Stephen E. Stein

An Integrated Method for Spectrum Extraction and Compound Identification from Gas Chromatography/Mass Spectrometry Data

A method is presented for extracting individual component spectra from gas chromatography/mass spectrometry (GC/MS) data files and then using these spectra to identify target compounds by matching spectra in a reference library. It extends a published “model peak” approach which uses selected ion chromatograms as models for component shape. On the basis of this shape, individual mass spectral peak abundance profiles are extracted to produce a “purified” spectrum. In the present work, ion-counting noise is explicitly treated and a number of characteristic features of GC/MS data are taken into account. This allows spectrum extraction to be reliably performed down to very low signal levels and for overlapping components. A spectrum match factor for compound identification is developed that incorporates a number of new corrections, some of which employ information derived from chromatographic behavior. Test results suggest that the ability of this system to identify compounds is comparable to that of conventional analysis.

Proteogenomic characterization of human colon and rectal cancer

Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA ‘microsatellite instability/CpG island methylation phenotype’ transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.

Detailed kinetic modeling of soot formation in shock-tube pyrolysis of acetylene

The chemical reaction pathways to soot were investigated by experimenting with detailed kinetic models of soot formation under the conditions used in shock-tube pyrolysis experiments. The analyses of the computational results revealed a single dominant route for the main soot mass growth. Fused polycyclic aromatics play a particularly important role: their formation reactions are essentially irreversible and have the effect of “pulling” chains of reversible reactions. Hydrogen atoms reactivate aromatic molecules to radicals by abstraction reactions. The main bottleneck appears at the formation of the first aromatic ring. The model explains the time scale of soot formation and soot yields obtained in shock-tube pyrolysis of acetylene and also is in accord with product distributions observed in flames.

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.

Deconvolution gas chromatography/mass spectrometry of urinary organic acids – potential for pattern recognition and automated identification of metabolic disorders

The National Institute of Standards and Technology (NIST) Automated Mass Spectral Deconvolution and Identification System (AMDIS) is applied to a selection of data files obtained from the gas chromatography/mass spectrometry (GC/MS) analysis of urinary organic acids. Mass spectra obtained after deconvolution are compared with a special user library containing both the mass spectra and retention indices of ethoxime-trimethylsilyl (EO-TMS) derivatives of a set of organic acids. Efficient identification of components is achieved and the potential of the procedure for automated diagnosis of inborn errors of metabolism and for related research is demonstrated.

Depletion of Abundant Plasma Proteins and Limitations of Plasma Proteomics

Immunoaffinity depletion with antibodies to the top 7 or top 14 high-abundance plasma proteins is used to enhance detection of lower abundance proteins in both shotgun and targeted proteomic analyses. We evaluated the effects of top 7/top 14 immunodepletion on the shotgun proteomic analysis of human plasma. Our goal was to evaluate the impact of immunodepletion on detection of proteins across detectable ranges of abundance. The depletion columns afforded highly repeatable and efficient plasma protein fractionation. Relatively few nontargeted proteins were captured by the depletion columns. Analyses of unfractionated and immunodepleted plasma by peptide isoelectric focusing (IEF), followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), demonstrated enrichment of nontargeted plasma proteins by an average of 4-fold, as assessed by MS/MS spectral counting. Either top 7 or top 14 immunodepletion resulted in a 25% increase in identified proteins compared to unfractionated plasma. Although 23 low-abundance (<10 ng mL(-1)) plasma proteins were detected, they accounted for only 5-6% of total protein identifications in immunodepleted plasma. In both unfractionated and immunodepleted plasma, the 50 most abundant plasma proteins accounted for 90% of cumulative spectral counts and precursor ion intensities, leaving little capacity to sample lower abundance proteins. Untargeted proteomic analyses using current LC-MS/MS platforms-even with immunodepletion-cannot be expected to efficiently discover low-abundance, disease-specific biomarkers in plasma.

Building Consensus Spectral Libraries for Peptide Identification in Proteomics

Spectral searching has drawn increasing interest as an alternative to sequence-database searching in proteomics. We developed and validated an open-source software toolkit, SpectraST, to enable proteomics researchers to build spectral libraries and to integrate this promising approach in their data-analysis pipeline. It allows individual researchers to condense raw data into spectral libraries, summarizing information about observed proteomes into a concise and retrievable format for future data analyses.

InChI - the worldwide chemical structure identifier standard.

Since its public introduction in 2005 the IUPAC InChI chemical structure identifier standard has become the international, worldwide standard for defined chemical structures. This article will describe the extensive use and dissemination of the InChI and InChIKey structure representations by and for the world-wide chemistry community, the chemical information community, and major publishers and disseminators of chemical and related scientific offerings in manuscripts and databases.

The critical evaluation of a comprehensive mass spectral library

A description of the methods used to build a high quality, comprehensive reference library of electron-ionization mass spectra is presented. Emphasis is placed on the most challenging part of this project—the improvement of quality by expert evaluation. The methods employed for this task were developed over the course of a spectrum-by-spectrum review of a library containing well over 100,000 spectra. Although the effectiveness of this quality improvement task depended critically on the expertise of the evaluators, a number of guidelines are discussed which were found to be effective in performing this onerous and often subjective task. A number of specific examples of the particularly challenging task of spectrum editing are given.

Integrated proteogenomic characterization of human high-grade serous ovarian cancer

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.