Single-Cell Sequencing Reveals Lineage-Specific Dynamic Genetic Regulation of Gene Expression During Human Cardiomyocyte Differentiation

Autor: Reem Elorbany, Alexis Battle, Yoav Gilad, Kenneth Barr, Katherine Rhodes, Guanghao Qi, Benjamin J. Strober, Popp Jm
Rok vydání: 2021
Předmět:
Cancer Research
Cell Lines
Cellular differentiation
Cell
Cell Culture Techniques
Gene Expression
QH426-470
Animal Cells
Medicine and Health Sciences
Myocytes
Cardiac
RNA-Seq
Induced pluripotent stem cell
Genetics (clinical)
Connective Tissue Cells
Cardiomyocytes
Regulation of gene expression
Stem Cells
Cell Differentiation
medicine.anatomical_structure
Connective Tissue
Biological Cultures
Single-Cell Analysis
Cellular Types
Anatomy
Research Article
Cell type
Lineage (genetic)
Induced Pluripotent Stem Cells
Muscle Tissue
Computational biology
Biology
Research and Analysis Methods
Cell Line
Genetics
medicine
Humans
Cell Lineage
Gene Regulation
Molecular Biology
Gene
Ecology
Evolution
Behavior and Systematics
Muscle Cells
Gene Expression Profiling
Biology and Life Sciences
Cell Biology
Fibroblasts
Biological Tissue
Gene Expression Regulation
Single cell sequencing
Cultured Fibroblasts
Developmental Biology
Zdroj: PLoS Genetics, Vol 18, Iss 1, p e1009666 (2022)
PLoS Genetics
Popis: Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.
Author summary Many complex traits and diseases are associated with genetic variants which are suspected to regulate the expression levels of nearby genes. However, we are still unable to identify many of the relevant variant-gene associations. Previous work has shown that regulation of gene expression is often specific to a biological context, suggesting that measuring gene expression in diverse contexts may reveal important associations. In this work, we identified genetic regulatory effects that are “dynamic” over time in a complex environment containing diverse and transient cell states. We collected single-cell gene expression data at several time points from cells differentiating, or changing state, from stem cells to cardiomyocytes. We characterized two distinct trajectories that cells undertake as they differentiate in vitro, and assigned each cell to a particular point along a specific trajectory. We then identified hundreds of dynamic associations between regulatory variants and gene expression levels, including many specific to a single trajectory. This work demonstrates the importance of searching for variant-gene associations in cell types that change over time or exist only during fleeting stages of cellular differentiation, and provides a framework for identifying these associations in the presence of bifurcating trajectories that are characteristic of human development.
Databáze: OpenAIRE
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