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      Spatial Multiomics of Lipids, N-Glycans, and Tryptic Peptides on a Single FFPE Tissue Section

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          Abstract

          Mass spectrometry imaging (MSI) is an emerging technology that is capable of mapping various biomolecules within their native spatial context, and performing spatial multiomics on formalin-fixed paraffin-embedded (FFPE) tissues may further increase the molecular characterization of pathological states. Here we present a novel workflow which enables the sequential MSI of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section and highlight the enhanced molecular characterization that is offered by combining the multiple spatial omics data sets. In murine brain and clear cell renal cell carcinoma (ccRCC) tissue, the three molecular levels provided complementary information and characterized different histological regions. Moreover, when the spatial omics data was integrated, the different histopathological regions of the ccRCC tissue could be better discriminated with respect to the imaging data set of any single omics class. Taken together, these promising findings demonstrate the capability to more comprehensively map the molecular complexity within pathological tissue.

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          Multi-omics approaches to disease

          High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of “integrative genetics”. Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.
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            GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans.

            Mass spectrometry is the main analytical technique currently used to address the challenges of glycomics as it offers unrivalled levels of sensitivity and the ability to handle complex mixtures of different glycan variations. Determination of glycan structures from analysis of MS data is a major bottleneck in high-throughput glycomics projects, and robust solutions to this problem are of critical importance. However, all the approaches currently available have inherent restrictions to the type of glycans they can identify, and none of them have proved to be a definitive tool for glycomics. GlycoWorkbench is a software tool developed by the EUROCarbDB initiative to assist the manual interpretation of MS data. The main task of GlycoWorkbench is to evaluate a set of structures proposed by the user by matching the corresponding theoretical list of fragment masses against the list of peaks derived from the spectrum. The tool provides an easy to use graphical interface, a comprehensive and increasing set of structural constituents, an exhaustive collection of fragmentation types, and a broad list of annotation options. The aim of GlycoWorkbench is to offer complete support for the routine interpretation of MS data. The software is available for download from: http://www.eurocarbdb.org/applications/ms-tools.
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              Multi-omics Data Integration, Interpretation, and Its Application

              To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.
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                Author and article information

                Journal
                J Proteome Res
                J Proteome Res
                pr
                jprobs
                Journal of Proteome Research
                American Chemical Society
                1535-3893
                1535-3907
                19 October 2022
                04 November 2022
                : 21
                : 11
                : 2798-2809
                Affiliations
                []Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca , 20854 Vedano al Lambro, Italy
                []Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, School of Medicine and Surgery, University of Milano-Bicocca , 20900 Monza, Italy
                Author notes
                [* ]Tel: +39 02 64488204. Email: andrew.smith@ 123456unimib.it .
                Author information
                https://orcid.org/0000-0001-6373-689X
                https://orcid.org/0000-0001-6530-6113
                Article
                10.1021/acs.jproteome.2c00601
                9639202
                36259755
                8058bd9d-a760-4e66-b64e-f46aad96dcdf
                © 2022 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 29 September 2022
                Funding
                Funded by: Fondazione Gigi e Pupa Ferrari, doi 10.13039/501100009700;
                Award ID: NA
                Funded by: Federal Acquisition Regulation, doi NA;
                Award ID: NA
                Funded by: Regione Lombardia, doi 10.13039/501100009882;
                Award ID: NA
                Categories
                Article
                Custom metadata
                pr2c00601
                pr2c00601

                Molecular biology
                maldi-ms imaging,lipidomics,n-glycomics,proteomics,spatial proteomics,renal cancer,tumor,multiomics

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