标题： 通过深度学习预测细胞特异性基因表达谱和敲除影响 显示英文标题

标题： Predicting cell-specific gene expression profile and knockout impact through deep learning

摘要： 基因表达数据对于理解基因在生物系统中的调控和相互作用至关重要，它为疾病通路和潜在治疗靶点提供了见解。 基因敲除已被证明是分子生物学中的基本技术，允许研究特定基因在生物体及其特定细胞类型中的功能。 然而，在均匀环境中的单细胞转录数据中，基因表达模式具有相当大的异质性，代表了不同的细胞状态，这导致了细胞类型和细胞特异性基因敲除的影响。 目前仍缺乏能够预测单细胞分辨率敲除影响的计算方法。 在此，我们提出了一种数据驱动的框架，用于学习从基因组合中衍生出的基因表达谱之间的映射，从而准确预测任何细胞在敲除后的扰动表达谱，而无需依赖先前的扰动数据。 我们使用从基因调控动力学模型生成的合成数据、两个小鼠敲除单细胞数据集以及高通量体外CRISPRi Perturb-seq数据对我们的框架进行了系统验证。 我们的结果表明，该框架可以准确预测单细胞水平上的表达谱和敲除效应。 我们的方法提供了一个可推广的工具，用于在单细胞分辨率下推断基因功能，为在大规模实验筛选不可行的情况下研究遗传扰动提供了新的机会。 显示英文摘要

摘要： Gene expression data is essential for understanding how genes are regulated and interact within biological systems, providing insights into disease pathways and potential therapeutic targets. Gene knockout has proven to be a fundamental technique in molecular biology, allowing the investigation of the function of specific genes in an organism, as well as in specific cell types. However, gene expression patterns are quite heterogeneous in single-cell transcriptional data from a uniform environment, representing different cell states, which produce cell-type and cell-specific gene knockout impacts. A computational method that can predict the single-cell resolution knockout impact is still lacking. Here, we present a data-driven framework for learning the mapping between gene expression profiles derived from gene assemblages, enabling the accurate prediction of perturbed expression profiles following knockout (KO) for any cell, without relying on prior perturbed data. We systematically validated our framework using synthetic data generated from gene regulatory dynamics models, two mouse knockout single-cell datasets, and high-throughput in vitro CRISPRi Perturb-seq data. Our results demonstrate that the framework can accurately predict both expression profiles and KO effects at the single-cell level. Our approach provides a generalizable tool for inferring gene function at single-cell resolution, offering new opportunities to study genetic perturbations in contexts where large-scale experimental screens are infeasible.