In this minireview, we discuss miniaturized proteomic and peptidomic approaches that have been applied in our laboratory in order to investigate the protein and peptide contents of body fluids (such as plasma, cerebrospinal and amniotic fluid), as well as extracted tissues. The methods involve miniaturized liquid separation, i.e., capillary liquid chromatography and capillary electrophoresis, combined with high resolution mass spectrometry (MS), i.e., Fourier transform ion cyclotron resonance MS. These approaches provide the opportunity to analyze samples of small volumes with high throughput, high sensitivity, good dynamic range and minimal sample handling. Also, the experiments are relatively easy to automate.     

A combined dataset of human cerebrospinal fluid proteins identified by multi-dimensional chromatography and tandem mass spectrometry

Human cerebrospinal fluid (CSF) is an important source for studying protein biomarkers of age-related neurodegenerative diseases. Before characterizing biomarkers unique to each disease, it is necessary to categorize CSF proteins systematically and extensively. However, the enormous complexity, great dynamic range of protein concentrations, and tremendous protein heterogeneity due to post-translational modification of CSF create significant challenges to the existing proteomics technologies for an in-depth, nonbiased profiling of the human CSF proteome. To circumvent these difficulties, in the last few years, we have utilized several different separation methodologies and mass spectrometric platforms that greatly enhanced the identification coverage and the depth of protein profiling of CSF to characterize CSF proteome. In total, 2594 proteins were identified in well-characterized pooled human CSF samples using stringent proteomics criteria. This report summarizes our efforts to comprehensively characterize the human CSF proteome to date.     

Structural analysis of 2D-gel-separated glycoproteins from human cerebrospinal fluid by tandem high-resolution mass spectrometry

The feasibility of global glycoprotein analysis by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry and infrared multiphoton dissociation (IRMPD) tandem mass spectrometry is demonstrated. Combined 2D gel glycoprotein separation and visualization, in-gel digestion, and accurate (<10 ppm) mass measurement allowed identification of human glycoproteins and revealed differences in glycosylation. IRMPD obviates the need for glycan release, which prevents sample dispersal, and allows the assignment of glycan structures to specific sites of N-glycosylation.     

Four-dimensional proteomics analysis of human cerebrospinal fluid with trapped ion mobility spectrometry using PASEF

A quadrupole time-of-flight mass spectrometer coupled with a trapped ion mobility spectrometry (timsTOF) operated in parallel accumulation-serial fragmentation (PASEF) mode has recently emerged as a platform capable of providing four-dimensional (4D) features comprising of elution time, collision cross section (CCS), mass-to-charge ratio, and intensity of peptides. The PASEF mode provides ∼100% ion sampling efficiency both in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes without sacrificing sensitivity. In addition, targeted measurements using PASEF integrated parallel reaction monitoring (PRM) mode have also been described. However, only limited number of studies have used timsTOF for analysis of clinical samples. Although Orbitrap mass spectrometers have been used for biomarker discovery from cerebrospinal fluid (CSF) in a variety of neurological diseases, these Orbitrap-derived datasets cannot readily be applied for driving experiments on timsTOF mass spectrometers. We generated a catalog of peptides and proteins in human CSF in DDA mode on a timsTOF mass spectrometer and used these data to build a spectral library. This strategy allowed us to use elution times and ion mobility values from the spectral library to design PRM experiments for quantifying previously discovered biomarkers from CSF samples in Alzheimer's disease. When the same samples were analyzed using a DIA approach combined with a spectral library search, a higher number of proteins were identified than in a library-free approach. Overall, we have established a spectral library of CSF as a resource and demonstrated its utility for PRM and DIA studies, which should facilitate studies of neurological disorders.     

Mass spectrometry imaging with high resolution in mass and space

Mass spectrometry (MS) imaging links molecular information and the spatial distribution of analytes within a sample. In contrast to most histochemical techniques, mass spectrometry imaging can differentiate molecular modifications and does not require labeling of targeted compounds. We have recently introduced the first mass spectrometry imaging method that provides highly specific molecular information (high resolution and accuracy in mass) at cellular dimensions (high resolution in space). This method is based on a matrix-assisted laser desorption/ionization (MALDI) imaging source working at atmospheric pressure which is coupled to an orbital trapping mass spectrometer. Here, we present a number of application examples and demonstrate the benefit of ‘mass spectrometry imaging with high resolution in mass and space.’ Phospholipids, peptides and drug compounds were imaged in a number of tissue samples at a spatial resolution of 5–10 μm. Proteins were analyzed after on-tissue tryptic digestion at 50-μm resolution. Additional applications include the analysis of single cells and of human lung carcinoma tissue as well as the first MALDI imaging measurement of tissue at 3 μm pixel size. MS image analysis for all these experiments showed excellent correlation with histological staining evaluation. The high mass resolution (R = 30,000) and mass accuracy (typically 1 ppm) proved to be essential for specific image generation and reliable identification of analytes in tissue samples. The ability to combine the required high-quality mass analysis with spatial resolution in the range of single cells is a unique feature of our method. With that, it has the potential to supplement classical histochemical protocols and to provide new insights about molecular processes on the cellular level.     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.

     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.     

Determination of baicalin in rat cerebrospinal fluid and blood using microdialysis coupled with ultra-performance liquid chromatography-tandem mass spectrometry

An in vivo microdialysis sampling method coupled with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed for continuous simultaneous monitoring of unbound baicalin in rat blood and brain. Microdialysis probes were inserted into the jugular vein and brain cerebrospinal fluid (CSF) of Sprague-Dawley rats then, following administration of baicalin at doses of 24mg/kg via the candal vein, samples were collected every 20min and injected directly into the UPLC-MS/MS system. In vitro recoveries of the probes were 19.26% and 18.38%, while in vivo recoveries of the probes were 15.0% and 17.52% for blood and brain, respectively. This improved method offers a rapid quantitative procedure for the determination of baicalin with a retention time of only 1.6min. The lower limit of quantification (LLOQ) and the lower limit of detection (LLOD) based on a signal-to-noise ratio of 5 were 2.37 and 0.1ng/ml for anticoagulant citrate dextrose (ACD) solution, and 1.185 and 0.3ng/ml for artificial cerebrospinal fluid (aCSF), respectively. The pharmacokinetics results indicated that baicalin could pass through the blood-brain barrier (BBB) and was detectable in brain dialysate. These in vivo microdialysis-based measurements provide a technique for simple sampling and rapid sensitive analysis of unbound baicalin in rat blood and CSF and for further application in pharmacokinetic studies.