Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices With Zeno SWATH Data-Independent Acquisition

Meissner, 2022, The emerging role of mass spectrometry-based proteomics in drug discovery, Nat. Rev. Drug Discov., 21, 637, 10.1038/s41573-022-00409-3 Goncalves, 2022, Pan-cancer proteomic map of 949 human cell lines, Cancer Cell, 40, 835, 10.1016/j.ccell.2022.06.010 Messner, 2020, Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection, Cell Syst., 11, 11, 10.1016/j.cels.2020.05.012 Demichev, 2021, A time-resolved proteomic and prognostic map of COVID-19, Cell Syst., 12, 780, 10.1016/j.cels.2021.05.005 Barkovits, 2020, Reproducibility, specificity and accuracy of relative quantification using spectral library-based data-independent acquisition, Mol. Cell Proteomics, 19, 181, 10.1074/mcp.RA119.001714 Ludwig, 2018, Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial, Mol. Syst. Biol., 14, 10.15252/msb.20178126 Collins, 2017, Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry, Nat. Commun., 8, 291, 10.1038/s41467-017-00249-5 Zaidman, 2016, Differentiation of human bronchial epithelial cells: Role of hydrocortisone in development of ion transport pathways involved in mucociliary clearance, Am. J. Physiol. Cell Physiol., 311, C225, 10.1152/ajpcell.00073.2016 Rayner, 2019, Optimization of normal human bronchial epithelial (NHBE) cell 3D cultures for in vitro lung model studies, Sci. Rep., 9, 500, 10.1038/s41598-018-36735-z Demichev, 2020, DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput, Nat. Met., 17, 41, 10.1038/s41592-019-0638-x Perez-Riverol, 2019, The PRIDE database and related tools and resources in 2019: improving support for quantification data, Nucl. Acids Res., 47, D442, 10.1093/nar/gky1106 Loboda, 2009, A novel ion trap that enables high duty cycle and wide m/z range on an orthogonal injection TOF mass spectrometer, J. Am. Soc. Mass Spectrom., 20, 1342, 10.1016/j.jasms.2009.03.018 Baba, 2021, Dissociation of biomolecules by an intense low-energy electron beam in a high sensitivity time-of-flight mass spectrometer, J. Am. Soc. Mass Spectrom., 32, 1964, 10.1021/jasms.0c00425 Wang, 2022, High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS, Elife, 11, 10.7554/eLife.83947 Uhlen, 2015, Proteomics. Tissue-based map of the human proteome, Science, 347, 10.1126/science.1260419 Ashburner, 2000, Gene ontology: tool for the unification of biology. The Gene Ontology Consortium, Nat. Genet., 25, 25, 10.1038/75556 Gene, 2021, Ontology, the Gene ontology resource: enriching a GOld mine, Nucl. Acids Res., 49, D325, 10.1093/nar/gkaa1113 He, 2021, LRG1 is an adipokine that mediates obesity-induced hepatosteatosis and insulin resistance, J. Clin. Invest., 131, 10.1172/JCI148545 Nakamura, 2019, Elevated levels of circulating ITIH4 are associated with hepatocellular carcinoma with nonalcoholic fatty liver disease: from pig model to human study, BMC Cancer, 19, 621, 10.1186/s12885-019-5825-8 Semmler, 2021, PNPLA3 and SERPINA1 variants are associated with severity of fatty liver disease at first referral to a tertiary center, J. Per. Med., 11, 165 Fan, 2019, ECM1 prevents activation of transforming growth factor beta, hepatic stellate cells, and fibrogenesis in mice, Gastroenterology, 157, 1352, 10.1053/j.gastro.2019.07.036 Gentric, 2015, Oxidative stress promotes pathologic polyploidization in nonalcoholic fatty liver disease, J. Clin. Invest., 125, 981, 10.1172/JCI73957 Ogresta, 2022, Coagulation and endothelial dysfunction associated with NAFLD: current status and therapeutic implications, J. Clin. Transl. Hepatol., 10, 339, 10.14218/JCTH.2021.00268 Jiao, 2015, Farnesoid X receptor: a master regulator of hepatic triglyceride and glucose homeostasis, Acta Pharmacol. Sin., 36, 44, 10.1038/aps.2014.116 Tilg, 2010, Evolution of inflammation in nonalcoholic fatty liver disease: the multiple parallel hits hypothesis, Hepatology, 52, 1836, 10.1002/hep.24001 Zhang, 2021, PPBP as a marker of diabetic nephropathy podocyte injury via Bioinformatics Analysis, Biochem. Biophys. Res. Commun., 577, 165, 10.1016/j.bbrc.2021.08.087 Saito, 2020, Selenoprotein P as an in vivo redox regulator: disorders related to its deficiency and excess, J. Clin. Biochem. Nutr., 66, 1, 10.3164/jcbn.19-31 Davi, 1997, Diabetes mellitus, hypercholesterolemia, and hypertension but not vascular disease per se are associated with persistent platelet activation in vivo. Evidence derived from the study of peripheral arterial disease, Circulation, 96, 69, 10.1161/01.CIR.96.1.69 Ekrikpo, 2017, Clinical utility of urinary beta2-microglobulin in detection of early nephropathy in african diabetes mellitus patients, Int. J. Nephrol., 2017, 10.1155/2017/4093171 Tanaka, 2018, Impact of serum cholesterol esterification rates on the development of diabetes mellitus in a general population, Lipids Health Dis., 17, 180, 10.1186/s12944-018-0822-5 Sobczak, 2019, Coagulatory defects in type-1 and type-2 diabetes, Int. J. Mol. Sci., 20, 6345, 10.3390/ijms20246345 Meier, 2018, BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes, Nat. Met., 15, 440, 10.1038/s41592-018-0003-5 Meier, 2020, diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition, Nat. Met., 17, 1229, 10.1038/s41592-020-00998-0 Meier, 2021, Trapped ion mobility spectrometry and parallel accumulation-serial fragmentation in proteomics, Mol. Cell Proteomics, 20, 10.1016/j.mcpro.2021.100138 Frohlich, 2022, Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity, Nat. Commun., 13, 2622, 10.1038/s41467-022-30094-0 Messner, 2021, Ultra-fast proteomics with scanning SWATH, Nat. Biotechnol., 39, 846, 10.1038/s41587-021-00860-4 Sun, 2020, Accelerated protein biomarker discovery from FFPE tissue samples using single-shot, short gradient microflow SWATH MS, J. Proteome Res., 19, 2732, 10.1021/acs.jproteome.9b00671 Buergel, 2022, Metabolomic profiles predict individual multidisease outcomes, Nat. Med., 28, 2309, 10.1038/s41591-022-01980-3 Bragg, 2022, Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study, BMC Med., 20, 159, 10.1186/s12916-022-02354-9 Wang, 2019, Metabolomic pattern predicts incident coronary heart disease, Arterioscler. Thromb. Vasc. Biol., 39, 1475, 10.1161/ATVBAHA.118.312236 Williams, 2022, A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk, Sci. Transl. Med., 14 Schussler-Fiorenza Rose, 2019, A longitudinal big data approach for precision health, Nat. Med., 25, 792, 10.1038/s41591-019-0414-6 Contrepois, 2020, Molecular choreography of acute exercise, Cell, 181, 1112, 10.1016/j.cell.2020.04.043 Stelzer, 2021, Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset, Sci. Transl. Med., 13, 10.1126/scitranslmed.abd9898 Williams, 2018, Improving assessment of drug safety through proteomics: early detection and mechanistic characterization of the unforeseen harmful effects of torcetrapib, Circulation, 137, 999, 10.1161/CIRCULATIONAHA.117.028213 Poulos, 2020, Strategies to enable large-scale proteomics for reproducible research, Nat. Commun., 11, 3793, 10.1038/s41467-020-17641-3 Cuklina, 2021, Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial, Mol. Syst. Biol., 17, 10.15252/msb.202110240