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.

阿尔茨海默病及相关痴呆（ADRD）是一种复杂的多因素疾病， 老年人逐渐丧失记忆、意识模糊和认知能力受损。除了 遗传变异，研究报告说，某些表观遗传，网络和基因组组织 干扰及其复杂的相互作用，有助于ADRD的进展，为新的细胞 病因学最近的单细胞革命，特别是多模式基因组分析， 以尽可能高的分辨率详细检查ADRD中的多尺度失调。但很少 已经开发了解决由于高缺失而引起的这一关键但具有挑战性的任务的方法， 维度和单细胞数据中的复杂特征交互。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，以突出多尺度失调 导致ADRD，包括遗传、表观遗传、网络和染色质结构改变， 单细胞分辨率。 与先前报道的一组一维（1D）功能性顺式调节蛋白不同， 元件（克雷斯），并将其应用于所有样品，我们的目标是构建个人， 来自sc-multiome数据的紧凑的、以基因为中心的和细胞类型特异性的脑调节组。具体地说， 我们将首先提出一种可扩展的多模态深度生成模型来集成大规模， 单细胞、多细胞和混合模式的异质性ADRD单细胞数据。区别于现有的 方法，我们将包括一个不变的表示学习计划，以获得潜在的细胞 与混杂因素不相关的表示（例如，年龄、性别、阅读深度和批次效应） 用于无偏倚转录组和表观基因组重建（Aim 1）。然后，我们将超越1D 通过破译多尺度基因调控代码（Aim 2）进行基因组注释，包括细胞类型- 特异性染色质区室化、克雷斯及其用于功能解释的靶基因，以及 转录因子（TF）调控网络（TRN）。最后，我们将开发可解释的深度学习。 将多尺度失调与ADRD联系起来的模型和机制解释（目标3）。 该提案是建立在张，Won和 Gerstein实验室起源于ENCODE和PsychENCODE项目，在以下方面拥有丰富的专业知识： 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 通过提供必要的工具来加快更广泛的科学界的研究， 基因组中的功能区域，并优先考虑ADRD的多尺度风险因素。