Seminal Plasma Proteome as an Indicator of Sperm Dysfunction and Low Sperm Motility in Chickens.

Molecular mechanisms underlying sperm motility have not been fully explained, particularly in chickens. The objective was to identify seminal plasma proteins associated with chicken sperm motility by comparing the seminal plasma proteomic profile of roosters with low sperm motility (LSM, n = 4) and high sperm motility (HSM, n = 4). Using a label-free MS-based method, a total of 522 seminal plasma proteins were identified, including 386 (∼74%) previously reported and 136 novel ones. A total of 70 differentially abundant proteins were defined, including 48 more-abundant, 15 less-abundant, and seven proteins unique to the LSM group (specific proteins). Key secretory proteins like less-abundant adhesion G-protein coupled receptor G2 (ADGRG2) and more-abundant serine peptidase inhibitor Kazal-type 2 (SPINK2) in the LSM suggested that the corresponding secretory tissues played a crucial role in maintaining sperm motility. Majority (80%) of the more-abundant and five specific proteins were annotated to the cytoplasmic domain which might be a result of higher plasma membrane damage and acrosome dysfunction in LSM. Additionally, more-abundant mitochondrial proteins were detected in LSM seminal plasma associated with lower spermatozoa mitochondrial membrane potential (ΔΨm) and ATP concentrations. Further studies showed that the spermatozoa might be suffering from oxidative stress, as the amount of spermatozoa reactive oxygen species (ROS) were largely enhanced, seminal malondialdehyde (MDA) concentrations were increased, and the seminal plasma total antioxidant capacity (T-AOC) were decreased. Our study provides an additional catalogue of chicken seminal plasma proteome and supports the idea that seminal plasma could be as an indicator of spermatozoa physiology. More-abundant of acrosome, mitochondria and sperm cytoskeleton proteins in the seminal plasma could be a marker of sperm dysfunction and loss of motility. The degeneration of spermatozoa caused by the reduced seminal T-AOC and enhanced oxidative stress might be potential determinants of low sperm motility. These results could extend our understanding of sperm motility and sperm physiology regulation.

Glycoproteogenomics: A Frequent Gene Polymorphism Affects the Glycosylation Pattern of the Human Serum Fetuin/α-2-HS-Glycoprotein.

Fetuin, also known as α-2-HS-glycoprotein (gene name: AHSG), is one of the more abundant glycoproteins secreted into the bloodstream. There are two frequently occurring alleles of human AHSG, resulting in three genotypes (AHSG*1, AHSG*2, and heterozygous AHSG1/2). The backbone amino acid sequences of fetuin coded by the AHSG*1 and AHSG*2 genes differ in two amino acids including one known O-glycosylation site (aa position 256). Although fetuin levels have been extensively studied, the originating genotype is often ignored in such analysis. As fetuin has been suggested repeatedly as a potential biomarker for several disorders, the question whether the gene polymorphism affects the fetuin profile is of great interest. In this work, we describe detailed proteoform profiles of fetuin, isolated from serum of 10 healthy and 10 septic patient individuals and investigate potential glycoproteogenomics correlations, e.g. how gene polymorphisms affect glycosylation. We established an efficient method for fetuin purification from individuals' serum using ion-exchange chromatography. Subsequently, we performed hybrid mass spectrometric approaches integrating data from native mass spectra and peptide-centric MS analysis. Our data reveal a crucial effect of the gene polymorphism on the glycosylation pattern of fetuin. Moreover, we clearly observed increased fucosylation in the samples derived from the septic patients. Our serum proteoform analysis, targeted at one protein obtained from 20 individuals, exposes the wide variability in proteoform profiles, which should be taken into consideration when using fetuin as biomarker. Importantly, focusing on a single or few proteins, the quantitative proteoform profiles can provide, as shown here, already ample data to classify individuals by genotype and disease state.

Immunoglobulin A N-glycosylation Presents Important Body Fluid-specific Variations in Lactating Mothers.

Secretory Immunoglobulin A (SIgA) is central to mucosal immunity: represents one of the main immunological mechanisms of defense against the potential attack of pathogens. During lactation SIgA is produced by plasmablasts in the mammary gland and is present in breast milk, playing a vital role in the passive immunity of the newborn. Interestingly, the different components of SIgA are highly N-glycosylated, and these N-Glycans have an essential role in health maintenance. In this work, we performed a glycomic study to compare N-glycosylation of SIgA purified from mature breast milk and saliva, and plasma IgA from the same lactating participants. Our results revealed a greater diversity than previously reported, with 89 glycan compositions that may correspond to over 250 structures. Among these glycans, 54 glycan compositions were characterized as body-fluid specific. Most of these unique N-Glycan compositions identified in SIgA from mature milk and IgA from plasma were fucosylated and both fucosylated and sialylated species, whereas in salivary SIgA the unique structures were mainly undecorated complex N-Glycans. In addition, we evaluated the effect of delivery mode on (S)IgA glycosylation. Lactating participants who had given birth by vaginal delivery presented an increased proportion of high mannose and fucosylated glycans in salivary SIgA, and selected high mannose, fucosylated, sialylated, and both fucosylated and sialylated glycans in plasma IgA, indicating that the hormonal changes during vaginal delivery could affect plasma and saliva IgA. These results reveal the structural details that provide a new dimension to the roles of (S)IgA N-Glycans in different tissues, and especially in maternal and new-born protection and infant development. The design of optimal recombinant IgA molecules specifically targeted to protect mucosal surfaces will need to include this dimension of structural detail.

Small-protein Enrichment Assay Enables the Rapid, Unbiased Analysis of Over 100 Low Abundance Factors from Human Plasma.

Unbiased and sensitive quantification of low abundance small proteins in human plasma (e.g. hormones, immune factors, metabolic regulators) remains an unmet need. These small protein factors are typically analyzed individually and using antibodies that can lack specificity. Mass spectrometry (MS)-based proteomics has the potential to address these problems, however the analysis of plasma by MS is plagued by the extremely large dynamic range of this body fluid, with protein abundances spanning at least 13 orders of magnitude. Here we describe an enrichment assay (SPEA), that greatly simplifies the plasma dynamic range problem by enriching small-proteins of 2-10 kDa, enabling the rapid, specific and sensitive quantification of >100 small-protein factors in a single untargeted LC-MS/MS acquisition. Applying this method to perform deep-proteome profiling of human plasma we identify C5ORF46 as a previously uncharacterized human plasma protein. We further demonstrate the reproducibility of our workflow for low abundance protein analysis using a stable-isotope labeled protein standard of insulin spiked into human plasma. SPEA provides the ability to study numerous important hormones in a single rapid assay, which we applied to study the intermittent fasting response and observed several unexpected changes including decreased plasma abundance of the iron homeostasis regulator hepcidin.

A Robust and Versatile Automated Glycoanalytical Technology for Serum Antibodies and Acute Phase Proteins: Ovarian Cancer Case Study.

The direct association of the genome, transcriptome, metabolome, lipidome and proteome with the serum glycome has revealed systems of interconnected cellular pathways. The exact roles of individual glycoproteomes in the context of disease have yet to be elucidated. In a move toward personalized medicine, it is now becoming critical to understand disease pathogenesis, and the traits, stages, phenotypes and molecular features that accompany it, as the disruption of a whole system. To this end, we have developed an innovative technology on an automated platform, "GlycoSeqCap," which combines N-glycosylation data from six glycoproteins using a single source of human serum. Specifically, we multiplexed and optimized a successive serial capture and glycoanalysis of six purified glycoproteins, immunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin A (IgA), transferrin (Trf), haptoglobin (Hpt) and alpha-1-antitrypsin (A1AT), from 50 µl of human serum. We provide the most comprehensive and in-depth glycan analysis of individual glycoproteins in a single source of human serum to date. To demonstrate the technological application in the context of a disease model, we performed a pilot study in an ovarian cancer cohort (n = 34) using discrimination and classification analyses to identify aberrant glycosylation. In our sample cohort, we exhibit improved selectivity and specificity over the currently used biomarker for ovarian cancer, CA125, for early stage ovarian cancer. This technology will establish a new state-of-the-art strategy for the characterization of individual serum glycoproteomes as a diagnostic and monitoring tool which represents a major step toward understanding the changes that take place during disease.

High-throughput Serum N-Glycomics: Method Comparison and Application to Study Rheumatoid Arthritis and Pregnancy-associated Changes.

N-Glycosylation is a fundamentally important protein modification with a major impact on glycoprotein characteristics such as serum half-life and receptor interaction. More than half of the proteins in human serum are glycosylated, and the relative abundances of protein glycoforms often reflect alterations in health and disease. Several analytical methods are currently capable of analyzing the total serum N-glycosylation in a high-throughput manner.Here we evaluate and compare the performance of three high-throughput released N-glycome analysis methods. Included were hydrophilic-interaction ultra-high-performance liquid chromatography with fluorescence detection (HILIC-UHPLC-FLD) with 2-aminobenzamide labeling of the glycans, multiplexed capillary gel electrophoresis with laser-induced fluorescence detection (xCGE-LIF) with 8-aminopyrene-1,3,6-trisulfonic acid labeling, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) with linkage-specific sialic acid esterification. All methods assessed the same panel of serum samples, which were obtained at multiple time points during the pregnancies and postpartum periods of healthy women and patients with rheumatoid arthritis (RA). We compared the analytical methods on their technical performance as well as on their ability to describe serum protein N-glycosylation changes throughout pregnancy, with RA, and with RA disease activity.Overall, the methods proved to be similar in their detection and relative quantification of serum protein N-glycosylation. However, the non-MS methods showed superior repeatability over MALDI-TOF-MS and allowed the best structural separation of low-complexity N-glycans. MALDI-TOF-MS achieved the highest throughput and provided compositional information on higher-complexity N-glycans. Consequentially, MALDI-TOF-MS could establish the linkage-specific sialylation differences within pregnancy and RA, whereas HILIC-UHPLC-FLD and xCGE-LIF demonstrated differences in α1,3- and α1,6-branch galactosylation. While the combination of methods proved to be the most beneficial for the analysis of total serum protein N-glycosylation, informed method choices can be made for the glycosylation analysis of single proteins or samples of varying complexity.

A Targeted Mass Spectrometry Strategy for Developing Proteomic Biomarkers: A Case Study of Epithelial Ovarian Cancer.

Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.

Delta-S-Cys-Albumin: A Lab Test that Quantifies Cumulative Exposure of Archived Human Blood Plasma and Serum Samples to Thawed Conditions.

Exposure of blood plasma/serum (P/S) to thawed conditions (> -30 °C) can produce biomolecular changes that skew measurements of biomarkers within archived patient samples, potentially rendering them unfit for molecular analysis. Because freeze-thaw histories are often poorly documented, objective methods for assessing molecular fitness before analysis are needed. We report a 10-µl, dilute-and-shoot, intact-protein mass spectrometric assay of albumin proteoforms called "ΔS-Cys-Albumin" that quantifies cumulative exposure of archived P/S samples to thawed conditions. The relative abundance of S-cysteinylated (oxidized) albumin in P/S increases inexorably but to a maximum value under 100% when samples are exposed to temperatures > -30 °C. The difference in the relative abundance of S-cysteinylated albumin (S-Cys-Alb) before and after an intentional incubation period that drives this proteoform to its maximum level is denoted as ΔS-Cys-Albumin. ΔS-Cys-Albumin in fully expired samples is zero. The range (mean ± 95% CI) observed for ΔS-Cys-Albumin in fresh cardiac patient P/S (n = 97) was, for plasma 12-29% (20.9 ± 0.75%) and for serum 10-24% (15.5 ± 0.64%). The multireaction rate law that governs S-Cys-Alb formation in P/S was determined and shown to predict the rate of formation of S-Cys-Alb in plasma and serum samples-a step that enables back-calculation of the time at which unknown P/S specimens have been exposed to room temperature. A blind challenge demonstrated that ΔS-Cys-Albumin can detect exposure of groups (n = 6 each) of P/S samples to 23 °C for 2 h, 4 °C for 16 h, or -20 °C for 24 h-and exposure of individual specimens for modestly increased times. An unplanned case study of nominally pristine serum samples collected under NIH-sponsorship demonstrated that empirical evidence is required to ensure accurate knowledge of archived P/S biospecimen storage history.

DIAlignR Provides Precise Retention Time Alignment Across Distant Runs in DIA and Targeted Proteomics.

Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH-MS) is widely used for proteomics analysis given its high throughput and reproducibility, but ensuring consistent quantification of analytes across large-scale studies of heterogeneous samples such as human plasma remains challenging. Heterogeneity in large-scale studies can be caused by large time intervals between data acquisition, acquisition by different operators or instruments, and intermittent repair or replacement of parts, such as the liquid chromatography column, all of which affect retention time (RT) reproducibility and, successively, performance of SWATH-MS data analysis. Here, we present a novel algorithm for RT alignment of SWATH-MS data based on direct alignment of raw MS2 chromatograms using a hybrid dynamic programming approach. The algorithm does not impose a chronological order of elution and allows for alignment of elution-order-swapped peaks. Furthermore, allowing RT mapping in a certain window around a coarse global fit makes it robust against noise. On a manually validated dataset, this strategy outperformed the current state-of-the-art approaches. In addition, on real-world clinical data, our approach outperformed global alignment methods by mapping 98% of peaks compared with 67% cumulatively. DIAlignR reduced alignment error up to 30-fold for extremely distant runs. The robustness of technical parameters used in this pairwise alignment strategy is also demonstrated. The source code is released under the BSD license at https://github.com/Roestlab/DIAlignR.

Identification of Tumor Antigens Among the HLA Peptidomes of Glioblastoma Tumors and Plasma.

Glioblastoma multiforme (GBM) is the most aggressive brain tumor with poor prognosis to most patients. Immunotherapy of GBM is a potentially beneficial treatment option, whose optimal implementation may depend on familiarity with tumor specific antigens, presented as HLA peptides by the GBM cells. Further, early detection of GBM, such as by a routine blood test, may improve survival, even with the current treatment modalities. This study includes large-scale analyses of the HLA peptidome (immunopeptidome) of the plasma-soluble HLA molecules (sHLA) of 142 plasma samples, and the membranal HLA of GBM tumors of 10 of these patients' tumor samples. Tumor samples were fresh-frozen immediately after surgery and the plasma samples were collected before, and at multiple visits after surgery. In total, this HLA peptidome analysis involved 52 different HLA allotypes and resulted in the identification of more than 35,000 different HLA peptides. Strong correlations were observed in the signal intensities and in the repertoires of identified peptides between the tumors and plasma-soluble HLA peptidomes of the individual patients, whereas low correlations were observed between these HLA peptidomes and the tumors' proteomes. HLA peptides derived from Cancer/Testis Antigens (CTAs) were selected based on their presence among the HLA peptidomes of the patients and absence of expression of their source genes from any healthy and essential human tissues, except from immune-privileged sites. Additionally, peptides were selected as potential biomarkers if their levels in the plasma-sHLA peptidome were significantly reduced after the removal of tumor mass. The CTAs identified among the analyzed HLA peptidomes provide new opportunities for personalized immunotherapy and for early diagnosis of GBM.

Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance.

Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.

Immunomic Identification of Malaria Antigens Associated With Protection in Mice.

Efforts to develop vaccines against malaria represent a major research target. The observations that 1) sterile protection can be obtained when the host is exposed to live parasites and 2) the immunity against blood stage parasite is principally mediated by protective antibodies suggest that a protective vaccine is feasible. However, only a small number of proteins have been investigated so far and most of the Plasmodium proteome has yet to be explored. To date, only few immunodominant antigens have emerged for testing in clinical trials but no formulation has led to substantial protection in humans. The nature of parasite molecules associated with protection remains elusive. Here, immunomic screening of mice immune sera with different protection efficiencies against the whole parasite proteome allowed us to identify a large repertoire of antigens validated by screening a library expressing antigens. The calculation of weighted scores reflecting the likelihood of protection of each antigen using five predictive criteria derived from immunomic and proteomic data sets, highlighted a priority list of protective antigens. Altogether, the approach sheds light on conserved antigens across Plasmodium that are amenable to targeting by the host immune system upon merozoite invasion and blood stage development. Most of these antigens have preliminary protection data but have not been widely considered as candidate for vaccine trials, opening new perspectives that overcome the limited choice of immunodominant, poorly protective vaccines currently being the focus of malaria vaccine researches.

Antigen Analysis of Pre-Eclamptic Plasma Antibodies Using Escherichia Coli Proteome Chips.

Pre-eclampsia is one of the main causes of perinatal mortality and morbidity. Many biomarkers for diagnosing pre-eclampsia have been found but most have low accuracy. Therefore, a potential marker that can detect pre-eclampsia with high accuracy is required. Infection has been reported as a cause of pre-eclampsia. In recent years, protein microarray chips have been recognized as a strong and robust tool for profiling antibodies for infection diagnoses. The purpose of the present study was to profile antibodies in the human plasma of healthy and pre-eclamptic pregnancies to identify suitable biomarkers. In this study, an Escherichia coli chip was probed with samples from 29 individuals (16 pre-eclamptic women and 13 healthy pregnant women) to profile plasma antibodies. Bioinformatics tools were used to analyze the results, discover conserved motifs, compare against the entire human proteome, and perform protein functional analysis. An antibody classifier was identified using k-top scoring pairs and additional samples for a blinded test were collected. The findings indicated that compared with the healthy women, the pre-eclamptic women exhibited 108 and 130 differentially immunogenic proteins against human immunoglobulins G and M, respectively. In addition, pre-eclamptic women developed more immunoglobulin G but less immunoglobulin M against bacterial surface proteins compared with healthy women. The k-top scoring pairs identified five pairs of immunogenic proteins as classifiers with a high accuracy of 90% in the blind test. [AG] [ISV] GV [AE] L [LF] and [IV] [IV] RI [AG] [AD] E were the consensus motifs observed in immunogenic proteins in the immunoglobulin G and immunoglobulin M of pre-eclamptic women, respectively, whereas GA [AG] [AL] L [LF] and [SRY] [IQML] [ILV] [ILV] [ACG] GI [GH] [AEF] [AK] [ATY] [RG] N [IV] were observed in the immunoglobulins G and immunoglobulin M of healthy women, respectively.

N- and O-glycosylation Analysis of Human C1-inhibitor Reveals Extensive Mucin-type O-Glycosylation.

Human C1-inhibitor (C1-Inh) is a serine protease inhibitor and the major regulator of the contact activation pathway as well as the classical and lectin complement pathways. It is known to be a highly glycosylated plasma glycoprotein. However, both the structural features and biological role of C1-Inh glycosylation are largely unknown. Here, we performed for the first time an in-depth site-specific N- and O-glycosylation analysis of C1-Inh combining various mass spectrometric approaches, including C18-porous graphitized carbon (PGC)-LC-ESI-QTOF-MS/MS applying stepping-energy collision-induced dissociation (CID) and electron-transfer dissociation (ETD). Various proteases were applied, partly in combination with PNGase F and exoglycosidase treatment, in order to analyze the (glyco)peptides. The analysis revealed an extensively O-glycosylated N-terminal region. Five novel and five known O-glycosylation sites were identified, carrying mainly core1-type O-glycans. In addition, we detected a heavily O-glycosylated portion spanning from Thr82-Ser121 with up to 16 O-glycans attached. Likewise, all known six N-glycosylation sites were covered and confirmed by this site-specific glycosylation analysis. The glycoforms were in accordance with results on released N-glycans by MALDI-TOF/TOF-MS/MS. The comprehensive characterization of C1-Inh glycosylation described in this study will form the basis for further functional studies on the role of these glycan modifications.

Identification of Tumor Antigens Among the HLA Peptidomes of Glioblastoma Tumors and Plasma.

Glioblastoma multiforme (GBM) is the most aggressive brain tumor with poor prognosis to most patients. Immunotherapy of GBM is a potentially beneficial treatment option, whose optimal implementation may depend on familiarity with tumor specific antigens, presented as HLA peptides by the GBM cells. Furthermore, early detection of GBM, such as by a routine blood test, may improve survival, even with the current treatment modalities. This study includes large-scale analyses of the HLA peptidome (immunopeptidome) of the plasma-soluble HLA molecules (sHLA) of 142 plasma samples, and the membranal HLA of GBM tumors of 10 of these patients' tumor samples. Tumor samples were fresh-frozen immediately after surgery and the plasma samples were collected before, and at multiple visits after surgery. In total, this HLA peptidome analysis involved 52 different HLA allotypes and resulted in the identification of more than 35,000 different HLA peptides. Strong correlations were observed in the signal intensities and in the repertoires of identified peptides between the tumors and plasma-soluble HLA peptidomes of the individual patients, whereas low correlations were observed between these HLA peptidomes and the tumors' proteomes. HLA peptides derived from Cancer/Testis Antigens (CTAs) were selected based on their presence among the HLA peptidomes of the patients and absence of expression of their source genes from any healthy and essential human tissues, except from immune-privileged sites. Additionally, peptides were selected as potential biomarkers if their levels in the plasma-sHLA peptidome were significantly reduced after the removal of tumor mass. The CTAs identified among the analyzed HLA peptidomes provide new opportunities for personalized immunotherapy and for early diagnosis of GBM.

An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples.

BACKGROUND: Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing. RESULTS: We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low µg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples. CONCLUSIONS: We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing.