Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and applications to B cell aging

Genome Biology

Research

Huiwen Zheng¹  Atefeh Taherian Fard¹  Jessica Cara Mar¹  Jan Vijg² 

[1] Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia;Department of Genetics, Albert Einstein College of Medicine, 10461, Bronx, NY, USA;Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China;

关键词: Cell-to-cell variability;    B lymphocytes differentiation;    Aging;    Single-cell RNA-seq;    Evaluation framework;   

DOI  :  10.1186/s13059-023-03036-2

 received in 2022-11-27, accepted in 2023-08-11,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently facilitate the study of cell-to-cell variability at the cell type level. Although different methods have been proposed to quantify cell-to-cell variability, it is unclear what the optimal statistical approach is, especially in light of challenging data structures that are unique to scRNA-seq data like zero inflation.ResultsWe systematically evaluate the performance of 14 different variability metrics that are commonly applied to transcriptomic data for measuring cell-to-cell variability. Leveraging simulations and real datasets, we benchmark the metric performance based on data-specific features, sparsity and sequencing platform, biological properties, and the ability to recapitulate true levels of biological variability based on known gene sets. Next, we use scran, the metric with the strongest all-round performance, to investigate changes in cell-to-cell variability that occur during B cell differentiation and the aging processes. The analysis of primary cell types from hematopoietic stem cells (HSCs) and B lymphopoiesis reveals unique gene signatures with consistent patterns of variable and stable expression profiles during B cell differentiation which highlights the significance of these methods. Identifying differentially variable genes between young and old cells elucidates the regulatory changes that may be overlooked by solely focusing on mean expression changes and we investigate this in the context of regulatory networks.ConclusionsWe highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and aging and emphasize the value of these findings at the level of individual cell types.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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