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Stress-Responsive Transcriptomic Signatures in Human iPSC-Derived Microglia Reveal Links to Alzheimer's Disease Risk
Abstract:
Cellular stress responses are essential for maintaining homeostasis in the face of environmental or internal challenges. In the central nervous system, microglia serve as key stress sensors and immune responders, shaping neuroinflammatory processes and disease progression. However, the molecular programs engaged by distinct stressors and their impact on microglial viability remain incompletely understood. In this study, we used human induced pluripotent stem cell-derived microglia-like cells to investigate stress responses to amyloid beta (Aβ), a chronic Alzheimer's disease-related stressor, and lipopolysaccharide (LPS), a classical acute inflammatory stimulus. Using single-cell RNA sequencing, we mapped the transcriptional programs activated by each condition and benchmarked these states against reference microglial datasets from mouse and human brains. In parallel, we performed a pooled CRISPR interference screen targeting Alzheimer's disease-associated microglial genes to identify genetic determinants of microglial survival. We found that Aβ and LPS elicit partially overlapping but distinct transcriptional responses. Aβ induced more focused and disease-associated gene expression changes, while LPS triggered broad inflammatory activation and stronger cell death signatures. A subset of genes activated by stress overlapped with Alzheimer's disease risk genes and with hits from the survival screen, suggesting that disease-associated microglial genes may contribute to stress adaptation and cellular fitness. These results demonstrate that iPSC-derived microglia-like cells can recapitulate in vivo-like stress-responsive states and offer a tractable platform to investigate genetic and environmental influences on microglial behavior. Together, our findings reveal transcriptional programs that link stress sensing, survival regulation, and Alzheimer's disease-associated gene networks, providing a foundation for future efforts to enhance microglial resilience in neurodegenerative disease contexts.
Insights
Microglia, key brain immune cells, respond differently to Alzheimer's amyloid beta and inflammatory LPS. Understanding these stress responses in human microglia models can improve neurodegenerative disease treatments.
Area of Science:
- Neuroscience
- Immunology
- Genetics
Background:
- Cellular stress responses are vital for homeostasis, particularly in the central nervous system where microglia act as immune responders.
- The specific molecular pathways microglia engage under different stress conditions and their effect on cell survival are not fully understood.
Purpose of the Study:
- To investigate stress responses in human induced pluripotent stem cell-derived microglia-like cells (iPSC-microglia) when exposed to amyloid beta (Aβ) and lipopolysaccharide (LPS).
- To identify genetic factors influencing microglial survival during stress using a CRISPR interference screen targeting Alzheimer's disease-associated genes.
Main Methods:
- Utilized single-cell RNA sequencing to map transcriptional programs in iPSC-microglia under Aβ and LPS stress.
- Benchmarked these transcriptional states against existing mouse and human microglial datasets.
- Conducted a pooled CRISPR interference screen to identify genes affecting microglial survival.
Main Results:
- Amyloid beta and LPS induced distinct yet partially overlapping transcriptional responses in iPSC-microglia.
- LPS triggered broader inflammatory activation and higher cell death rates compared to Aβ.
- A subset of stress-activated genes overlapped with Alzheimer's disease risk genes and genes identified in the survival screen.
Conclusions:
- Human iPSC-derived microglia-like cells effectively model in vivo-like stress responses.
- Disease-associated microglial genes may play a role in stress adaptation and cellular fitness.
- Findings provide a foundation for enhancing microglial resilience in neurodegenerative diseases by linking stress sensing, survival, and disease-associated gene networks.
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