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A lipid-associated macrophage lineage rewires the spatial landscape of adipose tissue in early obesity
Cooper M. Stansbury, Gabrielle A. Dotson, Harrison Pugh, Alnawaz Rehemtulla, Indika Rajapakse, Lindsey A. Muir
Cooper M. Stansbury, Gabrielle A. Dotson, Harrison Pugh, Alnawaz Rehemtulla, Indika Rajapakse, Lindsey A. Muir
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Resource and Technical Advance Metabolism

A lipid-associated macrophage lineage rewires the spatial landscape of adipose tissue in early obesity

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Abstract

Adipose tissue macrophage (ATM) infiltration is associated with adipose tissue dysfunction and insulin resistance in mice and humans. Recent single-cell data highlight increased ATM heterogeneity in obesity but do not provide a spatial context for ATM phenotype dynamics. We integrated single-cell RNA-Seq, spatial transcriptomics, and imaging of murine adipose tissue in a time course study of diet-induced obesity. Overall, proinflammatory immune cells were predominant in early obesity, whereas nonresident antiinflammatory ATMs predominated in chronic obesity. A subset of these antiinflammatory ATMs were transcriptomically intermediate between monocytes and mature lipid-associated macrophages (LAMs) and were consistent with a LAM precursor (pre-LAM). Pre-LAMs were spatially associated with early obesity crown-like structures (CLSs), which indicate adipose tissue dysfunction. Spatial data showed colocalization of ligand-receptor transcripts related to lipid signaling among monocytes, pre-LAMs, and LAMs, including Apoe, Lrp1, Lpl, and App. Pre-LAM expression of these ligands in early obesity suggested signaling to LAMs in the CLS microenvironment. Our results refine understanding of ATM diversity and provide insight into the dynamics of the LAM lineage during development of metabolic disease.

Authors

Cooper M. Stansbury, Gabrielle A. Dotson, Harrison Pugh, Alnawaz Rehemtulla, Indika Rajapakse, Lindsey A. Muir

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Figure 6

Histological quantification of CLSs.

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Histological quantification of CLSs.
(A) H&E-stained images captured...
(A) H&E-stained images captured during spatial transcriptomics library preparation (top) and segmentation results quantifying CLSs (bottom). (B) Segmentation class label proportions of 100 randomly sampled 500 μm regions from each diet condition. (C) Adipocyte area from images regions in B. (D) Spot correlation between myeloid cell-type proportions and segmentation results from a 150 μm region around each capture spot. Spots with read counts below the 0.05 quantile were removed. *P ≤ 0.01 by Pearson correlation. (E) Spot importance in global cell-type networks (eigenvector centrality) in HFD feeding conditions. Eigenvector centrality highlights regions of densely localized cells in the tissue. (F) CLShi segmentation results in 150 μm regions around each capture spot at 8 weeks (8w) and 14 weeks (14w). (G) CLS alignment represents the Pearson correlation between CLShi segmentation results and cell type–specific eigenvector centrality for each diet condition. *P ≤ 0.01 by Pearson correlation. Spots with read counts below the 0.05 quantile were removed.

Copyright © 2026 American Society for Clinical Investigation
ISSN 2379-3708

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