Causal modelling and adaptive experimental design for single-cell perturbation screens

单细胞扰动筛选的因果建模和自适应实验设计

• 批准号: 514206638
• 负责人: Dr. Moritz Mall
• 金额: --
• 依托单位: Klinik für Kardiologie, Angiologie und Pulmologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/514206638?language=en
• 关键词: Causal; modelling; adaptive; experimental; design

Gene expression is controlled by a complex network of transcription factors and epigenetic regulators and defines cellular, tissue and organismal phenotypes. Gene regulatory networks (GRNs) capture thee complex interactions and can be used to describe gene expression patterns in health and disease. A causal understanding of the underlying mechanisms within these networks is required to identify the factors and processes that regulate development and disease and predict druggable targets. However, functional data resolving these causal interactions is not available systematically and modelling approaches that provide quantitative predictions of perturbation effects across different GRNs and cell types are still in their infancy. In this project, we develop and validate an adaptive experimental design methodology, based on causal generative modelling, and apply it to dissect GRNs and their causal interactions in a systematic iterative fashion in human organoid model systems. Our project will apply the developed methods to increase our understanding of GRNs governing neural and cardiomyocyte differentiation, and their interaction with known risk-genes for psychiatric and cardiovascular disease. Thereby, we aim to learn more about the gene regulatory logic required for normal organ development and function, how gene regulation is changed during disease, and which genetic elements need to be targeted to achieve a maximum therapeutic effect. To these ends, we will systematically perturb GRNs by performing CRISPR/Cas9 pooled genetic screens in stem cell derived heart and brain organoid model systems. These will allow us to partially recreate the architecture and physiology of these human organs, which are associated with the highest global disease burden. Therefore, we will dissect GRNs relevant for cardiomyocyte and neural differentiation, which interact with disease associated genetic variants for a more precise prediction of therapeutic intervention points. To readout the effects of CRISPR perturbations, we will use targeted perturbation sequencing (TAP-seq, developed in the Steinmetz lab). The perturbation screens will be performed in an iterative fashion, and will employ an adaptive experimental design that utilises combinatorial gene-repressing (CRISPRi) and/or gene-activating (CRISPRa) perturbations guided by causal generative modelling. Thereby, we will leverage probabilistic estimates of causal relationships between genes to quantify the expected information gain associated with potential perturbations, and select a set of maximally informative interventions for the next experiment. This approach will allow us to efficiently probe GRNs in a causal fashion, provide a detailed understanding of gene regulatory mechanisms in health and disease and provide novel tools to predict the outcome of therapeutic interventions within those GRNs.

基因表达受转录因子和表观遗传调节因子的复杂网络控制，并定义细胞、组织和生物体表型。基因调控网络（GRNs）捕获这些复杂的相互作用，并可用于描述健康和疾病中的基因表达模式。需要对这些网络中的潜在机制进行因果理解，以确定调节发育和疾病的因素和过程，并预测可药物化的目标。然而，解决这些因果关系的相互作用的功能数据是不可用的系统和建模方法，提供定量预测的扰动效应在不同的GRNs和细胞类型仍处于起步阶段。在这个项目中，我们开发和验证了自适应实验设计方法，基于因果生成建模，并将其应用于解剖GRNs和它们的因果相互作用在人类类器官模型系统中的系统迭代方式。我们的项目将应用开发的方法来增加我们对控制神经和心肌细胞分化的GRN的了解，以及它们与已知的精神和心血管疾病风险基因的相互作用。因此，我们的目标是更多地了解正常器官发育和功能所需的基因调控逻辑，疾病期间基因调控如何改变，以及需要靶向哪些遗传元件以达到最大的治疗效果。为此，我们将通过在干细胞衍生的心脏和脑类器官模型系统中进行CRISPR/Cas9合并遗传筛选来系统地干扰GRNs。这将使我们能够部分重建这些人体器官的结构和生理学，这些器官与全球最高的疾病负担有关。因此，我们将剖析与心肌细胞和神经分化相关的GRNs，它们与疾病相关的遗传变异相互作用，以更精确地预测治疗干预点。为了读出CRISPR扰动的影响，我们将使用靶向扰动测序（TAP-seq，在Steinmetz实验室开发）。扰动筛选将以迭代方式进行，并将采用自适应实验设计，该设计利用由因果生成建模指导的组合基因抑制（CRISPRi）和/或基因激活（CRISPRa）扰动。 因此，我们将利用基因之间因果关系的概率估计来量化与潜在扰动相关的预期信息增益，并为下一个实验选择一组信息量最大的干预措施。这种方法将使我们能够以因果方式有效地探测GRNs，详细了解健康和疾病中的基因调控机制，并提供新的工具来预测这些GRNs中治疗干预的结果。