Reto Ossola

A High-Confidence Human Plasma Proteome Reference Set with Estimated Concentrations in PeptideAtlas

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.

High Sensitivity Detection of Plasma Proteins by Multiple Reaction Monitoring of N-Glycosites

The detection and quantification of plasma (serum) proteins at or below the ng/ml concentration range are of critical importance for the discovery and evaluation of new protein biomarkers. This has been achieved either by the development of high sensitivity ELISA or other immunoassays for specific proteins or by the extensive fractionation of the plasma proteome followed by the mass spectrometric analysis of the resulting fractions. The first approach is limited by the high cost and time investment for assay development and the requirement of a validated target. The second, although reasonably comprehensive and unbiased, is limited by sample throughput. Here we describe a method for the detection of plasma proteins at concentrations in the ng/ml or sub-ng/ml range and their accurate quantification over 5 orders of magnitude. The method is based on the selective isolation of N-glycosites from the plasma proteome and the detection and quantification of targeted peptides in a quadrupole linear ion trap instrument operated in the multiple reaction monitoring (MRM) mode. The unprecedented sensitivity of the mass spectrometric analysis of minimally fractionated plasma samples is the result of the significantly reduced sample complexity of the isolated N-glycosites compared with whole plasma proteome digests and the selectivity of the MRM process. Precise quantification was achieved via stable isotope dilution by adding 13C- and/or 15N-labeled reference analytes. We also demonstrate the possibility of significantly expanding the number of MRM measurements during one single LC-MS run without compromising sensitivity by including elution time constraints for the targeted transitions, thus allowing quantification of large sets of peptides in a single analysis.

Using iRT, a normalized retention time for more targeted measurement of peptides.

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide‐specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT‐peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.

Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer

A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.

N-glycoprotein profiling of lung adenocarcinoma pleural effusions by shotgun proteomics†

Malignant pleural effusion of advanced lung adenocarcinoma may be a valid source for detection of biomarkers, such as N‐glycosylated proteins (N‐GP), because tumor cells grow during weeks in this liquid. The authors aimed for creation of N‐GP effusion profiles from routine cytology specimens to detect relevant biomarkers.

N-Glycoprotein SRMAtlas A RESOURCE OF MASS SPECTROMETRIC ASSAYS FOR N-GLYCOSITES ENABLING CONSISTENT AND MULTIPLEXED PROTEIN QUANTIFICATION FOR CLINICAL APPLICATIONS

Protein biomarkers have the potential to transform medicine as they are clinically used to diagnose diseases, stratify patients, and follow disease states. Even though a large number of potential biomarkers have been proposed over the past few years, almost none of them have been implemented so far in the clinic. One of the reasons for this limited success is the lack of technologies to validate proposed biomarker candidates in larger patient cohorts. This limitation could be alleviated by the use of antibody-independent validation methods such as selected reaction monitoring (SRM). Similar to measurements based on affinity reagents, SRM-based targeted mass spectrometry also requires the generation of definitive assays for each targeted analyte. Here, we present a library of SRM assays for 5568 N-glycosites enabling the multiplexed evaluation of clinically relevant N-glycoproteins as biomarker candidates. We demonstrate that this resource can be utilized to select SRM assay sets for cancer-associated N-glycoproteins for their subsequent multiplexed and consistent quantification in 120 human plasma samples. We show that N-glycoproteins spanning 5 orders of magnitude in abundance can be quantified and that previously reported abundance differences in various cancer types can be recapitulated. Together, the established N-glycoprotein SRMAtlas resource facilitates parallel, efficient, consistent, and sensitive evaluation of proposed biomarker candidates in large clinical sample cohorts.

Protein expression changes in ovarian cancer during the transition from benign to malignant.

Epithelial ovarian carcinoma has in general a poor prognosis since the vast majority of tumors are genomically unstable and clinically highly aggressive. This results in rapid progression of malignancy potential while still asymptomatic and thus in late diagnosis. It is therefore of critical importance to develop methods to diagnose epithelial ovarian carcinoma at its earliest developmental stage, that is, to differentiate between benign tissue and its early malignant transformed counterparts. Here we present a shotgun quantitative proteomic screen of benign and malignant epithelial ovarian tumors using iTRAQ technology with LC-MALDI-TOF/TOF and LC-ESI-QTOF MS/MS. Pathway analysis of the shotgun data pointed to the PI3K/Akt signaling pathway as a significant discriminatory pathway. Selected candidate proteins from the shotgun screen were further confirmed in 51 individual tissue samples of normal, benign, borderline or malignant origin using LC-MRM analysis. The MRM profile demonstrated significant differences between the four groups separating the normal tissue samples from all tumor groups as well as perfectly separating the benign and malignant tumors with a ROC-area of 1. This work demonstrates the utility of using a shotgun approach to filter out a signature of a few proteins only that discriminates between the different sample groups.

Streptococcus pyogenes in human plasma: adaptive mechanisms analyzed by mass spectrometry based proteomics.

Background: The human pathogen Streptococcus pyogenes adapts to vascular leakage at the site of infection. Results: S. pyogenes modifies the production of 213 in plasma determined using quantitative proteomics. Conclusion: The results clarify the function of HSA-binding proteins in S. pyogenes. Significance: Our data demonstrates the power of the quantitative mass spectrometry strategy to investigate bacterial adaptation to a given environment. Streptococcus pyogenes is a major bacterial pathogen and a potent inducer of inflammation causing plasma leakage at the site of infection. A combination of label-free quantitative mass spectrometry-based proteomics strategies were used to measure how the intracellular proteome homeostasis of S. pyogenes is influenced by the presence of human plasma, identifying and quantifying 842 proteins. In plasma the bacterium modifies its production of 213 proteins, and the most pronounced change was the complete down-regulation of proteins required for fatty acid biosynthesis. Fatty acids are transported by albumin (HSA) in plasma. S. pyogenes expresses HSA-binding surface proteins, and HSA carrying fatty acids reduced the amount of fatty acid biosynthesis proteins to the same extent as plasma. The results clarify the function of HSA-binding proteins in S. pyogenes and underline the power of the quantitative mass spectrometry strategy used here to investigate bacterial adaptation to a given environment.

Biomarker Validation in Blood Specimens by Selected Reaction Monitoring Mass Spectrometry of N -Glycosites

Targeted mass spectrometry using selected reaction monitoring (SRM) has emerged as the method of choice for the validation in blood serum, plasma, or other clinically relevant specimens of biomarker candidates arising from comparative proteomics or other discovery strategies. Here, we describe a method in which N-glycosites are selectively enriched from biological specimens by solid phase capture and PNGase F release, and then analyzed by SRM. Focusing the highly sensitive targeted mass spectrometry method on a subproteome enriched for secreted and shed proteins reproducibly identifies and quantifies such proteins in serum and plasma at the low nanogram per milliliter (ng/mL) concentration range. This protocol is intended to give an introduction to SRM-based targeted mass spectrometry with a special focus on the validation of biomarker candidates.

Proteomic surfaceome analysis of mesothelioma

Identification of new markers for malignant pleural mesothelioma (MPM) is a challenging clinical need. Here, we propose a quantitative proteomics primary screen of the cell surface exposed MPM N-glycoproteins, which provides the basis for the development of new protein-based diagnostic assays. Using the antibody-independent mass-spectrometry based cell surface capturing (CSC) technology, we specifically investigated the N-glycosylated surfaceome of MPM towards the identification of protein-marker candidates discriminatory between MPM and lung adenocarcinoma (ADCA). Relative quantitative CSC analysis of MPM cell line ZL55 in comparison with ADCA cell line Calu-3 revealed a birds eye view of their respective surfaceomes. In a secondary screen of fifteen MPM and six ADCA, we used high throughput low density microarrays (LDAs) to verify specificity and sensitivity of nineteen N-glycoproteins overregulated in the surfaceome of MPM. This proteo-transcriptomic approach revealed thy-1/CD90 (THY1) and teneurin-2 (ODZ2) as protein-marker candidates for the discrimination of MPM from ADCA. Thy-1/CD90 was further validated by immunohistochemistry on frozen tissue sections of MPM and ADCA samples. Together, we present a combined proteomic and transcriptomic approach enabling the relative quantitative identification and pre-clinical selection of new MPM marker candidates.