{"id":481548,"date":"2025-12-31T04:12:20","date_gmt":"2025-12-31T04:12:20","guid":{"rendered":"https:\/\/www.europesays.com\/us\/481548\/"},"modified":"2025-12-31T04:12:20","modified_gmt":"2025-12-31T04:12:20","slug":"simultaneous-single-cell-proteomics-and-epigenetic-analysis-of-histone-deacetylase-inhibition-in-human-cells","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/481548\/","title":{"rendered":"Simultaneous single-cell proteomics and epigenetic analysis of histone deacetylase inhibition in human cells"},"content":{"rendered":"
  • \n

    Garcia, B. A. et al. Chemical derivatization of histones for facilitated analysis by mass spectrometry. Nat. Protoc. 2<\/b>, 933\u2013938 (2007).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    King, J., Patel, M. & Chandrasekaran, S. Metabolism, HDACs, and HDAC inhibitors: a systems biology perspective. Metabolites 11<\/b>, 792 (2021).<\/p>\n<\/li>\n

  • \n

    Boumber, Y., Younes, A. & Garcia-Manero, G. Mocetinostat (MGCD0103): a review of an isotype-specific histone deacetylase inhibitor. Expert Opin. Investig. Drugs 20<\/b>, 823\u2013829 (2011).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14<\/b>, 618\u2013630 (2013).<\/p>\n<\/li>\n

  • \n

    Meers, M. P., Llagas, G., Janssens, D. H., Codomo, C. A. & Henikoff, S. Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag. Nat. Biotechnol. 41<\/b>, 708\u2013716 (2023).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Orsburn, B. C., Yuan, Y. & Bumpus, N. N. Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry. Nat. Commun. 13<\/b>, 7237 (2022).<\/p>\n<\/li>\n

  • \n

    Orsburn, B. C. Metabolomic, proteomic, and single-cell proteomic analysis of cancer cells treated with the KRASG12D inhibitor MRTX1133. J. Proteome Res. 22<\/b>, 3703\u20133713 (2023).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Specht, H. et al. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol. 22<\/b>, 50 (2021).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wi\u015bniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A \u2018proteomic ruler\u2019 for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteomics 13<\/b>, 3497\u20133506 (2014).<\/p>\n<\/li>\n

  • \n

    Lanz, M. C., Fuentes Valenzuela, L., Elias, J. E. & Skotheim, J. M. Cell size contributes to single-cell proteome variation. J. Proteome Res. 23<\/b>, 221\u2013231 (2024).<\/p>\n<\/li>\n

  • \n

    Bache, N. et al. A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics. Mol. Cell. Proteomics 17<\/b>, 2284\u20132296 (2018).<\/p>\n<\/li>\n

  • \n

    Eberhard, C. D. & Orsburn, B. C. A. Multiplexed single-cell proteomic workflow applicable to drug treatment studies. in Proteomics for Drug Discovery (ed. Blonder, J.) 1\u201310 (Springer, 2024).<\/p>\n<\/li>\n

  • \n

    Gygi, J. P. et al. Web-based search tool for visualizing instrument performance using the triple knockout (TKO) proteome standard. J. Proteome Res. 18<\/b>, 687\u2013693 (2019).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Paulo, J. A., O\u2019Connell, J. D. & Gygi, S. P. A Triple Knockout (TKO) Proteomics Standard for Diagnosing Ion Interference in Isobaric Labeling Experiments. J. Am. Soc. Mass Spectrom. 27<\/b>, 1674\u20131681 (2016).<\/p>\n<\/li>\n

  • \n

    Yates, J. R., Ruse, C. I. & Nakorchevsky, A. Proteomics by mass spectrometry: approaches, advances, and applications. Annu. Rev. Biomed. Eng. 11<\/b>, 49\u201379 (2009).<\/p>\n<\/li>\n

  • \n

    Bian, Y. et al. Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC\u2013MS\/MS. Nat. Commun. 11<\/b>, 157 (2020).<\/p>\n<\/li>\n

  • \n

    Bruderer, R. et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell. Proteomics 14<\/b>, 1400\u20131410 (2015)<\/p>\n<\/li>\n

  • \n

    Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S. & Coon, J. J. Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol. Cell. Proteomics 11<\/b>, 1475\u201388 (2012).<\/p>\n<\/li>\n

  • \n

    Singh, C., Zampronio, C. G., Creese, A. J. & Cooper, H. J. Higher energy collision dissociation (HCD) product ion-triggered electron transfer dissociation (ETD) mass spectrometry for the analysis of N-linked glycoproteins. J. Proteome Res. 11<\/b>, 4517\u20134525 (2012).<\/p>\n<\/li>\n

  • \n

    Zolg, D. P. et al. ProteomeTools: systematic characterization of 21 post-translational protein modifications by liquid chromatography tandem mass spectrometry (LC-MS\/MS) using synthetic peptides. Mol. Cell. Proteomics 17<\/b>, 1850\u20131863 (2018).<\/p>\n<\/li>\n

  • \n

    Armony, G. et al. The GlycoPaSER prototype as a real-time N-glycopeptide identification tool based on the PaSER parallel computing platform. Int. J. Mol. Sci. 24<\/b>, 7869 (2023).<\/p>\n<\/li>\n

  • \n

    Toghi Eshghi, S. et al. Classification of tandem mass spectra for identification of N- and O-linked glycopeptides. Sci. Rep. 6<\/b>, 37189 (2016).<\/p>\n<\/li>\n

  • \n

    Jenkins, C. & Orsburn, B. C. Simple tool for rapidly assessing the quality of multiplexed single cell proteomics data. J. Am. Soc. Mass Spectrom. 34<\/b>, 1709\u20131711 (2023).<\/p>\n<\/li>\n

  • \n

    Gallia, J., Lavrich, K., Tan-Wilson, A. & Madden, P. H. Filtering of MS\/MS data for peptide identification. BMC Genomics 14<\/b>, S2 (2013).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chang, Y.-C. et al. Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics. Cell Rep. 43<\/b>, 113612 (2024).<\/p>\n<\/li>\n

  • \n

    Gatto, L. et al. Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nat. Methods 20<\/b>, 375\u2013386 (2023).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sui, J. et al. Plasma levels of S100A8\/A9, histone\/DNA complexes, and cell-free DNA predict adverse outcomes of immune thrombotic thrombocytopenic purpura. J. Thromb. Haemost. 19<\/b>, 370\u2013379 (2021).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Orsburn, B. C. An integrated method for single cell proteomics with simultaneous measurements of intracellular drug concentration implicates new mechanisms for adaptation to KRASG12D inhibitors. bioRxiv https:\/\/doi.org\/10.1101\/2023.11.18.567669<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Mardis, E. R. ChIP-seq: welcome to the new frontier. Nat. Methods 4<\/b>, 613\u2013614 (2007).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33<\/b>, 1165\u20131172 (2015).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Dickson, B. M., Kupai, A., Vaughan, R. M. & Rothbart, S. B. Streamlined quantitative analysis of histone modification abundance at nucleosome-scale resolution with siQ-ChIP version 2.0. Sci. Rep. 13<\/b>, 7508 (2023).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Berendzen, K. W., Grefen, C., Sakamoto, T. & Slane, D. Analysis of chromatin accessibility, histone modifications, and transcriptional states in specific cell types using flow cytometry. In Plant Gene Regulatory Networks: Methods and Protocols (eds. Kaufmann, K. & Vandepoele, K.) 57\u201373 (Springer US, New York, NY, 2023).<\/p>\n<\/li>\n

  • \n

    Cheung, P. et al. Single-cell epigenetics\u2014chromatin modification atlas unveiled by mass cytometry. Clin. Immunol. 196<\/b>, 40\u201348 (2018).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Mill\u00e1n-Zambrano, G., Burton, A., Bannister, A. J. & Schneider, R. Histone post-translational modifications \u2014 cause and consequence of genome function. Nat. Rev. Genet. 23<\/b>, 563\u2013580 (2022).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ctortecka, C. & Mechtler, K. The rise of single-cell proteomics. Anal. Sci. Adv. 2<\/b>, 240\u2013254 (2021).<\/p>\n<\/li>\n

  • \n

    Schoof, E. M. et al. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies. Nat. Commun. 12<\/b>, 3341 (2021).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Doerr, A. DIA mass spectrometry. Nat. Methods https:\/\/doi.org\/10.1038\/nmeth.3234<\/a> (2014).<\/p>\n<\/li>\n

  • \n

    Szyrwiel, L., Sinn, L., Ralser, M. & Demichev, V. Slice-PASEF: fragmenting all ions for maximum sensitivity in proteomics. bioRxiv https:\/\/doi.org\/10.1101\/2022.10.31.514544<\/a> (2022).<\/p>\n<\/li>\n

  • \n

    Kreimer, S. et al. High-throughput single-cell proteomic analysis of organ-derived heterogeneous cell populations by nanoflow dual-trap single-column liquid chromatography. Anal. Chem. 95<\/b>, 9145\u20139150 (2023).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Truong, T. et al. Data-dependent acquisition with precursor coisolation improves proteome coverage and measurement throughput for label-free single-cell proteomics**. Angew. Chem. Int. Ed. 62<\/b>, e202303415 (2023).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Orsburn, B. C. Proteome discoverer\u2014a community enhanced data processing suite for protein informatics. Proteomes 9<\/b>, 15 (2021).<\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"Garcia, B. A. et al. Chemical derivatization of histones for facilitated analysis by mass spectrometry. Nat. Protoc. 2,…\n","protected":false},"author":3,"featured_media":481549,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[218090,834,27531,123983,159,67,132,68],"class_list":{"0":"post-481548","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-science","8":"tag-epigenetics-analysis","9":"tag-general","10":"tag-life-sciences","11":"tag-proteomics","12":"tag-science","13":"tag-united-states","14":"tag-unitedstates","15":"tag-us"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@us\/115812233763071142","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/481548","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/comments?post=481548"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/481548\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media\/481549"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media?parent=481548"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/categories?post=481548"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/tags?post=481548"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}