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 来自异质组织微环境的谱系。然而，解释复杂的 来自高度异质细胞群体的单细胞数据。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聚类，这些方法不能直接将细胞聚类与 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 数千到数百万个细胞带来了更多的挑战，即精确定位哪个细胞簇用于进一步分析。给定 如此多的细胞，我们的“以表型为中心”的分析的基本原理是基于佛教理论，即“每一个 一个人只能喝一瓶整条河的水。“因此， 与基本表型相关的亚群比均匀地分析所有细胞簇更重要。 此外，临床表型信息，如治疗抗性、存活结果、癌症转移、 和疾病阶段，主要收集大量组织样品。因此，需要利用 广泛可用的临床表型信息，以帮助从单细胞数据中鉴定亚群。 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- 为每种情况富集亚群。总的来说，非常需要进一步的方法， “以表型为中心”的scSeq数据分析的进展。为此，我们建议制定一套监督- 基于学习的新方法，以准确识别最高度表型相关的细胞亚群 scSeq数据我们将（1）开发一个具有广泛实用性的平台，用于批量表型指导亚群 从scSeq鉴定;（2）建立一种新策略来学习高置信度表型富集的亚群 从scSeq数据;（3）建立一个新的平台，用于亚群的监督表型轨迹学习 scSeq数据所提出的方法将通过严格的模拟和真实的数据分析进行评估。这 该方案在以下几个方面具有创新性：（1）我们的批量表型指导的scSeq分析 能够从scSeq数据中无假设地鉴定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和识别表型相关的 （3）我们的方法来学习与连续的细胞亚群相关的细胞亚群， 表型具有恢复隐藏的表型阶段的独特特征。总而言之，我们希望这项提案 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， 使我们能够从细胞靶点的单细胞数据中精确定位疾病相关亚群 的发现