Project description:Data independent acquisition (DIA or DIA/SWATH ) mass spectrometry has emerged as a primary measurement strategy in the field of quantitative proteomics. diaPASEF is a recent adaptation that leverages trapped ion mobility spectrometry (TIMS) to improve selectivity and increase sensitivity. The complex fragmentation spectra generated by co-isolation of peptides in DIA mode are most typically analyzed with reference to prior knowledge in the form of spectral libraries. The best established method for generating libraries uses data dependent acquisition (DDA) mode, or DIA mode if appropriately deconvoluted, often including offline fractionation to increase depth of coverage,to create spectral libraries. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow window DIA methods designed to cover different slices of the precursor space, have been introduced and performed comparably to deep offline fractionation-based libraries for DIA data analysis. Here, we investigated whether an analogous GPF-based library building approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data and can remove the need for offline fractionation. To enable a rapid library development approach for diaPASEF we designed a GPF acquisition scheme covering the majority of multiply charged precursors in the m/z vs 1/K0 space requiring 7 injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation and ddaPASEF. . We found that the GPF based library outperformed library generation by direct deconvolution of the diaPASEF data, and performed comparably to deep offline fractionation libraries, when analysing diaPASEF data acquired from 200ng of commercial HeLa digest. With the ion mobility integrated GPF scheme we establish a pragmatic approach to rapid and comprehensive library generation for the analysis of diaPASEF data.
Project description:Data independent acquisition (DIA or DIA/SWATH ) mass spectrometry has emerged as a primary measurement strategy in the field of quantitative proteomics. diaPASEF is a recent adaptation that leverages trapped ion mobility spectrometry (TIMS) to improve selectivity and increase sensitivity. The complex fragmentation spectra generated by co-isolation of peptides in DIA mode are most typically analyzed with reference to prior knowledge in the form of spectral libraries. The best established method for generating libraries uses data dependent acquisition (DDA) mode, or DIA mode if appropriately deconvoluted, often including offline fractionation to increase depth of coverage,to create spectral libraries. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow window DIA methods designed to cover different slices of the precursor space, have been introduced and performed comparably to deep offline fractionation-based libraries for DIA data analysis. Here, we investigated whether an analogous GPF-based library building approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data and can remove the need for offline fractionation. To enable a rapid library development approach for diaPASEF we designed a GPF acquisition scheme covering the majority of multiply charged precursors in the m/z vs 1/K0 space requiring 7 injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation and ddaPASEF. . We found that the GPF based library outperformed library generation by direct deconvolution of the diaPASEF data, and performed comparably to deep offline fractionation libraries, when analysing diaPASEF data acquired from 200ng of commercial HeLa digest. With the ion mobility integrated GPF scheme we establish a pragmatic approach to rapid and comprehensive library generation for the analysis of diaPASEF data.
Project description:Phosphoproteomics and ubiquitinomics data-independent acquisition MS data is generally analyzed using a DDA spectral library. Performance of different library-free strategies of analyzing phosphoproteomics and ubiquitinomics DIA MS data are not evaluated. In this study, we assess three library-free approaches including DIA-Umpire, DIA-MSFragger and in silico-predicted library for analysis of phosphoproteomics SWATH, DIA and diaPASEF data as well as ubiquitinomics diaPASEF data. In silico-predicted library based on DIA-NN performs best among three library-free methods, but identify less or equal phosphopeptides compared to a DDA spectral library. Furthermore, the common phosphopeptides by the predicted library and DDA library are about 50%. This case is also observed for phospho-diaPASEF data. For ubiquitinomics diaPASEF data, in silico-predicted library detects about 50% more K-GG peptides than a project-specific DDA spectral library. Our results demonstrate that the predicted library, although performs best in library-free methods, requires improvement for phospho DIA MS data and displays substantial advantages for ubiquitinomics diaPASEF MS data.
Project description:Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from cerebrospinal fluid (CSF) and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass offset searches.
Project description:evaluated approaches for building a spectral library in plasma proteomics on a timsTOF HT system. Furthermore, the relationship between measurement time, library depth and number of protein and peptide identification for semi-high throughput plasma proteomics applications was assessed.
Project description:Ocular lens fiber cells degrade their organelles during differentiation to prevent light scattering. Organelle degradation occurs continuously throughout an individual’s lifespan, creating a spatial gradient of young cortical fiber cells in the lens periphery to older nuclear fiber cells in the center of the lens. Therefore, separation of cortical and nuclear regions enables examination of protein aging. Previously, the human lens cortex and nucleus have been studied using data-independent acquisition (DIA) proteomics, allowing for the identification of low-abundance protein groups. In this study, we employed data-independent acquisition parallel accumulation-serial fragmentation (diaPASEF) proteomics to study the zebrafish lens proteome and compared results to a standard orbitrap DIA method. Using the additional ion mobility gas phase separation of diaPASEF, peptide and protein group identifications increased by over 200% relative to an orbitrap DIA method in the zebrafish lens. With diaPASEF, we identified 13,721 and 11,996 unique peptides in the zebrafish lens cortex and nucleus, which correspond to 1,537 and 1,389 protein groups. Thus, separation of the zebrafish lens into cortical and nuclear regions followed by diaPASEF analysis produced the most comprehensive zebrafish lens proteomic dataset to date.
Project description:Data-independent mass spectrometry is the method of choice for deep, consistent and accurate single-shot profiling in bottom-up proteomics. While classic workflows required auxiliary DDA-MS analysis of subject samples to derive prior knowledge spectral libraries for targeted quantification from DIA-MS maps, library-free approaches based on in silico predicted libraries promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses, however, is limited, in part, by large library size and persistent deviations from experimental data. We present MSLibrarian, a workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library approach. Predicted-vs-observed comparisons enable optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups and optimization of library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded species mixture experiment and quantitative ratio-validation confirms gains of up to 9 % on precursor and 7 % protein level at equivalent FDR control and validation criteria. MSLibrarian has been implemented as open-source R software package and, with step-by-step usage instructions, is availabe at https://github.com/MarcIsak/MSLibrarian.
Project description:Ocean metaproteomics provides valuable insights into the structure and function of marine microbial communities. Yet, ocean samples are challenging due to their extensive biological diversity that results in a very large number of peptides with a large dynamic range. This study characterized the capabilities of data independent acquisition (DIA) mode for use in ocean metaproteomic samples. Spectral libraries were constructed from discovered peptides and proteins using machine learning algorithms to remove incorporation of false positives in the libraries. When compared with 1-dimensional and 2-dimensional data dependent acquisition analyses (DDA), DIA outperformed DDA both with and without gas phase fractionation. We found that larger discovered protein spectral libraries performed better, regardless of the geographic distance between where samples were collected for library generation and where the test samples were collected. Moreover, the spectral library containing all unique proteins present in the Ocean Protein Portal outperformed smaller libraries generated from individual sampling campaigns. However, a spectral library constructed from all open reading frames in a metagenome was found to be too large to be workable, resulting in low peptide identifications due to challenges maintaining a low false discovery rate with such a large database size. Given sufficient sequencing depth and validation studies, spectral libraries generated from previously discovered proteins can serve as a community resource, saving resequencing efforts. The spectral libraries generated in this study are available at the Ocean Protein Portal for this purpose.
Project description:We optimized the parameters of CCS range, polygon scan region, ramp time, and isolation window width to achieve high-depth proteome analysis using the timsTOF HT. As a result, we developed Thin-diaPASEF, which was optimized with a CCS range of 0.7–1.3, a ramp time of 150 ms, an isolation window width of 26 Th (with an overlap width of 1 Th), and a polygon area focused on regions where precursors accumulate. Thin-diaPASEF was then compared with existing methods, including py-diAID PASEF, slice-PASEF, and synchro-PASEF. The results demonstrated that Thin-diaPASEF achieved superior protein identification.
Performance evaluation of Thin-diaPASEF revealed the identification of approximately 9,400 proteins with a sample injection of 500 ng and an analysis time of 100 min. Finally, we applied Thin-diaPASEF to plasma proteome analysis. We compared three sample preparation methods: an improved LEL method based on our previously reported plasma and serum preparation approach, Seer’s nanoparticle-based method, and the conventional Top14 column-based method. As a result, the improved LEL method successfully identified 5,378 proteins at an analysis throughput of 24 samples per day.
Project description:Targeted proteomics by selected/multiple reaction monitoring or, on a larger scale, by SWATH MS relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of critical importance. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches, generation of consensus spectra and compilation of mass spectrometric coordinates that uniquely define each targeted peptide. Crucial steps of this process such as FDR control, retention time normalization and handling of post-translationally modified peptides are discussed in detail. Finally we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 days to complete, depending on the extent of the library and the computational resources available.