Project description:BackgroundMascot is a commonly used protein identification program for MS as well as for tandem MS data. When analyzing huge shotgun proteomics datasets with Mascot's native tools, limits of computing resources are easily reached. Up to now no application has been available as open source that is capable of converting the full content of Mascot result files from the original MIME format into a database-compatible tabular format, allowing direct import into database management systems and efficient handling of huge datasets analyzed by Mascot.ResultsA program called mres2x is presented, which reads Mascot result files, analyzes them and extracts either selected or all information in order to store it in a single file or multiple files in formats which are easier to handle downstream of Mascot. It generates different output formats. The output of mres2x in tab format is especially designed for direct high-performance import into relational database management systems using native tools of these systems. Having the data available in database management systems allows complex queries and extensive analysis. In addition, the original peak lists can be extracted in DTA format suitable for protein identification using the Sequest program, and the Mascot files can be split, preserving the original data format. During conversion, several consistency checks are performed. mres2x is designed to provide high throughput processing combined with the possibility to be driven by other computer programs. The source code including supplement material and precompiled binaries is available via http://www.protein-ms.de and http://sourceforge.net/projects/protms/.ConclusionThe database upload allows regrouping of the MS/MS results using a database management system and complex analyzing queries using SQL without the need to run new Mascot searches when changing grouping parameters.
Project description:Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either label-free or multiplexed) have enabled quantifying >1,000 proteins per cell while simultaneously increasing the specificity of protein quantification. Here we describe the Single Cell ProtEomics (SCoPE2) protocol, which uses an isobaric carrier to enhance peptide sequence identification. Single cells are isolated by FACS or CellenONE into multiwell plates and lysed by Minimal ProteOmic sample Preparation (mPOP), and their peptides labeled by isobaric mass tags (TMT or TMTpro) for multiplexed analysis. SCoPE2 affords a cost-effective single-cell protein quantification that can be fully automated using widely available equipment and scaled to thousands of single cells. SCoPE2 uses inexpensive reagents and is applicable to any sample that can be processed to a single-cell suspension. The SCoPE2 workflow allows analyzing ~200 single cells per 24 h using only standard commercial equipment. We emphasize experimental steps and benchmarks required for achieving quantitative protein analysis.
Project description:Please note most files are the "updates" subfolder. Processed protein and transcript data available at: https://scope2.slavovlab.net/docs/data
The fate and physiology of individual cells are controlled by protein interactions. Yet, our ability to quantitatively analyze proteins in single cells has remained limited. To overcome this barrier,we developed SCoPE2. It introduces automated and miniaturized sample preparation, substantially increasing quantitative accuracy while lowering cost and hands-on time. These advances enabled us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiated into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantified over 2,700proteins in 1,018 single monocytes and macrophages in ten days of instrument time, and the quantified proteins allowed us to discern single cells by cell type. Furthermore, the data uncovered acontinuous gradient of proteome states for the macrophage-like cells, suggesting that macrophage heterogeneity may emerge even in the absence of polarizing cytokines. Parallel measurements of transcripts by 10x Genomics scRNA-seq suggest that SCoPE2 samples 20-fold more copies per gene, thus supporting quantification with improved count statistics. Joint analysis of the data indicated that most genes had similar responses at the protein and RNA levels, though the responses of hundreds of genes differed. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry.