Back

Massively parallel in vivo Perturb-seq reveals cell type-specific transcriptional networks in cortical development

Zheng, X.; Wu, B.; Liu, Y.; Simmons, S. K.; Kim, K.; Clarke, G. S.; Ashiq, A.; Park, J.; Wang, Z.; Tong, L.; Wang, Q.; Xu, X.; Levin, J. Z.; Jin, X.

2023-09-18 neuroscience
10.1101/2023.09.18.558077 bioRxiv
Show abstract

Systematic analysis of gene function across diverse cell types in vivo is hindered by two challenges: obtaining sufficient cells from live tissues and accurately identifying each cells perturbation in high-throughput single-cell assays. Leveraging AAVs versatile cell type tropism and high labeling capacity, we expanded the resolution and scale of in vivo CRISPR screens: allowing phenotypic analysis at single-cell resolution across a multitude of cell types in the embryonic brain, adult brain, and peripheral nervous system. We undertook extensive tests of 86 AAV serotypes, combined with a transposon system, to substantially amplify labeling and accelerate in vivo gene delivery from weeks to days. Using this platform, we performed an in utero genetic screen as proof-of-principle and identified pleiotropic regulatory networks of Foxg1 in cortical development, including Layer 6 corticothalamic neurons where it tightly controls distinct networks essential for cell fate specification. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% (mediated by lentivirus), and achieve analysis of over 30,000 cells in one experiment, thus enabling massively parallel in vivo Perturb-seq. Compatible with various perturbation techniques (CRISPRa/i) and phenotypic measurements (single-cell or spatial multi-omics), our platform presents a flexible, modular approach to interrogate gene function across diverse cell types in vivo, connecting gene variants to their causal functions.

Matching journals

The top 3 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.