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Record W3203664141 · doi:10.1113/jp282344

What's up with WAT: attempting to mimic adipose tissue <i>in vitro</i>

2021· letter· en· W3203664141 on OpenAlexaff
Logan K. Townsend, Kyle D. Medak, Alyssa J. Weber

Bibliographic record

VenueThe Journal of Physiology · 2021
Typeletter
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of GuelphMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsAdipose tissueIn vitroWhite adipose tissueCell biologyChemistryBiologyEndocrinologyBiochemistry

Abstract

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White adipose tissue (WAT) has remarkable flexibility to expand and accommodate the storage of energy. Currently most humans live an obesogenic lifestyle that challenges the expansion and storage capacity of WAT forcing other tissues to store extra lipids. However, these tissues, including liver and skeletal muscle, are not suited for excessive long-term fat storage and this quickly leads to cellular dysfunction. Thus, proper WAT expansion is essential for maintaining healthy systemic metabolic health (Crewe et al. 2017). Within WAT there are mature adipocytes, the main lipid storing cells; blood vessels; and the stromal vascular fraction (SVF), which contains pre-adipocytes, endothelial cells, fibroblasts and numerous immune cells – all of which are held together by a dense extracellular matrix. WAT expansion requires adaptations in all these cell types making it difficult to imitate in vitro, which has limited mechanistic WAT studies. In a recent article published in The Journal of Physiology, Ioannidou and colleagues (2022) describe a new method for in vitro differentiation of three-dimensional vascularized white adipocytes from commercially available human SVF to yield a more physiological adipocyte model of human tissue. The authors describe a way to mimic obesity-associated adipocyte hypertrophy and subsequent cellular dysfunction. This new model is called human unilocular vascularized adipocyte spheroids (HUVAS) and represents a major advancement in the ability to perform mechanistic in vitro analysis of human WAT in health and obesity (Ioannidou et al. 2022). Classic white adipocytes are characterized by a single large lipid droplet (i.e. they are unilocular) that takes up the vast majority of the adipocyte. Whereas traditional cell culture protocols are able to produce lipid-filled adipocytes, they tend to be small and have numerous lipid droplets within each adipocyte (i.e. they are multilocular). Not only do HUVAS possess larger adipocytes, but they are also predominantly unilocular. This is important since adipocyte size and locularity affect several key characteristics of adipocyte function, including adipokine secretion and insulin sensitivity (Stenkula & Erlanson-Albertsson, 2018; Townsend et al. 2019). Indeed, HUVAS demonstrated greater secretion of leptin and adiponectin, classic WAT adipokines, and mRNA expression of WAT markers compared to other in vitro WAT methods, reflecting conserved features of WAT biology. Given the importance of vasculature and the extracellular matrix to adipocyte development and function (Gealekman et al. 2011; Crewe et al. 2017), the authors started by using endothelial cell medium to promote vascular sprouting from human SVF samples embedded in Matrigel to mimic the extracellular matrix prior to stimulating adipocyte differentiation. Vasculature is thought to be permissive in development of large unilocular adipocytes in HUVAS by increasing diffusion of nutrients and oxygen. The resulting three-dimensional cell clumps (i.e. spheroids) were then supplemented with adipocyte differentiation medium to promote adipogenesis, in which spheroid and adipocyte size rapidly expanded. Adipogenesis occurred along the vasculature, and prevention of vascular sprouting significantly reduced spheroid growth. Traditional two-dimensional cell culture and spheroids grown without Matrigel did not result in vasculature development and produced much smaller spheroids and adipocytes (Ioannidou et al. 2022). Taken together, the HUVAS method developed in the current report (Ioannidou et al. 2022) improves on many key features of WAT biology, including vasculature, extra cellular matrix, large unilocular adipocytes and adipokine secretion. WAT is at the forefront of the obesity crisis, so ways of accurately mimicking WAT hypertrophy are needed to understand the mechanistic changes associated with this state. In this regard, Ioannidou et al. (2022) show that lipids can be used to produce adipocyte and lipid droplet hypertrophy in HUVAS. To do this, the authors used Intralipid, which is a mixture of various fatty acids (mono- and poly-unsaturated and saturated) and cholesterol as distinct from most studies, which use a single, usually saturated, fatty acid, which is less physiological and can have unintended inflammatory consequences. By supplementing HUVAS with a low-dose (10 mg/dl) of Intralipid, Ioannidou and colleagues increased spheroid size by 25–50% accompanied by a more subtle (∼10%) increase in lipid droplet size. In humans, adipocytes range from ∼20 μm in lean WAT to reach ∼300 μm in obesity (Stenkula & Erlanson-Albertsson, 2018). In HUVAS, however, lipid-induced hypertrophy was much more modest, albeit significantly better than earlier methods. Along these lines, adipocyte differentiation was less efficient in the core of spheroids compared to the periphery, possibly suggesting limitations in vascularity and nutrient diffusion may still exist. Thus, future work will be needed to determine whether hypertrophy can be further augmented in HUVAS to match that seen in vivo, perhaps using a higher dose of lipids or insulin to mimic obesity-associated hyperinsulinaemia. Chronic adipocyte hypertrophy drives many local changes in WAT (Crewe et al. 2017). Inflammation is one of the early responses to hypertrophy and results partly from the accumulation and activation of macrophages, which secrete inflammatory cytokines like interleukin-6 and monocyte chemotactic protein-1. These inflammatory responses were evident in hypertrophied HUVAS (Ioannidou et al. 2022), but the macrophage response is unclear and would be interesting to explore since SVF used to differentiate HUVAS would contain immune cells. Another effect of obesity-associated WAT inflammation is that basal lipolysis is increased but resistant to stimulated lipolysis (Crewe et al. 2017; Stenkula & Erlanson-Albertsson, 2018). Importantly, both increased basal lipolysis and impaired stimulated lipolysis were apparent in hypertrophied HUVAS (Ioannidou et al. 2022). Additionally, WAT insulin resistance hinders the ability of insulin to suppress lipolysis and stimulate fatty acid and glucose uptake, ultimately contributing to systemic metabolic dysfunction. Hypertrophied HUVAS appear to become insulin resistant based on insulin receptor phosphorylation, but functional markers of insulin sensitivity, such as lipolysis or fatty acid/glucose uptake, would help to give a more comprehensive picture of insulin resistance in this model. Regardless, in response to lipids, HUVAS demonstrate many of the key characteristics of obese WAT, including hypertrophy, unilocularity, altered adipokine secretion, inflammation, impaired lipolysis and insulin resistance. The plasticity of WAT is dependent on sufficient vascularity and angiogenic potential (Gealekman et al. 2011; Crewe et al. 2017). Acute adipocyte hypertrophy normally leads to local hypoxia which promotes angiogenesis to improve oxygen supply to adipocytes. However, during obesity, angiogenic potential is exceeded leading to persistent hypoxia and WAT dysfunction (Gealekman et al. 2011; Crewe et al. 2017). Given this, it will be interesting to see how markers of hypoxia, angiogenesis and vascularity are affected by lipid-induced hypertrophy in HUVAS. Despite the necessity of sufficient vascularization to healthy WAT expansion, the mechanistic cause of disordered angiogenesis in obesity remains to be uncovered, but the HUVAS model may provide a great opportunity to explore these questions. Another important consideration is that distinct WAT depots have different characteristics (Gealekman et al. 2011). Visceral depots, those surrounding internal organs, tend to be more lipolytic, inflammatory and have larger adipocytes compared to subcutaneous WAT depots, which are directly beneath the skin. Subcutaneous adipose tissue may also have greater angiogenic potential compared to visceral adipose tissue (Gealekman et al. 2011). In the current report, the SVF was from subcutaneous human WAT, which could contribute to some of the current findings (Ioannidou et al. 2022). For instance, this may explain the modest hypertrophy and inflammatory response to lipids. Considering the distinct characteristics of subcutaneous and visceral WAT, it will be important to compare HUVAS derived from both depots. Similarly, beige/brite and brown adipose tissue are distinct forms of adipose tissue with very different characteristics (Townsend et al. 2019), and it will need to be determined whether HUVAS can be used to mimic these forms of adipose tissue. Indeed, brown adipose tissue is highly vascularized so HUVAS may be ideal to explore its physiology. In the current report, Ioannidou and colleagues (2022) describe a novel means of producing three-dimensional WAT spheroids from commercially available human SVF. The authors show that HUVAS are vascularized, develop large unilocular adipocytes, and have classic endocrine WAT characteristics. Moreover, the addition of lipids can induce adipocyte hypertrophy with concomitant inflammation, altered endocrine profile, insulin resistance and disturbed lipolysis. Taken together, Ioannidou and colleagues have developed and characterized a more physiological in vitro method to explore mechanisms of WAT function and dysfunction. 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. None declared. All authors have read and approved the final version of this manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. L.K.T. is supported by a CIHR Post-Doctoral Award and Michael De Groote Fellowship Award in Basic Biomedical Science. K.M. is supported by a NSERC Doctoral Award. A.W. is supported by an NSERC Masters Award.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.280
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
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