Optimized RNA sequencing deconvolution illustrates the impact of obesity and weight loss on cell composition of human adipose tissue
Cheehoon Ahn
Translational Research Institute, AdventHealth, Orlando, Florida, USA
Search for more papers by this authorAdeline Divoux
Translational Research Institute, AdventHealth, Orlando, Florida, USA
Search for more papers by this authorMingqi Zhou
Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, California, USA
Search for more papers by this authorMarcus M. Seldin
Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, California, USA
Search for more papers by this authorCorresponding Author
Lauren M. Sparks
Translational Research Institute, AdventHealth, Orlando, Florida, USA
Correspondence
Lauren M. Sparks and Katie L. Whytock, Translational Research Institute, AdventHealth, 301 E Princeton St, Orlando, FL 32804, USA.
Email: [email protected] and [email protected]
Search for more papers by this authorCorresponding Author
Katie L. Whytock
Translational Research Institute, AdventHealth, Orlando, Florida, USA
Correspondence
Lauren M. Sparks and Katie L. Whytock, Translational Research Institute, AdventHealth, 301 E Princeton St, Orlando, FL 32804, USA.
Email: [email protected] and [email protected]
Search for more papers by this authorCheehoon Ahn
Translational Research Institute, AdventHealth, Orlando, Florida, USA
Search for more papers by this authorAdeline Divoux
Translational Research Institute, AdventHealth, Orlando, Florida, USA
Search for more papers by this authorMingqi Zhou
Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, California, USA
Search for more papers by this authorMarcus M. Seldin
Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, California, USA
Search for more papers by this authorCorresponding Author
Lauren M. Sparks
Translational Research Institute, AdventHealth, Orlando, Florida, USA
Correspondence
Lauren M. Sparks and Katie L. Whytock, Translational Research Institute, AdventHealth, 301 E Princeton St, Orlando, FL 32804, USA.
Email: [email protected] and [email protected]
Search for more papers by this authorCorresponding Author
Katie L. Whytock
Translational Research Institute, AdventHealth, Orlando, Florida, USA
Correspondence
Lauren M. Sparks and Katie L. Whytock, Translational Research Institute, AdventHealth, 301 E Princeton St, Orlando, FL 32804, USA.
Email: [email protected] and [email protected]
Search for more papers by this authorLauren M. Sparks is the senior author.
Abstract
Objective
Cellular heterogeneity of human adipose tissue is linked to the pathophysiology of obesity and may impact the response to energy restriction and changes in fat mass. Herein, we provide an optimized pipeline to estimate cellular composition in human abdominal subcutaneous adipose tissue (ASAT) bulk RNA sequencing (RNA-seq) datasets using a single-nuclei RNA-seq signature matrix.
Methods
A deconvolution pipeline for ASAT was optimized by benchmarking publicly available algorithms using a signature matrix derived from ASAT single-nuclei RNA-seq data from 20 adults and then applied to estimate ASAT cell-type proportions in publicly available obesity and weight loss studies.
Results
Individuals with obesity had greater proportions of macrophages and lower proportions of adipocyte subpopulations and vascular cells compared with lean individuals. Two months of diet-induced weight loss increased the estimated proportions of macrophages; however, 2 years of diet-induced weight loss reduced the estimated proportions of macrophages, thereby suggesting a biphasic nature of cellular remodeling of ASAT during weight loss.
Conclusions
Our optimized high-throughput pipeline facilitates the assessment of composition changes of highly characterized cell types in large numbers of ASAT samples using low-cost bulk RNA-seq. Our data reveal novel changes in cellular heterogeneity and its association with cardiometabolic health in humans with obesity and following weight loss.
CONFLICT OF INTEREST STATEMENT
The authors declared no conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
Gene signature matrices and 6000 HVG list have been uploaded to the following link: https://github.com/KWhytock13/deconvolution-wat. Code for generating source data and running the deconvolution pipeline is also available at the following link: https://github.com/KWhytock13/deconvolution-wat
Supporting Information
| Filename | Description |
|---|---|
| oby24264-sup-0001-Supinfo.docxWord 2007 document , 37.1 KB | Data S1. Supporting Information. |
| oby24264-sup-0002-Tables.docxWord 2007 document , 15.8 KB | Table S1. CALERIE™ subject characteristics. Table S2. Subject characteristics of DioGenes and Aleman et al. [42]. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- 1Kershaw EE, Flier JS. Adipose tissue as an endocrine organ. J Clin Endocrinol Metab. 2004; 89(6): 2548-2556.
- 2Goodpaster BH, Sparks LM. Metabolic flexibility in health and disease. Cell Metab. 2017; 25(5): 1027-1036.
- 3Trayhurn P. Endocrine and signalling role of adipose tissue: new perspectives on fat. Acta Physiol Scand. 2005; 184(4): 285-293.
- 4Eto H, Suga H, Matsumoto D, et al. Characterization of structure and cellular components of aspirated and excised adipose tissue. Plast Reconstr Surg. 2009; 124(4): 1087-1097.
- 5Corvera S. Cellular heterogeneity in adipose tissues. Annu Rev Physiol. 2021; 83: 257-278.
- 6Frayn KN. Adipose tissue as a buffer for daily lipid flux. Diabetologia. 2002; 45(9): 1201-1210.
- 7Goossens GH. The metabolic phenotype in obesity: fat mass, body fat distribution, and adipose tissue function. Obes Facts. 2017; 10(3): 207-215.
- 8Klöting N, Fasshauer M, Dietrich A, et al. Insulin-sensitive obesity. Am J Physiol Endocrinol Metab. 2010; 299(3): E506-E515.
- 9Brotman SM, Oravilahti A, Rosen JD, et al. Cell-type composition affects adipose gene expression associations with cardiometabolic traits. Diabetes. 2023; 72(11): 1707-1718.
- 10Schleh MW, Ameka M, Rodriguez A, Hasty AH. Deficiency of the hemoglobin-haptoglobin receptor, CD163, worsens insulin sensitivity in obese male mice. Diabetes. 2024; 73: 1990-2002.
- 11Kosteli A, Sugaru E, Haemmerle G, et al. Weight loss and lipolysis promote a dynamic immune response in murine adipose tissue. J Clin Invest. 2010; 120(10): 3466-3479.
- 12Aron-Wisnewsky J, Tordjman J, Poitou C, et al. Human adipose tissue macrophages: m1 and m2 cell surface markers in subcutaneous and omental depots and after weight loss. J Clin Endocrinol Metabol. 2009; 94(11): 4619-4623.
- 13Whytock KL, Divoux A, Sun Y, et al. Aging human abdominal subcutaneous white adipose tissue at single cell resolution. Aging Cell. 2024; 23(11):e14287. doi:10.1111/acel.14287
- 14Emont MP, Jacobs C, Essene AL, et al. A single-cell atlas of human and mouse white adipose tissue. Nature. 2022; 603(7903): 926-933.
- 15Massier L, Jalkanen J, Elmastas M, et al. An integrated single cell and spatial transcriptomic map of human white adipose tissue. Nat Commun. 2023; 14(1): 1438.
- 16Hao Y, Hao S, Anderson-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184:3573-3587.e29.
- 17Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019;20:296. doi:10.1186/s13059-019-1874-1
- 18Pfister S, Kuettel V, Ferrero E. granulator: Rapid benchmarking of methods for *in silico* deconvolution of bulk RNA-seq data. R package version 1.14.0. https://github.com/xanibas/granulator
- 19Monaco G, Lee B, Xu W, et al. RNA-seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types. Cell Rep. 2019; 26(6): 1627-1640.
- 20Gong T, Szustakowski JD. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-seq data. Bioinformatics. 2013; 29(8): 1083-1085.
- 21Abbas AR, Wolslegel K, Seshasayee D, Modrusan Z, Clark HF. Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS One. 2009; 4(7):e6098.
- 22Hunt GJ, Freytag S, Bahlo M, Gagnon-Bartsch JA. dtangle: accurate and robust cell type deconvolution. Bioinformatics. 2019; 35(12): 2093-2099.
- 23Danziger SA, Gibbs DL, Shmulevich I, et al. ADAPTS: automated deconvolution augmentation of profiles for tissue specific cells. PLoS One. 2019; 14(11):e0224693.
- 24Altboum Z, Steuerman Y, David E, et al. Digital cell quantification identifies global immune cell dynamics during influenza infection. Mol Syst Biol. 2014; 10(2): 720.
- 25Wang X, Park J, Susztak K, Zhang NR, Li M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun. 2019; 10(1): 380.
- 26Racle J, Gfeller D. EPIC: a tool to estimate the proportions of different cell types from bulk gene expression data. Methods Mol Biol. 2020; 2120: 233-248.
- 27Chu T, Wang Z, Pe'er D, Danko CG. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat Cancer. 2022; 3(4): 505-517.
- 28Menden K, Marouf M, Oller S, et al. Deep learning–based cell composition analysis from tissue expression profiles. Sci Adv. 2020; 6(30):eaba2619.
- 29Raulerson CK, Ko A, Kidd JC, et al. Adipose tissue gene expression associations reveal hundreds of candidate genes for cardiometabolic traits. Am J Hum Genet. 2019; 105(4): 773-787.
- 30Laakso M, Kuusisto J, Stancakova A, et al. The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases. J Lipid Res. 2017; 58(3): 481-493.
- 31Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008; 9:559.
- 32Lee M-J, Wu Y, Fried SK. Adipose tissue heterogeneity: implication of depot differences in adipose tissue for obesity complications. Mol Aspects Med. 2013; 34(1): 1-11.
- 33Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, Ferrante AW. Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest. 2003; 112(12): 1796-1808.
- 34Yang CH, Fagnocchi L, Apostle S, et al. Independent phenotypic plasticity axes define distinct obesity sub-types. Nat Metab. 2022; 4(9): 1150-1165.
- 35Petersen MC, Smith GI, Palacios HH, et al. Cardiometabolic characteristics of people with metabolically healthy and unhealthy obesity. Cell Metab. 2024; 36(4): 745-761.
- 36Fryk E, Olausson J, Mossberg K, et al. Hyperinsulinemia and insulin resistance in the obese may develop as part of a homeostatic response to elevated free fatty acids: a mechanistic case-control and a population-based cohort study. EBioMedicine. 2021; 65:103264.
- 37Crewe C, An YA, Scherer PE. The ominous triad of adipose tissue dysfunction: inflammation, fibrosis, and impaired angiogenesis. J Clin Invest. 2017; 127(1): 74-82.
- 38Pasarica M, Sereda OR, Redman LM, et al. Reduced adipose tissue oxygenation in human obesity: evidence for rarefaction, macrophage chemotaxis, and inflammation without an angiogenic response. Diabetes. 2009; 58(3): 718-725.
- 39Bluher M. Adipose tissue dysfunction contributes to obesity related metabolic diseases. Best Pract Res Clin Endocrinol Metab. 2013; 27(2): 163-177.
- 40Magkos F, Fraterrigo G, Yoshino J, et al. Effects of moderate and subsequent progressive weight loss on metabolic function and adipose tissue biology in humans with obesity. Cell Metab. 2016; 23(4): 591-601.
- 41Imbert A, Vialaneix N, Marquis J, et al. Network analyses reveal negative link between changes in adipose tissue GDF15 and BMI during dietary-induced weight loss. J Clin Endocrinol Metab. 2022; 107(1): e130-e142.
- 42Aleman JO, Iyengar NM, Walker JM, et al. Effects of rapid weight loss on systemic and adipose tissue inflammation and metabolism in obese postmenopausal women. J Endocr Soc. 2017; 1(6): 625-637.
- 43Fritzen AM, Lundsgaard AM, Jordy AB, et al. New Nordic diet-induced weight loss is accompanied by changes in metabolism and AMPK signaling in adipose tissue. J Clin Endocrinol Metab. 2015; 100(9): 3509-3519.
- 44Rickman AD, Williamson DA, Martin CK, et al. The CALERIE study: design and methods of an innovative 25% caloric restriction intervention. Contemp Clin Trials. 2011; 32(6): 874-881.
- 45Russo L, Lumeng CN. Properties and functions of adipose tissue macrophages in obesity. Immunology. 2018; 155(4): 407-417.
- 46Åkra S, Aksnes TA, Flaa A, et al. Markers of remodeling in subcutaneous adipose tissue are strongly associated with overweight and insulin sensitivity in healthy non-obese men. Sci Rep. 2020; 10(1):14055.
- 47Caslin HL, Bhanot M, Bolus WR, Hasty AH. Adipose tissue macrophages: unique polarization and bioenergetics in obesity. Immunol Rev. 2020; 295(1): 101-113.
- 48Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med. 2011; 364(25): 2392-2404.
- 49Tam CS, Tordjman J, Divoux A, Baur LA, Clement K. Adipose tissue remodeling in children: the link between collagen deposition and age-related adipocyte growth. J Clin Endocrinol Metab. 2012; 97(4): 1320-1327.
- 50Spalding KL, Arner E, Westermark PO, et al. Dynamics of fat cell turnover in humans. Nature. 2008; 453(7196): 783-787.
- 51Landgraf K, Rockstroh D, Wagner IV, et al. Evidence of early alterations in adipose tissue biology and function and its association with obesity-related inflammation and insulin resistance in children. Diabetes. 2015; 64(4): 1249-1261.
- 52Ravussin E, Smith SR. Increased fat intake, impaired fat oxidation, and failure of fat cell proliferation result in ectopic fat storage, insulin resistance, and type 2 diabetes mellitus. Ann N Y Acad Sci. 2002; 967: 363-378.
- 53Cypess AM. Reassessing human adipose tissue. N Engl J Med. 2022; 386(8): 768-779.
- 54Shulman GI. Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N Engl J Med. 2014; 371(12): 1131-1141.
- 55Nicklas BJ, Ambrosius W, Messier SP, et al. Diet-induced weight loss, exercise, and chronic inflammation in older, obese adults: a randomized controlled clinical trial. Am J Clin Nutr. 2004; 79(4): 544-551.
- 56Christiansen T, Paulsen SK, Bruun JM, Pedersen SB, Richelsen B. Exercise training versus diet-induced weight-loss on metabolic risk factors and inflammatory markers in obese subjects: a 12-week randomized intervention study. Am J Physiol Endocrinol Metab. 2010; 298(4): E824-E831.
- 57Meydani SN, Das SK, Pieper CF, et al. Long-term moderate calorie restriction inhibits inflammation without impairing cell-mediated immunity: a randomized controlled trial in non-obese humans. Aging (Albany, NY). 2016; 8(7): 1416-1431.
- 58Capel F, Klimcakova E, Viguerie N, et al. Macrophages and adipocytes in human obesity: adipose tissue gene expression and insulin sensitivity during calorie restriction and weight stabilization. Diabetes. 2009; 58(7): 1558-1567.
- 59Asterholm IW, Tao C, Morley TS, et al. Adipocyte inflammation is essential for healthy adipose tissue expansion and remodeling. Cell Metab. 2014; 20(1): 103-118.
- 60Murphy J, Moullec G, Santosa S. Factors associated with adipocyte size reduction after weight loss interventions for overweight and obesity: a systematic review and meta-regression. Metabolism. 2017; 67: 31-40.
- 61Ejaz A, Mitterberger MC, Lu Z, et al. Weight loss upregulates the small GTPase DIRAS3 in human white adipose progenitor cells, which negatively regulates Adipogenesis and activates autophagy via Akt-mTOR inhibition. EBioMedicine. 2016; 6: 149-161.
- 62Rossmeislova L, Malisova L, Kracmerova J, et al. Weight loss improves the adipogenic capacity of human preadipocytes and modulates their secretory profile. Diabetes. 2013; 62(6): 1990-1995.
- 63Lofgren P, Andersson I, Adolfsson B, et al. Long-term prospective and controlled studies demonstrate adipose tissue hypercellularity and relative leptin deficiency in the postobese state. J Clin Endocrinol Metab. 2005; 90(11): 6207-6213.
- 64Prins JB, Walker NI, Winterford CM, Cameron DP. Human adipocyte apoptosis occurs in malignancy. Biochem Biophys Res Commun. 1994; 205(1): 625-630.
- 65Fischer-Posovszky P, Wang QA, Asterholm IW, Rutkowski JM, Scherer PE. Targeted deletion of adipocytes by apoptosis leads to adipose tissue recruitment of alternatively activated M2 macrophages. Endocrinology. 2011; 152(8): 3074-3081.
- 66Ding J, Adiconis X, Simmons SK, et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol. 2020; 38(6): 737-746.
- 67Oh JM, An M, Son DS, et al. Comparison of cell type distribution between single-cell and single-nucleus RNA sequencing: enrichment of adherent cell types in single-nucleus RNA sequencing. Exp Mol Med. 2022; 54(12): 2128-2134.
