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A generalizable framework for linking single-cell genomic states with cell fate outcomes in hematopoiesis

将单细胞基因组状态与造血细胞命运结果联系起来的通用框架

基本信息

批准号：
10645197
负责人：
H. LEIGHTON GRIMES
金额：
$ 106.15万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10645197
关键词：
Acceleration Address Adoptive Transfer Antibodies Architecture Bar Codes Biological Biological Assay Blood Cells Bone Marrow Catalogs Cell Maturation Cell physiology Cell surface Cells Cellular Indexing of Transcriptomes and Epitopes by Sequencing Chromatin Collaborations Colony-Forming Units Assay Communities Complement Confusion Coupled Data Set Development Disease Dissection Elements Enhancers Explosion Flow Cytometry Gene Expression Genes Genetic Genetic Transcription Genomics Health Hematologist Hematopoiesis Hematopoietic Hematopoietic Cell Production Hematopoietic System Hematopoietic stem cells Heterogeneity Hi-C Human Impairment In Vitro Interdisciplinary Study Link Macrophage Methods Molecular Multipotent Stem Cells Mus Myelogenous Myeloid Cells Oligonucleotides Outcome Population Production Reporter Sorting Specific qualifier value System Systems Biology Testing Transplantation Validation Work analytical method bioinformatics pipeline bioinformatics tool computer framework computerized tools disorder prevention functional genomics gene regulatory network genomic data in vivo indexing insight microscopic imaging neutrophil online resource progenitor programs promoter self-renewal single cell technology single-cell RNA sequencing tool transcription factor transcriptome

项目摘要

PROJECT SUMMARY Currently, conventional methods that have been utilized to characterize hematopoietic progenitors and their potentials including flow cytometry, in vitro colony-forming-unit assays and in vivo genetic marking are being vigorously complemented with genomics analyses such as single-cell RNA-Seq (scRNA-Seq) and scATACSeq. While these complementary analyses are defining a multitude of possible cell states, there is considerable confusion concerning the correspondence between such states and the heterogeneity/identity of cells captured within canonical flow cytometry gates, their developmental potentials, and mechanisms underlying lineage specification. To address this fundamental problem in the field we have assembled an interdisciplinary research team with deep expertise in the application of single-cell technologies, hematopoiesis, computational genomics and systems biology to develop and promote a unifying framework for the analysis of genomic states with their developmental potentials and trajectories. Specifically, we will define prevalent and rare hematopoietic intermediates as well as their developmental potencies, restrictions and trajectories, on the basis of their genomic states along with the optimal markers and flow gates necessary to isolate them. Using these genomic datasets coupled with analyses of poised or active enhancers interacting with promoters, we will infer gene regulatory networks (GRNs) that delineate the connectivity of transcription factors to their target genes thereby inferring control mechanisms underlying the distinctive genomic states. Thus, exploiting a consolidated biological, molecular and computational dissection of the hematopoietic system focusing on underlying genomic regulatory architectures, we will provide a new framework to incisively understand steady state hematopoiesis.

项目概要目前，用于表征造血祖细胞及其潜力的传统方法，包括流式细胞术、体外集落形成单位测定和体内遗传标记，正在与单细胞 RNA-Seq (scRNA-Seq) 和 scATACSeq 等基因组学分析大力补充。虽然这些补充分析定义了多种可能的细胞状态，但关于这些状态与规范流式细胞术门内捕获的细胞的异质性/同一性、它们的发育潜力以及谱系规范背后的机制之间的对应关系，存在相当大的混乱。为了解决该领域的这一基本问题，我们组建了一个跨学科研究团队，在单细胞技术、造血、计算基因组学和系统生物学应用方面拥有深厚的专业知识，以开发和推广用于分析基因组状态及其发育潜力和轨迹的统一框架。具体来说，我们将根据其基因组状态以及分离它们所需的最佳标记和流门来定义普遍和罕见的造血中间体及其发育效力、限制和轨迹。使用这些基因组数据集，结合对与启动子相互作用的平衡或活跃增强子的分析，我们将推断出基因调控网络（GRN），该网络描绘了转录因子与其靶基因的连接性，从而推断出独特基因组状态下的控制机制。因此，通过对造血系统进行综合的生物、分子和计算剖析，重点关注潜在的基因组调控架构，我们将提供一个新的框架来深入了解稳态造血作用。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Decision level integration of unimodal and multimodal single cell data with scTriangulate.

DOI：
10.1038/s41467-023-36016-y
发表时间：
2023-01-25
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Li, Guangyuan;Song, Baobao;Singh, Harinder;Surya Prasath, V. B.;Leighton Grimes, H.;Salomonis, Nathan
通讯作者：
Salomonis, Nathan

pyInfinityFlow: optimized imputation and analysis of high-dimensional flow cytometry data for millions of cells.

DOI：
10.1093/bioinformatics/btad287
发表时间：
2023-05-04
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
通讯作者：

Why Single-Cell Sequencing Has Promise in MDS.

DOI：
10.3389/fonc.2021.769753
发表时间：
2021
期刊：
Frontiers in oncology
影响因子：
4.7
作者：
Zhang X;Grimes HL
通讯作者：
Grimes HL

Assay optimization for the objective quantification of human multilineage colony-forming units.

人类多谱系集落形成单位客观定量的测定优化。

DOI：
10.1016/j.exphem.2023.05.007
发表时间：
2023
期刊：
Experimental hematology
影响因子：
2.6
作者：
Thompson,EvrettN;Carlino,MaximillianJ;Scanlon,VanessaM;Grimes,HLeighton;Krause,DianeS
通讯作者：
Krause,DianeS