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Manipulating gene expression in the host genome with high precision is crucial for controlling cellular function and behavior. Here, we present a precise, non-invasive, and tunable strategy for controlling the expression of multiple endogenous genes both in vitro and in vivo, utilizing ultrasound as the stimulus. By engineering a hyper-efficient dCas12a and effector under a heat shock promoter, we demonstrate a system that can be inducibly activated through thermal energy produced by ultrasound absorption. This system allows versatile thermal induction of gene activation or base editing across cell types, including primary T cells, and enables multiplexed gene activation using a single guide RNA array. In mouse models, localized temperature elevation guided by high-intensity focused ultrasound effectively triggers reporter gene expression in implanted cells. Our work underscores the potential of ultrasound as a clinically viable approach to enhance cell and gene-based therapies via precision genome and epigenome engineering.

 

The targeted insertion and stable expression of a large genetic payload in primary human cells demands methods that are robust, efficient and easy to implement. Large payload insertion via retroviruses is typically semi-random and hindered by transgene silencing. Leveraging homology-directed repair to place payloads under the control of endogenous essential genes can overcome silencing but often results in low knock-in efficiencies and cytotoxicity. Here we report a method for the knock-in and stable expression of a large payload and for the simultaneous knock-in of two genes at two endogenous loci. The method, which we named CLIP (for 'CRISPR for long-fragment integration via pseudovirus'), leverages an integrase-deficient lentivirus encoding a payload flanked by homology arms and 'cut sites' to insert the payload upstream and in-frame of an endogenous essential gene, followed by the delivery of a CRISPR-associated ribonucleoprotein complex via electroporation. We show that CLIP enables the efficient insertion and stable expression of large payloads and of two difficult-to-express viral antigens in primary T cells at low cytotoxicity. CLIP offers a scalable and efficient method for manufacturing engineered primary cells. 

Recent advances in deep neural networks have achieved outstanding success in natural language processing tasks. Interpretation methods that provide insight into the decision-making process of these models have received an influx of research attention because of the success and the black-box nature of the deep text classification models. Evaluation of these methods has been based on changes in classification accuracy or prediction confidence when removing important words identified by these methods. There are no measurements of the actual difference between the predicted important words and humans’ interpretation of ground truth because of the lack of interpretation ground truth. A large publicly available interpretation ground truth has the potential to advance the development of interpretation methods. Manual labeling important words for each document to create a large interpretation ground truth is very time-consuming. This paper presents (1) IDC, a new benchmark for quantitative evaluation of interpretation methods for deep text classification models, and (2) evaluation of six interpretation methods using the benchmark. The IDC benchmark consists of: (1) Three methods that generate three pseudo-interpretation ground truth datasets. (2) Three performance metrics: interpretation recall, interpretation precision, and Cohen’s kappa inter-agreement. Findings: IDC-generated interpretation ground truth agrees with human annotators on sampled movie reviews. IDC identifies Layer-wise Relevance Propagation and the gradient-by-input methods as the winning interpretation methods in this study.

 

Machine learning and multiplexed screening elucidates enhancer networks regulating gene expression in health and disease states.