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Extent, Heritability, and Functional Relevance of Single Cell Expression Variability in Highly Homogeneous Populations of Human Cells

83 Pages Posted: 29 Apr 2019 Sneak Peek Status: Review Complete

See all articles by Daniel Osorio

Daniel Osorio

Texas A&M University - Department of Veterinary Integrative Biosciences

Xue Yu

Texas A&M University - Department of Veterinary Pathobiology

Yan Zhong

Texas A&M University - Department of Statistics

Guanxun Li

Texas A&M University - Department of Statistics

Peng Yu

Texas A&M University - Department of Electrical and Computer Engineering

Erchin Serpedin

Texas A&M University - Department of Electrical and Computer Engineering

Jianhua Huang

Texas A&M University - Department of Statistics

James J. Cai

Texas A&M University - Department of Veterinary Integrative Biosciences

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Abstract

Because of recent technological developments, single-cell assays such as single-cell RNA sequencing (scRNA-seq) have become much more widely available and have achieved unprecedented resolution in revealing cell heterogeneity. The extent of intrinsic cell-to-cell variability in gene expression, or single cell expression variability (scEV), has thus been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what its implications are for multi-cellular organisms. We therefore analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs). For each of the three cell types, we estimated scEV in homogeneous populations of cells; we identified 465, 466, and 291 highly variable genes (HVGs), respectively. These HVGs were enriched with specific functions precisely relevant to the cell types, from which the scRNA-seq data used to identify HVGs were generated — e.g., HVGs identified in lymphoblastoid cells were enriched in cytokine signaling pathways, LAECs collagen formation, and DFs keratinization. HVGs were deeply embedded in gene regulatory networks specific to corresponding cell types. We also found that scEV is a heritable trait, partially determined by cell donors’ genetic makeups. Furthermore, across genes, especially immune genes, levels of scEV and between-individual variability in gene expression were positively correlated, suggesting a potential link between the two variabilities measured at different organizational levels. Taken together, our results support the “variation is function” hypothesis, which postulates that scEV is required for higher-level system function. Thus, we argue that quantifying and characterizing scEV in relevant cell types may deepen our understating of normal as well as pathological cellular processes.

Keywords: single-cell RNA sequencing, scRNA-Seq, single cell expression variability, cell-to-cell variation, lymphoblastoid cell line, lung airway epithelial cell, dermal fibroblast, Induced pluripotent stem cell

Suggested Citation

Osorio, Daniel and Yu, Xue and Zhong, Yan and Li, Guanxun and Yu, Peng and Serpedin, Erchin and Huang, Jianhua and Cai, James J., Extent, Heritability, and Functional Relevance of Single Cell Expression Variability in Highly Homogeneous Populations of Human Cells (April 24, 2019). CELL-REPORTS-D-19-01476. Available at SSRN: https://ssrn.com/abstract=3377618 or http://dx.doi.org/10.2139/ssrn.3377618
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Daniel Osorio

Texas A&M University - Department of Veterinary Integrative Biosciences

United States

Xue Yu

Texas A&M University - Department of Veterinary Pathobiology

United States

Yan Zhong

Texas A&M University - Department of Statistics

155 Ireland Street
447 Blocker
College Station, TX 77843
United States

Guanxun Li

Texas A&M University - Department of Statistics

155 Ireland Street
447 Blocker
College Station, TX 77843
United States

Peng Yu

Texas A&M University - Department of Electrical and Computer Engineering

United States

Erchin Serpedin

Texas A&M University - Department of Electrical and Computer Engineering

United States

Jianhua Huang

Texas A&M University - Department of Statistics ( email )

155 Ireland Street
447 Blocker
College Station, TX 77843
United States

James J. Cai (Contact Author)

Texas A&M University - Department of Veterinary Integrative Biosciences ( email )

United States

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