Quantitative Age-specific Variability of Plasma Proteins in Healthy Neonates, Children and Adults

Ontology-guided clustering enables proteomic analysis of rare pediatric disorders

The study of rare pediatric disorders is fundamentally limited by small patient numbers, making it challenging to draw meaningful biological conclusions. To address this, we developed a framework integrating clinical ontologies with proteomic profiling, enabling the systematic analysis of rare conditions in aggregate. We applied this approach to urine and plasma samples from 1140 children and adolescents, encompassing 394 distinct disease conditions and healthy controls. Using advanced mass spectrometry workflows, we quantified over 5000 proteins in urine, 900 in undepleted (neat) plasma, and 1900 in perchloric acid-depleted plasma. Embedding SNOMED CT clinical terminology in a network structure allowed us to group rare conditions based on their clinical relationships, enabling statistical analysis even for diseases with as few as two patients. This approach revealed molecular signatures across developmental stages and disease clusters while accounting for age- and sex-specific variation. Our framework provides a generalizable solution for studying heterogeneous patient populations where traditional case-control studies are impractical, bridging the gap between clinical classification and molecular profiling of rare diseases.

Distinct protein patterns related to postnatal development in small for gestational age preterm infants

BACKGROUND

Preterm infants, especially those born small for gestational age (SGA), are at risk of short-term and long-term health complications. Characterization of changes in circulating proteins postnatally in preterm infants may provide valuable fundamental insights into this population. Here, we investigated postnatal developmental patterns in preterm infants and explored protein signatures that deviate between SGA infants and appropriate for gestational age (AGA) infants using a mass spectrometry (MS)-based proteomics workflow.

METHODS

Longitudinal serum samples obtained at postnatal days 0, 3, 7, 14, and 28 from 67 preterm infants were analyzed using unbiased MS-based proteomics.

RESULTS

314 out of 833 quantified serum proteins change postnatally, including previously described age-related changes in immunoglobulins, hemoglobin subunits, and new developmental patterns, e.g. apolipoproteins (APOA4) and terminal complement cascade (C9) proteins. Limited differences between SGA and AGA infants were found at birth while longitudinal monitoring revealed 69 deviating proteins, including insulin-sensitizing hormone adiponectin, platelet proteins, and 24 proteins with an annotated function in the immune response.

CONCLUSIONS

This study shows the potential of MS-based serum profiling in defining circulating protein trajectories in the preterm infant population and its ability to identify longitudinal alterations in protein levels associated with SGA.

IMPACT

Postnatal changes of circulating proteins in preterm infants have not fully been elucidated but may contribute to development of health complications. Mass spectrometry-based analysis is an attractive approach to study circulating proteins in preterm infants with limited material. Longitudinal plasma profiling reveals postnatal developmental-related patterns in preterm infants (314/833 proteins) including previously described changes, but also previously unreported proteins. Longitudinal monitoring revealed an immune response signature between SGA and AGA infants. This study highlights the importance of taking postnatal changes into account for translational studies in preterm infants.

Changes in rat plasma proteomes during the first week after birth

Blood plasma is the most informative body fluid, containing large amounts of substances that are released by active secretion or leakage from tissues and cells. Therefore, plasma changes reflect the body state. To explore changes in plasma during the early life of Wistar-rats, the plasma proteomes of newborn and first-week rats were investigated using liquid chromatography-tandem mass spectrometry. A total of 639 proteins were identified at both developmental stages and 570 proteins were used for quantitative analysis. The plasma of first-week rats, compared to that in newborn rats using label-free quantification, showed that the levels of 42 proteins significantly increased while those of 17 proteins decreased. Plasma proteomic patterns at both developmental stages can be easily separated using differential protein cluster analysis. Using the Ingenuity Pathway analysis tool, some pathways including LXR/RXR Activation, DHCR24 Signaling Pathway, Acute Phase Response Signaling, and Detoxification of Reactive Oxygen Species were significantly enhanced. Over 10 categories related to the development and functions were enriched. Plasma proteomes of first-week rats were distinct from those of newborn rats. These changes would make it easier for newborn rats to survive. This is the first study using liquid chromatography-tandem mass spectrometry to investigate newborn rat plasma proteome changes, providing a basis and clues for studying animal development.

A Proteomic Examination of Plasma Extracellular Vesicles Across Colorectal Cancer Stages Uncovers Biological Insights That Potentially Improve Prognosis

BACKGROUND

Recent advancements in understanding plasma extracellular vesicles (EVs) and their role in disease biology have provided additional unique insights into the study of Colorectal Cancer (CRC).

METHODS

This study aimed to gain biological insights into disease progression from plasma-derived extracellular vesicle proteomic profiles of 80 patients (20 from each CRC stage I-IV) against 20 healthy age- and sex-matched controls using a high-resolution SWATH-MS proteomics with a reproducible centrifugation method to isolate plasma EVs.

RESULTS

We applied the High-Stringency Human Proteome Project (HPP) guidelines for SWATH-MS analysis, which refined our initial EV protein identification from 1362 proteins (10,993 peptides) to a more reliable and confident subset of 853 proteins (6231 peptides). In early-stage CRC, we identified 11 plasma EV proteins with differential expression between patients and healthy controls (three up-regulated and eight down-regulated), many of which are involved in key cancer hallmarks. Additionally, within the same cohort, we analysed EV proteins associated with tumour recurrence to identify potential prognostic indicators for CRC. A subset of up-regulated proteins associated with extracellular vesicle formation (GDI1, NSF, and TMED9) and the down-regulation of TSG101 suggest that micro-metastasis may have occurred earlier than previously anticipated.

DISCUSSION

By employing stringent proteomic analysis and a robust SWATH-MS approach, we identified dysregulated EV proteins that potentially indicate early-stage CRC and predict recurrence risk, including proteins involved in metabolism, cytoskeletal remodelling, and immune response. While our findings underline discrepancies with other studies due to differing isolation and stringency parameters, they provide valuable insights into the complexity of the EV proteome, emphasising the need for standardised protocols and larger, well-controlled studies to validate potential biomarkers.

Breast cancer metastasis progression is associated with elevated activity of kynurenine monooxygenase and kynureninase

INTRODUCTION

Metastasis remains the major cause of death in breast cancer (BrCa) and lacks specific treatment strategies. The kynurenine pathway (KP) has been suggested as a key mechanism facilitating progression of BrCa. While KP activity has been explored in primary BrCa, its role in metastasis remains unclear. To better understand this, we examined changes in the KP of BrCa with no metastasis compared to BCa that produced local or distant metastases. Given that the cancer cell secretome plays a role in metastasis, we also investigated the relationship between changes in KP activity and serum proteins of patients with local or distant metastases.

METHODS

To investigate changes in the KP in BrCa, with and without metastasis, we quantified KP metabolites in blood sera collected from patients with stage 1 BrCa (n = 34), BrCa with local metastases (n = 46), BrCa with distant metastases (n = 20) and healthy controls (n = 39). The serum protein profile of the BrCa patients with local or distant metastasis was determined before correlation analyses were carried out to examine the relationship between changes in the KP and cancer serum proteins using SPSS.

RESULTS

We found that the KP was elevated in BrCa patients with local and distant metastasis compared to healthy controls and stage 1 BrCa patients. The activity of kynurenine monooxygenase (KMO) and kynureninase (KYNU) A was positively associated with disease stage and was higher compared to healthy controls. Proteome analysis in patients with local or distant metastasis revealed the dysregulation of 14 proteins, 9 of which were up-regulated and 5 down-regulated at the distant metastasis stage. Importantly, three of these proteins have not been previously linked to BrCa metastasis. In the correlation studies between the KP profile, cancer serum proteins and metastasis status, KYNU A had the greatest number of significant associations with cancer serum protein, followed by KMO.

CONCLUSION

Our findings reveal that the KP was regulated differently at various stages of BrCa and was more dysregulated in patients with local or distant metastasis. These KP activity changes showed a significant association with cancer serum proteins in BrCa patients with local or distant metastasis, highlighting the potential role of KP in BrCa metastasis.

Discovery of Alpha-1-Antichymotrypsin as a Marker of Delayed Recovery from Concussion in Children

Of the four million children who experience a concussion each year, 30-50% of children will experience delayed recovery, where they will continue to experience symptoms more than two weeks after their injury. Delayed recovery from concussion encompasses emotional, behavioral, physical, and cognitive symptoms, and as such, there is an increased focus on developing an objective tool to determine risk of delayed recovery. This study aimed to identify a blood protein signature predictive of delayed recovery from concussion in children. Plasma samples were collected from children who presented to the Emergency Department at the Royal Children's Hospital, Melbourne, within 48h post-concussion. This study involved a discovery and validation phase. For the discovery phase, untargeted proteomics analysis was performed using single window acquisition of all theoretical mass spectra to identify blood proteins differentially abundant in samples from children with and without delayed recovery from concussion. A subset of these proteins was then validated in a separate participant cohort using multiple reaction monitoring and enzyme linked immunosorbent assay. A blood protein signature predictive of delayed recovery from concussion was modeled using a Support Vector Machine, a machine learning approach. In the discovery phase, 22 blood proteins were differentially abundant in age- and sex-matched samples from children with (n = 9) and without (n = 9) delayed recovery from concussion, six of whom were chosen for validation. In the validation phase, alpha-1-ACT was shown to be significantly lower in children with delayed recovery (n = 12) compared with those without delayed recovery (n = 28), those with orthopedic injuries (n = 7) and healthy controls (n = 33). A model consisting of alpha-1-ACT concentration stratified children based on recovery from concussion with an 0.88 area under the curve. We have identified that alpha-1-ACT differentiates between children at risk of delayed recovery from those without delayed recovery from concussion. To our knowledge, this is the first study to identify alpha-1-ACT as a potential marker of delayed recovery from concussion in children. Multi-site studies are required to further validate this finding before use in a clinical setting.

Serum proteomics of mother-infant dyads carrying HLA-conferred type 1 diabetes risk

In-utero and dietary factors make important contributions toward health and development in early childhood. In this respect, serum proteomics of maturing infants can provide insights into studies of childhood diseases, which together with perinatal proteomes could reveal further biological perspectives. Accordingly, to determine differences between feeding groups and changes in infancy, serum proteomics analyses of mother-infant dyads with HLA-conferred susceptibility to type 1 diabetes (n = 22), weaned to either an extensively hydrolyzed or regular cow's milk formula, were made. The LC-MS/MS analyses included samples from the beginning of third trimester, the time of delivery, 3 months postpartum, cord blood, and samples from the infants at 3, 6, 9, and 12 months. Correlations between ranked protein intensities were detected within the dyads, together with perinatal and age-related changes. Comparison with intestinal permeability data revealed a number of significant correlations, which could merit further consideration in this context.

Gestational Age-Related Urinary Peptidome Changes in Preterm and Term Born Infants

INTRODUCTION

Preterm infants are at risk for a variety of somatic and neurological disorders. In recent years, biofluid proteomics has emerged as a potential diagnostic tool for biomarker analysis. The aim of this study was to determine gestational age (GA)-related patterns of the urinary peptidome in preterm infants for researching potential novel prognostic biomarkers.

METHODS

We performed urinary peptidomics in longitudinal samples of 24 preterm (mean GA weeks 28 + 1 [24+1-31 + 6]) and 27 term born controls (mean GA weeks 39 + 2 [37+0-41 + 1]) using capillary electrophoresis combined with mass spectrometry (CE-MS). Peptides were sequenced using CE-MS/MS or LC-MS/MS analysis and were deposited, matched, and annotated in a Microsoft SQL database for statistical analysis. We compared their abundance in urine of preterm and term born infants and performed a validation analysis as well as correlations to GA and clinical risk scores.

RESULTS

Our results confirmed significant differences in the abundance of peptides and the hypothesis of age-dependent urinary peptidome changes in preterm and term infants. In preterm infants, SLC38A10 (solute carrier family 38 member 10) is one of the most abundant peptides. Combined urinary peptides correlated with clinical risk scores (p < 0.05).

CONCLUSION

This is the first study reporting GA-related urinary peptidome changes of preterm infants detected by CE-MS and a modulation of the peptidome with GA. Further research is required to locate peptidome clusters correlated with specific clinical complications and long-term outcome. This may identify preterm infants at higher risk for adverse outcome who would benefit from early intervention.

Advances in proteomics: characterization of the innate immune system after birth and during inflammation

Proteomics is the characterization of the protein composition, the proteome, of a biological sample. It involves the large-scale identification and quantification of proteins, peptides, and post-translational modifications. This review focuses on recent developments in mass spectrometry-based proteomics and provides an overview of available methods for sample preparation to study the innate immune system. Recent advancements in the proteomics workflows, including sample preparation, have significantly improved the sensitivity and proteome coverage of biological samples including the technically difficult blood plasma. Proteomics is often applied in immunology and has been used to characterize the levels of innate immune system components after perturbations such as birth or during chronic inflammatory diseases like rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). In cancers, the tumor microenvironment may generate chronic inflammation and release cytokines to the circulation. In these situations, the innate immune system undergoes profound and long-lasting changes, the large-scale characterization of which may increase our biological understanding and help identify components with translational potential for guiding diagnosis and treatment decisions. With the ongoing technical development, proteomics will likely continue to provide increasing insights into complex biological processes and their implications for health and disease. Integrating proteomics with other omics data and utilizing multi-omics approaches have been demonstrated to give additional valuable insights into biological systems.

MMP-9 plasma level as biomarker of cochlear implantation outcome in cohort study of deaf children

PURPOSE

If before cochlear implantation it was possible to assay biomarkers of neuroplasticity, we might be able to identify those children with congenital deafness who, later on, were at risk of poor speech and language rehabilitation outcomes.

METHODS

A group of 40 children aged up to 2 years with DFNB1-related congenital deafness was observed in this prospective cohort study over three follow-up intervals (0, 8, and 18 months) after cochlear implant (CI) activation. Children were assessed for auditory development using the LittlEARS Questionnaire (LEAQ) score, and at the same time, measurements were made of matrix metalloproteinase-9 (MMP-9) plasma levels.

RESULTS

There were significant negative correlations between plasma levels of MMP-9 at 8-month follow-up and LEAQ score at cochlear implantation (p = 0.04) and LEAQ score at 18-month follow-up (p = 0.02) and between MMP-9 plasma levels at 18-month follow-up and LEAQ score at cochlear implantation (p = 0.04). As already reported, we confirmed a significant negative correlation between MMP-9 plasma level at cochlear implantation and LEAQ score at 18-month follow-up (p = 0.005). Based on this latter correlation, two clusters of good and poor CI performers could be isolated.

CONCLUSIONS

The study shows that children born deaf who have an MMP-9 plasma level of less than 150 ng/ml at cochlear implantation have a good chance of attaining a high LEAQ score after 18 months of speech and language rehabilitation. This indicates that MMP-9 plasma level at cochlear implantation is a good prognostic marker for CI outcome.

Serum APOC1 levels are decreased in young autoantibody positive children who rapidly progress to type 1 diabetes

Better understanding of the early events in the development of type 1 diabetes is needed to improve prediction and monitoring of the disease progression during the substantially heterogeneous presymptomatic period of the beta cell damaging process. To address this concern, we used mass spectrometry-based proteomics to analyse longitudinal pre-onset plasma sample series from children positive for multiple islet autoantibodies who had rapidly progressed to type 1 diabetes before 4 years of age (n = 10) and compared these with similar measurements from matched children who were either positive for a single autoantibody (n = 10) or autoantibody negative (n = 10). Following statistical analysis of the longitudinal data, targeted serum proteomics was used to verify 11 proteins putatively associated with the disease development in a similar yet independent and larger cohort of children who progressed to the disease within 5 years of age (n = 31) and matched autoantibody negative children (n = 31). These data reiterated extensive age-related trends for protein levels in young children. Further, these analyses demonstrated that the serum levels of two peptides unique for apolipoprotein C1 (APOC1) were decreased after the appearance of the first islet autoantibody and remained relatively less abundant in children who progressed to type 1 diabetes, in comparison to autoantibody negative children.

Comparison of SPEED, S-Trap, and In-Solution-Based Sample Preparation Methods for Mass Spectrometry in Kidney Tissue and Plasma

Mass spectrometry is a powerful technique for investigating renal pathologies and identifying biomarkers, and efficient protein extraction from kidney tissue is essential for bottom-up proteomic analyses. Detergent-based strategies aid cell lysis and protein solubilization but are poorly compatible with downstream protein digestion and liquid chromatography-coupled mass spectrometry, requiring additional purification and buffer-exchange steps. This study compares two well-established detergent-based methods for protein extraction (in-solution sodium deoxycholate (SDC); suspension trapping (S-Trap)) with the recently developed sample preparation by easy extraction and digestion (SPEED) method, which uses strong acid for denaturation. We compared the quantitative performance of each method using label-free mass spectrometry in both sheep kidney cortical tissue and plasma. In kidney tissue, SPEED quantified the most unique proteins (SPEED 1250; S-Trap 1202; SDC 1197). In plasma, S-Trap produced the most unique protein quantifications (S-Trap 150; SDC 148; SPEED 137). Protein quantifications were reproducible across biological replicates in both tissue (R2 = 0.85–0.90) and plasma (SPEED R2 = 0.84; SDC R2 = 0.76, S-Trap R2 = 0.65). Our data suggest SPEED as the optimal method for proteomic preparation in kidney tissue and S-Trap or SPEED as the optimal method for plasma, depending on whether a higher number of protein quantifications or greater reproducibility is desired.

Proteomic Applications and Considerations: From Research to Patient Care

Despite technological advancements in the field of proteomics, the rate at which serum and plasma biomarkers identified using proteomic approaches are translated into clinical use remains extremely low. In this chapter, we describe recent technological advancements and analytical strategies in proteomic methods. We also describe the progress of proteomic blood-based biomarkers to date and discuss what the future of proteomics might entail with the use of multi-omic approaches and implementing machine learning on large proteomic datasets. Lastly, we provide several key considerations for biomarker studies, ranging from sample type to the use of reference samples, in order to achieve progress from bench to bedside, ultimately improving patient diagnosis, disease, and/or therapeutic monitoring and care.

Longitudinal Changes in BDNF and MMP-9 Protein Plasma Levels in Children after Cochlear Implantation

Congenitally deaf children who undergo cochlear implantation before 1 year of age develop their auditory skills faster than children who are implanted later. In this longitudinal study, a cohort of 59 implanted children were divided into two subgroups according to their ages at implantation-below or above 1 year old-and the plasma levels of matrix metalloproteinase-9 (MMP-9), brain-derived neurotrophic factor (BDNF), and pro-BDNF were measured at 0, 8, and 18 months after cochlear implant activation, while auditory development was simultaneously evaluated using the LittlEARs Questionnaire (LEAQ). A control group consisted of 49 age-matched healthy children. We identified statistically higher BDNF levels at 0 months and at the 18-month follow-ups in the younger subgroup compared to the older one and lower LEAQ scores at 0 months in the younger subgroup. Between the subgroups, there were significant differences in the changes in BDNF levels from 0 to 8 months and in LEAQ scores from 0 to 18 months. The MMP-9 levels significantly decreased from 0 to 18 months and from 0 to 8 months in both subgroups and from 8 to 18 months only in the older one. For all measured protein concentrations, significant differences were identified between the older study subgroup and the age-matched control group.

Quantification of cytosol and membrane proteins in rumen epithelium of sheep with low or high CH4 emission phenotype

BACKGROUND

Ruminant livestock are a major contributor to Australian agricultural sector carbon emissions. Variation in methane (CH4) produced from enteric microbial fermentation of feed in the reticulo-rumen of sheep differs with different digestive functions.

METHOD

We isolated rumen epithelium enzymatically to extract membrane and cytosol proteins from sheep with high (H) and low (L) CH4 emission. Protein abundance was quantified using SWATH-mass spectrometry.

RESULTS

The research found differences related to the metabolism of glucose, lactate and processes of cell defence against microbes in sheep from each phenotype. Enzymes in the methylglyoxal pathway, a side path of glycolysis, resulting in D-lactate production, differed in abundance. In the H CH4 rumen epithelium the enzyme hydroxyacylglutathione hydrolase (HAGH) was 2.56 fold higher in abundance, whereas in the L CH4 epithelium lactate dehydrogenase D (LDHD) was 1.93 fold higher. Malic enzyme 1 which converts D-lactate to pyruvate via the tricarboxylic cycle was 1.57 fold higher in the L CH4 phenotype. Other proteins that are known to regulate cell defence against microbes had differential abundance in the epithelium of each phenotype.

CONCLUSION

Differences in the abundance of enzymes involved in the metabolism of glucose were associated with H and L CH4 phenotype sheep. Potentially this represents an opportunity to use protein markers in the rumen epithelium to select low CH4 emitting sheep.

The Proteome Signatures of Fibroblasts from Patients with Severe, Intermediate and Mild Spinal Muscular Atrophy Show Limited Overlap

Most research to characterise the molecular consequences of spinal muscular atrophy (SMA) has focused on SMA I. Here, proteomic profiling of skin fibroblasts from severe (SMA I), intermediate (SMA II), and mild (SMA III) patients, alongside age-matched controls, was conducted using SWATH mass spectrometry analysis. Differentially expressed proteomic profiles showed limited overlap across each SMA type, and variability was greatest within SMA II fibroblasts, which was not explained by SMN2 copy number. Despite limited proteomic overlap, enriched canonical pathways common to two of three SMA severities with at least one differentially expressed protein from the third included mTOR signalling, regulation of eIF2 and eIF4 signalling, and protein ubiquitination. Network expression clustering analysis identified protein profiles that may discriminate or correlate with SMA severity. From these clusters, the differential expression of PYGB (SMA I), RAB3B (SMA II), and IMP1 and STAT1 (SMA III) was verified by Western blot. All SMA fibroblasts were transfected with an SMN-enhanced construct, but only RAB3B expression in SMA II fibroblasts demonstrated an SMN-dependent response. The diverse proteomic profiles and pathways identified here pave the way for studies to determine their utility as biomarkers for patient stratification or monitoring treatment efficacy and for the identification of severity-specific treatments.

Integrative genetic and immune cell analysis of plasma proteins in healthy donors identifies novel associations involving primary immune deficiency genes

BACKGROUND

Blood plasma proteins play an important role in immune defense against pathogens, including cytokine signaling, the complement system, and the acute-phase response. Recent large-scale studies have reported genetic (i.e., protein quantitative trait loci, pQTLs) and non-genetic factors, such as age and sex, as major determinants to inter-individual variability in immune response variation. However, the contribution of blood-cell composition to plasma protein heterogeneity has not been fully characterized and may act as a mediating factor in association studies.

METHODS

Here, we evaluated plasma protein levels from 400 unrelated healthy individuals of western European ancestry, who were stratified by sex and two decades of life (20-29 and 60-69 years), from the Milieu Intérieur cohort. We quantified 229 proteins by Luminex in a clinically certified laboratory and their levels of variation were analyzed together with 5.2 million single-nucleotide polymorphisms. With respect to non-genetic variables, we included 254 lifestyle and biochemical factors, as well as counts of seven circulating immune cell populations measured by hemogram and standardized flow cytometry.

RESULTS

Collectively, we found 152 significant associations involving 49 proteins and 20 non-genetic variables. Consistent with previous studies, age and sex showed a global, pervasive impact on plasma protein heterogeneity, while body mass index and other health status variables were among the non-genetic factors with the highest number of associations. After controlling for these covariates, we identified 100 and 12 pQTLs acting in cis and trans, respectively, collectively associated with 87 plasma proteins and including 19 novel genetic associations. Genetic factors explained the largest fraction of the variability of plasma protein levels, as compared to non-genetic factors. In addition, blood-cell fractions, including leukocytes, lymphocytes, monocytes, neutrophils, eosinophils, basophils, and platelets, had a larger contribution to inter-individual variability than age and sex and appeared as confounders of specific genetic associations. Finally, we identified new genetic associations with plasma protein levels of five monogenic Mendelian disease genes including two primary immunodeficiency genes (Ficolin-3 and FAS).

CONCLUSIONS

Our study identified novel genetic and non-genetic factors associated to plasma protein levels which may inform health status and disease management.

Mass spectrometry, data re-analysis, and homology modelling predict posttranslational modifications of leucine-rich alpha-2-glycoprotein as a marker of myelodysplastic syndrome

BACKGROUND

Leucine-rich alpha-2-glycoprotein (LRG) has been repeatedly proposed as a potential plasma biomarker for myelodysplastic syndrome (MDS).

OBJECTIVE

The goal of our work was to establish the total LRG plasma level and LRG posttranslational modifications (PTMs) as a suitable MDS biomarker.

METHODS

The total plasma LRG concentration was determined with ELISA, whilst the LRG-specific PTMs and their locations, were established using mass spectrometry and public mass spectrometry data re-analysis. Homology modelling and sequence analysis were used to establish the potential impact of PTMs on LRG functions via their impact on the LRG structure.

RESULTS

While the results showed that the total LRG plasma concentration is not a suitable MDS marker, alterations within two LRG sites correlated with MDS diagnosis (p= 0.0011). Sequence analysis and the homology model suggest the influence of PTMs within the two LRG sites on the function of this protein.

CONCLUSIONS

We report the presence of LRG proteoforms that correlate with diagnosis in the plasma of MDS patients. The combination of mass spectrometry, re-analysis of publicly available data, and homology modelling, represents an approach that can be used for any protein to predict clinically relevant protein sites for biomarker research despite the character of the PTMs being unknown.

Data-Independent Acquisition Enables Robust Quantification of 400 Proteins in Non-Depleted Canine Plasma

Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.

Leveraging homologies for cross-species plasma proteomics in ungulates using data-independent acquisition

The collection of blood plasma is minimally invasive, and the fluid is a rich source of proteins for biomarker studies in both humans and animals. Plasma protein analysis by mass spectrometry (MS) can be challenging, though modern data acquisition strategies, such as sequential window acquisition of all theoretical fragment ion spectra (SWATH), enable reproducible quantitation of hundreds of proteins in non-depleted plasma from humans and laboratory model animals. Although there is strong potential to enhance veterinary and translational research, SWATH-based plasma proteomics in non-laboratory animals is virtually non-existent. One limitation to date is the lack of comprehensively annotated genomes to aid protein identification. The current study established plasma peptide spectral repositories for sheep and cattle that enabled quantification of over 200 proteins in non-depleted plasma using SWATH approach. Moreover, bioinformatics pipeline was developed to leverage inter-species homologies to enhance the depth of baseline libraries and plasma protein quantification in bovids. Finally, the practical utility of using bovid libraries for SWATH data extraction in taxonomically related non-domestic ungulate species (giraffe) has been demonstrated. SIGNIFICANCE: Ability to quickly generate comprehensive spectral libraries is limiting the applicability of data-independent acquisition, such as SWATH, to study proteomes of non-laboratory animals. We describe an approach to obtain relatively shallow foundational plasma repositories from domestic ruminants and employ homology searches to increase the depth of data, which we subsequently extend to unsequenced ungulates using SWATH method. When applied to cross-species proteomics, the number of proteins quantified by our approach far exceeds what is traditionally used in plasma protein tests.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Drug penetration in pediatric brain tumors: Challenges and opportunities

Larger clinical trial enrollments and a greater understanding of biological heterogeneity have led to improved survival rates for children diagnosed with brain tumors in the last 50 years. However, reducing long-term morbidities and improving survival rates of high-risk tumors remain major challenges. Chemotherapy can reduce tumor burden, but effective drug penetration at the tumor site is limited by barriers in the route of drug administration and within the tumor microenvironment. Bioavailability of drugs is impeded by the blood-brain barrier, plasma protein binding, and structural components by the tumor including the matrix and vasculature contributing to increased interstitial fluid pressure, hypoxia, and acidity. Designing drug delivery systems to circumvent these barriers could lead to improved drug penetration at the tumor site and reduce adverse systemic side effects. In this review, we expand on how systemic and local barriers limit drug penetration and present potential methods to enhance drug penetration in pediatric brain tumors.

Protein profiles in plasma: Development from infancy to 5 years of age

PURPOSE

Little is known about the longitudinal development of different plasma protein levels during early childhood and particularly in relation to lifestyle factors. This study aimed to monitor the plasma proteome early in life and the influence of different lifestyles.

EXPERIMENTAL DESIGN

A multiplex bead-based immunoassay was used to analyze plasma levels of 97 proteins in 280 blood samples longitudinally collected in children at 6, 12, 24, and 60 months of age living in families with an anthroposophic (n = 15), partly anthroposophic (n = 27), or non-anthroposophic (n = 28) lifestyle.

RESULTS

A total of 68 proteins (70%) showed significantly altered plasma levels between 6 months and 5 years of age. In lifestyle stratified analysis, 59 of 97 (61%) proteins were altered over time within one or more of the three lifestyle groups. Nearly half of these proteins (28 out of 59) changed irrespective of lifestyle. The temporal changes represented four longitudinal trends of the plasma proteins during development, also following stratification of lifestyle.

CONCLUSIONS AND CLINICAL RELEVANCE

Our findings contribute to understand the development of the plasma proteome under the influence of lifestyle exposures in early childhood.

Investigating potential protein markers of cardiovascular disease in children with type 1 diabetes mellitus

BACKGROUND

Type 1 diabetes mellitus (T1DM) is a metabolic disease characterized by dysglycaemia. Cardiovascular disease (CVD) is a major complication among T1DM patients and the leading cause of mortality later in life.

METHODS

The study subjects consisted of T1DM children with poor glycemic control (HbA1c > 7.5%) and healthy age and gender matched controls. Venous blood samples were collected and tested by utilizing a novel immunoassay panel with 96 protein biomarkers. Data were analyzed using non-linear regression analysis and the expression of biomarkers was compared between T1DM and healthy control groups using an unpaired student's t-test. Dynamic principal component analysis (PCA) was operated based on the differentially expressed proteins.

RESULTS

Ten T1DM children and 10 healthy controls were analyzed. Twelve CVD markers show significant differential expression between T1DM patients and healthy controls (p < 0.05). Dynamic PCA clustering based on differentially expressed proteins demonstrated an obvious clustering between the two populations.

CONCLUSIONS

This preliminary study reveals the feasibility of utilizing a novel immunoassay panel to investigate potential biomarkers for predicting incipient CVD in children with T1DM. In future, longitudinal studies are required to track the relationships between measurements of the selected protein markers and the development of CVD in T1DM patients.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

Mass defect-based carbonyl activated tags (mdCATs) for multiplex data-independent acquisition proteome quantification

A novel eight-plex mass-defect-based carbonyl activated tag (mdCAT) has been designed for DIA quantification for the first time.

Data independent acquisition of plasma biomarkers of response to neoadjuvant chemotherapy in pancreatic ductal adenocarcinoma

The detection of disease-related plasma biomarkers has challenged the proteomic community for years. Attractive features for plasma proteomics includes the ease of collection and small volume needed for analysis, but on the other hand, the presence of highly abundant proteins complicates sample preparation procedures and reduces dynamic range. Data independent acquisition label free quantitation (DIA-LFQ) by mass spectrometry partly overcomes the dynamic range issue; however, generating the peptide spectral reference libraries that allow extensive analysis of the plasma proteome can be a slow and expensive task which is unattainable for many laboratories. We investigated the re-purposing of publically available plasma proteome datasets and the impact on peptide/protein detection for DIA-LFQ. We carried out these studies in the context of identifying putative biomarkers of response to neoadjuvant chemotherapy (NAC) for pancreatic ductal adenocarcinoma, as no useful plasma biomarkers have been clinically adopted. We demonstrated the benefit in searching DIA data against multiple spectral libraries to show that complement proteins were linked to NAC response in PDAC patients, confirming previous observations of the prognostic utility of complement following adjuvant chemotherapy. Our workflow demonstrates that DIA-LFQ can be readily applied in the oncology setting for the putative assignment of clinically relevant plasma biomarkers. STATEMENT OF SIGNIFICANCE: The proteomic mass spectrometry analysis of undepleted, unfractionated human plasma has benefits for sample throughput but remains challenging to obtain deep coverage. This work evaluated the re-purposing of open source peptide mass spectrometry data from human plasma to create spectral reference libraries for use in Data independent acquisition (DIA). We showed how seeding in locally acquired data to integrate iRT peptides into spectral libraries increased identification confidence by facilitating querying of multiple libraries. This workflow was applied to the discovery of putative plasma biomarkers for response to neoadjuvant chemotherapy (NAC) in pancreatic ductal adenocarcinoma patients. There is a paucity of prior information in the literature on this topic and we show that good responder patients have reduced levels of complement proteins.

Apolipoprotein Proteomic Profiling for the Prediction of Cardiovascular Death in Patients with Heart Failure

PURPOSE

Risk stratification in chronic systolic heart failure (HF) is critical to identify the patients who may benefit from advanced therapies. It is aimed at identifying new biomarkers to improve prognosis evaluation and help to better understand HF physiopathology.

EXPERIMENTAL DESIGN

Prognostic evaluation is performed in 198 patients with chronic systolic HF: 99 patients who died from cardiovascular cause within three years are individually matched for age, sex, and HF etiology (ischemic vs not) with 99 patients who are alive after three years of HF evaluation. A proteomic profiling of 15 apolipoproteins (Apo) is performed: Apo-A1, -A2, -A4, -B100, -C1, -C2, -C3, -C4, -D, -E, -F, -H, -J, -L1, and -M using LC-MRM-MS.

RESULTS

In univariate analysis, the levels of Apo-B100 and -L1 are significantly lower and the levels of Apo-C1, -J, and -M are significantly higher in patients who died from cardiovascular cause as compared with patients alive. In the final statistical model, Apo-C1, Apo-J, and Apo-M improve individually the prediction of cardiovascular death. Ingenuity pathway analysis indicates these three Apo in a network associated with lipid metabolism, atherosclerosis signaling, and retinoid X receptor activation.

CONCLUSIONS

Proteomic profiling of apolipoproteins using LC-MRM-MS might be useful in clinical practice for risk stratification of HF patients.

Hypoxia-induced small extracellular vesicle proteins regulate proinflammatory cytokines and systemic blood pressure in pregnant rats

Small extracellular vesicles (sEVs) released from the extravillous trophoblast (EVT) are known to regulate uterine spiral artery remodeling during early pregnancy. The bioactivity and release of these sEVs differ under differing oxygen tensions and in aberrant pregnancy conditions. Whether the placental cell-derived sEVs released from the hypoxic placenta contribute to the pathophysiology of preeclampsia is not known. We hypothesize that, in response to low oxygen tension, the EVT packages a specific set of proteins in sEVs and that these released sEVs interact with endothelial cells to induce inflammation and increase maternal systemic blood pressure. Using a quantitative MS/MS approach, we identified 507 differentially abundant proteins within sEVs isolated from HTR-8/SVneo cells (a commonly used EVT model) cultured at 1% (hypoxia) compared with 8% (normoxia) oxygen. Among these differentially abundant proteins, 206 were up-regulated and 301 were down-regulated (P < 0.05), and they were mainly implicated in inflammation-related pathways. In vitro incubation of hypoxic sEVs with endothelial cells, significantly increased (P < 0.05) the release of GM-CSF, IL-6, IL-8, and VEGF, when compared with control (i.e. cells without sEVs) and normoxic sEVs. In vivo injection of hypoxic sEVs into pregnant rats significantly increased (P < 0.05) mean arterial pressure with increases in systolic and diastolic blood pressures. We propose that oxygen tension regulates the release and bioactivity of sEVs from EVT and that these sEVs regulate inflammation and maternal systemic blood pressure. This novel oxygen-responsive, sEVs signaling pathway, therefore, may contribute to the physiopathology of preeclampsia.

Workflow for Rapidly Extracting Biological Insights from Complex, Multicondition Proteomics Experiments with WGCNA and PloGO2

We describe a useful workflow for characterizing proteomics experiments incorporating many conditions and abundance data using the popular weighted gene correlation network analysis (WGCNA) approach and functional annotation with the PloGO2 R package, the latter of which we have extended and made available to Bioconductor. The approach can use quantitative data from labeled or label-free experiments and was developed to handle multiple files stemming from data partition or multiple pairwise comparisons. The WGCNA approach can similarly produce a potentially large number of clusters of interest, which can also be functionally characterized using PloGO2. Enrichment analysis will identify clusters or subsets of proteins of interest, and the WGCNA network topology scores will produce a ranking of proteins within these clusters or subsets. This can naturally lead to prioritized proteins to be considered for further analysis or as candidates of interest for validation in the context of complex experiments. We demonstrate the use of the package on two published data sets using two different biological systems (plant and human plasma) and proteomics platforms (sequential window acquisition of all theoretical fragment-ion spectra (SWATH) and tandem mass tag (TMT)): an analysis of the effect of drought on rice over time generated using TMT and a pediatric plasma sample data set generated using SWATH. In both, the automated workflow recapitulates key insights or observations of the published papers and provides additional suggestions for further investigation. These findings indicate that the data set analysis using WGCNA combined with the updated PloGO2 package is a powerful method to gain biological insights from complex multifaceted proteomics experiments.

Plasma Proteomic Analysis Reveals Age-Specific Changes in Platelet- and Endothelial Cell-Derived Proteins and Regulators of Plasma Coagulation and Fibrinolysis

This post hoc study of a plasma proteomic database investigated hemostatic proteins in the context of developmental hemostasis. Twenty-seven hemostatic proteins changed expression with age, and the hemostatic profile of neonates was unique. Appreciating developmental hemostasis through proteomics may lead to more personalized medicine for hospitalized children.

Plasma proteomic profiling of young and old mice reveals cadherin-13 prevents age-related bone loss

The blood exhibits a dynamic flux of proteins that are secreted by the tissues and cells of the body. To identify novel aging-related circulating proteins, we compared the plasma proteomic profiles of young and old mice using tandem mass spectrometry. The expression of 134 proteins differed between young and old mice. We selected seven proteins that were expressed at higher levels in young mice, and confirmed their plasma expression in immunoassays. The plasma levels of anthrax toxin receptor 2 (ANTXR2), cadherin-13 (CDH-13), scavenger receptor cysteine-rich type 1 protein M130 (CD163), cartilage oligomeric matrix protein (COMP), Dickkopf-related protein 3 (DKK3), periostin, and secretogranin-1 were all confirmed to decrease with age. We then investigated whether any of the secreted proteins influenced bone metabolism and found that CDH-13 inhibited osteoclast differentiation. CDH 13 treatment suppressed the receptor activator of NF-κB ligand (RANKL) signaling pathway in bone marrow-derived macrophages, and intraperitoneal administration of CDH-13 delayed age-related bone loss in the femurs of aged mice. These findings suggest that low plasma CDH-13 expression in aged mice promotes aging-associated osteopenia by facilitating excessive osteoclast formation. Thus, CDH-13 could have therapeutic potential as a protein drug for the prevention of osteopenia.

Proteome of a Moraxella catarrhalis Strain under Iron-Restricted Conditions

Moraxella catarrhalis is a leading cause of otitis media and exacerbations of chronic obstructive pulmonary disease; however, its response to iron starvation during infection is not completely understood. Here, we announce a sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) data set describing the differential expression of the M. catarrhalis CCRI-195ME proteome under iron-restricted versus iron-replete conditions.

Moraxella catarrhalis is a leading cause of otitis media and exacerbations of chronic obstructive pulmonary disease; however, its response to iron starvation during infection is not completely understood. Here, we announce a sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) data set describing the differential expression of the M. catarrhalis CCRI-195ME proteome under iron-restricted versus iron-replete conditions.

Proteomic characterization and comparison of ram (Ovis aries) and buck (Capra hircus) spermatozoa proteome using a data independent acquisition mass spectometry (DIA-MS) approach

Fresh semen is most commonly used in an artificial insemination of small ruminants, because of low fertility rates of frozen sperm. Generally, when developing and applying assisted reproductive technologies, sheep and goats are classified as one species. In order to optimize sperm cryopreservation protocols in sheep and goat, differences in sperm proteomes between ram and buck are necessary to investigate, which may contribute to differences in function and fertility of spermatozoa. In the current work, a data-independent acquisition-mass spectrometry proteomic approach was used to characterize and make a comparison of ram (Ovis aries) and buck (Capra hircus) sperm proteomes. A total of 2,109 proteins were identified in ram and buck spermatozoa, with 238 differentially abundant proteins. Proteins identified in ram and buck spermatozoa are mainly involved in metabolic pathways for generation of energy and diminishing oxidative stress. Specifically, there are greater abundance of spermatozoa proteins related to the immune protective and capacity activities in ram, while protein that inhibit sperm capacitation shows greater abundance in buck. Our results not only provide novel insights into the characteristics and potential activities of spermatozoa proteins, but also expand the potential direction for sperm cryopreservation in ram and buck.

Age-Dependent Changes in the Plasma Proteome of Healthy Adults

OBJECTIVE

Human blood plasma is a complex that communicates with most parts of the body and reflects the changes in the state of an organism. Identifying age-related biomarkers can help predict and monitor age-related physiological decline and diseases and identify new treatments for diseases.

METHODS AND PARTICIPANTS

In this study, TMT-LC-MS/MS was utilized to screen differentially expressed plasma proteins in 118 healthy adults of different ages. Participants were divided into three groups: 21-30 years of age (Young), 41-50 years of age (Middle) and ≥60 years of age (Old).

RESULTS

The number of differentially expressed proteins in the comparisons of Young vs Middle, Middle vs Old and Young vs Old were 82, 22 and 99, respectively. These proteins were involved in numerous physiological processes, such as "negative regulation of smooth muscle cell proliferation" and "blood coagulation". Moreover, when Young was compared with Middle or Old, "complement and coagulation cascades" was the top enriched pathway by KEGG pathway enrichment analysis. Functional phenotyping of the proteome demonstrated that the plasma proteomic profiles of young adults were strikingly dissimilar to those of the middle-aged or older adults.

CONCLUSIONS

The results of this study mapped the variation in the expression of plasma proteins and provided information about possible biomarkers/treatments for different age-related functional disorders.

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.

Clinical biomarker discovery by SWATH-MS based label-free quantitative proteomics: impact of criteria for identification of differentiators and data normalization method

BACKGROUND

SWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification.

METHODS

The efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1.

RESULTS

Conventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally.

CONCLUSION

This is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.

Immunological biomarkers for the development and progression of type 1 diabetes

Immune biomarkers of type 1 diabetes are many and diverse. Some of these, such as the autoantibodies, are well established but not discriminative enough to deal with the heterogeneity inherent to type 1 diabetes progression. As an alternative, high hopes are placed on T cell assays, which give insight into the cells that actually target the beta cell or play a crucial role in maintaining tolerance. These assays are approaching a level of robustness that may allow for solid conclusions on both disease progression and therapeutic efficacy of immune interventions. In addition, 'omics' approaches to biomarker discovery are rapidly progressing. The potential emergence of novel biomarkers creates a need for the introduction of bioinformatics and 'big data' analysis systems for the integration of the multitude of biomarker data that will be available, to translate these data into clinical tools. It is worth noting that it is unlikely that the same markers will apply to all individuals. Instead, individualised signatures of biomarkers, combining autoantibodies, T cell profiles and other biomarkers, will need to be used to classify at-risk patients into various categories, thus enabling personalised prediction, prevention and treatment approaches. To achieve this goal, the standardisation of assays for biomarker discovery, the integration of analyses and data from biomarker studies and, most importantly, the careful clinical characterisation of individuals providing samples for these studies are critical. Longitudinal sample-collection initiatives, like INNODIA, should lead to novel biomarker discovery, not only providing a better understanding of type 1 diabetes onset and progression, but also yielding biomarkers of therapeutic efficacy of interventions to prevent or arrest type 1 diabetes.

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.

A Cost-Effective High-Throughput Plasma and Serum Proteomics Workflow Enables Mapping of the Molecular Impact of Total Pancreatectomy with Islet Autotransplantation

Blood is an ideal body fluid for the discovery or monitoring of diagnostic and prognostic protein biomarkers. However, discovering robust biomarkers requires the analysis of large numbers of samples to appropriately represent interindividual variability. To address this analytical challenge, we established a high-throughput and cost-effective proteomics workflow for accurate and comprehensive proteomics at an analytical depth applicable for clinical studies. For validation, we processed 1 μL each from 62 plasma samples in 96-well plates and analyzed the product by quantitative data-independent acquisition liquid chromatography/mass spectrometry; the data were queried using feature quantification with Spectronaut. To show the applicability of our workflow to serum, we analyzed a unique set of samples from 48 chronic pancreatitis patients, pre and post total pancreatectomy with islet autotransplantation (TPIAT) surgery. We identified 16 serum proteins with statistically significant abundance alterations, which represent a molecular signature distinct from that of chronic pancreatitis. In summary, we established a cost-efficient high-throughput workflow for comprehensive proteomics using PVDF-membrane-based digestion that is robust, automatable, and applicable to small plasma and serum volumes, e.g., finger stick. Application of this plasma/serum proteomics workflow resulted in the first mapping of the molecular implications of TPIAT on the serum proteome.

Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood

Children develop rapidly during the first years of life, and understanding the sources and associated levels of variation in the serum proteome is important when using serum proteins as markers for childhood diseases. The aim of this study was to establish a reference model for the evolution of a healthy serum proteome during early childhood. Label-free quantitative proteomics analyses were performed for 103 longitudinal serum samples collected from 15 children at birth and between the ages of 3–36 months. A flexible Gaussian process-based probabilistic modelling framework was developed to evaluate the effects of different variables, including age, living environment and individual variation, on the longitudinal expression profiles of 266 reliably identified and quantified serum proteins. Age was the most dominant factor influencing approximately half of the studied proteins, and the most prominent age-associated changes were observed already during the first year of life. High inter-individual variability was also observed for multiple proteins. These data provide important details on the maturing serum proteome during early life, and evaluate how patterns detected in cord blood are conserved in the first years of life. Additionally, our novel modelling approach provides a statistical framework to detect associations between covariates and non-linear time series data.

Mass spectrometry analysis of plasma from amyotrophic lateral sclerosis and control subjects

Mass spectrometry was used to study blood samples from patients with amyotrophic lateral sclerosis (ALS) and healthy controls. Addenbrooke's cognitive examination-III (ACE-III) was used to test for cognitive impairment (CI). Nano liquid chromatography and time of flight mass spectrometry (MS) were performed on samples from 42 ALS patients and 18 healthy controls. SWATH™ proteomic analysis was utilized to look for differences between groups. Western blot analysis was used to study levels of 4 proteins, selected as being of possible interest in ALS, in the MS discovery cohort and a second validation group of 10 ALS patients and 10 healthy controls. INGENUITY PATHWAY ANALYSIS (IPA) was applied to the final proteomic data. Between ALS patients and controls, there were significant differences in the expression of 30 proteins. Between controls and ALS patients without CI, there were significant differences in 15 proteins. Between controls and ALS patients with CI, there were significant differences in 32 proteins. Changes in levels of gelsolin, clusterin, and CD5L were validated by using western blot analysis in the discovery cohort. Changes in the expression of gelsolin, clusterin, and ficolin 3 were replicated in a validation group. In ALS, the LXR/RXR and coagulation pathways were downregulated whereas the complement pathway was upregulated. The proteomic data were used to produce two new networks, centered on IL1 and on NFkB, which showed altered levels in ALS. This study highlights the usefulness of MS of blood samples as a tool to study ALS.