Communications BiologyImmunologyPeer-reviewed journal article
Identification of conserved and tissue-restricted transcriptional profiles for lipid associated macrophages (LAMs)
Yingzheng XuiD, Hannah HillmaniD, Michael Chang, Fanta BarrowiD, Stoyan IvanoviD, Xavier S. ReveloiD, Jesse W. WilliamsiD
Abstract
Macrophages are essential immune cells present in all tissues, and are vital for maintaining tissue homeostasis, immune surveillance, and immune responses. Considerable efforts have identified shared and tissue-specific gene programs for macrophages across organs during homeostasis. This information has dramatically enhanced our understanding of tissue-restricted macrophage programming and function. However, few studies have addressed the overlapping and tissue-specific responses of macrophage subsets following inflammatory responses. One subset of macrophages that has been observed across several studies, lipid-associated macrophages (LAMs), have gained interest due to their unique role in lipid metabolism and potential as a therapeutic target. LAMs have been associated with regulating disease outcomes in metabolically related disorders including atherosclerosis, obesity, and nonalcoholic fatty liver disease (NAFLD). In this study, we utilized single-cell RNA sequencing (scRNAseq) data to profile LAMs across multiple tissues and sterile inflammatory conditions in mice and humans. Integration of data from various disease models revealed that LAMs share a set of conserved transcriptional profiles, including Trem2 and Lpl, but also identified key sets of tissue-specific LAM gene programs. Importantly, the shared LAM markers were highly conserved with human LAM populations that also emerge in chronic inflammatory settings. Overall, this analysis provides a detailed transcriptional landscape of tissue-restricted and shared LAM gene programs and offers insights into their roles in metabolic and chronic inflammatory diseases. These data may help instruct appropriate targets for broad or tissue-restricted therapeutic interventions to modulate LAM populations in disease.
- organism: mouse
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 32
- FWCI
- 10.78
- Field percentile
- top 1%
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