Characterizing phenotype-associated subpopulations from single-cell sequencing data

从单细胞测序数据表征表型相关的亚群

• 批准号: 10658611
• 负责人: Zheng Xia
• 金额: $ 30.8万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658611
• 关键词: Address; Automobile Driving; Biological; Biological Process; Biotechnology; Cell Count; Cell Therapy; Cells; Cellular Assay; Clinical; Clinical Research; Complex; Data; Data Analyses; Data Set; Detection; Development; Disease; Disease Progression; Genes; Genetic Transcription; Graph; Individual; Learning; Link; Measurement; Methodology; Methods; Modeling; Molecular; Mutation; Nail plate; Nature; Neoplasm Metastasis; Performance; Pharmacotherapy; Phenotype; Population; Population Heterogeneity; Prognostic Marker; Rationalization; Sample Size; Sampling; Series; Signal Transduction; Speed; The Cancer Genome Atlas; Time; Tissue Sample; Tissues; Water; cell type; cellular targeting; clinical phenotype; clinically significant; design; feature selection; flexibility; human disease; improved; innovation; learning strategy; loss of function; machine learning method; novel; novel strategies; public health relevance; simulation; single cell analysis; single cell sequencing; single cell technology; single-cell RNA sequencing; supervised learning; survival outcome; theories; therapy resistant; tool; transcriptome sequencing; treatment response

Project Summary Single-cell sequencing (scSeq) allows us to achieve new discoveries by distinguishing cell types, states, and lineages from heterogeneous tissue microenvironments. However, it remains challenging to interpret complex single-cell data from highly heterogeneous populations of cells. Currently, most existing single-cell data analyses focus on cell type clusters defined by unsupervised clustering methods that cannot directly link cell clusters with specific biological and clinical phenotypes. Additionally, the ever-increasing capability of scSeq in profiling thousands to millions of cells brings more challenges of pinpointing which cell cluster for further analysis. Given so many cells, the rationale of our "phenotype-centric" analysis is based on a Buddhist theory that "Each individual can only drink one bottle of water from the entire river." Therefore, focusing on specific cell subpopulations related to essential phenotypes is more important than evenly analyzing all cell clusters. Furthermore, clinical phenotype information, such as treatment resistance, survival outcomes, cancer metastasis, and disease stages, is primarily collected on bulk tissue samples. As a result, there is an unmet need to leverage widely available clinical phenotype information to aid subpopulation identification from single-cell data. Meanwhile, single-cell samples generated under different conditions require tools to identify the phenotype- enriched subpopulations for each condition. Taken together, there is a great need for further methodological progress for "phenotype-centric" scSeq data analysis. To this end, we propose to develop a suite of supervised- learning-based novel methods to accurately identify the most highly phenotype-associated cell subpopulations from scSeq data. We will (1) develop a platform with broad utilities for bulk phenotype-guided subpopulation identification from scSeq; (2) build a novel strategy to learn high-confidence phenotype-enriched subpopulations from scSeq data; (3) and establish a new platform for supervised phenotypic trajectory learning of subpopulations from scSeq data. The proposed methods will be evaluated by rigorous simulations and real data analyses. This proposal is conceptually innovative in the following aspects: (1) Our bulk phenotype-guided scSeq analysis enables hypothesis-free identification of clinically and biologically relevant cell subpopulations from scSeq data; (2) Our supervised learning frameworks can simultaneously select genes and identify phenotype-associated subpopulations from scSeq data; (3) Our method to learn cell subpopulations associated with continuous phenotypes has a unique feature to recover the hidden phenotypic stages. In summary, we expect this proposal to deliver a suite of novel machine learning methods for "phenotype-centric" single-cell data analysis, thus allowing us to precisely pinpoint disease-relevant subpopulations from single-cell data for cellular target discovery.

项目概要 单细胞测序 (scSeq) 使我们能够通过区分细胞类型、状态和 来自异质组织微环境的谱系。然而，解释复杂的情况仍然具有挑战性 来自高度异质性细胞群的单细胞数据。目前，大多数现有的单细胞数据分析 重点关注由无监督聚类方法定义的细胞类型簇，这些方法不能直接将细胞簇与 特定的生物学和临床表型。此外，scSeq 在分析方面的能力不断增强 数千到数百万个细胞给确定哪个细胞簇进行进一步分析带来了更多挑战。给定 如此多的细胞，我们“以表型为中心”分析的基本原理是基于佛教理论，即“每个 一个人只能喝整条河里的一瓶水。”因此，关注特定细胞 与基本表型相关的亚群比均匀分析所有细胞簇更重要。 此外，临床表型信息，如治疗耐药性、生存结果、癌症转移、 和疾病阶段，主要收集大量组织样本。因此，利用杠杆的需求尚未得到满足 广泛可用的临床表型信息可帮助从单细胞数据中识别亚群。 同时，在不同条件下产生的单细胞样本需要工具来识别表型- 丰富了每种条件的亚群。总而言之，非常需要进一步的方法论 “以表型为中心”的 scSeq 数据分析取得进展。为此，我们建议开发一套有监督的 基于学习的新方法可准确识别表型相关程度最高的细胞亚群 来自 scSeq 数据。我们将 (1) 开发一个具有广泛实用性的平台，用于批量表型引导的亚群 来自 scSeq 的识别； (2) 建立一种新的策略来学习高置信度表型丰富的亚群 来自 scSeq 数据； (3)并建立亚群有监督表型轨迹学习的新平台 来自 scSeq 数据。所提出的方法将通过严格的模拟和真实数据分析进行评估。这 该提案在以下几个方面在概念上有所创新：（1）我们的批量表型引导的 scSeq 分析 能够从 scSeq 数据中无假设地识别临床和生物学相关的细胞亚群； (2)我们的监督学习框架可以同时选择基因并识别与表型相关的 来自 scSeq 数据的亚群； (3)我们学习与连续相关的细胞亚群的方法 表型具有恢复隐藏表型阶段的独特功能。总而言之，我们期待这个提案 提供一套新颖的机器学习方法，用于“以表型为中心”的单细胞数据分析，从而 使我们能够从细胞目标的单细胞数据中精确定位与疾病相关的亚群 发现。