Mass spectrometry-based plasma proteomics: state of the art and future outlook

Comparative analysis of plasma affinity depletion methods: Impact on protein composition and phosphopeptide abundance in human plasma

Affinity-based protein depletion and TiO2 enrichment methods play a crucial role in detection of low-abundant proteins and phosphopeptides enrichment, respectively. Here, we assessed the effectiveness of HSA/IgG (HU2) and Human 7 (HU7) depletion methods and their impact on phosphopeptides coverage through comparative proteome analysis, utilizing in-solution digestion and nano-LC-Orbitrap mass spectrometry (MS). Our results demonstrated that both HU2 and HU7 affinity depletion significantly decreased high-abundant proteins by 1.5-7.8-fold (p < 0.001). A total of 1491 proteins were identified, with 48 proteins showing significant expression in the depleted groups. Notably, cadherin-13, neutrophil defensin 1, APM1, and desmoplakin variant protein were exclusively detected in the HU2/HU7-depleted groups. Furthermore, study on effect of depletion on phosphopeptides revealed an increase in tandem MS spectral counts with notable decrease (∼50%) in peptide spectrum matching in depleted groups, which was attributed to significant reduction in protein counts. Our post translation modification workflow for phosphoproteomics detected 42 phosphorylated peptides, corresponding to 12 phosphoproteins with unique peptide match ≥2 (high false discovery rates confidence). Among them, 10 phosphorylated proteins are highly expressed in depleted groups. Overall, these findings offer valuable insights in selection of protein depletion methods for comprehensive plasma proteomics analysis.

Novel proteomic technologies to address gaps in pre-clinical ovarian cancer biomarker discovery efforts

INTRODUCTION

An estimated 20,000 women in the United States will receive a diagnosis of ovarian cancer in 2023. Late-stage diagnosis is associated with poor prognosis. There is a need for novel diagnostic biomarkers for ovarian cancer to improve early-stage detection and novel prognostic biomarkers to improve patient treatment.

AREAS COVERED

This review provides an overview of the clinicopathological features of ovarian cancer and the currently available biomarkers and treatment options. Two affinity-based platforms using proximity extension assays (Olink) and DNA aptamers (SomaLogic) are described in the context of highly reproducible and sensitive multiplexed assays for biomarker discovery. Recent developments in ion mobility spectrometry are presented as novel techniques to apply to the biomarker discovery pipeline. Examples are provided of how these aforementioned methods are being applied to biomarker discovery efforts in various diseases, including ovarian cancer.

EXPERT OPINION

Translating novel ovarian cancer biomarkers from candidates in the discovery phase to bona fide biomarkers with regulatory approval will have significant benefits for patients. Multiplexed affinity-based assay platforms and novel mass spectrometry methods are capable of quantifying low abundance proteins to aid biomarker discovery efforts by enabling the robust analytical interrogation of the ovarian cancer proteome.

Comprehensive and deep profiling of the plasma proteome with protein corona on zeolite NaY

Proteomic characterization of plasma is critical for the development of novel pharmacodynamic biomarkers. However, the vast dynamic range renders the profiling of proteomes extremely challenging. Here, we synthesized zeolite NaY and developed a simple and rapid method to achieve comprehensive and deep profiling of the plasma proteome using the plasma protein corona formed on zeolite NaY. Specifically, zeolite NaY and plasma were co-incubated to form plasma protein corona on zeolite NaY (NaY-PPC), followed by conventional protein identification using liquid chromatography-tandem mass spectrometry. NaY was able to significantly enhance the detection of low-abundance plasma proteins, minimizing the "masking" effect caused by high-abundance proteins. The relative abundance of middle- and low-abundance proteins increased substantially from 2.54% to 54.41%, and the top 20 high-abundance proteins decreased from 83.63% to 25.77%. Notably, our method can quantify approximately 4000 plasma proteins with sensitivity up to pg/mL, compared to only about 600 proteins identified from untreated plasma samples. A pilot study based on plasma samples from 30 lung adenocarcinoma patients and 15 healthy subjects demonstrated that our method could successfully distinguish between healthy and disease states. In summary, this work provides an advantageous tool for the exploration of plasma proteomics and its translational applications.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Antibody-Based Affinity Capture Combined with LC-MS Analysis for Identification of COVID-19 Disease Serum Biomarkers

Blood serum or plasma proteins are potentially useful in COVID-19 research as biomarkers for risk prediction, diagnosis, stratification, and treatment monitoring. However, serum protein-based biomarker identification and validation is complicated due to the wide concentration range of these proteins, which spans more than ten orders of magnitude. Here we present a combined affinity purification-liquid chromatography mass spectrometry approach which allows identification and quantitation of the most abundant serum proteins along with the nonspecifically bound and interaction proteins. This led to the reproducible identification of more than 100 proteins that were not specifically targeted by the affinity column. Many of these have already been implicated in COVID-19 disease.

Evaluation of the Molecular Mechanisms of Sepsis Using Proteomics

Sepsis is a complex syndrome promoted by pathogenic and host factors; it is characterized by dysregulated host responses and multiple organ dysfunction, which can lead to death. However, its underlying molecular mechanisms remain unknown. Proteomics, as a biotechnology research area in the post-genomic era, paves the way for large-scale protein characterization. With the rapid development of proteomics technology, various approaches can be used to monitor proteome changes and identify differentially expressed proteins in sepsis, which may help to understand the pathophysiological process of sepsis. Although previous reports have summarized proteomics-related data on the diagnosis of sepsis and sepsis-related biomarkers, the present review aims to comprehensively summarize the available literature concerning “sepsis”, “proteomics”, “cecal ligation and puncture”, “lipopolysaccharide”, and “post-translational modifications” in relation to proteomics research to provide novel insights into the molecular mechanisms of sepsis.

TMT-Based Plasma Proteomics Reveals Dyslipidemia Among Lowlanders During Prolonged Stay at High Altitudes

Acute exposure to high altitude perturbs physiological parameters and induces an array of molecular changes in healthy lowlanders. However, activation of compensatory mechanisms and biological processes facilitates high altitude acclimatization. A large number of lowlanders stay at high altitude regions from weeks to months for work and professional commitments, and thus are vulnerable to altitude-associated disorders. Despite this, there is a scarcity of information for molecular changes associated with long-term stay at high altitudes. In the present study, we evaluated oxygen saturation (SpO₂), heart rate (HR), and systolic and diastolic blood pressure (SBP and DBP) of lowlanders after short- (7 days, HA-D7) and long-term (3 months, HA-D150) stay at high altitudes, and used TMT-based proteomics studies to decipher plasma proteome alterations. We observed improvements in SpO₂ levels after prolonged stay, while HR, SBP, and DBP remained elevated as compared with short-term stay. Plasma proteomics studies revealed higher levels of apolipoproteins APOB, APOCI, APOCIII, APOE, and APOL, and carbonic anhydrases (CA1 and CA2) during hypoxia exposure. Biological network analysis also identified profound alterations in lipoprotein-associated pathways like plasma lipoprotein assembly, VLDL clearance, chylomicron assembly, chylomicron remodeling, plasma lipoprotein clearance, and chylomicron clearance. In corroboration, lipid profiling revealed higher levels of total cholesterol (TC), triglycerides (TGs), low-density lipoprotein (LDL) for HA-D150 whereas high density lipoproteins (HDL) levels were lower as compared with HA-D7 and sea-level indicating dyslipidemia. We also observed higher levels of proinflammatory cytokines IL-6, TNFα, and CRP for HA-D150 along with oxidized LDL (oxLDL), suggesting vascular inflammation and proartherogenic propensity. These results demonstrate that long-term stay at high altitudes exacerbates dyslipidemia and associated disorders.

Quantitative proteomics by iTRAQ-PRM based reveals the new characterization for gout

Background

Gout is a common and complex form of immunoreactive arthritis based on hyperuricemia, while the symptoms would turn to remission or even got worse. So, it is hard to early identify whether an asymptomatic hyperuricemia (AHU) patient will be susceptible to get acute gout attack and it is also hard to predict the process of gout remission to flare. Here, we report that the plasma proteins profile can distinguish among acute gout (AG), remission of gout (RG), AHU patients, and healthy controls.

Methods

We established an isobaric tags for relative and absolute quantification (iTRAQ) and parallel reaction monitoring (PRM) based method to measure the plasma proteins for AG group (n = 8), RG group (n = 7), AHU group (n = 7) and healthy controls (n = 8).

Results

Eleven differentially expressed proteins such as Histone H2A, Histone H2B, Thrombospondin-1 (THBS1), Myeloperoxidase (MPO), Complement C2, Complement component C8 beta chain (C8B), Alpha-1-acid glycoprotein 1 (ORM1), Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), Carbonic anhydrase 1 (CA1), Serum albumin (ALB) and Multimerin-1 (MMRN1) were identified. Histone H2A, Histone H2B and THBS1 might be the strongest influential regulator to maintain the balance and stability of the gout process. The complement and coagulation cascades is one of the main functional pathways in the mechanism of gout process.

Conclusions

Histone H2A, Histone H2B and THBS1 are potential candidate genes for novel biomarkers in discriminating gout attack from AHU or RG, providing new theoretical insights for the prognosis, treatment, and management of gout process.

Trial registration

This study is not a clinical trial.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12953-021-00180-0.

Altered metabolic pathways in a transgenic mouse model suggest mechanistic role of amyloid precursor protein overexpression in Alzheimer's disease

INTRODUCTION

The mechanistic role of amyloid precursor protein (APP) in Alzheimer's disease (AD) remains unclear.

OBJECTIVES

Here, we aimed to identify alterations in cerebral metabolites and metabolic pathways in cortex, hippocampus and serum samples from Tg2576 mice, a widely used mouse model of AD.

METHODS

Metabolomic profilings using liquid chromatography-mass spectrometry were performed and analysed with MetaboAnalyst and weighted correlation network analysis (WGCNA).

RESULTS

Expressions of 11 metabolites in cortex, including hydroxyphenyllactate-linked to oxidative stress-and phosphatidylserine-lipid metabolism-were significantly different between Tg2576 and WT mice (false discovery rate < 0.05). Four metabolic pathways from cortex, including glycerophospholipid metabolism and pyrimidine metabolism, and one pathway (sulphur metabolism) from hippocampus, were significantly enriched in Tg2576 mice. Network analysis identified five pathways, including alanine, aspartate and glutamate metabolism, and mitochondria electron transport chain, that were significantly correlated with AD genotype.

CONCLUSIONS

Changes in metabolite concentrations and metabolic pathways are present in the early stage of APP pathology, and may be important for AD development and progression.

Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology

Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.

PSMD11, PTPRM and PTPRB as novel biomarkers of pancreatic cancer progression

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate of all major cancers. Surgery is the only curative intent therapy, but the majority of patients experience disease relapse. Thus, patients who do not benefit from highly morbid surgical resection needs to be identified and offered palliative chemotherapy instead. In this pilot study, we aimed to identify differentially regulated proteins in plasma and plasma derived microparticles from PDAC patients with poor and good prognosis.

METHODS

Plasma and plasma derived microparticle samples were obtained before surgical resection from PDAC patients. Sequential Windowed Acquisition of all Theoretical fragment ion spectra - Mass Spectrometry (SWATH-MS) proteomic analysis was performed to identify and quantify proteins in these samples. Statistical analysis was performed to identify biomarkers for poor prognosis.

RESULTS

A total of 482 and 1024 proteins were identified from plasma and microparticle samples, respectively, by SWATH-MS analysis. Statistical analysis of the data further identified nine and six differentially (log₂ratio > 1, p < .05) expressed proteins in plasma and microparticles, respectively. Protein tyrosine phosphatases, PTPRM and PTPRB, were decreased in plasma of patients with poor PDAC prognosis, while proteasomal subunit PSMD11 was increased in microparticles of patients with poor prognosis.

CONCLUSION AND GENERAL SIGNIFICANCE

A novel blood-based biomarker signature for PDAC prognosis was identified.

In situ analysis of liposome hard and soft protein corona structure and composition in a single label-free workflow

Methodological constraints have limited our ability to study protein corona formation, slowing nanomedicine development and their successful translation into the clinic. We determined hard and soft corona structural properties along with the corresponding proteomic compositions on liposomes in a label-free workflow: surface plasmon resonance and a custom biosensor for in situ structure determination on liposomes and corona separation, and proteomics using sensitive nanoliquid chromatography tandem mass spectrometry with open-source bioinformatics platforms. Undiluted human plasma under dynamic flow conditions was used for in vivo relevance. Proof-of-concept is presented with a regular liposome formulation and two light-triggered indocyanine green (ICG) liposome formulations in preclinical development. We observed formulation-dependent differences in corona structure (thickness, protein-to-lipid ratio, and surface mass density) and protein enrichment. Liposomal lipids induced the enrichment of stealth-mediating apolipoproteins in the hard coronas regardless of pegylation, and their preferential enrichment in the soft corona of the pegylated liposome formulation with ICG was observed. This suggests that the soft corona of loosely interacting proteins contributes to the stealth properties as a component of the biological identity modulated by nanomaterial surface properties. The workflow addresses significant methodological gaps in biocorona research by providing truly complementary hard and soft corona compositions with corresponding in situ structural parameters for the first time. It has been designed into a convenient and easily reproducible single-experiment format suited for preclinical development of lipid nanomedicines.

Plasma proteome profiling of freshwater and seawater life stages of rainbow trout (Oncorhynchus mykiss)

The sea-run phenotype of rainbow trout (Oncorhynchus mykiss), like other anadromous salmonids, present a juvenile stage fully adapted to life in freshwater known as parr. Development in freshwater is followed by the smolt stage, where preadaptations needed for seawater life are developed making fish ready to migrate to the ocean, after which event they become post-smolts. While these three life stages have been studied using a variety of approaches, proteomics has never been used for such purpose. The present study characterised the blood plasma proteome of parr, smolt and post-smolt rainbow trout using a gel electrophoresis liquid chromatography tandem mass spectrometry approach alone or in combination with low-abundant protein enrichment technology (combinatorial peptide ligand library). In total, 1,822 proteins were quantified, 17.95% of them being detected only in plasma post enrichment. Across all life stages, the most abundant proteins were ankyrin-2, DNA primase large subunit, actin, serum albumin, apolipoproteins, hemoglobin subunits, hemopexin-like proteins and complement C3. When comparing the different life stages, 17 proteins involved in mechanisms to cope with hyperosmotic stress and retinal changes, as well as the downregulation of nonessential processes in smolts, were significantly different between parr and smolt samples. On the other hand, 11 proteins related to increased growth in post-smolts, and also related to coping with hyperosmotic stress and to retinal changes, were significantly different between smolt and post-smolt samples. Overall, this study presents a series of proteins with the potential to complement current seawater-readiness assessment tests in rainbow trout, which can be measured non-lethally in an easily accessible biofluid. Furthermore, this study represents a first in-depth characterisation of the rainbow trout blood plasma proteome, having considered three life stages of the fish and used both fractionation alone or in combination with enrichment methods to increase protein detection.

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel

BACKGROUND

One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin).

METHODS

A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets.

RESULTS

Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC.

CONCLUSION

MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

The Relevance of Mass Spectrometry Analysis for Personalized Medicine through Its Successful Application in Cancer "Omics"

Mass spectrometry (MS) is an essential analytical technology on which the emerging omics domains; such as genomics; transcriptomics; proteomics and metabolomics; are based. This quantifiable technique allows for the identification of thousands of proteins from cell culture; bodily fluids or tissue using either global or targeted strategies; or detection of biologically active metabolites in ultra amounts. The routine performance of MS technology in the oncological field provides a better understanding of human diseases in terms of pathophysiology; prevention; diagnosis and treatment; as well as development of new biomarkers; drugs targets and therapies. In this review; we argue that the recent; successful advances in MS technologies towards cancer omics studies provides a strong rationale for its implementation in biomedicine as a whole.

Analysis of the plasma proteome using iTRAQ and TMT-based Isobaric labeling

Over the past decade, chemical labeling with isobaric tandem mass tags, such as isobaric tags for relative and absolute quantification reagents (iTRAQ) and tandem mass tag (TMT) reagents, has been employed in a wide range of different clinically orientated serum and plasma proteomics studies. In this review the scope of these works is presented with attention to the areas of research, methods employed and performance limitations. These applications have covered a wide range of diseases, disorders and infections, and have implemented a variety of different preparative and mass spectrometric approaches. In contrast to earlier works, which struggled to quantify more than a few hundred proteins, increasingly these studies have provided deeper insight into the plasma proteome extending the numbers of quantified proteins to over a thousand.

Ionizing radiation exposure: hazards, prevention, and biomarker screening

Radiation is a form of energy derived from a source that is propagated through material in space. It consists of ionizing radiation or nonionizing radiation. Ionizing radiation is a feature of the environment and an important tool in medical treatment, but it can cause serious damage to organisms. A number of protective measures and standards of protection have been proposed to protect against radiation. There is also a need for biomarkers to rapidly assess individual doses of radiation, which can not only estimate the dose of radiation but also determine its effects on health. Proteomics, genomics, metabolomics, and lipidomics have been widely used in the search for such biomarkers. These topics are discussed in depth in this review.

SWATH Mass Spectrometry for Proteomics of Non-Depleted Plasma

The limitations commonly observed in data-dependent acquisition (DDA) mass spectrometric investigation of non-depleted human plasma are mainly due to the large dynamic concentration range of protein expression. Less abundant proteins are usually masked by highly abundant proteins and are therefore difficult to reliably detect. Sequential window acquisition of all theoretical fragment-ion spectra (SWATH) mass spectrometry (MS), as a representative of data-independent acquisition (DIA) approaches, provides an opportunity to improve plasma-based biomarker discovery studies because this approach does not rely on precursor intensity for fragmentation selection but rather analyzes all precursors in specified mass ranges. Here, we describe a workflow for SWATH-MS-based analysis of non-depleted plasma including sample preparation, data acquisition, and statistical analysis.

Needle lost in the haystack: multiple reaction monitoring fails to detect Treponema pallidum candidate protein biomarkers in plasma and urine samples from individuals with syphilis

Background: Current syphilis diagnostic strategies are lacking a sensitive manner of directly detecting Treponema pallidum antigens. A diagnostic test that could directly detect T. pallidum antigens in individuals with syphilis would be of considerable clinical utility, especially for the diagnosis of reinfections and for post-treatment serological follow-up.

Methods: In this study, 11 candidate T. pallidum biomarker proteins were chosen according to their physiochemical characteristics, T. pallidum specificity and predicted abundance. Thirty isotopically labelled proteotypic surrogate peptides (hPTPs) were synthesized and incorporated into a scheduled multiple reaction monitoring assay. Protein extracts from undepleted/unenriched plasma (N = 18) and urine (N = 4) samples from 18 individuals with syphilis in various clinical stages were tryptically digested, spiked with the hPTP mixture and analysed with a triple quadruple mass spectrometer.

Results: No endogenous PTPs corresponding to the eleven candidate biomarkers were detected in any samples analysed. To estimate the Limit of Detection (LOD) of a comparably sensitive mass spectrometer (LTQ-Orbitrap), two dilution series of rabbit cultured purified T. pallidum were prepared in PBS. Polyclonal anti- T. pallidum antibodies coupled to magnetic Dynabeads were used to enrich one sample series; no LOD improvement was found compared to the unenriched series. The estimated LOD of MS instruments is 300 T. pallidum/ml in PBS.

Conclusions: Biomarker protein detection likely failed due to the low (femtomoles/liter) predicted concentration of T. pallidum proteins. Alternative sample preparation strategies may improve the detectability of T. pallidum proteins in biofluids.

Human follicular fluid proteomic and peptidomic composition quantitative studies by SWATH-MS methodology. Applicability of high pH RP-HPLC fractionation

Analysis of proteomic composition of human follicular fluid (hFF) has been previously proposed as a potential tool of oocyte quality evaluation. In order to develop an efficient method to investigate the hFF proteome and peptidome components, we applied and tested a few prefractionation schemes of hFF material consisting of ultrafiltration, optional immunodepletion, and high pH RP-HPLC separation by building spectral libraries and comparing their quantification capabilities of unfractionated samples. Low Molecular-Weight Fraction peptides (LMWF, <10 kDa) and High Molecular-Weight Fraction proteins (HMWF, >10 kDa) resulting from ultrafiltration were analyzed separately. We identified 302 proteins in HMWF and 161 proteins in LMWF in all qualitative experiments. All LMWF peptidomic libraries turned out to be of poor quantification quality, however they enabled measurement of higher numbers of peptides with increasing input of experiment data, in contrast to HMWF proteomic libraries. We were able to quantify a total of 108 HMWF proteins and 250 LMWF peptides (from 84 proteins) in all experiments. Employment of high RP-HPLC fractionation allowed for identification of a much broader set of proteins, however did not significantly improve the quantification capabilities of the applied method. Data are available via ProteomeXchange with identifier PXD008073. SIGNIFICANCE: In the search of biomarkers for assessment of oocyte quality in assisted reproductive technology, many studies are devoted to analysis of follicular fluid composition. Candidates for such biomarkers can be located in both the proteome and the recently investigated peptidome of hFF. Reliable qualitative and especially quantitative analysis of complex mixtures such as hFF, requires development of a fast and preferably inexpensive analytical procedure. The powerful SWATH-MS technique is well suited for quantitative label-free analysis of complex protein and peptide mixtures. However, for efficient usage it needs well designed and constructed MS-spectral libraries as well as a proper protocol for sample preparation. We investigated the influence of the size and quality of MS-spectral libraries (different spectral libraries are constructed using various sample prefractionation protocols) on SWATH experiments on hFF proteome and peptidome. In the case of peptidome investigation, increasing the size of spectral libraries led to quantification of more peptides in a single experiment. For the proteome, increasing the size of spectral libraries improved quantification only to a limited extend, and further extension of spectral libraries even worsened results. Nevertheless, using the best selected prefractionation schemes and spectral libraries we were able to quantify as many as 79 proteins of hFF proteome and 106 peptides (from 53 proteins) of hFF peptidome in single experiments. The spectral libraries and prefractionation protocols we developed allow for a large scale fast scan of hundreds of clinical hFF samples in the search for biomarkers for evaluation of oocyte quality.

Temporal expression profiling of plasma proteins reveals oxidative stress in early stages of Type 1 Diabetes progression

Blood markers other than islet autoantibodies are greatly needed to indicate the pancreatic beta cell destruction process as early as possible, and more accurately reflect the progression of Type 1 Diabetes Mellitus (T1D). To this end, a longitudinal proteomic profiling of human plasma using TMT-10plex-based LC-MS/MS analysis was performed to track temporal proteomic changes of T1D patients (n=11) across 9 serial time points, spanning the period of T1D natural progression, in comparison with those of the matching healthy controls (n=10). To our knowledge, the current study represents the largest (>2000 proteins measured) longitudinal expression profiles of human plasma proteome in T1D research. By applying statistical trend analysis on the temporal expression patterns between T1D and controls, and Benjamini-Hochberg procedure for multiple-testing correction, 13 protein groups were regarded as having statistically significant differences during the entire follow-up period. Moreover, 16 protein groups, which play pivotal roles in response to oxidative stress, have consistently abnormal expression trend before seroconversion to islet autoimmunity. Importantly, the expression trends of two key reactive oxygen species-decomposing enzymes, Catalase and Superoxide dismutase were verified independently by ELISA.

BIOLOGICAL SIGNIFICANCE

The temporal changes of >2000 plasma proteins (at least quantified in two subjects), spanning the entire period of T1D natural progression were provided to the research community. Oxidative stress related proteins have consistently different dysregulated patterns in T1D group than in age-sex matched healthy controls, even prior to appearance of islet autoantibodies - the earliest sign of islet autoimmunity and pancreatic beta cell stress.

Identification of candidate serum biomarkers of childhood-onset growth hormone deficiency using SWATH-MS and feature selection

A typical clinical manifestation of growth hormone deficiency (GHD) is a short stature resulting from delayed growth, but GHD affects bone health, cardiovascular function and metabolic profile and therefore quality of life. Although early GH treatment during childhood has been shown to improve outcomes, no single biochemical parameter is currently available for the accurate diagnosis of GHD in children. There is hence a need for non-invasive biomarkers. In this study, the relative abundance of serum proteins from GHD children and healthy controls was measured by next-generation proteomics SWATH-MS technology. The data generated was analysed by machine-learning feature-selection algorithms in order to discover the minimum number of protein biomarkers that best discriminate between both groups. The analysis of serum proteins by a SWATH-MS approach yielded a useful method for discovering potential biomarkers of GHD in children. A total of 263 proteins were confidently detected and quantified in each sample. Pathway analysis indicated an effect on tissue/organ structure and morphogenesis. The top ten serum protein biomarker candidates were identified after applying feature-selection data analysis. The combination of three proteins - apolipoprotein A-IV, complement factor H-related protein 4 and platelet basic protein - demonstrated the best classification performance for our data. In addition, the apolipoprotein group resulted in strong over-representation, thus highlighting these proteins as an additional promising biomarker panel.

SIGNIFICANCE

Currently there is no single biochemical parameter available for the accurate diagnosis of growth hormone (GH) deficiency (GHD) in children. Simple GH measurements are not an option: because GH is released in a pulsatile action, its blood levels fluctuate throughout the day and remain nearly undetectable for most of that time. This makes measurements of GH in a single blood sample useless for assessing GH deficiency. Actually, the diagnosis of GHD includes a combination of direct and indirect non-accurate measurements, such as taking several body measurements, testing GH levels in multiple blood samples after provocative tests (GH peak <7.3ng/mL, using radioimmunoassay), and conducting magnetic resonance imaging (MRI), among others. Therefore, there is a need for simple, non-invasive, accurate and cost-effective biomarkers. Here we report a case-control study, where relative abundance of serum proteins were measured by next-generation proteomics SWATH-MS technology in 15 GHD children and 15healthy controls matched by age, sex, and not receiving any treatment. Data generated was analysed by machine learning feature selection algorithms. 263 proteins could be confidently detected and quantified on each sample. The top 10 serum protein biomarker candidates could be identified after applying a feature selection data analysis. The combination of three proteins, apolipoprotein A-IV, complement factor H-related protein 4 and platelet basic protein, showed the best classification performance for our data. In addition, the fact that the pathway and GO analysis we performed pointed to the apolipoproteins as over-represented highlights this protein group as an additional promising biomarker panel for the diagnosis of GHD and for treatment evaluation.

Tissue and plasma proteomics for early stage cancer detection

The pursuit of novel and effective biomarkers is essential in the struggle against cancer, which is a leading cause of mortality worldwide. Here we discuss the relative advantages and disadvantages of the most frequently used proteomics techniques, concentrating on the latest advances and application of tissue and plasma proteomics for novel cancer biomarker discovery.

Discovery of a Potential Plasma Protein Biomarker Panel for Acute-on-Chronic Liver Failure Induced by Hepatitis B Virus

Hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF), characterized by an acute deterioration of liver function in the patients with chronic hepatitis B (CHB), is lack of predicting biomarkers for prognosis. Plasma is an ideal sample for biomarker discovery due to inexpensive and minimally invasive sampling and good reproducibility. In this study, immuno-depletion of high-abundance plasma proteins followed by iTRAQ-based quantitative proteomic approach was employed to analyze plasma samples from 20 healthy control people, 20 CHB patients and 20 HBV-ACLF patients, respectively. As a result, a total of 427 proteins were identified from these samples, and 42 proteins were differentially expressed in HBV-ACLF patients as compared to both CHB patients and healthy controls. According to bioinformatics analysis results, 6 proteins related to immune response (MMR), inflammatory response (OPN, HPX), blood coagulation (ATIII) and lipid metabolism (APO-CII, GP73) were selected as biomarker candidates. Further ELISA analysis confirmed the significant up-regulation of GP73, MMR, OPN and down-regulation of ATIII, HPX, APO-CII in HBV-ACLF plasma samples (p < 0.01). Moreover, receiver operating characteristic (ROC) curve analysis revealed high diagnostic value of these candidates in assessing HBV-ACLF. In conclusion, present quantitative proteomic study identified 6 novel HBV-ACLF biomarker candidates and might provide fundamental information for development of HBV-ACLF biomarker.

Polymeric ionic liquid-assembled graphene-immobilized silica composite for selective isolation of human serum albumin from human whole blood

Polymeric ionic liquids (PILs) with 1-vinyl-3-ethylimidazolium cations and two different anions of Br^- and PF₆^- were assembled onto the surface of graphene (G) nanosheets. The derived two composites, i.e., PIL(Br)-G and PIL(PF₆)-G, were further efficiently immobilized onto the surface of silica nanoparticles via self-assembly technique. The obtained two PIL-G/SiO₂ nanocomposites exhibited diverse adsorption performances toward proteins through adjusting the anions of PILs. Electrostatic attractions between proteins and the nanocomposites dominated protein adsorption, while the presence of PF₆^- anions weakened electrostatic interactions and deteriorated the selective adsorption of target protein, i.e., bovine serum albumin (BSA) in this case. Specifically, PIL(Br)-G/SiO₂ nanocomposite displayed high selectivity toward BSA and a high adsorption efficiency of ca. 98% was achieved for 100 mg L^-1 BSA in a Britton-Robinson (B-R) buffer at pH 5. HPLC analysis demonstrated the selectivity of PIL(Br)-G/SiO₂ nanocomposite toward BSA in the presence of abundant hemoglobin and cytochrome c. The practical applicability was verified by performing selective isolation of human serum albumin (HSA) from human whole blood. Graphical abstract Selective isolation of human serum albumin from blood by polymeric ionic liquid assembled graphene immobilized silica nanocomposite with tunable anions.

A plasma proteomics method reveals links between ischemic stroke and MTHFR C677T genotype

Methylene Tetrahydrofolate Reductase (MTHFR) catalyzes the conversion of methylene tetrahydrofolate to methylte trahydrofolate. The 677th nucleotide of the MTHFR gene is often regarded as a risk factor of cardiovascular disease. Previous studies demonstrated an elevated risk of ischemic stroke with the MTHFR677TT genotype. In this study, we employed a plasma proteomics method to investigate the connection between the polymorphism of the target nucleotide and stroke. In total, 28 protein spots were differentially expressed between the two groups, and of which, 25 protein spots were up-regulated and 3 were down-regulated. Five randomly selected spots were successfully identified as Haptoglobin (HPT) and Transferrin (TRFE). A functional analysis indicated that most of the differential expressed proteins (DEPs) were related to the inflammatory immune response. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that these DEPs were involved in the complement cascade reaction. Meanwhile, protein-protein interactions (PPIs) analysis highlighted the novel association between the C677T MTHFR genotype and Vitamin D binding protein (DBP), which was confirmed by a molecular genetic analysis. The results suggested that the phenotype of the MTHFR might be associated with multiple proteins that have a synergistic effect, which might be related to the mechanism of ischemic stroke.

Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry

Proteomic characterization of blood plasma is of central importance to clinical proteomics and particularly to biomarker discovery studies. The vast dynamic range and high complexity of the plasma proteome have, however, proven to be serious challenges and have often led to unacceptable tradeoffs between depth of coverage and sample throughput. We present an optimized sample-processing pipeline for analysis of the human plasma proteome that provides greatly increased depth of detection, improved quantitative precision and much higher sample analysis throughput as compared with prior methods. The process includes abundant protein depletion, isobaric labeling at the peptide level for multiplexed relative quantification and ultra-high-performance liquid chromatography coupled to accurate-mass, high-resolution tandem mass spectrometry analysis of peptides fractionated off-line by basic pH reversed-phase (bRP) chromatography. The overall reproducibility of the process, including immunoaffinity depletion, is high, with a process replicate coefficient of variation (CV) of <12%. Using isobaric tags for relative and absolute quantitation (iTRAQ) 4-plex, >4,500 proteins are detected and quantified per patient sample on average, with two or more peptides per protein and starting from as little as 200 μl of plasma. The approach can be multiplexed up to 10-plex using tandem mass tags (TMT) reagents, further increasing throughput, albeit with some decrease in the number of proteins quantified. In addition, we provide a rapid protocol for analysis of nonfractionated depleted plasma samples analyzed in 10-plex. This provides ∼600 quantified proteins for each of the ten samples in ∼5 h of instrument time.

Qualitative and Quantitative Analysis of Proteome and Peptidome of Human Follicular Fluid Using Multiple Samples from Single Donor with LC-MS and SWATH Methodology

Human follicular fluid (hFF) is a natural environment of oocyte maturation, and some components of hFF could be used to judge oocyte capability for fertilization and further development. In our pilot small-scale study three samples from four donors (12 samples in total) were analyzed to determine which hFF proteins/peptides could be used to differentiate individual oocytes and which are patient-specific. Ultrafiltration was used to fractionate hFF to high-molecular-weight (HMW) proteome (>10 kDa) and low-molecular-weight (LMW) peptidome (<10 kDa) fractions. HMW and LMW compositions were analyzed using LC-MS in SWATH data acquisition and processing methodology. In total we were able to identify 158 proteins, from which 59 were never reported before as hFF components. 55 (45 not reported before) proteins were found by analyzing LMW fraction, 67 (14 not reported before) were found by analyzing HMW fraction, and 36 were identified in both fractions of hFF. We were able to perform quantitative analysis for 72 proteins from HMW fraction of hFF. We found that concentrations of 11 proteins varied substantially among hFF samples from single donors, and those proteins are promising targets to identify biomarkers useful in oocyte quality assessment.

Affinity Proteomics Exploration of Melanoma Identifies Proteins in Serum with Associations to T-Stage and Recurrence

BACKGROUND

Blood-based proteomic profiling may aid and expand our understanding of diseases and their different phenotypes. The aim of the presented study was to profile serum samples from patients with malignant melanoma using affinity proteomic assays to describe proteins in the blood stream that are associated to stage or recurrence of melanoma.

MATERIAL AND METHODS

Multiplexed protein analysis was conducted using antibody suspension bead arrays. A total of 232 antibodies against 132 proteins were selected from (i) a screening with 4595 antibodies and 32 serum samples from melanoma patients and controls, (ii) antibodies used for immunohistochemistry, (iii) protein targets previously related with melanoma. The analysis was performed with 149 serum samples from patients with malignant melanoma. Antibody selectivity was then assessed by Western blot, immunocapture mass spectrometry, and epitope mapping. Lastly, indicative antibodies were applied for IHC analysis of melanoma tissues.

RESULTS

Serum levels of regucalcin (RGN) and syntaxin 7 (STX7) were found to be lower in patients with both recurring tumors and a high Breslow's thickness (T-stage 3/4) compared to low thickness (T-stage 1/2) without disease recurrence. Serum levels of methylenetetrahydrofolate dehydrogenase 1-like (MTHFD1L) were instead elevated in sera of T3/4 patients with recurrence. The analysis of tissue sections with S100A6 and MTHFD1L showed positive staining in a majority of patients with melanoma, and S100A6 was significantly associated to T-stage.

CONCLUSIONS

Our findings provide a starting point to further study RGN, STX7, MTHFD1L and S100A6 in serum to elucidate their involvement in melanoma progression and to assess a possible contribution to support clinical indications.

Human immune system variation

The human immune system is highly variable between individuals but relatively stable over time within a given person. Recent conceptual and technological advances have enabled systems immunology analyses, which reveal the composition of immune cells and proteins in populations of healthy individuals. The range of variation and some specific influences that shape an individual's immune system is now becoming clearer. Human immune systems vary as a consequence of heritable and non-heritable influences, but symbiotic and pathogenic microbes and other non-heritable influences explain most of this variation. Understanding when and how such influences shape the human immune system is key for defining metrics of immunological health and understanding the risk of immune-mediated and infectious diseases.

Isoelectric point region pI≈7.4 as a treasure island of abnormal proteoforms in blood

Theoretical distribution of isoelectric points (pI values) of human blood proteins exhibits multi-modality with a deep minimum in the range between pI 7.30 and 7.50. Considering that the pH of human blood is 7.4±0.1, normal forms of human proteins tend to eschew this specific pI region, thus avoiding charge neutrality that can result in enhanced precipitation. However, abnormal protein isoforms (proteoforms), which are the hallmarks and potential biomarkers of certain diseases, are likely to be found everywhere in the pI distribution, including this “forbidden” region. Therefore, we hypothesized that damaging proteoforms characteristic for neurodegenerative diseases are best detected around pI≈7.4. Blood serum samples from 14 Alzheimer's disease patients were isolated by capillary isoelectric focusing and analyzed by liquid chromatography hyphenated with tandem mass spectrometry. Consistent with the pI≈7.4 hypothesis, the 8 patients with fast memory decline had a significantly (p<0.003) higher concentration of proteoforms in the pI=7.4±0.1 region than the 6 patients with a slow memory decline. Moreover, protein compositions differed more from each other than for any other investigated pI region, providing absolute separation of the fast and slow decliner samples. The discovery of the “treasure island” of abnormal proteoforms in form of the pI≈7.4 region promises to boost biomarker development for a range of diseases.

Temporal profiles of plasma proteome during childhood development

Human blood plasma proteome reflects physiological changes associated with a child's development as well as development of disease states. While age-specific normative values are available for proteins routinely measured in clinical practice, there is paucity of comprehensive longitudinal data regarding changes in human plasma proteome during childhood. We applied TMT-10plex isobaric labeling-based quantitative proteomics to longitudinally profile the plasma proteome in 10 healthy children during their development, each with 9 serial time points from 9months to 15years of age. In total, 1828 protein groups were identified at peptide and protein level false discovery rate of 1% and with at least two razor and unique peptides. The longitudinal expression profiles of 1747 protein groups were statistically modeled and their temporal changes were categorized into 7 different patterns. The patterns and relative abundance of proteins obtained by LC-MS were also verified with ELISA. To our knowledge, this study represents the most comprehensive longitudinal profiling of human plasma proteome to date. The temporal profiles of plasma proteome obtained in this study provide a comprehensive resource and reference for biomarker studies in childhood diseases. Biological significance: A pediatric plasma proteome database with longitudinal expression patterns of 1747 proteins from neonate to adolescence was provided to the research community. 970 plasma proteins had age-dependent expression trends, which demonstrated the importance of longitudinal profiling study to identify the potential biomarkers specific to childhood diseases, and the requirement of strictly age-matched clinical samples in a cross-sectional study in pediatric population.

The Proteomic Characterization of Plasma or Serum from HIV-Infected Patients

Proteomics holds great promise for uncovering disease-related markers and mechanisms in human disorders. Recent advances have led to efficient, sensitive, and reproducible methods to quantitate the proteome in biological samples. Here we describe the techniques for processing, running, and analyzing samples from HIV-infected plasma or serum through quantitative mass spectroscopy.

A differential protein solubility approach for the depletion of highly abundant proteins in plasma using ammonium sulfate

Depletion of highly abundant proteins is an approved step in blood plasma analysis by mass spectrometry (MS). In this study, we explored a precipitation and differential protein solubility approach as a fractionation strategy for abundant protein removal from plasma. Total proteins from plasma were precipitated with 90% saturated ammonium sulfate, followed by differential solubilization in 55% and 35% saturated ammonium sulfate solutions. Using a four hour liquid chromatography (LC) gradient and an LTQ-Orbitrap XL mass spectrometer, a total of 167 and 224 proteins were identified from the 55% and 35% ammonium sulfate fractions, whereas 235 proteins were found in the remaining protein fractions with at least two unique peptides. SDS-PAGE and exclusive total spectrum counts from LC-MS/MS analyses clearly showed that majority of the abundant plasma proteins were solubilized in 55% and 35% ammonium sulfate solutions, indicating that the remaining protein fraction is of potential interest for identification of less abundant plasma proteins. Serum albumin, serotransferrin, alpha-1-antitrypsin and transthyretin were the abundant proteins that were highly enriched in 55% ammonium sulfate fractions. Immunoglobulins, complement system proteins, and apolipoproteins were among other abundant plasma proteins that were enriched in 35% ammonium sulfate fractions. In the remaining protein fractions a total of 40 unique proteins were identified of which, 32 proteins were identified with at least 10 exclusive spectrum counts. According to PeptideAtlas, 9 of these 32 proteins were estimated to be present at low μg ml(-1) (0.12-1.9 μg ml(-1)) concentrations in the plasma, and 17 at low ng ml(-1) (0.1-55 ng ml(-1)) range.

Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics

In the quest for deciphering disease-associated biomarkers, high-performing tools for multiplexed protein expression profiling of crude clinical samples will be crucial. Affinity proteomics, mainly represented by antibody-based microarrays, have during recent years been established as a proteomic tool providing unique opportunities for parallelized protein expression profiling. But despite the progress, several main technical features and assay procedures remains to be (fully) resolved. Among these issues, the handling of protein microarray data, i.e. the biostatistics parts, is one of the key features to solve. In this study, we have therefore further optimized, validated, and standardized our in-house designed recombinant antibody microarray technology platform. To this end, we addressed the main remaining technical issues (e.g. antibody quality, array production, sample labelling, and selected assay conditions) and most importantly key biostatistics subjects (e.g. array data pre-processing and biomarker panel condensation). This represents one of the first antibody array studies in which these key biostatistics subjects have been studied in detail. Here, we thus present the next generation of the recombinant antibody microarray technology platform designed for clinical immunoproteomics.

Comparative Analysis of Lipid Extracts and Imaging Mass Spectrometry for Evaluating Cerebral White Matter Biochemical Pathology in an Experimental Second-Hand Cigarette Smoke Exposure Model

Background

White matter injury and degeneration are common features of developmental and aging-associated diseases, yet their pathobiological bases are poorly understood. However, recent advances in Matrix-Assisted Laser Desorption Ionization (MALDI) instruments and chemistry have provided critical tools for myelin-lipid analytical research.

Design

This study characterizes Cigarette Smoke (CS) exposure effects on frontal lobe lipid ion profiles in adult male A/J mice that had been exposed to air for 8 weeks (A8), CS for 4 (CS4) or 8 weeks (CS8), or CS8 followed by 2 weeks recovery (CS8+R). MALDI data acquired by analysis of lipid extracts plated onto a ground steel target (high through-put) were compared with Imaging Mass Spectrometry (IMS).

Results

MALDI-time-of-flight (TOF) detected 120 lipid ions with m/z’s of 600 to 1300 (phospholipids and sulfatides) in samples plated onto the steel target or analyzed by IMS, but just 25 ions (18%) were detected by both methods. IMS more effectively detected ions in the highest m/z range, whereas the extracts had abundant middle-range m/z ions. The experimental groups were better discriminated by PCA and R-generated heat map hierarchical clustering of IMS data than lipid extract data. On the other hand, both methods clearly delineated the CS4, CS8 and CS8+R experimental groups from control.

Conclusions

MALDI analysis of brain lipid extracts plated onto a ground steel target for high through-put studies, or imaged directly in tissue can be used to assess biochemical pathology of white matter neurodegeneration and responses to treatment.

Comprehensive and quantitative proteomic analyses of zebrafish plasma reveals conserved protein profiles between genders and between zebrafish and human

Omic approaches have been increasingly used in the zebrafish model for holistic understanding of molecular events and mechanisms of tissue functions. However, plasma is rarely used for omic profiling because of the technical challenges in collecting sufficient blood. In this study, we employed two mass spectrometric (MS) approaches for a comprehensive characterization of zebrafish plasma proteome, i.e. conventional shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) for an overview study and quantitative SWATH (Sequential Window Acquisition of all THeoretical fragment-ion spectra) for comparison between genders. 959 proteins were identified in the shotgun profiling with estimated concentrations spanning almost five orders of magnitudes. Other than the presence of a few highly abundant female egg yolk precursor proteins (vitellogenins), the proteomic profiles of male and female plasmas were very similar in both number and abundance and there were basically no other highly gender-biased proteins. The types of plasma proteins based on IPA (Ingenuity Pathway Analysis) classification and tissue sources of production were also very similar. Furthermore, the zebrafish plasma proteome shares significant similarities with human plasma proteome, in particular in top abundant proteins including apolipoproteins and complements. Thus, the current study provided a valuable dataset for future evaluation of plasma proteins in zebrafish.

Multijunction Capillary Isoelectric Focusing Device Combined with Online Membrane-Assisted Buffer Exchanger Enables Isoelectric Point Fractionation of Intact Human Plasma Proteins for Biomarker Discovery

Prefractionation of proteins is often employed to improve analysis specificity in proteomics. Prefractionation based on the isoelectric point (pI) is particularly attractive because pI is a well-defined parameter and it is orthogonal to hydrophobicity on which reversed-phase chromatography is based. However, direct capillary electrophoresis of blood proteins is challenging due to its high content of salts and charged small molecules. Here, we couple an online desalinator device to our multijunction capillary isoelectric focusing (MJ-CIEF) instrument and perform direct isoelectric separation of human blood plasma. In a proof-of-principle experiment, pooled samples of patients with progressive mild cognitive impairment and corresponding healthy controls were investigated. Injection of 3 μL of plasma containing over 100 μg of proteins into the desalinator was followed by pI fractionation with MJ-CIEF in less than 1 h. Shotgun proteomics of 12 collected fractions from each of the 5 replicates of pooled samples resulted in the identification and accurate quantification (median CV between the replicates is <4%) of nearly 365 protein groups from 4030 unique peptides (with <1% FDR for both peptides and proteins). The obtained results include several proteins previously reported as AD markers. The isoelectric point of each quantified protein was calculated using a set of 7 synthetic peptides spiked into the samples. Several proteins with a significant pI shift between their isoforms in the patient and control samples were identified. The presented method is straightforward, robust, and scalable; therefore, it can be used in both biological and clinical applications.

Recent applications of CE- and HPLC-MS in the analysis of human fluids

The present review intends to cover the literature on the use of CE-/LC-MS for the analysis of human fluids, from 2010 until present. It has been planned to provide an overview of the most recent practical applications of these techniques to less extensively used human body fluids, including, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate, tear fluid, breast fluid, amniotic fluid, and cerumen. Potential pitfalls related to fluid collection and sample preparation, with particular attention to sample clean-up procedures, and methods of analysis, from the research laboratory to a clinical setting will also be addressed. While being apparent that proteomics/metabolomics represent the most prominent approaches for global identification/quantification of putative biomarkers for a variety of human diseases, evidence is also provided of the suitability of these sophisticated techniques for the detection of heterogeneous components carried by these fluids.

Proteomic methods for biomarker discovery and validation. Are we there yet?

Noninvasive molecular biomarkers are becoming attractive tools to monitor disease progression, aid drug development programs and use as surrogate outcome measures in clinical trials. Cutting edge proteomic methods to assay biomarkers in body fluids have been developed in the past few years, but transitioning them to clinical practice has been slow and depends on the qualification of both the method and the biomarker.

Multiplexed, Quantitative Workflow for Sensitive Biomarker Discovery in Plasma Yields Novel Candidates for Early Myocardial Injury

We have developed a novel plasma protein analysis platform with optimized sample preparation, chromatography, and MS analysis protocols. The workflow, which utilizes chemical isobaric mass tag labeling for relative quantification of plasma proteins, achieves far greater depth of proteome detection and quantification while simultaneously having increased sample throughput than prior methods. We applied the new workflow to a time series of plasma samples from patients undergoing a therapeutic, "planned" myocardial infarction for hypertrophic cardiomyopathy, a unique human model in which each person serves as their own biologic control. Over 5300 proteins were confidently identified in our experiments with an average of 4600 proteins identified per sample (with two or more distinct peptides identified per protein) using iTRAQ four-plex labeling. Nearly 3400 proteins were quantified in common across all 16 patient samples. Compared with a previously published label-free approach, the new method quantified almost fivefold more proteins/sample and provided a six- to nine-fold increase in sample analysis throughput. Moreover, this study provides the largest high-confidence plasma proteome dataset available to date. The reliability of relative quantification was also greatly improved relative to the label-free approach, with measured iTRAQ ratios and temporal trends correlating well with results from a 23-plex immunoMRM (iMRM) assay containing a subset of the candidate proteins applied to the same patient samples. The functional importance of improved detection and quantification was reflected in a markedly expanded list of significantly regulated proteins that provided many new candidate biomarker proteins. Preliminary evaluation of plasma sample labeling with TMT six-plex and ten-plex reagents suggests that even further increases in multiplexing of plasma analysis are practically achievable without significant losses in depth of detection relative to iTRAQ four-plex. These results obtained with our novel platform provide clear demonstration of the value of using isobaric mass tag reagents in plasma-based biomarker discovery experiments.