Back

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

Saunders, D.; Sultan, F.; Vialle, R. A.; Kearns, N. A.; Ng, B.; Clark, E. M.; Vyas, H.; Tissera, S. D.; Xu, J.; Bennett, D. A.; Wang, Y.

2025-09-03 neuroscience
10.1101/2025.08.30.673269 bioRxiv
Show abstract

Commercially available human iPSC lines with inducible CRISPR interference (CRISPRi) systems offer scalable platforms for gene function studies. One widely available line, the AICS-0090 dCas9-KRAB iPSC line developed by the Allen Institute, has been extensively validated for genomic integrity and stem cell potency. However, its utility in modeling specialized immune cell types such as microglia -- and in assessing their functional responses to disease-relevant stimuli -- has not been fully established. Here, we evaluated the AICS-0090 line for its ability to differentiate into microglia-like cells, support efficient gene knockdown, and respond to environmental stressors. We assessed its differentiation capacity by qPCR, flow cytometry, and immunocytochemistry, confirming reproducible expression of microglial surface markers at early and late timepoints. Gene knockdown efficiency was validated both at the single-gene level and in pooled CRISPRi screens. Focusing on functional responsiveness, we exposed the microglia-like cells to two distinct stimuli: amyloid-{beta} (A{beta}), a disease-associated trigger in Alzheimers disease, and lipopolysaccharide (LPS), a classical inflammatory signal. Transcriptomic and functional analyses revealed stimulus-specific responses: A{beta} induced limited activation of stress and inflammation pathways, whereas LPS elicited broader transcriptional reprogramming and cytokine release. Signatures from both conditions partially overlapped with ex vivo human and mouse microglial states. Together, these findings support the use of the AICS-0090 dCas9-KRAB iPSC-derived microglia-like cells as a flexible and tractable model for gene function interrogation under defined inflammatory contexts, with potential for future applications in neuroimmune modeling and perturbation-based screening.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.