Details

Autor(en) / Beteiligte

• Lin, Cheng-Hui
• Sun, Yue
• Chan, Candace S.Y.
• Wu, Man-Ru
• Gu, Lei
• Davis, Alexander E.
• Gu, Baokun
• Zhang, Wenlin
• Tanasa, Bogdan
• Zhong, Lei R.
• Emerson, Mark M.
• Chen, Lu
• Ding, Jun B.
• Wang, Sui

Titel

Identification of cis-regulatory modules for adeno-associated virus-based cell-type-specific targeting in the retina and brain

Ist Teil von

• The Journal of biological chemistry, 2022-04, Vol.298 (4), p.101674, Article 101674

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• Adeno-associated viruses (AAVs) targeting specific cell types are powerful tools for studying distinct cell types in the central nervous system (CNS). Cis-regulatory modules (CRMs), e.g., enhancers, are highly cell-type-specific and can be integrated into AAVs to render cell type specificity. Chromatin accessibility has been commonly used to nominate CRMs, which have then been incorporated into AAVs and tested for cell type specificity in the CNS. However, chromatin accessibility data alone cannot accurately annotate active CRMs, as many chromatin-accessible CRMs are not active and fail to drive gene expression in vivo. Using available large-scale datasets on chromatin accessibility, such as those published by the ENCODE project, here we explored strategies to increase efficiency in identifying active CRMs for AAV-based cell-type-specific labeling and manipulation. We found that prescreening of chromatin-accessible putative CRMs based on the density of cell-type-specific transcription factor binding sites (TFBSs) can significantly increase efficiency in identifying active CRMs. In addition, generation of synthetic CRMs by stitching chromatin-accessible regions flanking cell-type-specific genes can render cell type specificity in many cases. Using these straightforward strategies, we generated AAVs that can target the extensively studied interneuron and glial cell types in the retina and brain. Both strategies utilize available genomic datasets and can be employed to generate AAVs targeting specific cell types in CNS without conducting comprehensive screening and sequencing experiments, making a step forward in cell-type-specific research.