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Stress-Responsive Transcriptomic Signatures in Human iPSC-Derived Microglia Reveal Links to Alzheimer's Disease Risk
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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