DEIMoS Example LC-IMS-MS/MS Files
Ontology highlight
ABSTRACT:
INSTRUMENT(S): 6560 Q-TOF LC/MS
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Sean M. Colby
PROVIDER: MSV000091746 | MassIVE | Wed Apr 19 10:26:00 BST 2023
REPOSITORIES: MassIVE
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Project description:Example LC-IMS-MS/MS data files for use with the data extraction for multidimensional spectrometry (DEIMoS) user guide.
2023-04-19 | MSV000091746 | GNPS
Project description:The advent of on-line multidimensional liquid chromatography-mass spectrometry has significantly impacted proteomic analyses of complex biological fluids such as plasma. However, there is general agreement that additional advances to enhance the peak capacity of such platforms are required to enhance the accuracy and coverage of proteome maps of such fluids. Here, we describe the combination of strong-cation-exchange and reversed-phase liquid chromatographies with ion mobility and mass spectrometry as a means of characterizing the complex mixture of proteins associated with the human plasma proteome. The increase in separation capacity associated with inclusion of the ion mobility separation leads to generation of one of the most extensive proteome maps to date. The map is generated by analyzing plasma samples of five healthy humans; we report a preliminary identification of 9087 proteins from 37,842 unique peptide assignments. An analysis of expected false-positive rates leads to a high-confidence identification of 2928 proteins. The results are catalogued in a fashion that includes positions and intensities of assigned features observed in the datasets as well as pertinent identification information such as protein accession number, mass, and homology score/confidence indicators. Comparisons of the assigned features reported here with other datasets shows substantial agreement with respect to the first several hundred entries; there is far less agreement associated with detection of lower abundance components.
| S-EPMC2195767 | biostudies-literature
Project description:Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Although single cell technologies have emerged as valuable tools to address this cellular heterogeneity, a majority of these workflows lack sufficient in situ resolution for functional classification of cells and are associated with extremely long analysis time, especially when it comes to in situ proteomics. In addition, lack of understanding of single cell dynamics within their native environment limits our ability to explore the altered physiology in disease development. This limitation is particularly relevant in the mammalian brain, where different cell types perform unique functions and exhibit varying sensitivities to insults. The hippocampus, a brain region crucial for learning and memory, is of particular interest due to its obvious involvement in various neurological disorders. Here, we present a combination of experimental and data integration approaches for investigation of cellular heterogeneity and functional disposition within the mouse brain hippocampus using MALDI Imaging mass spectrometry (MALDI-IMS) and shotgun proteomics (LC-MS/MS) coupled with laser-capture microdissection (LCM) along with spatial transcriptomics. Within the dentate gyrus granule cells we identified two proteomically distinct cellular subpopulations that are characterized by a substantial number of discriminative proteins. These cellular clusters contribute to the overall functionality of the dentate gyrus by regulating redox homeostasis, mitochondrial organization, RNA processing, and microtubule organization. Importantly, most of the identified proteins matched their transcripts, verifying the in situ protein identification and supporting their functional analyses. By combining high-throughput spatial proteomics with transcriptomics, our approach enables reliable near-single-cell scale identification of proteins and profiling of inter-cellular heterogeneity within similar cell-types in tissues. This methodology has the potential to be applied to different biological conditions and tissues, providing a deeper understanding of cellular subpopulations in situ.
2024-07-24 | GSE242688 | GEO
Project description:Chemical cross-linking of proteins followed by proteolysis and mass spectrometric analysis of the resulting cross-linked peptides provides powerful insight into the quaternary structure of protein complexes. Mixed-isotope cross-linking (a method for distinguishing intermolecular cross-links) was coupled with liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS) to provide an additional separation dimension to the traditional cross-linking approach. This method produced multiplet m/z peaks that are aligned in the IMS drift time dimension and serve as signatures of intermolecular cross-linked peptides. We developed an informatics tool to use the amino acid sequence information inherent in the multiplet spacing for accurate identification of the cross-linked peptides. Because of the separation of cross-linked and non-cross-linked peptides in drift time, our LC-IMS-MS approach was able to confidently detect more intermolecular cross-linked peptides than LC-MS alone.
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Project description:Digenome-seq example data from HAP1 cell line
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Project description:MotivationThe addition of ion mobility spectrometry to liquid chromatography-mass spectrometry experiments requires new, or updated, software tools to facilitate data processing.ResultsWe introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension.AvailabilityLC-IMS-MS Feature Finder is available as a command-line tool for download at http://omics.pnl.gov/software/LC-IMS-MS_Feature_Finder.php. The Microsoft.NET Framework 4.0 is required to run the software. All other dependencies are included with the software package. Usage of this software is limited to non-profit research to use (see README).Contactrds@pnnl.gov.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Matrix effects (MEs) continue to be an obstacle in the development of the LC-MS/MS method, with phospholipids being the major cause of MEs. Changing the mobile phase has been a common strategy to reduce MEs; however, the underlying mechanism is unclear. "In-source multiple-reaction monitoring" (IS-MRM) for glycerophosphocholines (PCs) has been commonly applied in many bioanalytical methods. "Visualized MEs" is a suitable term to describe the application of IS-MRM to visualize the elution pattern of phospholipids. We selected a real case to discuss the relationship of MEs and phospholipids in different mobile phases by quantitative, qualitative, and visualized MEs in LC-MS/MS bioanalysis. The application of visualized MEs not only predicts the ion-suppression zone but also helps in selecting an appropriate (1) mobile phase, (2) column, (3) needle wash solvent for the residue of analyte and phospholipids, and (4) evaluates the clean-up efficiency of sample preparation. The TRAM-34 LC-MS/MS method, improved by using visualized MEs, was shown to be a precise and accurate analytical method. All data indicated that the use of visualized MEs indeed provided useful information about the LC-MS/MS method development and improvement. In this study, an integrative approach for the qualitative, quantitative, and visualized MEs was used to decipher the complexity of MEs.
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