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      Spatially-Resolved Proteomics: Rapid Quantitative Analysis of Laser Capture Microdissected Alveolar Tissue Samples

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          Abstract

          Laser capture microdissection (LCM)-enabled region-specific tissue analyses are critical to better understand complex multicellular processes. However, current proteomics workflows entail several manual sample preparation steps and are challenged by the microscopic mass-limited samples generated by LCM, impacting measurement robustness, quantification and throughput. Here, we coupled LCM with a proteomics workflow that provides fully automated analysis of proteomes from microdissected tissues. Benchmarking against the current state-of-the-art in ultrasensitive global proteomics (FASP workflow), our approach demonstrated significant improvements in quantification (~2-fold lower variance) and throughput (>5 times faster). Using our approach we for the first time characterized, to a depth of >3,400 proteins, the ontogeny of protein changes during normal lung development in microdissected alveolar tissue containing only 4,000 cells. Our analysis revealed seven defined modules of coordinated transcription factor-signaling molecule expression patterns, suggesting a complex network of temporal regulatory control directs normal lung development with epigenetic regulation fine-tuning pre-natal developmental processes.

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          Most cited references 70

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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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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              Molecular mechanisms of epithelial-mesenchymal transition.

              The transdifferentiation of epithelial cells into motile mesenchymal cells, a process known as epithelial-mesenchymal transition (EMT), is integral in development, wound healing and stem cell behaviour, and contributes pathologically to fibrosis and cancer progression. This switch in cell differentiation and behaviour is mediated by key transcription factors, including SNAIL, zinc-finger E-box-binding (ZEB) and basic helix-loop-helix transcription factors, the functions of which are finely regulated at the transcriptional, translational and post-translational levels. The reprogramming of gene expression during EMT, as well as non-transcriptional changes, are initiated and controlled by signalling pathways that respond to extracellular cues. Among these, transforming growth factor-β (TGFβ) family signalling has a predominant role; however, the convergence of signalling pathways is essential for EMT.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                22 December 2016
                2016
                : 6
                Affiliations
                [1 ]Biological Science Division, Pacific Northwest National Laboratory , Richland, WA 99352, USA
                [2 ]Department of Pediatrics, University of Alabama at Birmingham , Birmingham, AL 35249, USA
                [3 ]Division of Pulmonary Biology, Cincinnati Children’s Hospital Medical Center , Cincinnati, OH 45229, USA
                [4 ]Texas Advanced Computing Center, University of Texas at Austin , Austin, TX 78712, USA
                srep39223
                10.1038/srep39223
                5177886
                28004771
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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