Deglycosylation and label-free quantitative LC-MALDI MS applied to efficient serum biomarker discovery of lung cancer

Background  

Serum is an ideal source of biomarker discovery and proteomic profiling studies are continuously pursued on serum samples. However, serum is featured by high level of protein glycosylations that often cause ionization suppression and confound accurate quantification analysis by mass spectrometry. Here we investigated the effect of N-glycan and sialic acid removal from serum proteins on the performance of label-free quantification results.     

Global and targeted quantitative proteomics for biomarker discovery

The extraordinary developments made in proteomic technologies in the past decade have enabled investigators to consider designing studies to search for diagnostic and therapeutic biomarkers by scanning complex proteome samples using unbiased methods. The major technology driving these studies is mass spectrometry (MS). The basic premises of most biomarker discovery studies is to use the high data-gathering capabilities of MS to compare biological samples obtained from healthy and disease-afflicted patients and identify proteins that are differentially abundant between the two specimen. To meet the need to compare the abundance of proteins in different samples, a number of quantitative approaches have been developed. In this article, many of these will be described with an emphasis on their advantageous and disadvantageous for the discovery of clinically useful biomarkers.     

A proteomic approach for plasma biomarker discovery with 8-plex iTRAQ labeling and SCX-LC-MS/MS

Plasma is recognized as a promising source of disease-related biomarkers, and proteomic approaches for identifying novel plasma biomarkers are in great demand. However, the complexity and dynamic protein concentration range of plasma remain the main obstacles for current research in this field. In this study, plasma proteins were prefractioned by immunodepletion and Protein Equalizer Technology to remove high abundant proteins, then labeled with an 8-plex isobaric tags for relative and absolute quantitation (iTRAQ) to improve the peptide ionization, and analyzed by strong-cation-exchange(SCX) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Our results showed that both prefraction methods were complementary, with regard to the number of identified proteins. Good chromatographic technique is important to further fractionate the iTRAQ labeling peptides, which allowed 320 and 248 different proteins to be characterized from two prefraction methods, respectively, encompassing a wide array of biological functions and a broad dynamic range of 10⁷. Furthermore, the accuracy of iTRAQ relative quantitation for differentially expressed proteins is associated with the number of peptides hits per protein.     

Application of a peptide-based PF2D platform for quantitative proteomics in disease biomarker discovery

A peptide-based 2-D liquid phase fractionation (PF2D) system was used in a quantitative proteomic analysis of hepatocellular carcinoma. 2-D liquid maps of peptide specimens showed better resolution than those of proteins, leading to the identification of differentially expressed proteins. Peptide-based PF2D gave well-matched theoretical and experimental pI values and was proven to be a very efficient and versatile analytical tool for both large-scale profiling and quantification of phosphoproteins in disease biomarker discovery.     

Systematic discovery of ectopic pregnancy serum biomarkers using 3-D protein profiling coupled with label-free quantitation

Ectopic pregnancy (EP) and normal intrauterine pregnancy (IUP) serum proteomes were quantitatively compared to systematically identify candidate biomarkers. A 3-D biomarker discovery strategy consisting of abundant protein immunodepletion, SDS gels, LC-MS/MS, and label-free quantitation of MS signal intensities identified 70 candidate biomarkers with differences between groups greater than 2.5-fold. Further statistical analyses of peptide quantities were used to select the most promising 12 biomarkers for further study, which included known EP biomarkers, novel EP biomarkers (ADAM12 and ISM2), and five specific isoforms of the pregnancy specific beta-1-glycoprotein family. Technical replicates showed good reproducibility and protein intensities from the label-free discovery analysis compared favorably with reported abundance levels of several known reference serum proteins over at least 3 orders of magnitude. Similarly, relative abundances of candidate biomarkers from the label-free discovery analysis were consistent with relative abundances from pilot validation assays performed for five of the 12 most promising biomarkers using label-free multiple reaction monitoring of both the patient serum pools used for discovery and the individual samples that constituted these pools. These results demonstrate robust, reproducible, in-depth 3-D serum proteome discovery, and subsequent pilot-scale validation studies can be achieved readily using label-free quantitation strategies.     

Development of glycoprotein capture-based label-free method for the high-throughput screening of differential glycoproteins in hepatocellular carcinoma

A robust, reproducible, and high throughput method was developed for the relative quantitative analysis of glycoprotein abundances in human serum. Instead of quantifying glycoproteins by glycopeptides in conventional quantitative glycoproteomics, glycoproteins were quantified by nonglycosylated peptides derived from the glycoprotein digest, which consists of the capture of glycoproteins in serum samples and the release of nonglycopeptides by trypsin digestion of captured glycoproteins followed by two-dimensional liquid chromatography-tandem MS analysis of released peptides. Protein quantification was achieved by comparing the spectrum counts of identified nonglycosylated peptides of glycoproteins between different samples. This method was demonstrated to have almost the same specificity and sensitivity in glycoproteins quantification as capture at glycopeptides level. The differential abundance of proteins present at as low as nanogram per milliliter levels was quantified with high confidence. The established method was applied to the analysis of human serum samples from healthy people and patients with hepatocellular carcinoma (HCC) to screen differential glycoproteins in HCC. Thirty eight glycoproteins were found with substantial concentration changes between normal and HCC serum samples, including α-fetoprotein, the only clinically used marker for HCC diagnosis. The abundance changes of three glycoproteins, i.e. galectin-3 binding protein, insulin-like growth factor binding protein 3, and thrombospondin 1, which were associated with the development of HCC, were further confirmed by enzyme-linked immunosorbent assay. In conclusion, the developed method was an effective approach to quantitatively analyze glycoproteins in human serum and could be further applied in the biomarker discovery for HCC and other cancers.     

Evaluation of multiprotein immunoaffinity subtraction for plasma proteomics and candidate biomarker discovery using mass spectrometry

Strategies for removal of high abundance proteins have been increasingly utilized in proteomic studies of serum/plasma and other body fluids to enhance the detection of low abundance proteins and achieve broader proteome coverage; however, both the reproducibility and specificity of the high abundance protein depletion process still represent common concerns. Here we report a detailed evaluation of immunoaffinity subtraction performed applying the ProteomeLab IgY-12 system that is commonly used in human serum/plasma proteome characterization in combination with high resolution LC-MS/MS. Plasma samples were repeatedly processed using this approach, and the resulting flow-through fractions and bound fractions were individually analyzed for comparison. The removal of target proteins by the immunoaffinity subtraction system and the overall process was highly reproducible. Non-target proteins, including one spiked protein standard (rabbit glyceraldehyde-3-phosphate dehydrogenase), were also observed to bind to the column at different levels but also in a reproducible manner. The results suggest that multiprotein immunoaffinity subtraction systems can be readily integrated into quantitative strategies to enhance detection of low abundance proteins in biomarker discovery studies.     

Proteomics and its applications for biomarker discovery in human saliva

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.     

Proteomics and its applications for biomarker discovery in human saliva

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.     

A comparison of MS2-based label-free quantitative proteomic techniques with regards to accuracy and precision

The advent of algorithms for fragmentation spectrum-based label-free quantitative proteomics has enabled straightforward quantification of shotgun proteomic experiments. Despite the popularity of these approaches, few studies have been performed to assess their performance. We have therefore profiled the precision and the accuracy of three distinct relative label-free methods on both the protein and the proteome level. We derived our test data from two well-characterized publicly available quantitative data sets.     

Proteomics of human cerebrospinal fluid: discovery and verification of biomarker candidates in neurodegenerative diseases using quantitative proteomics

There is an urgent need for novel biomarkers that can be used to improve the diagnosis, predict the disease progression, improve our understanding of the pathology or serve as therapeutic targets for neurodegenerative diseases. Cerebrospinal fluid (CSF) is in direct contact with the CNS and reflects the biochemical state of the CNS under different physiological and pathological settings. Because of this, CSF is regarded as an excellent source for identifying biomarkers for neurological diseases and other diseases affecting the CNS. Quantitative proteomics and sophisticated computational software applied to analyze the protein content of CSF has been fronted as an attractive approach to find novel biomarkers for neurological diseases. This review will focus on some of the potential pitfalls in biomarker studies using CSF, summarize the status of the field of CSF proteomics in general, and discuss some of the most promising proteomics biomarker study approaches. A brief status of the biomarker discovery efforts in multiple sclerosis, Alzheimer's disease, and Parkinson's disease is also given.     

In-depth discovery of protein lactylation in hepatocellular carcinoma

As a prevalent cancer type, hepatocellular carcinoma (HCC) accounts for a number of tumor-related deaths worldwide. Substantial efforts from various aspects, including RNA and proteins, have been devoted to understanding the mechanisms of HCC and proposing therapeutic schemes correspondingly. Within one of the important fields in cancer research – protein post-translational modifications (PTMs), recent discoveries revealed much broader landscapes of lysine lactylation (Kla) distributed in whole human proteome. Upon realizing the relation between Kla and cancers, Hong et al. (Proteomics 2023, 23, 2200432) comprehensively profiled lactylproteome in HCC tissues for the first time. All collected and processed samples were categorized into normal liver tissues, HCC without metastasis or HCC with lung metastasis tissues. As a result, 2045 Kla modification sites from 960 proteins were identified and 1438 sites from 772 proteins were quantifiably measured. Many differentially expressed Kla-proteins emerged and meant to contribute HCC formation and metastatsis. Among them, specific Kla sites from ubiquitin specific peptidase 14 (USP14) and ATP-binding cassette family 1 (ABCF1) were respectively verified as diagnostic indicators to characterize HCC and its metastasis. This work was of great significance, and made impacts in terms of further discovery of HCC rationale, as well as diagnosis of HCC status and targeted therapies.     

Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery

A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a q-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An a priori power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a post hoc power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes>2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This q-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation.     

An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters

Background  

Many studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins.     

Identification of long non-coding RNA p34822 as a potential plasma biomarker for the diagnosis of hepatocellular carcinoma

正Dear Editor,Hepatocellular carcinoma(HCC)is the third leading cause of cancer-related deaths worldwide(Li and Satomura,2015).Early diagnosis and treatment are vital for reducing mortality.Currently,biomarkers such as serum alpha fetoprotein(AFP),AFP-L2,and carcinoembryonic antigen are widely used tumor markers for managing HCC in patients(Shi et al.,2016).However,because of their low diagnostic sensitivity and specificity,novel biomarkers with high sensitivity and specificity for early HCC diagnosis are needed.     

Analysis of the HUPO Brain Proteome reference samples using 2-D DIGE and 2-D LC-MS/MS

Within the Human Proteome Organization (HUPO) Brain Proteome Project, a pilot study was launched with reference samples shipped to nine international laboratories (see Hamacher et al., this Special Issue) to evaluate different proteome approaches in neuroscience and to build up a first version of a brain protein database. One part of the study addresses quantitative proteome alterations between three developmental stages (embryonic day 16; postnatal day 7; 8 weeks) of mouse brains. Five brains per stage were differentially analyzed by 2-D DIGE using internal standardization and overlapping pH gradients (pH 4-7 and 6-9). In total, 214 protein spots showing stage-dependent intensity alterations (> two-fold) were detected, 56 of which were identified. Several of them, e.g. members of the dihydropyrimidinase family, are known to be associated with brain development. To feed the HUPO BPP brain protein database, a robust 2-D LC-MS/MS method was applied to murine postnatal day 7 and human post-mortem brain samples. Using MASCOT and the IPI database, 350 human and 481 mouse proteins could be identified by at least two different peptides. The data are accessible through the PRIDE database (http://www.ebi.ac.uk/pride/).     

CDKN2A is a prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma

Cyclin dependent kinase inhibitor 2A (CDKN2A) is an essential regulator of immune cell functionality, but the mechanisms whereby it drives immune infiltration in hepatocellular carcinoma (HCC) remain unclear. In the present study, we studied the association with CDKN2A expression and immune invasion with the risk of developing HCC. A totally of 2207 different genes were found between HCC and adjacent liver tissues from TCGA and GEO databases. CDKN2A was highly expressed in HCC and associated with poorer overall survival and disease-free survival. Notably, CDKN2A expression was positively correlated with infiltrating levels into purity, B cell, CD+8 T cell, CD+4 T cell, macrophage, neutrophil, and dendritic cells in HCC. CDKN2A expression showed strong correlations between diverse immune marker sets in HCC. These findings suggest that CDKN2A expression potentially contributes to regulation of tumor-associated macrophages and can be used as a prognostic biomarker for determining prognosis and immune infiltration in HCC.     

Evaluation of three principally different intact protein prefractionation methods for plasma biomarker discovery

The aim of this study was to evaluate three principally different top-down protein prefractionation methods for plasma: high-abundance protein depletion, size fractionation and peptide ligand affinity beads, focusing in particular on compatibility with downstream analysis, reproducibility and analytical depth. Our data clearly demonstrates the benefit of high-abundance protein depletion. However, MS/MS analysis of the proteins eluted from the high-abundance protein depletion column show that more proteins than aimed for are removed and, in addition, that the depletion efficacy varies between the different high-abundance proteins. Although a smaller number of proteins were identified per fraction using the peptide ligand affinity beads, this technique showed to be both robust and versatile. Size fractionation, as performed in this study, focusing on the low molecular weight proteome using a combination of gel filtration chromatography and molecular weight cutoff filters, showed limitations in the molecular weight cutoff precision leading detection of high molecular weight proteins and, in the case of the cutoff filters, high variability. GeLC-MS/MS analysis of the fractionation methods in combination with pathway analysis demonstrates that increased fractionation primarily leads to high proteome coverage of pathways related to biological functions of plasma, such as acute phase reaction, complement cascade and coagulation. Further, the prefractionation methods in this study induces limited effect on the proportion of tissue proteins detected, thereby highlighting the importance of extensive or targeted downstream fractionation.     

Development and validation of an LC-MS/MS method for the quantitative determination of aripiprazole and its main metabolite, OPC-14857, in human plasma

An accurate, sensitive, reproducible, and selective liquid chromatography/tandem mass spectrometry (LC-MS/MS) method for determination of aripiprazole and its main metabolite, OPC-14857, in human plasma was developed and validated. Chromatographic separation was achieved isocratically on a C18 reversed-phase column within 7.5 min. The calibration curve, ranging from 0.1 to 100 ng/ml, was fitted to a 1/y2-weighted linear regression model. The assay showed no significant interference. Lower limit of quantitation (LLOQ) for both analytes was 0.1 ng/ml using 0.4 ml of plasma. Intra- and inter-assay precision and accuracy values for aripiprazole and OPC-14857 were within regulatory limits.