Usage Information
Abstract
Increased adipose tissue macrophages (ATMs) correlate with metabolic dysfunction in humans and are causal in development of insulin resistance in mice. Recent bulk and single-cell transcriptomics studies reveal a wide spectrum of gene expression signatures possible for macrophages that depends on context, but the signatures of human ATM subtypes are not well defined in obesity and diabetes. We profiled 3 prominent ATM subtypes from human adipose tissue in obesity and determined their relationship to type 2 diabetes. Visceral adipose tissue (VAT) and s.c. adipose tissue (SAT) samples were collected from diabetic and nondiabetic obese participants to evaluate cellular content and gene expression. VAT CD206+CD11c− ATMs were increased in diabetic participants, were scavenger receptor–rich with low intracellular lipids, secreted proinflammatory cytokines, and diverged significantly from 2 CD11c+ ATM subtypes, which were lipid-laden, were lipid antigen presenting, and overlapped with monocyte signatures. Furthermore, diabetic VAT was enriched for CD206+CD11c− ATM and inflammatory signatures, scavenger receptors, and MHC II antigen presentation genes. VAT immunostaining found CD206+CD11c– ATMs concentrated in vascularized lymphoid clusters adjacent to CD206–CD11c+ ATMs, while CD206+CD11c+ were distributed between adipocytes. Our results show ATM subtype–specific profiles that uniquely contribute to the phenotypic variation in obesity.
Authors
Lindsey A. Muir, Kae Won Cho, Lynn M. Geletka, Nicki A. Baker, Carmen G. Flesher, Anne P. Ehlers, Niko Kaciroti, Stephen Lindsly, Scott Ronquist, Indika Rajapakse, Robert W. O’Rourke, Carey N. Lumeng
Usage data is cumulative from May 2025 through May 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 1,701 | 210 |
| 216 | 53 | |
| Figure | 436 | 0 |
| Table | 165 | 0 |
| Supplemental data | 86 | 12 |
| Citation downloads | 146 | 0 |
| Totals | 2,750 | 275 |
| Total Views | 3,025 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
