Deep learning-guided design of cell type-specific AAV promoters
Year: 2026
Type: preprint
Abstract: Precise cell type targeting is critical for both clinical and experimental applications of adeno-associated viral (AAV) vectors, yet engineering vectors with cell type-specific activity remains a challenge. Here, we compared three strategies leveraging single-cell chromatin accessibility data to design cell type-specific AAV promoters, including a deep learning-based method to generate de novo regulatory sequences. When applied to target retinal ganglion cells or horizontal cells in mouse retina, deep learning-guided design consistently outperformed rational approaches, yielding synthetic promoters with stronger and more specific expression in vivo. Synthetic AAV promoters supported diverse transgenes, enabling the recording and ablation of targeted cells. Promoter activity was also maintained in human retinal organoids, suggesting that deep learning-designed sequences may be suitable for translation. Our findings highlight the potential of deep learning to synthesize cell type-specific AAV promoters and establish a versatile platform for cell type targeting with broad implications for gene therapy and basic research.
Authors: Sean K Wang, Sean K Wang, Boxiong Deng, Surag Nair, Xiaobai Ren, Jiaying Li, Jenna Tijerina, Praveen Prakhar, Ziming Luo, Chelsea Nnebe, Samuel H Kim, Yimin Zhou, Sahil H Shah, Alexander Davis, Rohan Mahajan, Yilin Qiao, Yuxi Zhou, Jenny Zhang, Yunlu Xue, Jeffrey L Goldberg, Wei Wei, Anshul Kundaje, Howard Y Chang, Sui Wang, Sui Wang
Institutions: Stanford University, University of Chicago
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7124212842