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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.

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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

                Journal
                Nat Commun
                Nature communications
                Springer Science and Business Media LLC
                2041-1723
                2041-1723
                January 07 2020
                : 11
                : 1
                Affiliations
                [1 ] Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
                [2 ] The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA.
                [3 ] National Security Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
                [4 ] Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
                [5 ] The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA. ryan.kelly@byu.edu.
                [6 ] Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA. ryan.kelly@byu.edu.
                [7 ] Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA. kristin.burnum-johnson@pnnl.gov.
                Article
                10.1038/s41467-019-13858-z
                10.1038/s41467-019-13858-z
                6946663
                31911630
                1acd9298-d392-48c7-9acb-0b218ec3049f
                History

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