bioRxivNeurosciencePreprint
Integrated morphological, multi-omics, and functional profiling reveals microglial plasticity driven by AD risk genes
Yingjie ZhangiD, Ai Zhang, Sang Seo, Craig Fredrickson, Min JungiD, Xiwei ShaniD, Lilian Phu, Amber L. Cramer, Carlos Sánchez-PriegoiD, Vineet Vinay KulkarniiD, Christopher M. RoseiD, Casper C. HoogenraadiD, Claire G. JeongiD
Abstract
Microglia, the resident macrophages of the central nervous system, are highly dynamic cells essential for brain homeostasis. While genome-wide association studies (GWAS) strongly implicate microglial dysfunction in Alzheimer's disease (AD), the mechanistic links coordinating their diverse transcriptional, morphological, and functional states remain poorly understood. Using human induced pluripotent stem cell-derived microglia (iMicroglia) and single-cell RNA sequencing, we identified six distinct transcriptional profiles and mapped them to specific morphological phenotypes via targeted immunofluorescence, establishing a link between microglial morphology and molecular identity. Transcriptomic and morphological profiling further demonstrated profound microglial plasticity, revealing distinct, stimulus-specific responses to AD-relevant pathologies, including Tau PFF, amyloid-beta, and apoptotic neurons. To assess how AD risk variants perturb these states, we performed high-efficiency CRISPR-Cas9 ribonucleoprotein (RNP) knockouts of specific AD GWAS genes. Bulk RNA-seq profiling revealed extensive transcriptional remodeling following genetic perturbation. Crucially, we show that depletion of these AD risk genes disrupts baseline morpho-transcriptomic coupling and fundamentally alters microglial phagocytic capacity. Together, this study reveals how AD GWAS genes may drive microglia into dysfunctional states characterized by altered morphology, distinct multi-omic signatures, and impaired functions.
- organism: human
- assay: scRNA-seq
- assay: imaging
- research focus: cell type annotation
- research focus: differential expression
- research focus: perturbation response prediction
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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