Abstract
Obesity is a major public health challenge affecting an ever-increasing proportion of the global population. It is associated with numerous comorbidities. Progressive expansion and remodeling of adipose tissue may lead to depot specific changes in adipose tissue biology and energy partitioning. Such changes likely precede the development of obesity-related complications. To facilitate a deeper understanding of adipose tissue biology, a comprehensive quantitative proteomic dataset is presented at the peptide and protein level. Data-independent acquisition LC-MS/MS data were acquired from matched subcutaneous and omental adipose tissues from metabolically healthy individuals with no comorbidities and covering a wide range of body mass indexes. Adipose tissue samples were collected during elective surgeries and immediately processed for histology or frozen until proteomic analysis. Internal and external quality control systems ensured high quality data. All data presented are available via ProteomeXchange. This dataset will allow new insights into biological changes that evolve with increasing adiposity captured before the onset of comorbidities. Matched sampling across fat depots provides an opportunity to uncover depot-specific physiological signatures.
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Data availability
All data presented in the current work are available on Panorama Public30 (https://panoramaweb.org/human-adipose.url) and were assigned the ProteomeXchange39 ID: PXD067514 (https://doi.org/10.6069/sq0n-1x32)40.
Code availability
MSConvert is available from https://proteowizard.sourceforge.io/. DIA-NN is available from https://github.com/vdemichev/DiaNN. Carafe is available from: https://github.com/Noble-Lab/Carafe. EncyclopeDIA is available from https://bitbucket.org/searleb/encyclopedia/. Skyline is available from https://skyline.ms/skyline.url. All Nextflow workflows used in the current work to generate spectral libraries, search, quantify and normalize the MS data presented here are publicly available at: https://nf-carafe-ai-ms.readthedocs.io/en/latest/, for generation of spectral libraries using Carafe and DIA-NN; https://nf-teirex-dia.readthedocs.io/en/latest/, for generation of on-column chromatogram libraries followed by quantification using EncyclopeDIA and Skyline; https://github.com/uw-maccosslab/nf-dia-batch-correction, for peptide and protein level normalization. All in house code written for the analysis of the data presented in this work is available online at https://github.com/Isoherranen-Lab/Human-Adipose-Depot-DIA.
References
Stierman, B. et al. National Health and Nutrition Examination Survey 2017–March 2020 Prepandemic Data Files–Development of Files and Prevalence Estimates for Selected Health Outcomes. Natl. Health Stat. Report. 2021 (2021).
Busebee, B., Ghusn, W., Cifuentes, L. & Acosta, A. Obesity: A Review of Pathophysiology and Classification. Mayo Clin. Proc. 98, 1842–1857 (2023).
Heymsfield, S. B. & Wadden, T. A. Mechanisms, Pathophysiology, and Management of Obesity. New England Journal of Medicine 376, 254–266 (2017).
Ibrahim, M. M. Subcutaneous and visceral adipose tissue: Structural and functional differences. Obesity Reviews 11, 11–18 (2010).
Hruska, P. et al. Proteomic Signatures of Human Visceral and Subcutaneous Adipocytes. Journal of Clinical Endocrinology and Metabolism 107, 755–775 (2022).
Raajendiran, A. et al. Proteome analysis of human adipocytes identifies depot-specific heterogeneity at metabolic control points. Am. J. Physiol. Endocrinol. Metab. 320, E1068–E1084 (2021).
Gómez-Serrano, M. et al. Proteome-wide alterations on adipose tissue from obese patients as age-, diabetes- and gender-specific hallmarks. Scientific Reports 2016 6:1 6, 1–15 (2016).
Larsen, J. K. et al. High-throughput proteomics uncovers exercise training and type 2 diabetes-induced changes in human white adipose tissue. Sci. Adv. 9, eadi7548 (2023).
Madsen, S. et al. Deep Proteome Profiling of White Adipose Tissue Reveals Marked Conservation and Distinct Features Between Different Anatomical Depots. Molecular and Cellular Proteomics 22 (2023).
Harney, D. J. et al. Proteomics analysis of adipose depots after intermittent fasting reveals visceral fat preservation mechanisms. Cell Rep. 34, 108804 (2021).
Venable, J. D., Dong, M.-Q., Wohlschlegel, J., Dillin, A. & Yates, J. R. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).
Egertson, J. D., MacLean, B., Johnson, R., Xuan, Y. & MacCoss, M. J. Multiplexed peptide analysis using data-independent acquisition and Skyline. Nat. Protoc. 10, 887–903 (2015).
Gillet, L. C. et al. Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis. Molecular & Cellular Proteomics 11, O111.016717 (2012).
Angulo, P. et al. The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology 45, 846–854 (2007).
Batth, T. S. et al. Protein Aggregation Capture on Microparticles Enables Multipurpose Proteomics Sample Preparation. Mol. Cell. Proteomics 18, 1027–1035 (2019).
Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).
Tsantilas, K. A. et al. A Framework for Quality Control in Quantitative Proteomics. J. Proteome Res. https://doi.org/10.1021/acs.jproteome.4c00363 (2024).
Egertson, J. D. et al. Multiplexed MS/MS for improved data-independent acquisition. Nature Methods 2013 10:8 10, 744–746 (2013).
Amodei, D. et al. Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows. J. Am. Soc. Mass Spectrom. 30, 669–684 (2019).
Searle, B. C. et al. Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nat. Commun. 9, 5128 (2018).
Pino, L. K., Just, S. C., MacCoss, M. J. & Searle, B. C. Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries. Molecular and Cellular Proteomics 19, 1088–1103 (2020).
Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 30, 918–920 (2012).
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).
Wen, B. et al. Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics. Nature Communications 2025 16:1 16, 9815 (2025).
DI Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).
MacLean, B. et al. Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).
Pino, L. K. et al. The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom. Rev. https://doi.org/10.1002/mas.21540 (2017).
Sharma, V., Eng, J. K., Maccoss, M. J. & Riffle, M. A mass spectrometry proteomics data management platform. Mol Cell Proteomics 11, 824–831 (2012).
Webb-Robertson, B. J. M., Matzke, M. M., Jacobs, J. M., Pounds, J. G. & Waters, K. M. A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors. Proteomics 11, 4736–4741 (2011).
Sharma, V. et al. Panorama Public: A Public Repository for Quantitative Data Sets Processed in Skyline. Mol. Cell. Proteomics 17, 1239–1244 (2018).
Merrihew, G. E. et al. A peptide-centric quantitative proteomics dataset for the phenotypic assessment of Alzheimer’s disease. Sci. Data 10 (2023).
Ammar, C., Schessner, J. P., Willems, S., Michaelis, A. C. & Mann, M. Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes. Molecular and Cellular Proteomics 22 (2023).
Harris, C. R. et al. Array programming with NumPy. Nature 2020 585:7825 585, 357–362 (2020).
McKinney, W. Data Structures for Statistical Computing in Python. scipy 56–61, https://doi.org/10.25080/MAJORA-92BF1922-00A (2010).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Pedregosa Fabianpedregosa, F. et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011).
Waskom, M. L. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Vizcaíno, J. A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nature Biotechnology 2014 32:3 32, 223–226 (2014).
Zelter, A. et al. Proteomic Profiling of Human Omental and Subcutaneous Adipose Tissue in Individuals with a Broad Range of Body Mass Indexes. ProteomeXchange https://doi.org/10.6069/sq0n-1x32 (2026).
Furuhashi, M., Saitoh, S., Shimamoto, K. & Miura, T. Fatty Acid-Binding Protein 4 (FABP4): Pathophysiological Insights and Potent Clinical Biomarker of Metabolic and Cardiovascular Diseases. Clin. Med. Insights Cardiol. 8, 23 (2015).
Li, J. et al. Vinculin: A new target for the diagnosis and treatment of disease. Prog. Biophys. Mol. Biol. 195, 157–166 (2025).
Sohn, J. H. et al. Perilipin 1 (Plin1) deficiency promotes inflammatory responses in lean adipose tissue through lipid dysregulation. Journal of Biological Chemistry 293, 13974–13988 (2018).
Straub, L. G. & Scherer, P. E. Metabolic Messengers: Adiponectin. Nat. Metab. 1, 334 (2019).
Bäckdahl, J. et al. Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin. Cell Metab. 33, 1869–1882.e6 (2021).
Rubinow, K. B. et al. Evidence of depot-specific regulation of all-trans-retinoic acid biosynthesis in human adipose tissue. Clin. Transl. Sci. 15, 1460–1471 (2022).
Acknowledgements
This work was supported in part by National Institutes of Health Grants R01GM111772, R01DK143511, T32DK007247, T32GM007750, TL1TR002318, R24GM141156, U01DK137097 and R01DK143511, as well as by the University of Washington’s Proteomics Resource (UWPR95794). We thank the research participants for their altruism and the nursing staff at the UW general surgery clinic for their assistance. We thank the UW Histology and Imaging Core for technical expertise with histology and immunohistochemistry.
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K.B.R. and N.I. conceived the experiments. A.Z., M.J.M., J.S. and N.I. designed experiments. K.B.R. and L.C.C. identified and consented participants. S.K., J.Y.C., E.W., Z.P. and D.K. performed surgical biopsies. J.L. and L.C.C. performed initial tissue processing. J.M.S., J.Z. and A.S.Y. performed adipose tissue pathology and cell counting. A.Z. performed proteomic sample preparation, mass spectrometry and data analysis. A.M. and M.R. contributed new analytical tools to perform proteomics data analysis. Y.W.W. performed the statistical analysis of proteomic data with help from M.R. The manuscript was written by A.Z., Y.W.W. and N.I. with contributions from all authors. All authors discussed the results and commented on the manuscript. All authors have given approval to the final version of the manuscript.
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N.I. has consultancy agreements with Merck and Boehringer-Ingelheim. The MacCoss Lab at the University of Washington has a sponsored research agreement with Thermo Fisher Scientific, the manufacturer of the instrumentation used in this research. M.J.M. is a paid consultant for Thermo Fisher Scientific. The remaining authors declare no competing interests.
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Zelter, A., Wen, Y.W., Riffle, M. et al. Proteomic profiling of human omental and subcutaneous adipose tissue in individuals with a broad range of BMI. Sci Data (2026). https://doi.org/10.1038/s41597-026-06948-3
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DOI: https://doi.org/10.1038/s41597-026-06948-3
