Dataset Information

ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data.

ABSTRACT: Imaging mass spectrometry (IMS) has developed into a powerful tool allowing label-free detection of numerous biomolecules in situ. In contrast to shotgun proteomics, proteins/peptides can be detected directly from biological tissues and correlated to its morphology leading to a gain of crucial clinical information. However, direct identification of the detected molecules is currently challenging for MALDI-IMS, thereby compelling researchers to use complementary techniques and resource intensive experimental setups. Despite these strategies, sufficient information could not be extracted because of lack of an optimum data combination strategy/software. Here, we introduce a new open-source software ImShot that aims at identifying peptides obtained in MALDI-IMS. This is achieved by combining information from IMS and shotgun proteomics (LC-MS) measurements of serial sections of the same tissue. The software takes advantage of a two-group comparison to determine the search space of IMS masses after deisotoping the corresponding spectra. Ambiguity in annotations of IMS peptides is eliminated by introduction of a novel scoring system that identifies the most likely parent protein of a detected peptide in the corresponding IMS dataset. Thanks to its modular structure, the software can also handle LC-MS data separately and display interactive enrichment plots and enriched Gene Ontology terms or cellular pathways. The software has been built as a desktop application with a conveniently designed graphic user interface to provide users with a seamless experience in data analysis. ImShot can run on all the three major desktop operating systems and is freely available under Massachusetts Institute of Technology license.

SUBMITTER: Aftab W

PROVIDER: S-EPMC9194865 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data.

Aftab Wasim W Lahiri Shibojyoti S Imhof Axel A

Molecular & cellular proteomics : MCP 20220513 6

Imaging mass spectrometry (IMS) has developed into a powerful tool allowing label-free detection of numerous biomolecules in situ. In contrast to shotgun proteomics, proteins/peptides can be detected directly from biological tissues and correlated to its morphology leading to a gain of crucial clinical information. However, direct identification of the detected molecules is currently challenging for MALDI-IMS, thereby compelling researchers to use complementary techniques and resource intensive ...[more]

PMID: 35569805

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Project description:In order that biological meaning may be derived and testable hypotheses may be built from proteomics experiments, assignments of proteins identified by mass spectrometry or other techniques must be supplemented with additional notation, such as information on known protein functions, protein-protein interactions, or biological pathway associations. Collecting, organizing, and interpreting this data often requires the input of experts in the biological field of study, in addition to the time-consuming search for and compilation of information from online protein databases. Furthermore, visualizing this bulk of information can be challenging due to the limited availability of easy-to-use and freely available tools for this process. In response to these constraints, we have undertaken the design of software to automate annotation and visualization of proteomics data in order to accelerate the pace of research. Here we present the Software Tool for Researching Annotations of Proteins (STRAP), a user-friendly, open-source C# application. STRAP automatically obtains gene ontology (GO) terms associated with proteins in a proteomics results ID list using the freely accessible UniProtKB and EBI GOA databases. Summarized in an easy-to-navigate tabular format, STRAP results include meta-information on the protein in addition to complementary GO terminology. Additionally, this information can be edited by the user so that in-house expertise on particular proteins may be integrated into the larger data set. STRAP provides a sortable tabular view for all terms, as well as graphical representations of GO-term association data in pie charts (biological process, cellular component, and molecular function) and bar charts (cross comparison of sample sets) to aid in the interpretation of large data sets and differential analyses experiments. Furthermore, proteins of interest may be exported as a unique FASTA-formatted file to allow for customizable re-searching of mass spectrometry data, and gene names corresponding to the proteins in the lists may be encoded in the Gaggle microformat for further characterization, including pathway analysis. STRAP, a tutorial, and the C# source code are freely available from http://cpctools.sourceforge.net.

Project description:BackgroundRepair of complete atrioventricular canal (CAVC) is often complicated by residual left atrioventricular valve regurgitation. The structure of the mitral and tricuspid valves in biventricular hearts has previously been shown to be associated with valve dysfunction. However, the three-dimensional (3D) structure of the entire unrepaired CAVC valve has not been quantified. Understanding the 3D structure of the CAVC may inform optimized repair.MethodsNovel open-source work flows were created in SlicerHeart for the modeling and quantification of CAVC valves on the basis of 3D echocardiographic images. These methods were applied to model the annulus, leaflets, and papillary muscle (PM) structure of 35 patients (29 with trisomy 21) with CAVC using transthoracic 3D echocardiography. The mean leaflet and annular shapes were calculated and visualized using shape analysis. Metrics of the complete native CAVC valve structure were compared with those of normal mitral valves using the Mann-Whitney U test. Associations between CAVC structure and atrioventricular valve regurgitation were analyzed.ResultsCAVC leaflet metrics varied throughout systole. Compared with normal mitral valves, the left CAVC PMs were more acutely angled in relation to the annular plane (P < .001). In addition, the anterolateral PM was laterally and inferiorly rotated in CAVC, while the posteromedial PM was more superiorly and laterally rotated, relative to normal mitral valves (P < .001). Lower native CAVC atrioventricular valve annular height and annular height-to-valve width ratio before repair were both associated with moderate or greater left atrioventricular valve regurgitation after repair (P < .01).ConclusionsIt is feasible to model and quantify 3D CAVC structure using 3D echocardiographic images. The results demonstrate significant variation in CAVC structure across the cohort and differences in annular, leaflet, and PM structure compared with the mitral valve. These tools may be used in future studies to catalyze future research intended to identify structural associations of valve dysfunction and to optimize repair in this vulnerable and complex population.

Dataset Information

ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data.

Publications

ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data.

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