Summary

Isolation of Nuclei from Human Intermuscular Adipose Tissue and Downstream Single-Nuclei RNA Sequencing

Published: May 03, 2024
doi:

Summary

The biology of intermuscular adipose tissue (IMAT) is largely unexplored due to the limited accessibility of human tissue. Here, we present a detailed protocol for nuclei isolation and library preparation of frozen human IMAT for single nuclei RNA sequencing to identify the cellular composition of this unique adipose depot.

Abstract

Intermuscular adipose tissue (IMAT) is a relatively understudied adipose depot located between muscle fibers. IMAT content increases with age and BMI and is associated with metabolic and muscle degenerative diseases; however, an understanding of the biological properties of IMAT and its interplay with the surrounding muscle fibers is severely lacking. In recent years, single-cell and nuclei RNA sequencing have provided us with cell type-specific atlases of several human tissues. However, the cellular composition of human IMAT remains largely unexplored due to the inherent challenges of its accessibility from biopsy collection in humans. In addition to the limited amount of tissue collected, the processing of human IMAT is complicated due to its proximity to skeletal muscle tissue and fascia. The lipid-laden nature of the adipocytes makes it incompatible with single-cell isolation. Hence, single nuclei RNA sequencing is optimal for obtaining high-dimensional transcriptomics at single-cell resolution and provides the potential to uncover the biology of this depot, including the exact cellular composition of IMAT. Here, we present a detailed protocol for nuclei isolation and library preparation of frozen human IMAT for single nuclei RNA sequencing. This protocol allows for the profiling of thousands of nuclei using a droplet-based approach, thus providing the capacity to detect rare and low-abundant cell types.

Introduction

Intermuscular adipose tissue (IMAT) is an ectopic adipose depot residing between and around muscle fibers1. As described in detail in a recent review by Goodpaster et al., IMAT can be detected using high-resolution computed tomography (CT) and magnetic resonance imaging (MRI) (Figure 1A,B) and is found around and within muscle fibers throughout the entire body1. The quantity of IMAT varies greatly between individuals and is influenced by BMI, age, sex, race, and sedentariness2,3,4. Moreover, IMAT deposition is commonly seen in pathological conditions associated with muscle degeneration5, and numerous studies have documented increased IMAT mass in individuals with obesity, type 2 diabetes, metabolic syndrome, and insulin resistance6,7,8,9. Nonetheless, the cellular and biological properties of IMAT are only beginning to be unraveled. The limited accessibility and the variation in IMAT locations and content throughout the body have challenged the collection of samples from this unique adipose depot2. Moreover, samples are easily 'contaminated' with skeletal muscle (SM) upon collection, making the separation between the biological contribution from the different tissues difficult to decipher (Figure 1C). To this end, single nuclei RNA sequencing (snRNA-seq), which has gained considerable attention during the last decade, serves as an ideal methodology to allow for the separation of IMAT- and SM-derived gene expression patterns with single-cell resolution. Moreover, nuclei isolation is particularly useful for adipose tissue due to the large lipid-laden adipocytes, which are impossible to dissociate into single-cell suspension without compromising the integrity of the cells. Lastly, this technology holds the potential to discover novel markers of IMAT-specific adipocytes and uncover the composition and presence of different progenitor cell populations, as well as study the variation of the cell composition in pathological and normal conditions.

Figure 1
Figure 1: Images of IMAT. Representative magnetic resonance (MRI) image of IMAT from (A) a middle-aged lean female and (B) a middle-aged male with obesity. Red: subcutaneous adipose tissue, yellow: intermuscular adipose tissue, green: skeletal muscle, blue: bone. Image courtesy of Heather Cornnell, AdventHealth Translational Research Institute. (C) Fresh tissue sample with IMAT (encircled by dashed black line). Image courtesy of Meghan Hopf, AdventHealth Translational Research Institute and Bryan Bergman, University of Colorado. This figure has been modified with permission from Goodpaster et al.1. Please click here to view a larger version of this figure.

A number of studies have been published from the livestock industry investigating the marbling of meat (IMAT in particular) in pigs, chickens, and cattle using single-cell (sc) and snRNA-seq10. These studies have identified several subpopulations of adipocytes and markers of potential progenitor cells of IMAT11,12,13; however, whether these cellular compositions translate to human IMAT is unknown. To our knowledge, only one study has looked into the cellular heterogeneity of human muscle with fatty infiltration, obtained from male patients with hip osteoarthritis, using snRNA-seq14. The investigators reported a small adipocyte population and several fibro-adipogenic progenitor (FAP) subpopulations within the large population of myonuclei14. Our study is the first to develop a method to directly interrogate IMAT manually dissected from human muscle for cellular composition using snRNA-seq.

Importantly, protocols for snRNA-seq need to be customized for the specific tissue studied, as the amount of tissue available and the physical properties of the specific tissue will dictate the optimal processing steps. The tissue yield for IMAT is typically small, often not exceeding 50 mg, even when performing ultrasound-guided biopsies. Hence, proper processing of this scarce tissue is essential. We believe that this protocol will serve as a valuable resource for researchers studying human IMAT.

Protocol

The sample used for this protocol was part of the Study of Muscle, Mobility, and Aging (SOMMA)15, which was approved by the Western IRB-Copernicus Group (WCG) Institutional Review Board and was carried out in accordance with the Declaration of Helsinki. Participants provided written informed consent for their participation in the study. NOTE : This protocol is adapted from a previous protocol using 100 mg of human abdominal subcutaneous adipose tissue on a na…

Representative Results

This workflow was designed to guide the processing of frozen human IMAT samples to obtain gene expression profiles at single nuclei resolution, enabling cell type identification. Here, one representative IMAT sample from a participant in the SOMMA study is presented. The first step of any analysis of snRNA-seq data is to evaluate the quality of the data to identify poor-quality nuclei, which should potentially be removed from the dataset. Importantly, the filtering steps and thresholds should …

Discussion

There are several inherent challenges to working with IMAT. In addition to its limited accessibility, the yield of sample material is often very scarce, and "contamination" of skeletal muscle is almost impossible to avoid. To obtain the best quality sample, one should penetrate the muscle fascia when inserting the biopsy needle (to make sure not to collect subcutaneous adipose tissue) and remove as much muscle tissue as possible by dissecting the sample under a microscope immediately after collection, followed by…

Divulgaciones

The authors have nothing to disclose.

Acknowledgements

The authors would like to acknowledge Bryan Bergman, PhD at University of Colorado for providing the image of the IMAT biopsy in Figure 1C from the MoTrIMAT study (R01AG077956). We are grateful for the Study of Muscle, Mobility and Aging providing the IMAT sample from which data is shown in the representative results section. The National Institute on Aging (NIA) funded the Study of Muscle, Mobility and Aging (SOMMA; R01AG059416) and its ancillary studies SOMMA AT (R01AG066474) and SOMMA Knee OA (R01AG070647). Study infrastructure support was funded in part by NIA Claude D. Pepper Older American Independence Centers at University of Pittsburgh (P30AG024827) and Wake Forest University (P30AG021332) and the Clinical and Translational Science Institutes, funded by the National Center for Advancing Translational Science, at Wake Forest University (UL1 0TR001420).

Materials

0.2 µm corning syringe filters  Millipore Sigma CLS431229
1.7 mL DNA LoBind tubes Eppendorf 22431021 low-bind tubes
10% Tween 20 Bio-Rad 1662404
100x protease inhibitor Thermo Fisher Scientific 78437
10X Magnetic Separator 10X Genomics 230003
10X Vortex Adapter 10X Genomics 330002
15 mL canonical tubes Sarstedt 6,25,54,502
2100 Bioanalyzer  Agilent G2939BA
50 mL conical tubes Sarstedt 6,25,47,254
CellRanger Genomics N/A
Chromium iX accesory kit 10X Genomics PN1000323
Chromium iX Controller 10X Genomics PN1000326
Chromium Next GEM Chip G Single Cell Kit  10X Genomics PN1000127
Chromium Next GEM Single Cell 3'  Kit v 3.1 10X Genomics PN1000269
Chromium Next GEM Single Cell 3' Gel Bead Kit v3.1  10X Genomics PN1000129
Chromium Next GEM Single Cell GEM Kit v3.1 10X Genomics PN1000130
Countess 3 Automated Cell Counter Thermo Fisher Scientific AMQAX2000 Automated cell counter
Countess cell counting chamber slides Thermo Fisher Scientific C10228
DoubletFinder N/A
DPBS (no calcium, no magnesium) Thermo Fisher Scientific 14190144
DTT Thermo Fisher Scientific R0861
Dual Index Kit TT Set A, 96 rxns 10X Genomics PN1000215
Dynabeads MyOne SILANE  10X Genomics PN2000048
Falcon 100 µm Cell strainer Corning Life Science 352360
Falcon 40 µm Cell strainer Corning Life Science 352340
Glycerin (glycerol), 50% (v/v) Aqueous Solution Ricca Chemical Company 3290-32
KCL Thermo Fisher Scientific AM9640G
Library Construction Kit v3.1 10X Genomics PN1000196
MACS SmartStrainers (30µm) Miltenyi Biotec 130-098-458
Mastercycler Nexus Gradient Thermal cycler Eppendorf 6331000017
MgCl2 Ambion AM9530G
Mortar and pestel Health care logistics  14075
NucBlue Live Ready Probes Reagent Thermo Fisher Scientific R37605
Nuclease Free Water (not DEPC treated) Thermo Fisher Scientific AM9930
Probumin Bovine Serum Albumin Fatty Acid Free, Powder Sigma-Aldrich 820024
Qiagen Buffer EB Qiagen 19086
Ribolock RNAse inhibitor Thermo Fisher Scientific EO0382
Seurat N/A
Sucrose Sigma-Aldrich S0389
SUPERasin 20 U/µL Thermo Fisher Scientific AM2695
ThermoMixer C Eppendorf  5382000015
Tissue homogenizer Glass-Col 099C K54
Tris buffer pH 8.0 Thermo Fisher Scientific AM9855G
Triton X-100 Thermo Fisher Scientific AC327372500
UltraPure 0.5M EDTA pH 8.0 Gibco 15575020

Referencias

  1. Goodpaster, B. H., Bergman, B. C., Brennan, A. M., Sparks, L. M. Intermuscular adipose tissue in metabolic disease. Nat Rev Endocrinol. 19 (5), 285-298 (2023).
  2. Sparks, L. M., Goodpaster, B. H., Bergman, B. C. The metabolic significance of intermuscular adipose tissue: Is IMAT a friend or a foe to metabolic health. Diabetes. 70 (11), 2457-2467 (2021).
  3. Gallagher, D., et al. Adipose tissue in muscle: A novel depot similar in size to visceral adipose tissue. Am J Clin Nutr. 81 (4), 903-910 (2005).
  4. Manini, T. M., et al. Reduced physical activity increases intermuscular adipose tissue in healthy young adults. Am J Clin Nutr. 85 (2), 377-384 (2007).
  5. Addison, O., Marcus, R. L., LaStayo, P. C., Ryan, A. S. Intermuscular fat: A review of the consequences and causes. Int J Endocrinol. 2014, 309570 (2014).
  6. Goodpaster, B. H., et al. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med. 165 (7), 777-783 (2005).
  7. Goodpaster, B. H., Thaete, F. L., Kelley, D. E. Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus. Am J Clin Nutr. 71 (4), 885-892 (2000).
  8. Goodpaster, B. H., et al. Association between regional adipose tissue distribution and both type 2 diabetes and impaired glucose tolerance in elderly men. Diabetes Care. 26 (2), 372-379 (2003).
  9. Sachs, S., et al. Intermuscular adipose tissue directly modulates skeletal muscle insulin sensitivity in humans. Am J Physiol Endocrinol Metab. 316 (5), E866-E879 (2019).
  10. Ford, H., Liu, Q., Fu, X., Strieder-Barboza, C. White adipose tissue heterogeneity in the single-cell era: From mice and humans to cattle. Biology (Basel). 12 (10), 1289 (2023).
  11. Wang, L., et al. Single-nucleus and bulk RNA sequencing reveal cellular and transcriptional mechanisms underlying lipid dynamics in high marbled pork. NPJ Sci Food. 7 (1), 23 (2023).
  12. Li, J., et al. Identification of diverse cell populations in skeletal muscles and biomarkers for intramuscular fat of chicken by single-cell RNA sequencing. BMC Genomics. 21 (1), 752 (2020).
  13. Lyu, P., Qi, Y., Tu, Z. J., Jiang, H. Single-cell RNA sequencing reveals heterogeneity of cultured bovine satellite cells. Front Genet. 12, 742077 (2021).
  14. Fitzgerald, G., et al. MME+ fibro-adipogenic progenitors are the dominant adipogenic population during fatty infiltration in human skeletal muscle. Commun Biol. 6 (1), 111 (2023).
  15. Cummings, S. R., et al. The study of muscle, mobility and aging (SOMMA): A unique cohort study about the cellular biology of aging and age-related loss of mobility. J Gerontol A Biol Sci Med Sci. 78 (11), 2083-2093 (2023).
  16. Whytock, K. L., et al. Isolation of nuclei from frozen human subcutaneous adipose tissue for full-length single-nuclei transcriptional profiling. STAR Protoc. 4 (1), 102054 (2023).
  17. 10x Genomics. Chromium Single Cell 3′ Reagent Kits User Guide (v3.1 Chemistry Dual Index), Document Number CG000315 RevE Available from: https://cdn.10xgenomics.com/image/upload/v1668017706/support-documents/CG000315_ChromiumNextGEMSingleCell3-_GeneExpression_v3.1_DualIndex__RevE.pdf (2022)
  18. Heumos, L., et al. Best practices for single-cell analysis across modalities. Nat Rev Genet. 24 (1), 550-572 (2023).
  19. Hao, Y., et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol. 42 (2), 293-304 (2023).
  20. McGinnis, C. S., Murrow, L. M., Gartner, Z. J. DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8 (4), 329-337 (2019).
  21. Yang, S., et al. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol. 21 (2), 57 (2020).
  22. Common considerations for quality control filters for single cell RNA-seq data. 10X Genomics Available from: https://www.10xgenomics.com/analysis-guides/common-considerations-for-quality-control-filters-for-single-cell-rna-seq-data (2022)
  23. Luecken, M. D., Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 15 (6), e8746 (2019).
  24. Emont, M. P., et al. A single-cell atlas of human and mouse white adipose tissue. Nature. 603 (7903), 926-933 (2022).
  25. Hildreth, A. D., et al. Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity. Nat Immunol. 22 (5), 639-653 (2021).
  26. Whytock, K. L., et al. Single cell full-length transcriptome of human subcutaneous adipose tissue reveals unique and heterogeneous cell populations. iScience. 25 (8), 104772 (2022).
  27. Probst, V., et al. Benchmarking full-length transcript single cell mRNA sequencing protocols. BMC Genomics. 23 (1), 860 (2022).
  28. CG000148 Rev A Technical Note – Resolving cell types as a function of read depth and cell number. Technical note. 10X Genomics Available from: https://assets.ctfassets.net/an68im79xiti/6gDArDPBTOg4IIkYEO2Sis/803be2286bb (2018)
  29. Gupta, A., et al. Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages. Genome Res. 32 (2), 242-257 (2022).
  30. Bakken, T. E., et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS One. 13 (12), e0209648 (2018).
  31. Wu, H., Kirita, Y., Donnelly, E. L., Humphreys, B. D. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: Rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol. 30 (1), 23-32 (2019).
  32. Kim, N., Kang, H., Jo, A., Yoo, S. -. A., Lee, H. -. O. Perspectives on single-nucleus RNA sequencing in different cell types and tissues. J Pathol Transl Med. 57 (1), 52-59 (2023).
  33. Avila Cobos, F., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P., De Preter, K. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun. 11 (1), 5650 (2020).
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Elingaard-Larsen, L. O., Whytock, K. L., Divoux, A., Hopf, M., Kershaw, E. E., Justice, J. N., Goodpaster, B. H., Lane, N. E., Sparks, L. M. Isolation of Nuclei from Human Intermuscular Adipose Tissue and Downstream Single-Nuclei RNA Sequencing. J. Vis. Exp. (207), e66784, doi:10.3791/66784 (2024).

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