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.

项目摘要 单细胞测序(ScSeq)使我们能够通过区分细胞类型、状态和 来自不同组织微环境的血统。然而，要解释复杂的问题仍然具有挑战性。 来自高度异质性细胞群体的单细胞数据。目前，大多数现有的单元格数据分析 重点关注由非监督聚类方法定义的细胞类型聚类，这些方法无法直接将细胞聚类与 特定的生物学和临床表型。此外，scSeq在分析方面不断增强的能力 数以千计到数百万个的细胞带来了更多的挑战，即精确定位哪个细胞群进行进一步分析。vt.给出 如此之多的细胞，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 每个人只能喝一瓶来自整条河的水。 与基本表型相关的亚群比平均分析所有细胞团更重要。 此外，临床表型信息，如治疗耐药，生存结果，癌症转移， 和疾病阶段，主要是从大量组织样本中收集。因此，存在一种未得到满足的杠杆需求。 广泛可用的临床表型信息，以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- 每种情况下都有丰富的亚群。综上所述，非常需要进一步的方法论 “以表型为中心”的scSeq数据分析进展。为此，我们建议发展一套受监管的- 基于学习的准确识别最高表型相关细胞亚群的新方法 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 从scSeq中识别；(2)建立一种新的策略来学习高置信度表型丰富的亚群 (3)建立一个新的有监督的亚群表型轨迹学习平台 来自scSeq数据。所提出的方法将通过严格的模拟和实际数据分析进行评估。这 该方案在以下几个方面具有创新性：(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和识别与表型相关的基因 从scSeq数据中学习细胞亚群；(3)学习与连续序列相关的细胞亚群的方法 表型有一个独特的功能，可以恢复隐藏的表型阶段。总而言之，我们期待这项提案 为“以表型为中心”的单细胞数据分析提供一套新颖的机器学习方法，因此 使我们能够准确地从单细胞数据中定位与疾病相关的亚群作为细胞靶点 发现号。