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This paper discusses the successful adoption of a subscription-based, open-access model of service delivery for a mass spectrometry and proteomics facility. In 2009, the Mass Spectrometry and Proteomics Facility at the University of Melbourne (Australia) moved away from the standard fee for service model of service provision. Instead, the facility adopted a subscription- or membership-based, open-access model of service delivery. For a low fixed yearly cost, users could directly operate the instrumentation but, more importantly, there were no limits on usage other than the necessity to share available instrument time with all other users. All necessary training from platform staff and many of the base reagents were also provided as part of the membership cost. These changes proved to be very successful in terms of financial outcomes for the facility, instrument access and usage, and overall research output. This article describes the systems put in place as well as the overall successes and challenges associated with the operation of a mass spectrometry/proteomics core in this manner. Graphical abstract ᅟ.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometric imaging (MSI) is a powerful tool to map the spatial distribution of biomolecules on tissue sections. Recent developments of hybrid MS instruments allow combination of different types of data acquisition by various mass analyzers into a single MSI analysis, which reduces experimental time and sample consumptions. Here, using the well-characterized crustacean nervous system as a test-bed, we explore the utility of high resolution and accurate mass (HRAM) MALDI Orbitrap platform for enhanced in situ characterization of the neuropeptidome with improved chemical information. Specifically, we report on a multiplex-MSI method, which combines HRAM MSI with data dependent acquisition (DDA) tandem MS analysis in a single experiment. This method enables simultaneous mapping of neuropeptide distribution, sequence validation, and novel neuropeptide discovery in crustacean neuronal tissues. To enhance the dynamic range and efficiency of in situ DDA, we introduced a novel approach of fractionating full m/z range into several sub-mass ranges and embedding the setup using the multiplex-DDA-MSI scan events to generate pseudo fractionation before MS/MS scans. The division of entire m/z into multiple segments of m/z sub-ranges for MS interrogation greatly decreased the complexity of molecular species from tissue samples and the heterogeneity of the distribution and variation of intensities of m/z peaks. By carefully optimizing the experimental conditions such as the dynamic exclusion, the multiplex-DDA-MSI approach demonstrates better performance with broader precursor coverage, less biased MS/MS scans towards high abundance molecules, and improved quality of tandem mass spectra for low intensity molecular species. Graphical Abstract ᅟ.

The comprehensive MS analysis of the peptidome, the intracellular and intercellular products of protein degradation, has the potential to provide novel insights on endogenous proteolytic processing and its utility in disease diagnosis and prognosis. Along with the advances in MS instrumentation and related platforms, a plethora of proteomics data analysis tools have been applied for direct use in peptidomics; however, an evaluation of the currently available informatics pipelines for peptidomics data analysis has yet to be reported. In this study, we began by evaluating the results of several popular MS/MS database search engines, including MS-GF+, SEQUEST, and MS-Align+, for peptidomics data analysis, followed by identification and label-free quantification using the well-established accurate mass and time (AMT) tag and newly developed informed quantification (IQ) approaches, both based on direct LC-MS analysis. Our results demonstrated that MS-GF+ outperformed both SEQUEST and MS-Align+ in identifying peptidome peptides. Using a database established from MS-GF+ peptide identifications, both the AMT tag and IQ approaches provided significantly deeper peptidome coverage and less missing data for each individual data set than the MS/MS methods, while achieving robust label-free quantification. Besides having an excellent correlation with the AMT tag quantification results, IQ also provided slightly higher peptidome coverage. Taken together, we propose an optimized informatics pipeline combining MS-GF+ for initial database searching with IQ (or AMT tag) approaches for identification and label-free quantification for high-throughput, comprehensive, and quantitative peptidomics analysis. Graphical Abstract ᅟ.