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（ADRDs）是一种复杂的多因素疾病