BRITE-Eye: An integrated discovery engine for CNS therapeutic targets driven by high throughput genetic screens, functional readouts in human neurons, and machine learning

BRITE-Eye:由高通量遗传筛选、人类神经元功能读数和机器学习驱动的中枢神经系统治疗靶点的集成发现引擎

基本信息

  • 批准号:
    10699137
  • 负责人:
  • 金额:
    $ 172.19万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-09-19 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

Project Summary Neurological disorders affect millions of patients worldwide and represent a major unmet medical need. Recent progress on developing new classes of central nervous system (CNS) therapeutics has lagged compared to other disease areas. A key obstacle in the CNS drug discovery process has been a need for cellular models, assays, and technologies that can more reliably assess disease-relevant neurophysiological parameters in a human cellular context at the level of individual neurons and synapses, with the scale and resolution to capture the complexity and variability of these systems. We propose to address this need through the integration of three key technologies – (i) our high throughput BRITETM platform for all-optical physiology in human neurons, which achieves single-cell and single-action-potential resolution with a throughput of ~500,000 neurons per day per instrument; (ii) genomic screens using CRISPR nuclease to disrupt gene function; (iii) machine learning for identification of fingerprints that represent complex physiological phenotypes with single-cell resolution. This Phase II program includes four key objectives. 1) Establish CRISPRn screening conditions in human neurons. We will select 20 candidate target genes, including epilepsy and neurodevelopmental targets to further optimize assay conditions compatible with all-optical physiology phenotyping, including timing of genetic disruption and concentration of CRISPRn/gRNA components for effective knockdown of gene targets. 2) Build deep-learning- powered analytical tools for single-cell phenotyping. We will use deep neural networks to learn a compact vector representation of neuronal behavior after pharmacological intervention that leverages our single cell resolution measurements and accommodates potential heterogeneity in the population of neurons. 3) Identify genetic modulators of neuronal function using a genome-wide CRISPRn screen. We will combine experimental conditions and analytical models established in Aims 1-2 to carry out a genome-wide CRISPRn screen (>18,000 gene targets) with arrayed gRNA libraries in wild-type human iPSC-excitatory neurons. We will identify gene targets whose downregulation leads to significant changes in functional parameters. Potential hits and specificity of target knockdown will be confirmed in independent rounds using single gRNA and qPCR and immunoblotting assays. 4) Predict and validate phenotypic rescue in a human iPSC-neuronal model of Fragile X Syndrome. Finally, we will assess the predictive value of the functional fingerprints developed in Aim 3 to generate a candidate list of gene targets that can rescue (suppress) phenotypic parameters we have identified in a human cellular model of the neurodevelopmental disorder, Fragile X syndrome. We will modulate the expression of these potential genetic suppressors with CRISPRn in FMR1-/y iPSC-neurons and benchmark phenotypic rescue using genetic re-introduction of FMRP. Successful completion of the proposed work has potential to yield a new understanding of the molecular architecture of human neurophysiology and a platform for novel therapeutic target identification focused on the molecular basis for modulation of neurophysiological disease mechanisms.
项目摘要 神经疾病影响着全球数百万患者,是一种尚未得到满足的主要医疗需求。近期 与开发新型中枢神经系统(CNS)疗法相比,进展滞后 其他疫区。中枢神经系统药物发现过程中的一个关键障碍是对细胞模型的需求, 可以更可靠地评估与疾病相关的神经生理参数的分析和技术 单个神经元和突触水平上的人类细胞背景,以及捕捉的规模和分辨率 这些系统的复杂性和多变性。我们建议通过整合三个方面来满足这一需求 关键技术-(I)我们用于人类神经元全光生理学的高通量BRITETM平台, 实现单细胞和单动作电位分辨率,每天的吞吐量约为500,000个神经元 仪器;(2)使用CRISPR核酸酶干扰基因功能的基因组筛选;(3)机器学习 用单细胞分辨率识别代表复杂生理表型的指纹。这 第二阶段计划包括四个关键目标。1)建立人神经元CRISPRn筛选条件。 我们将选择20个候选靶基因,包括癫痫和神经发育靶点,以进一步优化 与全光生理学表型鉴定相兼容的检测条件,包括基因中断的时间和 用于有效敲除基因靶点的CRISPRn/gRNA组分的浓度。2)构建深度学习-- 用于单细胞表型鉴定的动力分析工具。我们将使用深度神经网络来学习紧凑的向量 利用我们的单细胞分辨率的药物干预后神经元行为的表现 测量和适应神经元群体中的潜在异质性。3)识别基因 使用全基因组CRISPRn屏幕的神经功能调节器。我们将结合实验 在AIMS 1-2中建立的进行全基因组CRISPRn筛查(>18,000)的条件和分析模型 基因靶点)与野生型人类IPSC兴奋性神经元中排列的gRNA文库。我们将鉴定基因 下调监管导致功能参数发生重大变化的目标。潜在命中率和特异性 将使用单个gRNA和qPCR以及免疫印迹在独立的几轮中确认靶基因敲除 化验。4)在脆性X综合征的人IPSC神经元模型中预测和验证表型救援。 最后,我们将评估在目标3中开发的功能指纹的预测价值,以生成 可以挽救(抑制)我们在人类中确定的表型参数的候选基因靶点列表 神经发育障碍的细胞模型,脆性X综合征。我们将调节基因的表达 FMR1/y IPSC神经元中CRISPRn的潜在遗传抑制因子与基准表型拯救 利用FMRP的遗传再导入。拟议工作的成功完成有可能产生一个新的 对人类神经生理学分子结构的理解和新疗法的平台 靶点识别侧重于神经生理学疾病调控机制的分子基础研究。

项目成果

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