Plasma/serum proteomics: pre-analytical issues

Expression pattern of ATG4C and its effect on early embryonic development of porcine oocytes

Autophagy is essential for oocyte maturation and preimplantation embryo development. ATG4C, a member of the ATG4 family, plays a crucial role in the autophagy process. The effect of ATG4C on the early embryonic development in pig has not been studied. In this study, the expression patterns of ATG4C were explored using qRT-PCR and immunofluorescence staining. Different concentrations of serum were added to in vitro maturation (IVM) medium to investigate its effects on oocyte maturation and embryonic development. Finally, the developmental potential of parthenogenetic embryos was detected by downregulating ATG4C in MII stage oocytes under 0 % serum condition. The results revealed that ATG4C was highly expressed in porcine oocytes matured in vitro and in parthenogenetic embryos. Compared with the 10 % serum group, the cumulus cell expansion, first polar body (PB1) extrusion rate, and subsequent developmental competence of embryos were reduced in the 0 % and 5 % serum groups. The mRNA levels of LC3, ATG5, BECLIN1, TFAM, PGC1α, and PINK1 were significantly increased (P < 0.05) in the 0 % serum group. ATG4C was significantly upregulated in the embryos at the 1-cell, 2-cell, 8-cell, and 16-cell stages in the 0 % serum group (P < 0.05). Compared with the negative control group, downregulation of ATG4C significantly decreased the 4-cell, 8-cell, and blastocyst rates (P < 0.05), and the expression of genes related to autophagy, mitochondria, and zygotic genome activation (ZGA) was significantly decreased (P < 0.05). The relative fluorescence intensity of LC3 and mitochondrial content in the ATG4C siRNA group was significantly reduced (P < 0.05). Collectively, the results indicate that ATG4C is highly expressed in porcine oocytes matured in vitro and in early embryos, and inhibition of ATG4C effects embryonic developmental competence by decreasing autophagy, mitochondrial content, and ZGA under serum-free condition.

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis

Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids.

Advancements in automation for plasma proteomics sample preparation

Automation is necessary to increase sample processing throughput for large-scale clinical analyses. Replacement of manual pipettes with robotic liquid handler systems is especially helpful in processing blood-based samples, such as plasma and serum. These samples are very heterogenous, and protein expression can vary greatly from sample-to-sample, even for healthy controls. Detection of true biological changes requires that variation from sample preparation steps and downstream analytical detection methods, such as mass spectrometry, remains low. In this mini-review, we discuss plasma proteomics protocols and the benefits of automation towards enabling detection of low abundant proteins and providing low sample error and increased sample throughput. This discussion includes considerations for automation of major sample depletion and/or enrichment strategies for plasma toward mass spectrometry detection.

Urea Nitrogen Metabolite Can Contribute to Implementing the Ideal Protein Concept in Monogastric Animals

Simple Summary

Can urea nitrogen metabolite contribute to implementing the ideal protein concept in monogastric animals? This work aims to critically analyse how this metabolite can contribute to accurately implementing the ideal protein concept in monogastric animals, particularly in pig, poultry, and rabbit nutrition. This information will contribute to evaluating its potential and limitations as biomarker, as well as to standardizing the use of this metabolite in precise amino acidic monogastric nutrition.

Abstract

The ideal protein concept refers to dietary protein with an amino acid profile that exactly meets an animal’s requirement. Low-quality protein levels in the diet have negative implications for productive and reproductive traits, and a protein oversupply is energetically costly and leads to an excessive N excretion, with potentially negative environmental impact. Urea Nitrogen (UN), which corresponds to the amount of nitrogen in the form of urea circulating in the bloodstream, is a metabolite that has been widely used to detect amino acid imbalances and deficiencies and protein requirements. This review aims to critically analyse how UN can contribute to accurately implementing the ideal protein concept in monogastric animals, particularly in pig, poultry, and rabbit nutrition (14,000 animals from 76 published trials). About 59, 37, and 4% of trials have been conducted in pigs, poultry, and rabbits, respectively. UN level was negatively correlated to main performance traits (Pearson Correlation Coefficient [PCC] of −0.98 and −0.76, for average daily gain and feed conversion ratio, respectively), and lower UN level was related to higher milk yield and concentration. High level of UN was positively correlated to N excretion (PCC = 0.99) and negatively correlated to protein retention (PCC = −0.99). Therefore, UN in blood seems to be a proper indicator of amino acid imbalance in monogastric animals. Great variability in the use of UN was observed in the literature, including uses as determination medium (blood, plasma, or serum), units, and feeding system used (ad libitum or restricted), among others. A standardization of the methods in each of the species, with the aim to harmonize comparison among works, is suggested. After review, UN measurement in plasma and, whenever possible, the utilization of the same nutritional methodology (ad libitum conditions or restriction with blood sampling after refeeding at standardised time) are recommended. More studies are necessary to know the potential of UN and other bioindicators for amino acid deficiencies evaluation to get closer to the ideal protein concept.

Biomarker Assay Validation by Mass Spectrometry

Decades of discussion and publication have gone into the guidance from the scientific community and the regulatory agencies on the use and validation of pharmacokinetic and toxicokinetic assays by chromatographic and ligand binding assays for the measurement of drugs and metabolites. These assay validations are well described in the FDA Guidance on Bioanalytical Methods Validation (BMV, 2018). While the BMV included biomarker assay validation, the focus was on understanding the challenges posed in validating biomarker assays and the importance of having reliable biomarker assays when used for regulatory submissions, rather than definition of the appropriate experiments to be performed. Different from PK bioanalysis, analysis of biomarkers can be challenging due to the presence of target analyte(s) in the control matrices used for calibrator and quality control sample preparation, and greater difficulty in procuring appropriate reference standards representative of the endogenous molecule. Several papers have been published offering recommendations for biomarker assay validation. The situational nature of biomarker applications necessitates fit-for-purpose (FFP) assay validation. A unifying theme for FFP analysis is that method validation requirements be consistent with the proposed context of use (COU) for any given biomarker. This communication provides specific recommendations for biomarker assay validation (BAV) by LC-MS, for both small and large molecule biomarkers. The consensus recommendations include creation of a validation plan that contains definition of the COU of the assay, use of the PK assay validation elements that support the COU, and definition of assay validation elements adapted to fit biomarker assays and the acceptance criteria for both.

Assessing the Preanalytical Variability of Plasma and Cerebrospinal Fluid Processing and Its Effects on Inflammation-Related Protein Biomarkers

Proteomics studies are important for the discovery of new biomarkers as clinical tools for diagnosis and disease monitoring. However, preanalytical variations caused by differences in sample handling protocol pose challenges for assessing biomarker reliability and comparability between studies. The purpose of this study was to examine the effects of delayed centrifuging on measured protein levels in plasma and cerebrospinal fluid (CSF). Blood from healthy individuals and patients with multiple sclerosis along with CSF from patients with suspected neurological disorders were left at room temperature for different periods (blood: 1, 24, 48, 72 h; CSF: 1 and 6 h) prior to centrifuging. Ninety-one inflammation-related proteins were analyzed using a proximity extension assay, a high-sensitivity multiplex immunoassay. Additional metabolic and neurology-related markers were also investigated in CSF. In summary, many proteins, particularly in plasma, had increased levels with longer delays in processing likely due in part to intracellular leakage. Levels of caspase 8, interleukin 8, interleukin 18, sirtuin 2, and sulfotransferase 1A1 increased 2-fold to 10-fold in plasma after 24 h at room temperature. Similarly, levels of cathepsin H, ectonucleoside triphosphate diphosphohydrolase 5, and WW domain containing E3 ubiquitin protein ligase 2 differentiated in CSF with <6 h delay in processing. However, the rate of change for many proteins was relatively consistent; therefore, we were able to characterize biomarkers for detecting sample handling variability. Our findings highlight the importance of timely and consistent sample collection and the need for increased awareness of protein susceptibility to sample handling bias. In addition, suggested biomarkers may be used in certain situations to detect and correct for preanalytical variation in future studies.

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Several blood and cerebrospinal fluid proteins are affected by sample handling.

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Plasma protein levels increased with longer centrifugation delay from hemolysis.

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Certain proteins may assess sample handling variability and predict delay time.

Microheterogeneity and preanalytical stability of protein biomarkers of inflammation and renal function

Protein biomarker microheterogeneity has attracted increasing attention in epidemiological and clinical research studies. Knowledge concerning the preanalytical stability of proteins is paramount to assess the biological significance of their proteoforms. We investigated the stability of the inflammatory markers C-reactive protein (CRP), serum amyloid A (SAA), and calprotectin (S100A8/9), and the renal function marker, cystatin C (CnC). In total 16 proteoforms were quantified by immuno-MALDI-TOF MS in EDTA plasma and serum samples from 15 healthy volunteers. Prior to analysis blood samples were stored at either room temperature from 1 h up to 8 days, or underwent up to 9 consecutive freeze/thaw cycles. Pearson's correlation coefficient and t-test, intra-class correlation coefficient (ICC), and Autoregressive Integrated Moving-Average (ARIMA) models were used to investigate the stability of proteoform concentrations and distributions in blood. Plasma and serum concentrations of CRP and SAA proteoforms were highly stable during room temperature exposure and repeated freeze/thaw cycles, demonstrating excellent reproducibility (ICC > 0.75), no serial dependency in ARIMA models, and stable distribution of proteoforms. Stability analyses for proteoforms of S100A8/9 and CnC identified only minor preanalytical changes in concentrations and distributions, and none of the proteoforms were produced during prolonged exposure to room temperature or repeated freezing/thawing. The four proteins and their proteoforms are stable during sub-optimal sample handling, and represent robust biomarker candidates for future biobank studies aimed at investigating the microheterogeneity of SAA, S100A8/9, and CnC in relation to inflammation, renal dysfunction and various clinical outcomes.

Neuro biomarker levels measured with high-sensitivity digital ELISA differ between serum and plasma

Aim: Digital ELISA-based assays for blood biomarkers of neurological disease are on the verge of clinical use. Here, we aimed to determine whether different preanalytical blood processing techniques influence results. Materials & methods: Concentrations of neurofilament light chain (NfL), Tau and amyloid beta (Aβ) were measured in human plasma and serum specimens using digital ELISA and compared between blood products. Measured levels of NfL were highly equivalant between serum and plasma in all analyses, however, measured levels of Tau and Aβ were consistently lower in serum relative to plasma. Conclusion: Tau and Aβ are likely lost during clotting in serum preparations, and should be assayed in plasma to get an accurate measure of circulating levels.

Autologous plasma versus fetal calf serum as a supplement for the culture of neutrophils

OBJECTIVE

Currently, the replacement of fetal calf serum (FCS) by a more suitable alternative is a sought aim in the field of tissue and cell culture research. Autologous plasma (AP) and especially autologous serum (AS) have been shown to be effective substitutes of FCS in culture media for some of the cell types. Nevertheless, there is no comparative data on the most appropriate supplement for cell media in neutrophil studies, it is now unclear whether AP have a relatively equal, superior or inferior performance to FCS in neutrophil cell culture. In the present study, human blood neutrophils were isolated and cultured in FCS- or AP-supplemented medium. After 12, 36 and 60 h of incubation, cell viability, oxidative burst and CD11b expression were determined by flow cytometry.

RESULTS

Compared to the culture of neutrophils in FCS 10% medium, the culture of neutrophils in a medium with AP 10% could prolong their life span without affecting their function. The findings introduce AP as a better supplement for human neutrophil cell culture than FCS and propose a simple and economical procedure for neutrophil isolation and culture.

Proteomic Biomarkers for the Detection of Endometrial Cancer

Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.

Obtaining High-Quality Blood Specimens for Downstream Applications: A Review of Current Knowledge and Best Practices

Blood is a biological fluid that contains multiple blood fraction and cellular components. High-quality blood specimens are essential prerequisites for various downstream applications such as molecular epidemiology studies, genomics, and proteomics studies. Currently, protocols and research publications concerning the collection, handling, preservation, and stability of blood or blood fractions are constantly emerging. Moreover, standardized guidelines are a requirement for biorepositories to tightly control preanalytical variables originating from these procedures and obtain high-quality blood specimen for downstream analyses. In this review article, we summarize the best practices and fit-for-purpose protocols regarding blood collection, processing, storage, and stability. In addition, we present some typical quality biomarkers, which could be used to evaluate the integrity of blood specimens.

Human PBMCs fight or flight response to starvation stress: Increased T-reg, FOXP3, and TGF-β1 with decreased miR-21 and Constant miR-181c levels

Regulatory T-lymphocytes play a prominent role in autoimmunity, allergy, and cancer. In some conditions such as inflammation and tumor, immune cells are encountered with metabolic stress. Emerging evidence indicates the contribution of microRNAs in both metabolism and immune regulation. Herewith, we have examined the in vitro effects of serum starvation for 16, 48, 72 and 96 h on the expression of T-reg differentiation markers (CD4, CD25, CD127, and FOXP3) as well as on the Transforming Growth Factor-β1 (TGF-β1) and some microRNAs (miR-21,-29a,-31,146a,-155,-181a and -181c) levels in human Peripheral Blood Mononuclear Cells (PBMCs). The percentage of CD4⁺CD25⁺CD127low/-FOXP3⁺ T-regs, as well as FOXP3 expression, was increased in starved lymphocytes (p < 0.01). 96 h-starved PBMCs had the lowest T-eff/T-reg ratio (p < 0.05). All the studied miRNAs except miR-181c were significantly down-regulated in those cells (p < 0.05), in particular, miR-29a and miR-155 were sharply declined in 48h-starved PBMCs (p < 0.01). There was a negative correlation between time of starvation and microRNAs expression, except for miR-181c (r-value = -0. 61 to -0.9 and p-value = 0.037 to 0). The percentage of T-reg was inversely correlated with all miRNAs levels except for miR-31 and miR-181c (r-value = -0.68 to -0.78 and p-value = 0.015 to 0.003). FOXP3 expression exhibited a same degree of negative correlation with miR-31 and miR-155 expression levels (r = -0.57 and p =  0.05, for both). Increasing starvation duration led to a rise inTGF-β1 protein levels (p<0.01), especially its active form (P<0.001). This study introduced the serum starvation as a tool for immunoregulation which acts probably through increasing TGF-β1 production and inducing some alterations in microRNAs expression.

Impact of biological matrix on inflammatory protein biomarker quantification based on targeted mass spectrometry

Background: Serum and plasma are widely used matrices in biological and clinical studies. To improve reliability and consistency of markers quantification, the influence of these matrices on proteins was evaluated by targeted mass spectrometry. Results: 65 proteins were quantified in matched blood samples collected in serum, ethylenediaminetetraacetic acid and heparin plasma tubes from 40 healthy and 10 pathological individuals. Only 52% of the proteins were not impacted by any of the biological matrices tested, and the effects on quantification of proteins affected was matrix and protein dependent. Conclusion: Matrix comparisons using mass spectrometry is therefore recommended to assess the relevance of using surrogate matrix, performing biomarker discovery study or evaluating the clinical use of biomarkers in large clinical cohorts.

The use of in-strip digestion for fast proteomic analysis on tear fluid from dry eye patients

Tear is an accessible fluid for exploring biomarkers of dry eye disease. This study describes a fast proteomic method by LC-Q-orbitrap-MS analysis with in-strip digestion and investigates the tear proteome of dry eye patients. Schirmer’s strips were used for collection of tear fluid from patients. These strips were cut into pieces and directly digested with trypsin before mass spectrometry analysis. The data showed that more than 50 proteins were found in tear fluid from dry eye patients. Gene Ontology (GO) annotation showed that most of proteins were transfer/carrier proteins, hydrolyses, enzyme modulators and signaling molecules. Targeted proteomics strategy revealed that 18 proteins were differentially expressed in dry eye patients. Furthermore, it was showed that the common post-translational modification in tear proteins is deamidation of Asn.

Stability and reproducibility of proteomic profiles measured with an aptamer-based platform

The feasibility of SOMAscan, a multiplex, high sensitivity proteomics platform, for use in studies using archived plasma samples has not yet been assessed. We quantified 1,305 proteins from plasma samples donated by 16 Nurses' Health Study (NHS) participants, 40 NHSII participants, and 12 local volunteers. We assessed assay reproducibility using coefficients of variation (CV) from duplicate samples and intra-class correlation coefficients (ICC) and Spearman correlation coefficients (r) of samples processed (i.e., centrifuged and aliquoted into separate components) immediately, 24, and 48 hours after collection, as well as those of samples collected from the same individuals 1 year apart. CVs were <20% for 99% of proteins overall and <10% for 92% of proteins in heparin samples compared to 66% for EDTA samples. We observed ICC or Spearman r (comparing immediate vs. 24-hour delayed processing) ≥0.75 for 61% of proteins, with some variation by anticoagulant (56% for heparin and 70% for EDTA) and protein class (ranging from 49% among kinases to 83% among hormones). Within-person stability over 1 year was good (ICC or Spearman r ≥ 0.4) for 91% of proteins. These results demonstrate the feasibility of SOMAscan for analyses of archived plasma samples.

Understanding preanalytical variables and their effects on clinical biomarkers of oncology and immunotherapy

Identifying a suitable course of immunotherapy treatment for a given patient as well as monitoring treatment response is heavily reliant on biomarkers detected and quantified in blood and tissue biospecimens. Suboptimal or variable biospecimen collection, processing, and storage practices have the potential to alter clinically relevant biomarkers, including those used in cancer immunotherapy. In the present review, we summarize effects reported for immunologically relevant biomarkers and highlight preanalytical factors associated with specific analytical platforms and assays used to predict and gauge immunotherapy response. Given that many of the effects introduced by preanalytical variability are gene-, transcript-, and protein-specific, biospecimen practices should be standardized and validated for each biomarker and assay to ensure accurate results and facilitate clinical implementation of newly identified immunotherapy approaches.

Mesoporous silica chip: enabled peptide profiling as an effective platform for controlling bio-sample quality and optimizing handling procedure

Background

High quality clinical samples are critical for meaningful interpretation of data obtained in both basic and translational medicine. More specifically, optimized pre-analysis handling to bio-sample is crucial for avoiding biased analysis in a clinical setting. A universally applicable method for the evaluation of sample quality and pre-analysis handling is therefore in great demand.

Methods

The fingerprint pattern of low molecular weight (LMW) peptides in sera is directly associated with sample quality and handling process. Previous studies for enrichment/isolation of LMW peptides have shown that LMW peptides can be enriched by silica meso-porous material in a sensitive and high-throughput manner. Here, a peptide profile approach utilizing mesoporous silica chip-based sample preparation combined with MALDI MS analysis was used as a new platform for evaluation of bio-sample quality. Rat sera were selected as model sample and analyzed according to their LMW peptide fingerprint spectra.

Results

This novel method can complete the entire sample preparation procedure in a short period of time (<40 min), requires minimum amounts of sample (<10 µL), is of high sensitivity (LOD 10 ng/mL) as well as high reproducibility (CV% < 15%). According to the acquired LMW peptide spectra, we were able to distinguish the serum samples processed under different conditions (including different storage temperature, time, and freezing/thaw cycles) with the help of bioinformatics tools (principle composition analysis and significant difference analysis), and identify the samples that had significantly changed due to the inappropriate processing. Based on the percentage of significantly changed peaks in LMW peptide mass spectrum after handling, a judgment standard was established that can be used to evaluate the status of preservation of a biological sample. In addition, our principle study established recommendations for storage time, storage temperature and freeze/thaw conditions.

Conclusion

Our novel method for analysis of bio-samples allows for effective identification of variations in composition within samples, and provides a cost-effective tool for simple sample manipulation in a clinical setting.

Effects of pre-analytical processes on blood samples used in metabolomics studies

Every day, analytical and bio-analytical chemists make sustained efforts to improve the sensitivity, specificity, robustness, and reproducibility of their methods. Especially in targeted and non-targeted profiling approaches, including metabolomics analysis, these objectives are not easy to achieve; however, robust and reproducible measurements and low coefficients of variation (CV) are crucial for successful metabolomics approaches. Nevertheless, all efforts from the analysts are in vain if the sample quality is poor, i.e. if preanalytical errors are made by the partner during sample collection. Preanalytical risks and errors are more common than expected, even when standard operating procedures (SOP) are used. This risk is particularly high in clinical studies, and poor sample quality may heavily bias the CV of the final analytical results, leading to disappointing outcomes of the study and consequently, although unjustified, to critical questions about the analytical performance of the approach from the partner who provided the samples. This review focuses on the preanalytical phase of liquid chromatography–mass spectrometry-driven metabolomics analysis of body fluids. Several important preanalytical factors that may seriously affect the profile of the investigated metabolome in body fluids, including factors before sample collection, blood drawing, subsequent handling of the whole blood (transportation), processing of plasma and serum, and inadequate conditions for sample storage, will be discussed. In addition, a detailed description of latent effects on the stability of the blood metabolome and a suggestion for a practical procedure to circumvent risks in the preanalytical phase will be given.

Graphical Abstract

Comparative proteomic analysis of plasma proteins in patients with age-related macular degeneration

AIM

To find the significant altered proteins in age-related macular degeneration (AMD) patients as potential biomarkers of AMD.

METHODS

A comparative analysis of the protein pattern of AMD patients versus healthy controls was performed by means of proteomic analysis using two-dimensional gel electrophoresis followed by protein identification with MALDI TOF/TOF mass spectrometry.

RESULTS

We identified 28 proteins that were significantly altered with clinical relevance in AMD patients. These proteins were involved in a wide range of biological functions including immune responses, growth cytokines, cell fate determination, wound healing, metabolism, and anti-oxidance.

CONCLUSION

These results demonstrate the capacity of proteomic analysis of AMD patient plasma. In addition to the utility of this approach for biomarker discovery, identification of alterations in endogenous proteins in the plasma of AMD patient could improve our understanding of the disease pathogenesis.

Joint MS-based platforms for comprehensive comparison of rat plasma and serum metabolic profiling

Metabolomics is a rapidly growing field in the comprehensive understanding of cellular and organism-specific responses associated with perturbations induced by medicines, chemicals and environment. Blood matrices are frequently used in clinical and biological studies. In this study, we compared metabolic profiling between rat plasma and serum using complementary platforms of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-quadruple time-of-flight-mass spectrometry (LC-QTOF-MS). The sample types that were tested included plasma prepared with K2 EDTA and serum collected using venous blood collection protocols. The results of peak area variation for each detected metabolite/feature in the quality control samples showed a good reproducibility in LC-QTOF-MS and better reproducibility in GC-MS. In GC-MS analysis: (a) 25.8% of the defined metabolites differed serum from plasma profiling (t-test, p < 0.05); and (b) serum possessed higher sensitivity than plasma for its generally higher peak intensity in the metabolic profiling. In LC-QTOF-MS analysis, 13 (in positive ion mode) and seven (in negative ion mode) important metabolites were identified as mainly contributing to the separation between serum and plasma.

Cytokines as biomarkers in rheumatoid arthritis

RA is a complex disease that develops as a series of events often referred to as disease continuum. RA would benefit from novel biomarker development for diagnosis where new biomarkers are still needed (even if progresses have been made with the inclusion of ACPA into the ACR/EULAR 2010 diagnostic criteria) and for prognostic notably in at risk of evolution patients with autoantibody-positive arthralgia. Risk biomarkers for rapid evolution or cardiovascular complications are also highly desirable. Monitoring biomarkers would be useful in predicting relapse. Finally, predictive biomarkers for therapy outcome would allow tailoring therapy to the individual. Increasing numbers of cytokines have been involved in RA pathology. Many have the potential as biomarkers in RA especially as their clinical utility is already established in other diseases and could be easily transferable to rheumatology. We will review the current knowledge's relation to cytokine used as biomarker in RA. However, given the complexity and heterogeneous nature of RA, it is unlikely that a single cytokine may provide sufficient discrimination; therefore multiple biomarker signatures may represent more realistic approach for the future of personalised medicine in RA.

Proteomics applied to transfusion plasma: the beginning of the story

'Safe blood' is and has always been the major concern in transfusion medicine. Plasma can undergo virus inactivation treatments based on physicochemical, photochemical or thermal methodologies for pathogen inactivation. The validation of these treatments is essentially based on clottability assays and clotting factors' titration; however, their impact on plasma proteins at the molecular level has not yet been evaluated. Proteomics appears as particularly adapted to identify, to localize and, consequently, to correlate these modifications to the biological activity change. At the crossroads of biology and analytical sciences, proteomics is the large-scale study of proteins in tissues, physiological fluids or cells at a given moment and in a precise environment. The proteomic strategy is based on a set of methodologies involving separative techniques like mono- and bidimensional gel electrophoresis and chromatography, analytical techniques, especially mass spectrometry, and bioinformatics. Even if plasma has been extensively studied since the very beginning of proteomics, its application to transfusion medicine has just begun. In the first part of this review, we present the principles of proteomics analysis. Then, we propose a state of the art of proteomics applied to plasma analysis. Finally, the use of proteomics for the evaluation of the impact of storage conditions and pathogen inactivation treatments applied to transfusion plasma and for the evaluation of therapeutic protein fractionated is discussed.

An automated plasma protein fractionation design: high-throughput perspectives for proteomic analysis

Background

Human plasma, representing the most complete record of the individual phenotype, is an appealing sample for proteomics analysis in clinical applications. Up to today, the major obstacle in a proteomics study of plasma is the large dynamic range of protein concentration and the efforts of many researchers focused on the resolution of this important drawback.

Findings

In this study, proteins from pooled plasma samples were fractionated according to their chemical characteristics on a home-designed SPE automated platform. The resulting fractions were digested and further resolved by reversed-phase liquid chromatography coupled with MALDI TOF/TOF mass spectrometry. A total of 712 proteins were successfully identified until a concentration level of ng/mL. Pearson correlation coefficient was used to test reproducibility.

Conclusions

Our multidimensional fractionation approach reduced the analysis time (2 days are enough to process 16 plasma samples filling a 96-well plate) over the conventional gel-electrophoresis or multi-LC column based methods. The robotic processing, avoiding contaminants or lack of sample handling skill, promises highly reproducible specimen analyses (more than 85% Pearson correlation). The automated platform here presented is flexible and easily modulated changing fractioning elements or detectors.

Impact of preanalytic factors on the design and application of integral biomarkers for directing patient therapy

Molecular assays have been routinely applied to improve diagnosis for the last 25 years. Assays that guide therapy have a similar history; however, their evolution has lacked the focus on analytic integrity that is required for the molecularly targeted therapies of today. New molecularly targeted agents require assays of greater precision/quantitation to predict the likelihood of response, i.e., to identify patients whose tumors will respond, while at the same time excluding and protecting those patients whose tumors will not respond or in whom treatment will cause unacceptable toxicity. The handling of tissue has followed a fit-for-purpose approach focused on appropriateness for diagnostic needs, which is less rigorous than the demands of new molecular assays that interrogate DNA, RNA, and proteins in a quantitative, multiplex manner. There is a new appreciation of the importance and fragility of tissue specimens as the source of analytes to direct therapy. By applying a total test paradigm and defining and measuring sources of variability in specimens, we can develop a set of specifications that can be incorporated into the clinical-care environment to ensure that a specimen is appropriate for analysis and will return a true result.

Differences between human plasma and serum metabolite profiles

BACKGROUND

Human plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized.

METHODOLOGY/PRINCIPAL FINDINGS

We analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p = 0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r = 0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices.

CONCLUSIONS/SIGNIFICANCE

Our study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.

Blood-based biomarkers of Alzheimer's disease: challenging but feasible

Blood-based biomarkers present a considerable challenge: technically, as blood is a complex tissue and conceptually, as blood lacks direct contact with brain. Nonetheless, increasing evidence suggests that there is a blood protein signature, and possibly a transcript signature, that might act to increase confidence in diagnosis, be used to predict progression in either disease or prodromal states, and that may also be used to monitor disease progression. Evidence for this optimism comes partly from candidate protein studies, including those suggesting that amyloid-beta measures might have value in prediction and those studies of inflammatory markers that consistently show change in Alzheimer's disease, and partly from true proteomics studies that are beginning to identify markers in blood that replicate across studies and populations.

Challenges for biomarker discovery in body fluids using SELDI-TOF-MS

Protein profiling using SELDI-TOF-MS has gained over the past few years an increasing interest in the field of biomarker discovery. The technology presents great potential if some parameters, such as sample handling, SELDI settings, and data analysis, are strictly controlled. Practical considerations to set up a robust and sensitive strategy for biomarker discovery are presented. This paper also reviews biological fluids generally available including a description of their peculiar properties and the preanalytical challenges inherent to sample collection and storage. Finally, some new insights for biomarker identification and validation challenges are provided.

Analysis of human plasma proteins: a focus on sample collection and separation using free-flow electrophoresis

Due to ease of accessibility, plasma has become the sample of choice for proteomics studies directed towards biomarker discovery intended for use in diagnostics, prognostics and even in theranostics. The result of these extensive efforts is a long list of potential biomarkers, very few of which have led to clinical utility. Why have so many potential biomarkers failed validation? Herein, we address certain issues encountered, which complicate biomarker discovery efforts originating from plasma. The advantages of stabilizing the sample at collection by the addition of protease inhibitors are discussed. The principles of free-flow electrophoresis (FFE) separation are provided together with examples applying to various studies. Finally, particular attention is given to plasma or serum analysis using multidimensional separation strategies into which the FFE is incorporated. The advantages of using FFE separation in these workflows are discussed.

Multidimensional protein fractionation using ProteomeLab PF 2D for profiling amyotrophic lateral sclerosis immunity: A preliminary report

Background

The ProteomeLab™ PF 2D platform is a relatively new approach to global protein profiling. Herein, it was used for investigation of plasma proteome changes in amyotrophic lateral sclerosis (ALS) patients before and during immunization with glatiramer acetate (GA) in a clinical trial.

Results

The experimental design included immunoaffinity depletion of 12 most abundant proteins from plasma samples with the ProteomeLab™ IgY-12 LC10 column kit as first dimension separation, also referred to as immuno-partitioning. Second and third dimension separations of the enriched proteome were performed on the PF 2D platform utilizing 2D isoelectric focusing and RP-HPLC with the resulting fractions collected for analysis. 1D gel electrophoresis was added as a fourth dimension when sufficient protein was available. Protein identification from collected fractions was performed using nano-LC-MS/MS approach. Analysis of differences in the resulting two-dimensional maps of fractions obtained from the PF 2D and the ability to identify proteins from these fractions allowed sensitivity threshold measurements. Masked proteins in the PF 2D fractions are discussed.

Conclusion

We offer some insight into the strengths and limitations of this emerging proteomic platform.

Lost in translation: five grand challenges for proteomic biomarker discovery

The biomedical community has the imperative to develop reliable, clinically relevant and generalizable bioassays that can be used to accurately recognize those individuals with early-stage disease or those patients who will respond to therapy, with the ultimate aim of achieving individualized medicine. In recent years, increasingly sophisticated proteomic screening technologies have been introduced, providing the biomedical community with a valuable new approach for the systematic discovery and validation of novel diagnostic, prognostic and therapeutic tools. Nevertheless, the complexity of the cellular milieu wherein a variety of macromolecules interact in dynamic fashion, combined with the complex clinical manifestation of chronic pathologies and widespread diversity of patient populations, mean that universal biomarkers will not be easily developed. In this review, the five key challenges that must be surmounted in order to advance the clinical impact of this nascent field are described, and plausible solutions based on the authors' own ongoing proteomic profiling of cardiovascular disease is outlined.

How will haematologists use proteomics?

Proteomics technologies are emerging as a useful tool in the identification of disease biomarkers, and in defining and characterising both normal physiological and disease processes. Many cellular changes in protein expression in response to an external stimulus or mutation can only be characterised at the proteome level. In these cases protein expression is often controlled by altered rates of translation and/or degradation, making proteomics an important tool in the analysis of biological systems. In the leukaemias, post-translational modification of proteins (e.g. phosphorylation, acetylation) plays a key role in the molecular pathology of the disease: such modifications can now be detected with novel proteomic methods. In a clinical setting, serum remains a relatively un-mined source of information for prognosis and response to therapy. This protein rich fluid represents an opportunity for proteomics research to benefit hematologists and others. In this review, we discuss the technologies available for the study of the proteome that offer realistic opportunities in haematology.