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Cell type-specific transgene expression in mice has broad utility in biomedical research. We developed a versatile system for in vivo transgene delivery using adeno-associated virus (AAV). Efficient and tissue-specific transgene expression is achieved by regulating the expression of the gene encoding the AAV receptor, thereby precisely targeting AAV to the cell type of interest.
Our study introduces conditional autoencoder for multiplexed pixel analysis (CAMPA), a deep-learning framework that uses highly multiplexed imaging to identify consistent subcellular landmarks across heterogeneous cell populations and experimental perturbations. Generating interpretable cellular phenotypes revealed links between subcellular organization and perturbations of RNA production, RNA processing and cell size.
This paper proposes two new anisotropy metrics—the Fourier shell occupancy and the Bingham test—that can be used to understand the quality of cryogenic electron microscopy maps.
CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis) learns representations of molecular pixel profiles from multiplexed images that can be clustered to quantify subcellular landmarks and capture interpretable cellular phenotypes.
SIMBA learns a co-embedding space of single cells and multiple features such as genes, chromatin-accessible regions and transcription-factor-binding sequences, boosting the performances of various analyses of cellular diversity and regulation.
MISAR-seq combines spatial-ATAC-seq and RNA-seq for spatial profiling of both chromatin accessibility and gene expression, as demonstrated in the developing mouse brain.
The miniature RNA-guided endonuclease IscB and its ωRNA were engineered for efficient gene editing in mammalian cells. Fusions of ‘enIscB’ to T5 exonuclease and cytosine or adenosine deaminase yield versatile tools for genome engineering.
Nano-DMS-MaP focuses in on the structures of individual RNA isoforms, enabling direct examination of the structural diversity of different RNAs inside cells.
