Interpretable Deep Learning Methods to Investigate Genetics and Epigenetics of Alzheimer's Disease at a Single-Cell Resolution

可解释的深度学习方法以单细胞分辨率研究阿尔茨海默病的遗传学和表观遗传学

• 批准号: 10698166
• 负责人: JING ZHANG
• 金额: $ 63.43万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-30 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698166
• 关键词: 3-Dimensional; Acceleration; Address; Adult; Affect; Age; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Autopsy; Benchmarking; Biological Assay; Boundary Elements; Brain; Brain region; Cells; Chromatin; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Collaborations; Communities; Complex; Computer Models; Computer software; Confusion; Data; Dimensions; Disease; Dropout; Elderly; Epigenetic Process; Etiology; Event; Excision; Family; Gender; Gene Expression Regulation; Genes; Genetic; Genome; Genome engineering; Genomics; Genotype; Graph; Health Care Costs; Heterogeneity; Hi-C; Hybrids; Impaired cognition; Individual; Knowledge; Learning; Life; Link; Memory Loss; Methods; Modality; Modeling; Molecular; Multiomic Data; Neurosciences; Patients; Pattern; Personal Satisfaction; Phenotype; Prevention strategy; Regulator Genes; Regulatory Element; Reporting; Research; Resolution; Risk Factors; Sampling; Scheme; Series; Software Tools; Specificity; System; Transcriptional Regulation; Variant; cell type; cognitive ability; computer science; deep learning; deep learning model; differential expression; effective therapy; epigenome; epigenomics; flexibility; functional genomics; genetic variant; genome annotation; genome wide association study; genomic data; high dimensionality; infancy; learning strategy; member; multimodal data; multimodality; multiple omics; neural; novel; open source; predictive modeling; reconstruction; risk variant; sequencing platform; single cell sequencing; supervised learning; tool; transcription factor; transcriptome; transcriptomics; treatment strategy

Alzheimer's disease and related dementias (ADRDs) are complex multifactorial disorders characterized by progressive memory loss, confusion, and impaired cognitive abilities in older adults. In addition to genetic variants, studies have reported that certain epigenetic, network, and genome organizational perturbations, and their complex interplay, contribute to ADRD progression, informing new cellular etiologies. The recent single-cell revolution, especially multimodal genomic profiling, makes it possible to scrutinize multi-scale dysregulations in ADRDs at the finest possible resolution. However, few methods have been developed to address this critical yet challenging task due to the high missingness, dimensionality, and complex feature interactions in single-cell data. In this project, we will develop interpretable deep learning methods and software tools to highlight multi-scale dysregulations contributing to ADRDs, including genetic, epigenetic, network, and chromatin structural alterations at a single-cell resolution. Distinct from previous efforts reporting a set of one-dimensional (1D) functional cis-regulatory elements (CREs) from only one genome and applying it to all samples, we aim to construct personal, compact, gene-centric, and cell-type-specific brain regulome from sc-multiome data. Specifically, we will first propose a scalable multimodal deep generative model to integrate large-scale, heterogeneous ADRD single-cell data with single-, multi-, and hybrid modalities. Distinct to existing methods, we will include an invariant representation learning scheme to derive latent cell representations uncorrelated with confounding factors (e.g., age, gender, read depth, and batch effects) for bias-free transcriptome and epigenome reconstruction (Aim 1). Then, we will go beyond the 1D genome annotation by deciphering the multi-scale gene regulation code (Aim 2), including cell-type- specific chromatin compartmentation, CREs and their target genes for functional interpretation, and transcription factor (TF) regulatory networks (TRNs). Lastly, we will develop interpretable deep learning models to link multi-scale dysregulations to ADRD with mechanistic explanation (Aim 3). This proposal is built on an existing multi-year collaboration among the Zhang, Won, and Gerstein labs that originated from the ENCODE and PsychENCODE projects, with diverse expertise in computer science, neuroscience, and genomics. Upon completion, our proposal will significantly accelerate research in a broader scientific community by providing essential tools to investigate functional regions in the genome and prioritize multi-scale risk factors for ADRD.

阿尔茨海默氏病和相关痴呆症（ADRDS）是复杂的多因素疾病。 通过渐进的记忆力丧失，混乱和老年人认知能力受损。此外 遗传变异，研究报告说，某些表观遗传学，网络和基因组组织 扰动及其复杂的相互作用，有助于ADRD进展，告知新的细胞 病因。最近的单细胞革命，尤其是多模式基因组分析，使其成为可能 以最好的分辨率仔细检查ADRD中的多尺度失调。但是，很少 由于缺失高，已经开发了解决这一关键但具有挑战性的任务的方法 单细胞数据中的维度和复杂特征相互作用。在这个项目中，我们将开发 可解释的深度学习方法和软件工具突出显示多尺度失调 为ADRD做出贡献，包括遗传，表观遗传学，网络和染色质结构改变 单细胞分辨率。 与以前报告一组一维（1D）功能顺式调节的努力不同 元素（CRE）仅从一个基因组中，并将其应用于所有样本，我们的目标是建造个人， 来自SC-Multiome数据的紧凑，以基因为中心和细胞类型的大脑调节组。具体来说， 我们将首先提出一个可扩展的多模式深生成模型，以整合大规模， 具有单，多和混合方式的异质ADRD单细胞数据。与现有不同 方法，我们将包括一个不变的表示学习方案，以得出潜在的细胞 与混杂因素（例如年龄，性别，阅读深度和批处理效应）不相关的表示形式 用于无偏置转录组和表观基因组重建（AIM 1）。然后，我们将超越1D 通过解读多尺度基因调节代码（AIM 2）的基因组注释，包括细胞类型 特定的染色质隔室，CRE及其靶基因用于功能解释，并且 转录因子（TF）调节网络（TRN）。最后，我们将发展可解释的深度学习 将多尺度失调与机械解释联系起来的模型（AIM 3）。 该提案建立在张，胜利和 Gerstein Labs源自编码和心理码项目，具有多样的专业知识 计算机科学，神经科学和基因组学。完成后，我们的建议将大大 通过提供基本工具来调查，加速更广泛的科学界的研究 基因组中的功能区域，并确定ADRD的多尺度风险因素。