Tissue-specific loss-of-function (LOF) analysis is essential for characterizing gene function. Here, we present a simple, yet highly efficient, clustered regularly interspaced short palindromic repeats (CRISPR)-mediated tissue-restricted mutagenesis (CRISPR-TRiM) method for ablating gene function in Drosophila. This binary system consists of a tissue-specific Cas9 and a ubiquitously expressed multi-guide RNA (gRNA) transgene. We describe convenient toolkits for making enhancer-driven Cas9 lines and multi-gRNAs that are optimized for mutagenizing somatic cells. We demonstrate that insertions or deletions in coding sequences more reliably cause somatic mutations than DNA excisions induced by two gRNAs. We further show that enhancer-driven Cas9 is less cytotoxic yet results in more complete LOF than Gal4-driven Cas9 in larval sensory neurons. Finally, CRISPR-TRiM efficiently unmasks redundant soluble N-ethylmaleimide-sensitive factor attachment protein receptor gene functions in neurons and epidermal cells. Importantly, Cas9 transgenes expressed at different times in the neuronal lineage reveal the extent to which gene products persist in cells after tissue-specific gene knockout. These CRISPR tools can be applied to analyze tissue-specific gene function in many biological processes.

Phenotype-based compound screening has advantages over target-based drug discovery, but is unscalable and lacks understanding of mechanism. Chemical-induced gene expression profile provides a mechanistic signature of phenotypic response. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput. Few methods can perform phenotype-based de novo chemical compound screening. Here, we propose a mechanism-driven neural network-based method DeepCE, which utilizes graph neural network and multi-head attention mechanism to model chemical substructure-gene and gene-gene associations, for predicting the differential gene expression profile perturbed by de novo chemicals. Moreover, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves superior performances to state-of-the-art methods. The effectiveness of gene expression profiles generated from DeepCE is further supported by comparing them with observed data for downstream classification tasks. To demonstrate the value of DeepCE, we apply it to drug repurposing of COVID-19, and generate novel lead compounds consistent with clinical evidence. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data and screening novel chemicals for the modulation of a systemic response to disease.

R47H- TREM2 mutation enhances AKT signaling in human AD microglia and mediates proinflammatory and synaptic toxicity in a tauopathy mouse model.