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.
项目概要 神经系统疾病影响着全世界数百万患者,是一个未得到满足的重大医疗需求。最近的 与其他国家相比,开发新型中枢神经系统(CNS)疗法的进展滞后 其他疾病领域。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, 可以更可靠地评估疾病相关神经生理学参数的测定和技术 单个神经元和突触水平的人类细胞环境,具有捕获的规模和分辨率 这些系统的复杂性和可变性。我们建议通过整合三者来满足这一需求 关键技术 – (i) 我们用于人类神经元全光学生理学的高通量 BRITETM 平台,该平台 实现单细胞和单动作电位分辨率,每天吞吐量约为 500,000 个神经元 乐器; (ii) 使用 CRISPR 核酸酶破坏基因功能的基因组筛选; (iii) 机器学习 以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) 建立人类神经元中的CRISPRn筛选条件。 我们将筛选20个候选靶基因,包括癫痫和神经发育靶点进一步优化 与全光学生理学表型兼容的测定条件,包括遗传破坏的时间和 CRISPRn/gRNA 成分的浓度,用于有效敲低基因靶标。 2)建立深度学习- 用于单细胞表型分析的动力分析工具。我们将使用深度神经网络来学习紧凑向量 利用我们的单细胞分辨率来表征药物干预后的神经元行为 测量并适应神经元群体中潜在的异质性。 3)识别遗传 使用全基因组 CRISPRn 筛选神经元功能的调节剂。我们将结合实验 目标 1-2 中建立的条件和分析模型用于进行全基因组 CRISPRn 筛选(>18,000 基因目标)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 其下调会导致功能参数发生显着变化的目标。潜在的命中率和特异性 目标敲除的效果将在独立回合中使用单一 gRNA、qPCR 和免疫印迹进行确认 化验。 4) 预测并验证脆性 X 综合征人类 iPSC 神经元模型的表型拯救。 最后,我们将评估目标 3 中开发的功能指纹的预测价值,以生成 我们在人类中发现的可以挽救(抑制)表型参数的基因靶标候选列表 神经发育障碍、脆性 X 综合征的细胞模型。我们将调整表达 FMR1-/y iPSC 神经元中这些潜在的 CRISPRn 基因抑制因子和基准表型拯救 使用 FMRP 的基因重新引入。成功完成拟议的工作有可能产生新的成果 了解人类神经生理学的分子结构和新治疗的平台 目标识别侧重于调节神经生理疾病机制的分子基础。

项目成果

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