Tony Tegeler

Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.

Verification of candidate biomarkers relies upon specific, quantitative assays optimized for selective detection of target proteins, and is increasingly viewed as a critical step in the discovery pipeline that bridges unbiased biomarker discovery to preclinical validation. Although individual laboratories have demonstrated that multiple reaction monitoring (MRM) coupled with isotope dilution mass spectrometry can quantify candidate protein biomarkers in plasma, reproducibility and transferability of these assays between laboratories have not been demonstrated. We describe a multilaboratory study to assess reproducibility, recovery, linear dynamic range and limits of detection and quantification of multiplexed, MRM-based assays, conducted by NCI-CPTAC. Using common materials and standardized protocols, we demonstrate that these assays can be highly reproducible within and across laboratories and instrument platforms, and are sensitive to low μg/ml protein concentrations in unfractionated plasma. We provide data and benchmarks against which individual laboratories can compare their performance and evaluate new technologies for biomarker verification in plasma.

Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography−Tandem Mass Spectrometry

The complexity of proteomic instrumentation for LC-MS/MS introduces many possible sources of variability. Data-dependent sampling of peptides constitutes a stochastic element at the heart of discovery proteomics. Although this variation impacts the identification of peptides, proteomic identifications are far from completely random. In this study, we analyzed interlaboratory data sets from the NCI Clinical Proteomic Technology Assessment for Cancer to examine repeatability and reproducibility in peptide and protein identifications. Included data spanned 144 LC-MS/MS experiments on four Thermo LTQ and four Orbitrap instruments. Samples included yeast lysate, the NCI-20 defined dynamic range protein mix, and the Sigma UPS 1 defined equimolar protein mix. Some of our findings reinforced conventional wisdom, such as repeatability and reproducibility being higher for proteins than for peptides. Most lessons from the data, however, were more subtle. Orbitraps proved capable of higher repeatability and reproducibility, but aberrant performance occasionally erased these gains. Even the simplest protein digestions yielded more peptide ions than LC-MS/MS could identify during a single experiment. We observed that peptide lists from pairs of technical replicates overlapped by 35-60%, giving a range for peptide-level repeatability in these experiments. Sample complexity did not appear to affect peptide identification repeatability, even as numbers of identified spectra changed by an order of magnitude. Statistical analysis of protein spectral counts revealed greater stability across technical replicates for Orbitraps, making them superior to LTQ instruments for biomarker candidate discovery. The most repeatable peptides were those corresponding to conventional tryptic cleavage sites, those that produced intense MS signals, and those that resulted from proteins generating many distinct peptides. Reproducibility among different instruments of the same type lagged behind repeatability of technical replicates on a single instrument by several percent. These findings reinforce the importance of evaluating repeatability as a fundamental characteristic of analytical technologies.

Performance Metrics for Liquid Chromatography-Tandem Mass Spectrometry Systems in Proteomics Analyses

A major unmet need in LC-MS/MS-based proteomics analyses is a set of tools for quantitative assessment of system performance and evaluation of technical variability. Here we describe 46 system performance metrics for monitoring chromatographic performance, electrospray source stability, MS1 and MS2 signals, dynamic sampling of ions for MS/MS, and peptide identification. Applied to data sets from replicate LC-MS/MS analyses, these metrics displayed consistent, reasonable responses to controlled perturbations. The metrics typically displayed variations less than 10% and thus can reveal even subtle differences in performance of system components. Analyses of data from interlaboratory studies conducted under a common standard operating procedure identified outlier data and provided clues to specific causes. Moreover, interlaboratory variation reflected by the metrics indicates which system components vary the most between laboratories. Application of these metrics enables rational, quantitative quality assessment for proteomics and other LC-MS/MS analytical applications.

Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance

Optimal performance of LC-MS/MS platforms is critical to generating high quality proteomics data. Although individual laboratories have developed quality control samples, there is no widely available performance standard of biological complexity (and associated reference data sets) for benchmarking of platform performance for analysis of complex biological proteomes across different laboratories in the community. Individual preparations of the yeast Saccharomyces cerevisiae proteome have been used extensively by laboratories in the proteomics community to characterize LC-MS platform performance. The yeast proteome is uniquely attractive as a performance standard because it is the most extensively characterized complex biological proteome and the only one associated with several large scale studies estimating the abundance of all detectable proteins. In this study, we describe a standard operating protocol for large scale production of the yeast performance standard and offer aliquots to the community through the National Institute of Standards and Technology where the yeast proteome is under development as a certified reference material to meet the long term needs of the community. Using a series of metrics that characterize LC-MS performance, we provide a reference data set demonstrating typical performance of commonly used ion trap instrument platforms in expert laboratories; the results provide a basis for laboratories to benchmark their own performance, to improve upon current methods, and to evaluate new technologies. Additionally, we demonstrate how the yeast reference, spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concentration in a complex matrix, thereby providing a metric to evaluate and minimize preanalytical and analytical variation in comparative proteomics experiments.

LC/MS-Based Quantitative Proteomic Analysis of Paraffin-Embedded Archival Melanomas Reveals Potential Proteomic Biomarkers Associated with Metastasis

Background Melanoma metastasis status is highly associated with the overall survival of patients; yet, little is known about proteomic changes during melanoma tumor progression. To better understand the changes in protein expression involved in melanoma progression and metastasis, and to identify potential biomarkers, we conducted a global quantitative proteomic analysis on archival metastatic and primary melanomas. Methodology and Findings A total of 16 metastatic and 8 primary cutaneous melanomas were assessed. Proteins were extracted from laser captured microdissected formalin fixed paraffin-embedded archival tissues by liquefying tissue cells. These preparations were analyzed by a LC/MS-based label-free protein quantification method. More than 1500 proteins were identified in the tissue lysates with a peptide ID confidence level of >75%. This approach identified 120 significant changes in protein levels. These proteins were identified from multiple peptides with high confidence identification and were expressed at significantly different levels in metastases as compared with primary melanomas (q-Value<0.05). Conclusions and Significance The differentially expressed proteins were classified by biological process or mapped into biological system networks, and several proteins were implicated by these analyses as cancer- or metastasis-related. These proteins represent potential biomarkers for tumor progression. The study successfully identified proteins that are differentially expressed in formalin fixed paraffin-embedded specimens of metastatic and primary melanoma.

Differential effects of AKT1(p.E17K) expression on human mammary luminal epithelial and myoepithelial cells

Recently, we identified a somatic mutation in AKT1, which results in a glutamic acid to lysine substitution (p.Glu17Lys or E17K). E17K mutations appear almost exclusively in breast cancers of luminal origin. Cellular models involving cell lines such as human mammary epithelial and MCF10 are model systems that upon transformation lead to rare forms of human breast cancer. Hence, we studied the effects of E17K using a clinically pertinent luminal cell line model while providing evidence to explain why E17K mutations do not occur in the mammary myoepithelium. Thus the purpose of our study was to perform a functional and differential proteomics study to assess the role of AKT1(E17K) in the development of breast cancer. We used a set of genetically matched nontumorigenic and tumorigenic mammary luminal and myoepithelial cells. We demonstrated that in myoepithelial cells, expression of E17K inhibited growth, migration, and protein synthesis compared with wild‐type AKT1. In luminal cells, E17K enhanced cell survival and migration, possibly offering a selective advantage in this type of cell. However, antineoplastic effects of E17K in luminal cells, such as inhibition of growth and protein synthesis, may ultimately be associated with favorable prognosis. Our study illustrates the importance of cellular context in determining phenotypic effects of putative oncogenic mutations. Hum Mutat 33:1216–1227, 2012.

Immunoprecipitation and mass spectrometry defines an extensive RBM45 protein-protein interaction network.

The pathological accumulation of RNA-binding proteins (RBPs) within inclusion bodies is a hallmark of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). RBP aggregation results in both toxic gain and loss of normal function. Determining the protein binding partners and normal functions of disease-associated RBPs is necessary to fully understand molecular mechanisms of RBPs in disease. Herein, we characterized the protein-protein interactions (PPIs) of RBM45, a RBP that localizes to inclusions in ALS/FTLD. Using immunoprecipitation coupled to mass spectrometry (IP-MS), we identified 132 proteins that specifically interact with RBM45 within HEK293 cells. Select PPIs were validated by immunoblot and immunocytochemistry, demonstrating that RBM45 associates with a number of other RBPs primarily via RNA-dependent interactions in the nucleus. Analysis of the biological processes and pathways associated with RBM45-interacting proteins indicates enrichment for nuclear RNA processing/splicing via association with hnRNP proteins and cytoplasmic RNA translation via eiF2 and eiF4 pathways. Moreover, several other ALS-linked RBPs, including TDP-43, FUS, Matrin-3, and hnRNP-A1, interact with RBM45, consistent with prior observations of these proteins within intracellular inclusions in ALS/FTLD. Taken together, our results define a PPI network for RBM45, suggest novel functions for this protein, and provide new insights into the contributions of RBM45 to neurodegeneration in ALS/FTLD. This article is part of a Special Issue entitled SI:RNA Metabolism in Disease.

Isolation and characterization of muscle fatigue substance with anti-tumor activities.

Research during the 1950s indicated that exercise played a role in the reduction of tumor growth. In the 1960s our studies confirmed that tumor-bearing rats, exercised to fatigue, demonstrated tumor inhibition. Our further studies isolated an extract (Fatigue Substance, or F-Substance) from rectus femoris muscles of rats which had been electrically stimulated to fatigue. This extract significantly inhibited growth of transplanted rat tumors. Research continued until 1978 when it became apparent the methodology at that time was not able to further identify the substances active components. Using current technology, we now report on the further isolation and characterization of F-Substance. In cell proliferation assays, extracts from electrically stimulated rat rectus femoris muscles had more significant inhibitory effect on the breast cancer cell line MCF-7 than those isolated from unstimulated muscles. To identify the molecule(s) responsible for the antitumor activity, a rat cytokine antibody array was used to profile the cytokines in the substances. Among the 29 different cytokines contained on the array, 3 showed greater than 3-fold difference between the substances isolated from the stimulated and unstimulated muscles. LIX (also known as CXCL5) is 6-fold higher in the substances isolated from stimulated muscles than those from the unstimulated muscles. TIMP-1 is 4.6 fold higher and sICAM is 3.6 fold higher in the substances from the stimulated muscles. Our results indicated that cytokines released from contracting muscles might be responsible for the antitumor effect of F-Substance.

Accurate LC Peak Boundary Detection for 16O/18O Labeled LC-MS Data

In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance. In this paper, we propose to apply accurate LC peak boundary detection to remove the corrupted part of LC peaks. Accurate LC peak boundary detection is achieved by checking the consistency of intensity patterns within peptide elution time ranges. In addition, we remove peptides with erroneous mass assignment through model fitness check, which compares observed intensity patterns to theoretically constructed ones. The proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements.

Quantitative Proteomic Approach for MicroRNA Target Prediction Based on 18O/16O Labeling

Motivation Among many large-scale proteomic quantification methods, 18O/16O labeling requires neither specific amino acid in peptides nor label incorporation through several cell cycles, as in metabolic labeling; it does not cause significant elution time shifts between heavy- and light-labeled peptides, and its dynamic range of quantification is larger than that of tandem mass spectrometry-based quantification methods. These properties offer 18O/16O labeling the maximum flexibility in application. However, 18O/16O labeling introduces large quantification variations due to varying labeling efficiency. There lacks a processing pipeline that warrants the reliable identification of differentially expressed proteins (DEPs). This motivates us to develop a quantitative proteomic approach based on 18O/16O labeling and apply it on Kaposi sarcoma-associated herpesvirus (KSHV) microRNA (miR) target prediction. KSHV is a human pathogenic y-herpesvirus strongly associated with the development of B-cell proliferative disorders, including primary effusion lymphoma. Recent studies suggest that miRs have evolved a highly complex network of interactions with the cellular and viral transcriptomes, and relatively few KSHV miR targets have been characterized at the functional level. While the new miR target prediction method, photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP), allows the identification of thousands of miR targets, the link between miRs and their targets still cannot be determined. We propose to apply the developed proteomic approach to establish such links. Method We integrate several 18O/16O data processing algorithms that we published recently and identify the messenger RNAs of downregulated proteins as potential targets in KSHV miR-transfected human embryonic kidney 293T cells. Various statistical tests are employed for picking DEPs, and we select the best test by examining the enrichment of PAR-CLIP-reported targets with seed match to the miRs of interest among top ranked DEPs returned by statistical tests. Subsequently, the list of DEPs picked by the selected statistical test is filtered with the criteria that they must have downregulated gene expressions, must have reported as targets by an miR target prediction algorithm SVMcrio, and must have reported as targets by PAR-CLIP. Result We test the developed approach in the problem of finding targets of KSHV miR-K1. The RNAs of three DEPs are identified as miR-K1 targets, among which RAB23 and HNRNPU are novel. Results from both Western blotting and Luciferase reporter assays confirm the novel targets. These results show that the developed quantitative approach based on 18O/16O labeling can be combined with genomic, PAR-CLIP, and target prediction algorithms for the confident identification of KSHV miR targets. The developed approach could also be applied in other applications.