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MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.

In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein ( e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats., (© 2020 Tsai et al.)

Purpose: Human pluripotent stem cell (hPSC)-derived cellular models have great potential to enable drug discovery and improve translation of preclinical insights to the clinic. We have developed a hPSC-derived neural precursor cell model for studying early events in human brain development. We present protein-level characterization of this model, using a multiplexed SRM approach, to establish reproducibility and physiological relevance; essential prerequisites for utilization of the neuronal development model in phenotypic screening-based drug discovery., Experimental Design: Profiles of 246 proteins across three key stages of in vitro neuron differentiation were analyzed by SRM. Three independently hPSC-derived isogenic neural stem cell (NSC) lines were analyzed across five to nine independent neuronal differentiations., Results: One hundred seventy-five proteins were reliably quantified revealing a time-dependent pattern of protein regulation that reflected protein dynamics during in vivo brain development and that was conserved across replicate differentiations and multiple cell lines., Conclusions and Clinical Relevance: SRM-based protein profiling enabled establishment of the reproducibility and physiological relevance of the hPSC-derived neuronal model. Combined with the successful quantification of proteins relevant to neurodevelopmental diseases, this validates the platform for use as a model to enable neuroscience drug discovery., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Purpose: Beagle dogs are used to study oral pharmacokinetics and guide development of drug formulations for human use. Since mechanistic insight into species differences is needed to translate findings in this species to human, abundances of cytochrome P450 (CYP) and uridine diphosphate glucuronosyltransferase (UGT) drug metabolizing enzymes have been quantified in dog liver and intestine., Methods: Abundances of enzymes were measured in Beagle dog intestine and liver using selected reaction monitoring mass spectrometry., Results: Seven and two CYPs were present in the liver and intestine, respectively. CYP3A12 was the most abundant CYP in both tissues. Seven UGT enzymes were quantified in the liver and seven in the intestine although UGT1A11 and UGT1A9 were present only in the intestine and UGT1A7 and UGT2B31 were found only in the liver. UGT1A11 and UGT1A2 were the most abundant UGTs in the intestine and UGT2B31 was the most abundant UGT in the liver. Summed abundance of UGT enzymes was similar to the sum of CYP enzymes in the liver whereas intestinal UGTs were up to four times more abundant than CYPs. The estimated coefficients of variation of abundance estimates in the livers of 14 donors were separated into biological and technical components which ranged from 14 to 49% and 20 to 39%, respectively., Conclusions: Abundances of canine CYP enzymes in liver and intestine have been confirmed in a larger number of dogs and UGT abundances have been quantified for the first time. The biological variability in hepatic CYPs and UGTs has also been estimated.

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.