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      Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution

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          Abstract

          Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation.

          Abstract

          Imaging mass spectrometry is a powerful emerging tool for mapping the spatial distribution of biomolecules across tissue surfaces. Here the authors showcase an automated technology for deep proteome imaging that utilizes ultrasensitive microfluidics and a mass spectrometry workflow to analyze tissue voxels, generating quantitative cell-type-specific images.

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          Most cited references51

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          MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

          Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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            Andromeda: a peptide search engine integrated into the MaxQuant environment.

            A key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Here we describe Andromeda, a novel peptide search engine using a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used commercial search engine, as judged by sensitivity and specificity analysis based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform and both are freely available at www.maxquant.org. The combination enables analysis of large data sets in a simple analysis workflow on a desktop computer. For searching individual spectra Andromeda is also accessible via a web server. We demonstrate the flexibility of the system by implementing the capability to identify cofragmented peptides, significantly improving the total number of identified peptides.
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              Identifying biological themes within lists of genes with EASE.

              EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'.
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                Author and article information

                Contributors
                ryan.kelly@byu.edu
                kristin.burnum-johnson@pnnl.gov
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                7 January 2020
                7 January 2020
                2020
                : 11
                : 8
                Affiliations
                [1 ]ISNI 0000 0001 2218 3491, GRID grid.451303.0, Biological Sciences Division, , Pacific Northwest National Laboratory, ; Richland, WA USA
                [2 ]ISNI 0000 0001 2218 3491, GRID grid.451303.0, The Environmental Molecular Sciences Laboratory, , Pacific Northwest National Laboratory, ; Richland, WA USA
                [3 ]ISNI 0000 0001 2218 3491, GRID grid.451303.0, National Security Directorate, , Pacific Northwest National Laboratory, ; Richland, WA USA
                [4 ]ISNI 0000 0000 9025 8099, GRID grid.239573.9, Cincinnati Children’s Hospital Medical Center, ; Cincinnati, OH USA
                [5 ]ISNI 0000 0004 1936 9115, GRID grid.253294.b, Department of Chemistry and Biochemistry, , Brigham Young University, ; Provo, UT USA
                Author information
                http://orcid.org/0000-0001-5108-2227
                http://orcid.org/0000-0002-5416-0566
                http://orcid.org/0000-0002-0253-6859
                http://orcid.org/0000-0002-4807-8646
                http://orcid.org/0000-0003-1646-4515
                http://orcid.org/0000-0001-9159-186X
                http://orcid.org/0000-0002-2722-4149
                Article
                13858
                10.1038/s41467-019-13858-z
                6946663
                31911630
                1acd9298-d392-48c7-9acb-0b218ec3049f
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 November 2018
                : 20 November 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100009633, U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD);
                Award ID: R21 HD084788
                Award ID: R01 HD068524
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100006086, U.S. Department of Health & Human Services | NIH | Office of Strategic Coordination (OSC);
                Award ID: UG3HL145593
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);
                Award ID: R33 CA225248
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                analytical biochemistry,microfluidics,proteomics
                Uncategorized
                analytical biochemistry, microfluidics, proteomics

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