High throughput and accurate serum proteome profiling by integrated sample preparation technology and single-run data independent mass spectrometry analysis

Mass spectrometry analysis of phosphotyrosine-containing proteins

Tyrosine phosphorylation is a crucial posttranslational modification that is involved in various aspects of cell biology and often has functions in cancers. It is necessary not only to identify the specific phosphorylation sites but also to quantify their phosphorylation levels under specific pathophysiological conditions. Because of its high sensitivity and accuracy, mass spectrometry (MS) has been widely used to identify endogenous and synthetic phosphotyrosine proteins/peptides across a range of biological systems. However, phosphotyrosine-containing proteins occur in extremely low abundance and they degrade easily, severely challenging the application of MS. This review highlights the advances in both quantitative analysis procedures and enrichment approaches to tyrosine phosphorylation before MS analysis and reviews the differences among phosphorylation, sulfation, and nitration of tyrosine residues in proteins. In-depth insights into tyrosine phosphorylation in a wide variety of biological systems will offer a deep understanding of how signal transduction regulates cellular physiology and the development of tyrosine phosphorylation-related drugs as cancer therapeutics.

Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy

OBJECTIVES

To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers.

METHODS

A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile. Differentially expressed proteins between groups were identified, with gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further developed. Classifying performance of biomarkers for distinguishing DR from DM was compared by the combination of three machine-learning algorithms. The selected biomarker panel was tested in the validation cohort using parallel reaction monitoring mass spectrometry.

RESULTS

Among 3364 proteins quantified, 235 and 88 differentially expressed proteins were identified for DR when compared to HC and DM, respectively, which were fundamentally related to retina homeostasis, inflammation and immunity, oxidative stress, angiogenesis and coagulation, metabolism, and cellular adhesion processes. The biomarker panel consisting of NAD-dependent protein deacetylase sirtuin-2 (SIR2), amine oxidase [flavin-containing] B (AOFB), and U8 snoRNA-decapping enzyme (NUD16) exhibited the best diagnostic performance in discriminating DR from DM, with AUCs of 0.933 and 0.881 in the discovery and validation set, respectively.

CONCLUSIONS

Tear protein dysregulation is comprehensively revealed to be associated with DR onset. The combination of tear SIR2, AOFB, and NUD16 can be a novel potential approach for non-invasive detection or pre-screening of DR.

CLINICAL TRIAL REGISTRATION

Chinese Clinical Trial Registry Identifier: ChiCTR2100054263. https://www.chictr.org.cn/showproj.html?proj=143177 . Date of registration: 2021/12/12.

Characterization of effective, simple, and low-cost precipitation methods for depleting abundant plasma proteins to enhance the depth and breadth of plasma proteomics

Plasma is an abundant source of proteins and potential biomarkers to aid in the detection, diagnosis, and prognosis of human diseases. These proteins are often present at low levels in the blood and difficult to identify and measure due to the large dynamic range of proteins. The goal of this work was to characterize and compare various protein precipitation methods related to how they affect the depth and breadth of plasma proteomic studies. Abundant protein precipitation with perchloric acid (PerCA) can increase protein identifications and depth of plasma proteomic studies. Three acid- and four solvent-based precipitation methods were evaluated. All methods tested provided excellent plasma proteomic coverage (>600 identified protein groups) and detected protein in the low pg/mL range. Functional enrichment analysis revealed subtle differences within and larger changes between the precipitant groups. Methanol-based precipitation outperformed the other methods based on identifications and reproducibility. The methods' performance was verified using eight lung cancer patient samples, where >700 protein groups were measured and proteins with an estimated plasma concentration of ∼10 pg/mL were detected. Various protein precipitation agents are amenable to extending the depth and breadth of plasma proteomes. These data can guide investigators to implement inexpensive, high-throughput methods for their plasma proteomic workflows.

Screening of novel biomarkers for acute kidney transplant rejection using DIA-MS based proteomics

BACKGROUND

Kidney transplantation is the preferred treatment for patients with end-stage renal disease. However, acute rejection poses a threat to the graft long-term survival. The aim of this study was to identify novel biomarkers to detect acute kidney transplant rejection.

METHODS

The serum proteomic profiling of kidney transplant patients with T cell-mediated acute rejection (TCMR) and stable allograft function (STA) was analyzed using data-independent acquisition mass spectrometry (DIA-MS). The differentially expressed proteins (DEPs) of interest were further verified by enzyme-linked immunosorbent assay (ELISA).

RESULTS

A total of 131 DEPs were identified between STA and TCMR patients, 114 DEPs were identified between mild and severe TCMR patients. The verification results showed that remarkable higher concentrations of serum amyloid A protein 1 (SAA1) and insulin like growth factor binding protein 2 (IGFBP2), and lower fetuin-A (AHSG) concentration were found in TCMR patients when compared with STA patients. We also found higher SAA1 concentration in severe TCMR group when compared with mild TCMR group. The receiver operating characteristics (ROC) analysis further confirmed that combination of SAA1, AHSG, and IGFBP2 had excellent performance in the acute rejection diagnosis.

CONCLUSIONS

Our data demonstrated that serum SAA1, AHSG, and IGFBP2 could be effective biomarkers for diagnosing acute rejection after kidney transplantation. DIA-MS has great potential in biomarker screening of kidney transplantation.

Plasma proteomic characterization of colorectal cancer patients with FOLFOX chemotherapy by integrated proteomics technology

BACKGROUND

Colorectal Cancer (CRC) is a prevalent form of cancer, and the effectiveness of the main postoperative chemotherapy treatment, FOLFOX, varies among patients. In this study, we aimed to identify potential biomarkers for predicting the prognosis of CRC patients treated with FOLFOX through plasma proteomic characterization.

METHODS

Using a fully integrated sample preparation technology SISPROT-based proteomics workflow, we achieved deep proteome coverage and trained a machine learning model from a discovery cohort of 90 CRC patients to differentiate FOLFOX-sensitive and FOLFOX-resistant patients. The model was then validated by targeted proteomics on an independent test cohort of 26 patients.

RESULTS

We achieved deep proteome coverage of 831 protein groups in total and 536 protein groups in average for non-depleted plasma from CRC patients by using a Orbitrap Exploris 240 with moderate sensitivity. Our results revealed distinct molecular changes in FOLFOX-sensitive and FOLFOX-resistant patients. We confidently identified known prognostic biomarkers for colorectal cancer, such as S100A4, LGALS1, and FABP5. The classifier based on the biomarker panel demonstrated a promised AUC value of 0.908 with 93% accuracy. Additionally, we established a protein panel to predict FOLFOX effectiveness, and several proteins within the panel were validated using targeted proteomic methods.

CONCLUSIONS

Our study sheds light on the pathways affected in CRC patients treated with FOLFOX chemotherapy and identifies potential biomarkers that could be valuable for prognosis prediction. Our findings showed the potential of mass spectrometry-based proteomics and machine learning as an unbiased and systematic approach for discovering biomarkers in CRC.

Uncommon posttranslational modifications in proteomics: ADP-ribosylation, tyrosine nitration, and tyrosine sulfation

Posttranslational modifications (PTMs) are covalent modifications of proteins that modulate the structure and functions of proteins and regulate biological processes. The development of various mass spectrometry-based proteomics workflows has facilitated the identification of hundreds of PTMs and aided the understanding of biological significance in a high throughput manner. Improvements in sample preparation and PTM enrichment techniques, instrumentation for liquid chromatography-tandem mass spectrometry (LC-MS/MS), and advanced data analysis tools enhance the specificity and sensitivity of PTM identification. Highly prevalent PTMs like phosphorylation, glycosylation, acetylation, ubiquitinylation, and methylation are extensively studied. However, the functions and impact of less abundant PTMs are not as well understood and underscore the need for analytical methods that aim to characterize these PTMs. This review focuses on the advancement and analytical challenges associated with the characterization of three less common but biologically relevant PTMs, specifically, adenosine diphosphate-ribosylation, tyrosine sulfation, and tyrosine nitration. The advantages and disadvantages of various enrichment, separation, and MS/MS techniques utilized to identify and localize these PTMs are described.

Differential protein analysis of saline-alkali promoting the oil accumulation in Nitzschia palea

BACKGROUND

The increasingly severe salinization of the aquatic environment has led to serious damage to the habitats of aquatic organisms. Benthic diatoms are commonly employed as indicator species for assessing water quality and serve as a reflection of the overall health of the aquatic ecosystem. Nitzschia palea is a common diatom found in freshwater, with high oil content, rapid reproductive rate, and it is a commonly dominant species in various rivers.

RESULTS

The results showed that after 4 days (d) of saline-alkali stress, the cell density and chlorophyll a content of Nitzschia palea reached their maximum values. Therefore, we selected Nitzschia palea under 4 d stress for Tandem Mass Tag (TMT) quantitative proteomic analysis to explore the molecular adaptation mechanism of freshwater diatoms under saline-alkali stress. Totally, 854 proteins were enriched, of which 439 differentially expressed proteins were identified. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and subcellular fractionation analysis revealed that these proteins were mainly enriched in the photosynthesis pathway, citric acid cycle (TCA cycle), fatty acid synthesis, and glutathione cycle.

CONCLUSIONS

This study aims to reveal the physiological, biochemical and proteomic mechanisms of salt and alkali tolerance and molecular adaptation of Nitzschia palea under different saline-alkali concentrations. This study showed that Nitzschia palea is one candidate of the environmental friendly, renewable bioenergy microalgae. Meantime, Nitzschia palea reveals for the proteome of the freshwater and provides the basis, it became a model algal species for freshwater diatoms.

A Galaxy of informatics resources for MS-based proteomics

INTRODUCTION

Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software.

AREAS COVERED

The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses.

EXPERT OPINION

The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

Bottom-Up Proteomics: Advancements in Sample Preparation

Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics is a powerful technique for profiling proteomes of cells, tissues, and body fluids. Typical bottom-up proteomic workflows consist of the following three major steps: sample preparation, LC–MS/MS analysis, and data analysis. LC–MS/MS and data analysis techniques have been intensively developed, whereas sample preparation, a laborious process, remains a difficult task and the main challenge in different applications. Sample preparation is a crucial stage that affects the overall efficiency of a proteomic study; however, it is prone to errors and has low reproducibility and throughput. In-solution digestion and filter-aided sample preparation are the typical and widely used methods. In the past decade, novel methods to improve and facilitate the entire sample preparation process or integrate sample preparation and fractionation have been reported to reduce time, increase throughput, and improve reproducibility. In this review, we have outlined the current methods used for sample preparation in proteomics, including on-membrane digestion, bead-based digestion, immobilized enzymatic digestion, and suspension trapping. Additionally, we have summarized and discussed current devices and methods for integrating different steps of sample preparation and peptide fractionation.

Data Independent Acquisition Reveals In-Depth Serum Proteome Changes in Canine Leishmaniosis

Comprehensive profiling of serum proteome provides valuable clues of health status and pathophysiological processes, making it the main strategy in biomarker discovery. However, the high dynamic range significantly decreases the number of detectable proteins, obstructing the insights into the underlying biological processes. To circumvent various serum enrichment methods, obtain high-quality proteome wide information using the next-generation proteomic, and study host response in canine leishmaniosis, we applied data-independent acquisition mass spectrometry (DIA-MS) for deep proteomic profiling of clinical samples. The non-depleted serum samples of healthy and naturally Leishmania-infected dogs were analyzed using the label-free 60-min gradient sequential window acquisition of all theoretical mass spectra (SWATH-MS) method. As a result, we identified 554 proteins, 140 of which differed significantly in abundance. Those were included in lipid metabolism, hematological abnormalities, immune response, and oxidative stress, providing valuable information about the complex molecular basis of the clinical and pathological landscape in canine leishmaniosis. Our results show that DIA-MS is a method of choice for understanding complex pathophysiological processes in serum and serum biomarker development.

Combination of automated sample preparation and micro-flow LC-MS for high-throughput plasma proteomics

BACKGROUND

Non-invasive detection of blood-based markers is a critical clinical need. Plasma has become the main sample type for clinical proteomics research because it is easy to obtain and contains measurable protein biomarkers that can reveal disease-related physiological and pathological changes. Many efforts have been made to improve the depth of its identification, while there is an increasing need to improve the throughput and reproducibility of plasma proteomics analysis in order to adapt to the clinical large-scale sample analysis.

METHODS

We have developed and optimized a robust plasma analysis workflow that combines an automated sample preparation platform with a micro-flow LC-MS-based detection method. The stability and reproducibility of the workflow were systematically evaluated and the workflow was applied to a proof-of-concept plasma proteome study of 30 colon cancer patients from three age groups.

RESULTS

This workflow can analyze dozens of samples simultaneously with high reproducibility. Without protein depletion and prefractionation, more than 300 protein groups can be identified in a single analysis with micro-flow LC-MS system on a Orbitrap Exploris 240 mass spectrometer, including quantification of 35 FDA approved disease markers. The quantitative precision of the entire workflow was acceptable with median CV of 9%. The preliminary proteomic analysis of colon cancer plasma from different age groups could be well separated with identification of potential colon cancer-related biomarkers.

CONCLUSIONS

This workflow is suitable for the analysis of large-scale clinical plasma samples with its simple and time-saving operation, and the results demonstrate the feasibility of discovering significantly changed plasma proteins and distinguishing different patient groups.

Longitudinal serum proteomics analyses identify unique and overlapping host response pathways in Lyme disease and West Nile virus infection

Advancement in proteomics methods for interrogating biological samples has helped identify disease biomarkers for early diagnostics and unravel underlying molecular mechanisms of disease. Herein, we examined the serum proteomes of 23 study participants presenting with one of two common arthropod-borne infections: Lyme disease (LD), an extracellular bacterial infection or West Nile virus infection (WNV), an intracellular viral infection. The LC/MS based serum proteomes of samples collected at the time of diagnosis and during convalescence were assessed using a depletion-based high-throughput shotgun proteomics (dHSP) pipeline as well as a non-depleting blotting-based low-throughput platform (MStern). The LC/MS integrated analyses identified host proteome responses in the acute and recovery phases shared by LD and WNV infections, as well as differentially abundant proteins that were unique to each infection. Notably, we also detected proteins that distinguished localized from disseminated LD and asymptomatic from symptomatic WNV infection. The proteins detected in both diseases with the dHSP pipeline identified unique and overlapping proteins detected with the non-depleting MStern platform, supporting the utility of both detection methods. Machine learning confirmed the use of the serum proteome to distinguish the infection from healthy control sera but could not develop discriminatory models between LD and WNV at current sample numbers. Our study is the first to compare the serum proteomes in two arthropod-borne infections and highlights the similarities in host responses even though the pathogens and the vectors themselves are different.

Effects of Alternating Electric Field Assisted Freezing-Thawing-Aging Sequence on Data-Independent Acquisition Quantitative Proteomics of Longissimus dorsi Muscle

This study was designed to investigate the differences in the proteomes of bovine Longissimus dorsi (LD) muscle during an alternating electric field (AEF)-assisted freezing-thawing-aging sequence based on a data-independent acquisition strategy. When compared to that of the only postmortem aging (OA) group, the meat quality of the freezing-thawing-aging sequence (FA) and AEF-assisted freezing-thawing-aging sequence (EA) groups showed a declining trend. However, the group assisted by AEF was significantly enhanced in color, water-holding capacity, and tenderness. Three hundred fifty-two proteins in LD muscle were differentially abundant proteins (DAPs) among FA, EA, and OA treatments. Furthermore, among the 40 DAPs in the FA versus EA comparison, 5 DAPs with variable importance in projection scores higher than 1 were identified as biochemical markers of beef quality. Bioinformatic analysis revealed that most of these proteins were involved in structural constituents of ribosome and catalytic activity. These results provide a basis for further understanding the quality of beef following a freezing-thawing-aging sequence assisted by AEF.

Novel serum proteomic biomarkers for early diagnosis and aggressive grade identification of prostate cancer

BACKGROUND

Prostate cancer (PCa) is one of the most common tumors and the second leading cause of cancer-related death in men. The discovery of novel biomarkers for PCa diagnosis in the early stage, as well as discriminating aggressive PCa from non-aggressive PCa continue to pose a challenge. The aim of this study was to identify serum proteins that were sensitive and specific enough to detect early-stage and aggressive PCa.

METHODS

The serum proteomic profiling of patients with PCa and benign prostatic hyperplasia (BPH) was comprehensively analyzed using data-independent acquisition mass spectrometry (DIA-MS), and the bioinformatics analysis was performed. The differentially expressed proteins (DEPs) of interest were further verified by enzyme-linked immunosorbent assay (ELISA) and immunoturbidimetry assay.

RESULTS

Statistically significant difference in abundance showed 56 DEPs between early-stage PCa and BPH and 47 DEPs between aggressive and non-aggressive PCa patients. In addition, the verification results showed that serum L-selectin concentration was significantly higher (p<0.05) in Gleason 6 PCa when compared with BPH, and the concentration of osteopontin (SPP1) and ceruloplasmin (CP) increased with higher Gleason score.

CONCLUSIONS

DIA-MS has great potential in cancer-related biomarker screening. Our data demonstrated that adding SPP1 and CP to PSA improved the separation of Gleason 7 (4 + 3) or above from Gleason 7 (3 + 4) or below compared with PSA diagnosis alone. Serum SPP1 and CP could be effective biomarkers to differentiate aggressive PCa (especially Gleason 7 (4 + 3) or above) from non-aggressive disease.

Recent advances in isobaric labeling and applications in quantitative proteomics

Mass spectrometry (MS) has emerged at the forefront of quantitative proteomic techniques. Liquid chromatography-mass spectrometry (LC-MS) can be used to determine abundances of proteins and peptides in complex biological samples. Several methods have been developed and adapted for accurate quantification based on chemical isotopic labeling. Among various chemical isotopic labeling techniques, isobaric tagging approaches rely on the analysis of peptides from MS2-based quantification rather than MS1-based quantification. In this review, we will provide an overview of several isobaric tags along with some recent developments including complementary ion tags, improvements in sensitive quantitation of analytes with lower abundance, strategies to increase multiplexing capabilities, and targeted analysis strategies. We will also discuss limitations of isobaric tags and approaches to alleviate these restrictions through bioinformatic tools and data acquisition methods. This review will highlight several applications of isobaric tags, including biomarker discovery and validation, thermal proteome profiling, cross-linking for structural investigations, single-cell analysis, top-down proteomics, along with applications to different molecules including neuropeptides, glycans, metabolites, and lipids, while providing considerations and evaluations to each application.

Targeted proteomics on its way to discovery

For a long time, targeted and discovery proteomics covered different corners of the detection spectrum, with targeted proteomics focused on small target sets. This changed with the recent advances in highly multiplexed analysis. While discovery proteomics still pushes higher numbers of identified and quantified proteins, the advances in targeted proteomics rose to cover large parts of less complex proteomes or proteomes with low protein detection counts due to dynamic range restrictions, like the blood proteome. These new developments will impact, especially on the field of biomarker discovery and the possibility of using targeted proteomics for diagnostic purposes. This article is protected by copyright. All rights reserved.

Serum Proteomic Analysis Identifies SAA1, FGA, SAP, and CETP as New Biomarkers for Eosinophilic Granulomatosis With Polyangiitis

Background

Eosinophilic granulomatosis with polyangiitis (EGPA) is characterized by asthma-like attacks in its early stage, which is easily misdiagnosed as severe asthma. Therefore, new biomarkers for the early diagnosis of EGPA are needed, especially for differentiating the diagnosis of asthma.

Objectives

To identify serum biomarkers that can be used for early diagnosis of EGPA and to distinguish EGPA from severe asthma.

Method

Data-independent acquisition (DIA) analysis was performed to identify 45 healthy controls (HC), severe asthma (S-A), and EGPA patients in a cohort to screen biomarkers for early diagnosis of EGPA and to differentiate asthma diagnosis. Subsequently, parallel reaction monitoring (PRM) analysis was applied to a validation cohort of 71 HC, S-A, and EGPA patients.

Result

Four candidate biomarkers were identified from DIA and PRM analysis-i.e., serum amyloid A1 (SAA1), fibrinogen-α (FGA), and serum amyloid P component (SAP)-and were upregulated in the EGPA group, while cholesteryl ester transfer protein (CETP) was downregulated in the EGPA group compared with the S-A group. Receiver operating characteristics analysis shows that, as biomarkers for early diagnosis of EGPA, the combination of SAA1, FGA, and SAP has an area under the curve (AUC) of 0.947, a sensitivity of 82.35%, and a specificity of 100%. The combination of SAA1, FGA, SAP, and CETP as biomarkers for differential diagnosis of asthma had an AUC of 0.921, a sensitivity of 78.13%, and a specificity of 100%, which were all larger than single markers. Moreover, SAA1, FGA, and SAP were positively and CETP was negatively correlated with eosinophil count.

Conclusion

DIA-PRM combined analysis screened and validated four previously unexplored but potentially useful biomarkers for early diagnosis of EGPA and differential diagnosis of asthma.

The Investigation of Protein Profile and Meat Quality in Bovine Longissimus thoracic Frozen under Different Temperatures by Data-Independent Acquisition (DIA) Strategy

The influence of freezing on the protein profile and quality traits in bovine Longissimus thoracic (LT) muscle was investigated by the data-independent acquisition (DIA) technique. Compared to fresh meat, a total of 262 proteins were identified as differential abundance proteins (DAPs) in four frozen groups (−12 °C, −18 °C, −38 °C, and −80 °C). According to the bioinformatics analysis, most of the DAPs in the significant Go terms and the KEGG pathway were structure proteins and enzymes. Proteome changes in the frozen bovine muscle at −12 °C and −18 °C were more significant than those at −38 °C and −80 °C. The result was consistent with the deterioration trend of the meat quality. The correlation analysis revealed that 17 proteins were correlated closely with the color, shear force, thawing loss, and cooking loss of the frozen meat, which could be used as putative biomarkers for frozen meat quality. MYO18A and ME3 are newly discovered proteins that are associated with frozen beef quality. In addition, CTTN and SERPINB6 were identified in frozen groups, which exhibited a significant inverse correlation with thawing loss (p < 0.01). These findings reveal the quality changes induced by freezing at the protein molecular level and provide new insights into the control of quality deterioration.

Nephrotoxicity evaluation of 3-monochloropropane-1,2-diol exposure in Sprague-Dawley rats using data-independent acquisition-based quantitative proteomics analysis

3-Monochloropropane-1,2-diol (3-MCPD), as a heat-induced food process contaminant, possesses strongly toxic effect on kidney. The present study focuses on characterizing the proteome and clarifying the underlying molecular regulatory mechanisms in a model of kidney injury in rats treated with 3-MCPD. Data-independent acquisition (DIA)-mass spectrometry (MS) based proteomics was used to identify dysregulated proteins in kidney tissues of Sprague-Dawley (SD) rats treated with 30 mg/kg/day 3-MCPD by gavage for 28 days. It was found that a total of 975 proteins were deregulated after 3-MCPD treatment. Bioinformatic analyses revealed that several enzymes related to the metabolisms of amino acid, lipid and carbohydrate in endogenous metabolism were altered in response to 3-MCPD treatment. Moreover, some proteins involved in these pathways were also changed, mainly including oxidative stress, oxidative phosphorylation, apoptosis and autophagy. Our study unravels the vital roles of loss of mitochondrial homeostasis and function and cell death pathways in the development of renal damage induced by 3-MCPD, which provides further valuable insights into the initiation and resolution of 3-MCPD nephrotoxicity. The proposed DIA-MS workflow not only provides a choice for proteomic analysis in toxicological research, but also provides a more comprehensive understanding of the molecular mechanisms of nephrotoxicity induced by toxins.

Investigation of cattle plasma proteome in response to pain and inflammation using next generation proteomics technique, SWATH-MS

Pain assessment in farm animals has primarily relied on a combination of behavioral and physiological responses, although these are relatively subjective and difficult to quantify. It is essential to develop more effective biomarkers of pain in production animals since they are frequently exposed to routine surgical husbandry procedures. More effective biomarkers of pain would improve welfare, limit the loss of productivity associated with pain and permit better assessment of analgesics. This study aimed to investigate the use of a modern mass spectrometry data independent acquisition strategy, termed Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS), to detect candidate protein biomarkers that are known to associate with nociceptive and inflammatory processes in cattle, which could then be used to assess the efficacy of potential analgesics. Calves were randomly divided into two groups that were either surgically dehorned or subjected to restraint stress, without provision of anaesthesia or analgesia in accordance with current industry standards. Samples were analysed before and after dehorning at multiple timepoints. Significant changes in protein concentrations were detected predominantly at 24 and 96 h following dehorning, including kininogens, proteins associated with the coagulation and complement cascades and serine protease inhibitors. Gene ontology analysis revealed that the identified candidate biomarkers were associated with stress, wound healing, immune response, blood coagulation and the inflammatory and acute phase responses, which could be expected following surgical damage to tissues, but can now be more objectively assessed. These results offer more definitive and quantitative monitoring of response to tissue injury induced pain and inflammation.

Evaluation of the Molecular Mechanisms of Sepsis Using Proteomics

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

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/

Longitudinal Bottom-Up Proteomics of Serum, Serum Extracellular Vesicles, and Cerebrospinal Fluid Reveals Candidate Biomarkers for Early Detection of Glioblastoma in a Murine Model

Glioblastoma Multiforme (GBM) is a brain tumor with a poor prognosis and low survival rates. GBM is diagnosed at an advanced stage, so little information is available on the early stage of the disease and few improvements have been made for earlier diagnosis. Longitudinal murine models are a promising platform for biomarker discovery as they allow access to the early stages of the disease. Nevertheless, their use in proteomics has been limited owing to the low sample amount that can be collected at each longitudinal time point. Here we used optimized microproteomics workflows to investigate longitudinal changes in the protein profile of serum, serum small extracellular vesicles (sEVs), and cerebrospinal fluid (CSF) in a GBM murine model. Baseline, pre-symptomatic, and symptomatic tumor stages were determined using non-invasive motor tests. Forty-four proteins displayed significant differences in signal intensities during GBM progression. Dysregulated proteins are involved in cell motility, cell growth, and angiogenesis. Most of the dysregulated proteins already exhibited a difference from baseline at the pre-symptomatic stage of the disease, suggesting that early effects of GBM might be detectable before symptom onset.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Proteomic analysis of differentially expressed whey proteins in Saanen goat milk from different provinces in China using a data-independent acquisition technique

Whey proteins of Saanen goat milk samples from 3 provinces in China (Guangdong, GD; Inner Mongolia, IM; Shaanxi, SX) were characterized and compared using data-independent acquisition quantitative proteomics technique. A total of 550 proteins were quantified in all 3 samples. There were 44, 44, and 33 differentially expressed proteins (DEP) for GD versus IM, GD versus SX, and IM versus SX, respectively. Gene ontology annotation analysis showed that the largest number of DEP for the 3 comparisons were as follows: for biological processes: response to progesterone, glyceraldehyde-3-phosphate metabolic process, and negative regulation of megakaryocyte differentiation; for molecular functions: antioxidant activity, binding, and peroxiredoxin activity; and for cellular components: the same category of extracellular regions for the 3 comparisons, respectively. Pathways for the DEP of 3 comparisons were (1) disease; (2) synthesis and metabolism; and (3) synthesis, degradation, and metabolism. Protein-protein interaction network analysis showed that DEP for GD versus SX had the most interactions.

New Approaches to Profile the Microbiome for Treatment of Neurodegenerative Disease

Progressive neurodegenerative diseases represent some of the largest growing treatment challenges for public health in modern society. These diseases mainly progress due to aging and are driven by microglial surveillance and activation in response to changes occurring in the aging brain. The lack of efficacious treatment options for Alzheimer's disease (AD), as the focus of this review, and other neurodegenerative disorders has encouraged new approaches to address neuroinflammation for potential treatments. Here we will focus on the increasing evidence that dysbiosis of the gut microbiome is characterized by inflammation that may carry over to the central nervous system and into the brain. Neuroinflammation is the common thread associated with neurodegenerative diseases, but it is yet unknown at what point and how innate immune function turns pathogenic for an individual. This review will address extensive efforts to identify constituents of the gut microbiome and their neuroactive metabolites as a peripheral path to treatment. This approach is still in its infancy in substantive clinical trials and requires thorough human studies to elucidate the metabolic microbiome profile to design appropriate treatment strategies for early stages of neurodegenerative disease. We view that in order to address neurodegenerative mechanisms of the gut, microbiome and metabolite profiles must be determined to pre-screen AD subjects prior to the design of specific, chronic titrations of gut microbiota with low-dose antibiotics. This represents an exciting treatment strategy designed to balance inflammatory microglial involvement in disease progression with an individual's manifestation of AD as influenced by a coercive inflammatory gut.

Identifying Candidate Biomarkers of Ionizing Radiation in Human Pulmonary Microvascular Lumens Using Microfluidics-A Pilot Study

The microvasculature system is critical for the delivery and removal of key nutrients and waste products and is significantly damaged by ionizing radiation. Single-cell capillaries and microvasculature structures are the primary cause of circulatory dysfunction, one that results in morbidities leading to progressive tissue and organ failure and premature death. Identifying tissue-specific biomarkers that are predictive of the extent of tissue and organ damage will aid in developing medical countermeasures for treating individuals exposed to ionizing radiation. In this pilot study, we developed and tested a 17 µL human-derived microvascular microfluidic lumen for identifying candidate biomarkers of ionizing radiation exposure. Through mass-spectrometry-based proteomics, we detected 35 proteins that may be candidate early biomarkers of ionizing radiation exposure. This pilot study demonstrates the feasibility of using humanized microfluidic and organ-on-a-chip systems for biomarker discovery studies. A more elaborate study of sufficient statistical power is needed to identify candidate biomarkers and test medical countermeasures of ionizing radiation.

Data-Independent Acquisition Proteomics Reveals Long-Term Biomarkers in the Serum of C57BL/6J Mice Following Local High-Dose Heart Irradiation

Background and Purpose: Cardiotoxicity is a well-known adverse effect of radiation therapy. Measurable abnormalities in the heart function indicate advanced and often irreversible heart damage. Therefore, early detection of cardiac toxicity is necessary to delay and alleviate the development of the disease. The present study investigated long-term serum proteome alterations following local heart irradiation using a mouse model with the aim to detect biomarkers of radiation-induced cardiac toxicity.

Materials and Methods: Serum samples from C57BL/6J mice were collected 20 weeks after local heart irradiation with 8 or 16 Gy X-ray; the controls were sham-irradiated. The samples were analyzed by quantitative proteomics based on data-independent acquisition mass spectrometry. The proteomics data were further investigated using bioinformatics and ELISA.

Results: The analysis showed radiation-induced changes in the level of several serum proteins involved in the acute phase response, inflammation, and cholesterol metabolism. We found significantly enhanced expression of proinflammatory cytokines (TNF-α, TGF-β, IL-1, and IL-6) in the serum of the irradiated mice. The level of free fatty acids, total cholesterol, low-density lipoprotein (LDL), and oxidized LDL was increased, whereas that of high-density lipoprotein was decreased by irradiation.

Conclusions: This study provides information on systemic effects of heart irradiation. It elucidates a radiation fingerprint in the serum that may be used to elucidate adverse cardiac effects after radiation therapy.

Dissecting the multi-omics atlas of the exosomes released by human lung adenocarcinoma stem-like cells

Lung adenocarcinoma is heterogeneous and hierarchically organized, with a subpopulation of stem-like cells (CSCs) that reside at the apex of the hierarchy, in which exosomes act as important mediators by transporting specific molecules among different cell populations. Although there have been numerous studies on tumor exosomes, the constituents and functional properties of CSC-derived exosomes are still poorly characterized. Here we present a detail transcriptome and proteome atlas of the exosomes released by human lung adenocarcinoma stem-like cells (LSLCs). The transcriptome analysis indicates the specific patterns of exosomal constituents, including the fragmentation of transcripts and the low-level presence of circular RNAs, and identifies multiple exosomal-enriched mRNAs and lncRNAs. Integrative analysis of transcriptome and proteome data reveals the diverse functions of exosomal-enriched RNAs and proteins, many of which are associated with tumorigenesis. Importantly, several LSLC markers we identified are highly expressed in LSLC-derived exosomes and associate with poor survival, which may serve as promising liquid biopsy biomarkers for lung adenocarcinoma diagnosis. Our study provides a resource for the future elucidation of the functions of tumor-derived exosomes and their regulatory mechanisms in mediating lung cancer development.

Marker peptide screening and species-specific authentication of Pheretima using proteomics

Pheretima is a common and valuable animal-derived medication used in traditional Chinese medicine. There are four species of Pheretima specified in the Chinese Pharmacopoeia (2015 edition), i.e. Pheretima aspergillum, P. vulgaris, P. guillelmi, and P. pectinifera. A recent report revealed ~ 55% of Pheretima in the commercial marketplace may be adulterated by other species, contrary to the Pharmacopoeia standard. The safety, efficacy, and authenticity of Pheretima is an important issue. Currently, the availability of specific quality-markers for the various species and effective identification methods are still limited. In this study, label-free quantification proteomics of species from Pheretima and Amynthas was carried out using nanoscale liquid chromatography coupled to tandem mass spectrometry (nano LC-MS/MS), and marker peptides were identified based on their ion intensities using multivariate data analysis (principal component analysis and supervised partial least-squares discriminant analysis). A total of 48,476 peptides with high confidence corresponding to 13,397 proteins were identified from all samples. The marker peptides were validated by comparison with synthetic peptide reference standards using LC-MS/MS operating in a multiple-reaction monitoring mode. A multiple-peptide identification strategy was proposed for the authentication of Pheretima and subsequently applied to samples obtained from retail outlets in various regions of China. The results showed that eight out of the 15 samples tested were deemed authentic Pheretima.

Integrated proteomic sample preparation with combination of on-line high-abundance protein depletion, denaturation, reduction, desalting and digestion to achieve high throughput plasma proteome quantification

In this study, we developed an integrated plasma proteome sample preparation system, by which high-abundance proteins from human plasma were first depleted by immunoaffinity column, followed by on-line middle and low-abundance proteins denaturation, reduction, desalting and tryptic digestion. To evaluate the performance of such a system, 20 μL plasma was processed automatically, followed by 1-h gradient liquid chromatography-mass spectrometry analysis (LC-MS). Compared to conventional in-solution protocols, not only the sample preparation time could be shortened from 20 h to 20 min, but also the number of identified proteins were greatly increased by 1.4-2.0 times. Such an integrated system allows us to process 36 human plasma samples per day, with more than 300 proteins and 52 FDA approved disease markers per sample being identified. With combination of such an integrated sample preparation system with label-free single-shot LC-MS/MS, the human plasma proteins could be quantified across more than 6 orders of magnitude of abundance range with high reproducibility (Pearson R = 0.99, n = 9). In addition, the relative quantification of human plasma samples from diabetic retinopathy patients and diabetic patients demonstrated the feasibility of our developed workflow for clinic plasma proteome profiling. All these results demonstrated that our developed integrated plasma proteome sample preparation system would provide a new tool for high throughput biomarker discovery.

Fully integrated protein absolute quantification platform for analysis of multiple tumor markers in human plasma

In this study, we developed a fully integrated protein absolute quantification platform for simultaneous analysis of multiple tumor markers in human plasma, by which multiple target proteins (alpha-fetoprotein, prostate-specific antigen, carcino-embryonic antigen and mucin-1) were firstly enriched by aptamers immobilized capillary column using graphene oxide modified polymer microsphere as the separation matrix, and then the eluted target proteins were online denatured, reduced, desalted and digested by our developed fully automated sample treatment device (FAST), finally the resulting peptides were analyzed by parallel reaction monitoring (PRM) on LTQ-orbitrap velos mass spectrometry. Compared to traditional ELISA assay, the platform exhibited significant advantages such as short analysis time, low limit of detection, and ease of automation. Furthermore, our developed platform was also applied in the absolute quantification of tumor markers from clinical human plasma samples, and the results were comparable to those obtained by clinical immunoassay. All the results demonstrated that such a platform could provide a promising tool for achieving high sensitivity, high accuracy, and high throughput detection of disease related protein markers in the routine physical examination and clinical disease diagnosis.

Serum-Derived Exosomal Proteins as Potential Candidate Biomarkers for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common form of hepatic malignancies. The diagnosis of HCC remains challenging due to the low sensitivity and specificity of the diagnostic method. Exosomes, which are abundant in various proteins from parent cells, play pivotal roles in intercellular communication and have been confirmed as promising sources of disease biomarkers. Herein, we performed a simple but robust proteomic profiling on exosomes derived from 1 μL of serum using a data-independent acquisition (DIA) method for the first time, to screen potential biomarkers for the diagnosis of HCC. Ten pivotal differentially expressed proteins (DEPs) (von Willebrand factor (VWF), LGALS3BP, TGFB1, SERPINC1, HPX, HP, HBA1, FGA, FGG, and FGB) were screened as a potential candidate biomarker panel, which could completely discriminate patients with HCC from normal control (NC). Interestingly, Gene Expression Profiling Interactive Analysis (GEPIA) revealed that the expression levels of four genes increased and those of six genes decreased in HCC tissues compared with normal tissues, which were in concordance with protein expression levels. In conclusion, we screened 10 exosomal proteins holding promise for acting as a potential candidate biomarker panel for detection of HCC through a simple but robust proteomic profiling.

SWATH-MS Protocols in Human Diseases

Identification of molecular biomarkers for human diseases is one of the most important disciplines in translational science as it helps to elucidate their origin and early progression. Thus, it is a key factor in better diagnosis, prognosis, and treatment. Proteomics can help to solve the problem of sample complexity when the most common primary sample specimens were analyzed: organic fluids of easy access. The latest developments in high-throughput and label-free quantitative proteomics (SWATH-MS), together with more advanced liquid chromatography, have enabled the analysis of large sample sets with the sensitivity and depth needed to succeed in this task. In this chapter, we show different sample processing methods (major protein depletion, digestion, etc.) and a micro LC-SWATH-MS protocol to identify/quantify several proteins in different types of samples (serum/plasma, saliva, urine, tears).

Multiomic analysis of a dried single-drop plasma sample using an integrated mass spectrometry approach

An easy-to-use and fast approach was developed for integrated proteomic and metabolic profiling in a dried single-drop plasma sample. Plasma collection, room temperature storage, and sample preparation for both proteins and metabolites were seamlessly integrated in one spintip device. MS-based multiomic profiling using the same nano LC-MS system identified more than 150 proteins and 160 metabolites from the 1 μL plasma sample in 6 hours. Further combination with micro-flow LC and targeted MS made it a promising approach for the fast profiling of molecular biomarkers with high sensitivity and accuracy.

Proteomics reveals surface electrical property-dependent toxic mechanisms of silver nanoparticles in Chlorella vulgaris

Silver nanoparticles (AgNPs) are known to exert adverse effects on both humans and aquatic organisms; however, the toxic mechanisms underlying these effects remain unclear. In this study, we investigated the toxic mechanisms of various AgNPs with different surface electrical properties in the freshwater algae Chlorella vulgaris using an advanced proteomics approach with Data-Independent Acquisition. Citrate-coated AgNPs (Cit-AgNPs) and polyethyleneimine-coated AgNPs (PEI-AgNPs) were selected as representatives of negatively and positively charged nanoparticles, respectively. Our results demonstrated that the AgNPs exhibited surface electrical property-dependent effects on the proteomic profile of C. vulgaris. In particular, the negatively charged Cit-AgNPs specifically regulated mitochondrial function-related proteins, resulting in the disruption of several associated metabolic pathways, such as those related to energy metabolism, oxidative phosphorylation, and amino acid synthesis. In contrast, the positively charged PEI-AgNPs primarily targeted ribosome function-related proteins and interrupted pathways of protein synthesis and DNA genetic information transmission. In addition, Ag⁺ ions released from the AgNPs had a significant influence on protein regulation and the induction of cellular stress. Collectively, our findings provide new insight into the surface electrical property-dependent proteomic effects of AgNPs on C. vulgaris and should improve our understanding of the toxic mechanisms of AgNPs in freshwater algae.

Study on the Diagnosis of Gastric Cancer by Magnetic Beads Extraction and Mass Spectrometry

Objective. This study constructed a model for the early diagnosis of gastric cancer by comparing the serum peptides profiles of patients with advanced gastric cancer and healthy people. And that model may be the potential to be applied for the efficacy evaluation and recurrence monitoring in gastric cancer. Methods. Serums of 30 healthy people and 30 advanced gastric cancer patients were matched by age and gender were collected. The serum peptide spectrum was obtained by MB-WCX concentration and MALDI-TOF MS analysis. Based on the analysis of the efficiency of differential peptides in the diagnosis of gastric cancer, we first established a model for the diagnosis of gastric cancer based on differential peptides and then carried out external verification. The diagnostic reliability of this model was further tested by compared with carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9). Results. In this present study, we found the expression of two peptide peaks with a molecular weight of 2863 Da and 2953 Da were significantly increased in gastric cancer serum, while the expression of two peptide peaks with a molecular weight of 1945 Da and 2082 Da were significantly decreased. Depending on the characteristics of peptide expression, we constructed a diagnostic model, we compared the sensitivity and specificity of the model established by 2953 Da/1945 Da, and found this model is significantly higher than CEA and CA19-9. Conclusion. There were some differences in serum peptides profiles between patients with advanced gastric cancer and healthy people. The serum peptide diagnostic models based on 2953 Da and 1945 Da have high diagnostic efficiency for advanced gastric cancer. Our result indicated that this model was well worth further validation for clinical diagnosis.

Proteomic approaches for characterizing renal cell carcinoma

Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.

Comparative Proteomic Analysis of Adhesion/Invasion Related Proteins in Cronobacter sakazakii Based on Data-Independent Acquisition Coupled With LC-MS/MS

Cronobacter sakazakii is foodborne pathogen that causes serious illnesses such as necrotizing enterocolitis, meningitis and septicemia in infants. However, the virulence determinants and mechanisms of pathogenicity of these species remain unclear. In this study, multilocus sequence typing (MLST) was performed on 34 C. sakazakii strains and two strains with the same sequence type (ST) but distinct adhesion/invasion capabilities were selected for identification of differentially expressed proteins using data-independent acquisition (DIA) proteomic analysis. A total of 2,203 proteins were identified and quantified. Among these proteins, 210 exhibited differential expression patterns with abundance ratios ≥3 or ≤0.33 and P values ≤0.05. Among these 210 proteins, 67 were expressed higher, and 143 were expressed lower in C. sakazakii SAKA80220 (strongly adhesive/invasive strain) compared with C. sakazakii SAKA80221 (weakly adhesive/invasive strain). Based on a detailed analysis of the differentially expressed proteins, the highly expressed genes involved in flagellar assembly, lipopolysaccharide synthesis, LuxS/AI-2, energy metabolic pathways and iron-sulfur cluster may be associated with the adhesion/invasion capability of C. sakazakii. To verify the accuracy of the proteomic results, real-time qPCR was used to analyze the expression patterns of some genes at the transcriptional level, and consistent results were observed. This study, for the first time, used DIA proteomic to investigate potential adhesion/invasion related factors as a useful reference for further studies on the pathogenic mechanism of C. sakazakii.

Prognostic significance of proteomics and multi-omics studies in renal carcinoma

INTRODUCTION

Renal carcinoma, and in particular its most common variant, the clear cell subtype, is often diagnosed incidentally through abdominal imaging and frequently, the tumor is discovered at an early stage. However, 20% to 40% of patients undergoing nephrectomy for clinically localized renal cancer, even after accurate histological and clinical classification, will develop metastasis or recurrence, justifying the associated mortality rate. Therefore, even if renal carcinoma is not among the most frequent nor deadly cancers, a better prognostication is needed.

AREAS COVERED

Recently proteomics or other omics combinations have been applied to both cancer tissues, on the neoplasia itself and surrounding microenvironment, cultured cells, and biological fluids (so-called liquid biopsy) generating a list of prognostic molecular tools that will be reviewed in the present paper.

EXPERT OPINION

Although promising, none of the approaches listed above has been yet translated in clinics. This is likely due to the peculiar genetic and phenotypic heterogeneity of this cancer, which makes nearly each tumor different from all the others. Attempts to overcome this issue will be also revised. In particular, we will discuss how the application of omics-integrated approaches could provide the determinants of response to the different targeted drugs.

The Influence of Blood Collection Tubes in Biomarkers' Screening by Mass Spectrometry

PURPOSE

Mass spectrometry is one of the rapidly developing bio-analytical techniques in recent years, and it shows that the results of biomarkers' screening can be influenced by pre-analytical process. The selection of the blood collection tubes is one of the most significant steps of pre-analytical process which is often neglected by researchers. So, it is urgent to define the influence of blood collection tubes clearly in biomarkers' screening.

EXPERIMENTAL DESIGN

Two types of blood collection tubes, non-additive tubes and coagulant activator tubes, are used to collect serum samples from patients and healthy controls. All samples are analyzed using matrix-assisted laser desorption ionization-time of flight mass spectrum in this study.

RESULTS

The serum protein profile changes while using coagulant tubes whether for patients or healthy controls. It is found that the effect of coagulant on serum protein of patients is smaller than that of control group. There are 27 significantly different peaks between the control group and the control coagulant group. However, between patient group and patient coagulant group, only one differential peak is obtained. Coagulant changes the expression differences between patients and healthy controls, making the differences expand, shrink or reverse, and most of the polypeptides are small molecule, which will change the results of biomarker's screening.

CONCLUSIONS AND CLINICAL RELEVANCE

This research suggested that different types of blood collection tubes would influence the final laboratory results. So it's important for clinicians to choose the proper tubes to detect biomarkers and make correct diagnoses.

Proteomic profiling analysis of postmenopausal osteoporosis and osteopenia identifies potential proteins associated with low bone mineral density

Postmenopausal osteoporosis (PMOP) is a major global public health concern and older women are more susceptible to experiencing fragility fractures. Our study investigated the associations between circulating proteins with bone mineral density (BMD) in postmenopausal women with or without low BMD (osteoporosis and osteopenia) using a tandem mass tag (TMT) labeling proteomic experiment and parallel reaction monitoring testing. Across all plasma samples, we quantitatively measured 1,092 proteins, and the OP and normal control (NC) samples were differentiated by principal component analysis and a partial least squares-discrimination analysis model based on the protein profiling data. The differentially abundant proteins between the low BMD and NC samples mostly exhibited binding, molecular function regulator, transporter and molecular transducer activity, and were involved in metabolic and cellular processes, stimulus response, biological regulation, immune system processes and so forth. TMT analysis and RRM validation indicated that the expression of protein Lysozyme C (P61626) was negatively related to BMD, while the expression of proteins Glucosidase (A0A024R592) and Protein disulfideisomerase A5 (Q14554) was positively related to BMD values. Collectively, our results suggest that postmenopausal women with low BMD have a different proteomic profile or signature. Protein alterations may play an important role in the pathogenesis of PMOP, and they may act as novel biomarkers and targets of therapeutic agents for this disease.

Affinity-Bead Assisted Mass Spectrometry (Affi-BAMS): A Multiplexed Microarray Platform for Targeted Proteomics

The ability to quantitatively probe diverse panels of proteins and their post-translational modifications (PTMs) across multiple samples would aid a broad spectrum of biological, biochemical and pharmacological studies. We report a novel, microarray analytical technology that combines immuno-affinity capture with Matrix Assisted Laser Desorption Ionization Mass Spectrometry (MALDI MS), which is capable of supporting highly multiplexed, targeted proteomic assays. Termed "Affinity-Bead Assisted Mass Spectrometry" (Affi-BAMS), this LC-free technology enables development of highly specific and customizable assay panels for simultaneous profiling of multiple proteins and PTMs. While affinity beads have been used previously in combination with MS, the Affi-BAMS workflow uses enrichment on a single bead that contains one type of antibody, generally capturing a single analyte (protein or PTM) while having enough binding capacity to enable quantification within approximately 3 orders of magnitude. The multiplexing capability is achieved by combining Affi-BAMS beads with different protein specificities. To enable screening of bead-captured analytes by MS, we further developed a novel method of performing spatially localized elution of targets from individual beads arrayed on a microscope slide. The resulting arrays of micro spots contain highly concentrated analytes localized within 0.5 mm diameter spots that can be directly measured using MALDI MS. While both intact proteins and protein fragments can be monitored by Affi-BAMS, we initially focused on applying this technology for bottom-up proteomics to enable screening of hundreds of samples per day by combining the robust magnetic bead-based workflow with the high throughput nature of MALDI MS acquisition. To demonstrate the variety of applications and robustness of Affi-BAMS, several studies are presented that focus on the response of 4EBP1, RPS6, ERK1/ERK2, mTOR, Histone H3 and C-MET to stimuli including rapamycin, H2O2, EPO, SU11274, Staurosporine and Vorinostat.

Identification of tumor-associated antigens with diagnostic ability of colorectal cancer by in-depth immunomic and seroproteomic analysis

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer related death worldwide. Its diagnosis at early stages would significantly improve the survival of CRC patients. The humoral immune response has been demonstrated useful for cancer diagnosis, predating clinical symptoms up to 3 years. Here, we employed an in-depth seroproteomic approach to identify proteins that elicit a humoral immune response in CRC patients. The seroproteomic approach relied on the immunoprecipitation with patient-derived autoantibodies of proteins from CRC cell lines with different metastatic properties followed by LC-MS/MS. After bioinformatics, we focused on 31 targets of CRC autoantibodies. After WB and IHC validation, ERP44 and TALDO1 showed potential to discriminate disease-free and metastatic CRC patients, and time to recurrence of CRC patients in stage II. Using plasma samples of 30 healthy individuals, 28 premalignant individuals, and 32 CRC patients, nine out of 13 selected targets for seroreactive analysis showed significant diagnostic ability to discriminate either CRC patients or premalignant subjects from controls. Our results suggest that the here defined panel of CRC autoantibodies and their target proteins should be included in CRC blood-based biomarker panels to get a clinically useful blood-based diagnostic signature for CRC detection. SIGNIFICANCE: Colorectal cancer is one of the deadliest cancer types mainly due to its late diagnosis. Its early diagnosis, therefore, is of great importance since it would significantly improve the survival of CRC patients. In our work, the in-depth seroproteomic analysis of colorectal cancer using isolated IgGs from colorectal cancer patients and controls and protein extract of colorectal cancer cells provide the identification of valuable biomarkers with diagnostic and prognostic ability of the disease.

Investigation of serum proteome homeostasis during radiation therapy by a quantitative proteomics approach

The purpose of the present study is to analyze the serum proteome of patients receiving Radiation Therapy (RT) at different stages of their treatment to discovery candidate biomarkers of the radiation-induced skin lesions and the molecular pathways underlying the radiation signatures. Six stages of RT treatment were monitored from patients treated because of brain cancer: before starting the treatment, during the treatment (four time points), and at 4 weeks from the last RT dose. Serum samples were analyzed by a proteomics approach based on the Data Independent Acquisition (DIA) mass spectrometry (MS). RT induced clear changes in the expression levels of 36 serum proteins. Among these, 25 proteins were down- or up-regulated significantly before the emergence of skin lesions. Some of these were still deregulated after the completion of the treatment. Few days before the appearance of the skin lesions, the levels of some proteins involved in the wound healing processes were down-regulated. The pathway analysis indicated that after partial body irradiation, the expression levels of proteins functionally involved in the acute inflammatory and immune response, lipoprotein process and blood coagulation, were deregulated.

In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine

Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.

Protein Biomarker Discovery in Non-depleted Serum by Spectral Library-Based Data-Independent Acquisition Mass Spectrometry

In discovery proteomics experiments, tandem mass spectrometry and data-dependent acquisition (DDA) are classically used to identify and quantify peptides and proteins through database searching. This strategy suffers from known limitations such as under-sampling and lack of reproducibility of precursor ion selection in complex proteomics samples, leading to somewhat inconsistent analytical results across large datasets. Data-independent acquisition (DIA) based on fragmentation of all the precursors detected in predetermined isolation windows can potentially overcome this limitation. DIA promises reproducible peptide and protein quantification with deeper proteome coverage and fewer missing values than DDA strategies. This approach is particularly attractive in the field of clinical biomarker discovery, where large numbers of samples must be analyzed. Here, we describe a DIA workflow for non-depleted serum analysis including a straightforward approach through which to construct a dedicated spectral library, and indications on how to optimize chromatographic and mass spectrometry analytical methods to produce high-quality DIA data and results.

Fast quantitative urinary proteomic profiling workflow for biomarker discovery in kidney cancer

Background

Urine has evolved as a promising body fluids in clinical proteomics because it can be easily and noninvasively obtained and can reflect physiological and pathological status of the human body. Many efforts have been made to characterize more urinary proteins in recent years, but few have focused on the analysis throughput and detection reproducibility. Increasing the urine proteomic profiling throughput and reproducibility is urgently needed for discovering potential biomarker in large cohorts.

Methods

In this study, we developed a fast and robust workflow for streamlined urinary proteome analysis. The workflow integrate highly efficient sample preparation technique and urinary specific data-independent acquisition (DIA) approach. The performance of the workflow was systematically evaluated and the workflow was subsequently applied in a proof-of-concept urine proteome study of 21 kidney cancer (KC) patients and 22 healthy controls.

Results

With this workflow, the entire sample preparation process takes less than 3 h and allows multiplexing on standard centrifuges. Without pre-fractionation, our newly developed DIA method allows quantitative analysis of ~ 1000 proteins within 80 min of MS time (~ 15 samples/day). The quantitation accuracy of the whole workflow was excellent with median CV of 9.1%. The preliminary study on KC identified 125 significantly changed proteins.

Conclusions

The result suggested the feasibility of applying the high throughput workflow in extensive urinary proteome profiling and clinical relevant biomarker discovery.

Electronic supplementary material

The online version of this article (10.1186/s12014-018-9220-2) contains supplementary material, which is available to authorized users.