Nature GeneticsGenetic & Genomic MedicinePeer-reviewed journal article
Plasticity of Human Microglia and Brain Perivascular Macrophages in Aging and Alzheimer's Disease
Donghoon LeeiD, James M. VicariiD, Christian PorrasiD, Colleen SpenceriD, Milos PjaniciD, Xinyi WangiD, Seon KinrotiD, Philipp WeileriD, Roman KosoyiD, Jaroslav M. BendliD, N. M. PrashantiD, Konstantina PsychogyiouiD, Periklis MalakatesiD, Evelyn HenniganiD, Jennifer Monteiro FortesiD, Shiwei ZhengiD, Karen TherrieniD, Deepika Dayal MathuriD, Steven P. KleopoulosiD, Zhiping ShaoiD, Stathis ArgyriouiD, Marcela AlviaiD, Clara CaseyiD, Aram HongiD, Kristin G. BeaumontiD, , Panos RoussosiD
Abstract
The complex roles of myeloid cells, including microglia and perivascular macrophages, are central to the neurobiology of Alzheimers disease (AD), yet they remain incompletely understood. Here, we profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 unique donors covering the human lifespan and varying degrees of AD neuropathology. We delineated 13 transcriptionally distinct myeloid subtypes organized into 6 subclasses and identified AD-associated adaptive changes in myeloid cells over aging and disease progression. The GPNMB subtype, linked to phagocytosis, increased significantly with AD burden and correlated with polygenic AD risk scores. By organizing AD-risk genes into a regulatory hierarchy, we identified and validated MITF as an upstream transcriptional activator of GPNMB, critical for maintaining phagocytosis. Through cell-to-cell interaction networks, we prioritized APOE-SORL1 and APOE-TREM2 ligand-receptor pairs, associated with AD progression. In both human and mouse models, TREM2 deficiency disrupted GPNMB expansion and reduced phagocytic function, suggesting that GPNMBs role in neuroprotection was TREM2-dependent. Our findings clarify myeloid subtypes implicated in aging and AD, advancing the mechanistic understanding of their role in AD and aiding therapeutic discovery.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 22
- FWCI
- 30.31
- Field percentile
- top 1%
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