scholarly journals Extent, heritability, and functional relevance of single cell expression variability in highly homogeneous populations of human cells

2019 ◽  
Author(s):  
Daniel Osorio ◽  
Xue Yu ◽  
Yan Zhong ◽  
Guanxun Li ◽  
Peng Yu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Regulatory Networks ◽  
Cell Types ◽  
Airway Epithelial Cells ◽  
Dermal Fibroblasts ◽  
Cytokine Signaling ◽  
Expression Variability ◽  
Collagen Formation ◽  
Cell Expression

AbstractBecause 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, orsingle 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.

scholarly journals Single-Cell Expression Variability Implies Cell Function

Cells ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 14 ◽  
Author(s):  
Daniel Osorio ◽  
Xue Yu ◽  
Yan Zhong ◽  
Guanxun Li ◽  
Erchin Serpedin ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Function ◽  
Expression Profiles ◽  
Airway Epithelial Cells ◽  
Dermal Fibroblasts ◽  
Cytokine Signaling ◽  
Cell Type ◽  
Expression Variability ◽  
Collagen Formation ◽  
Cell Expression

As single-cell RNA sequencing (scRNA-seq) data becomes widely available, cell-to-cell variability in gene expression, or single-cell expression variability (scEV), has been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what are its implications for multi-cellular organisms. Here, we analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs) and, for each cell type, selected a group of homogenous cells with highly similar expression profiles. We estimated the scEV levels for genes after correcting the mean-variance dependency in that data and identified 465, 466, and 364 highly variable genes (HVGs) in LCLs, LAECs, and DFs, respectively. Functions of these HVGs were found to be enriched with those biological processes precisely relevant to the corresponding cell type’s function, from which the scRNA-seq data used to identify HVGs were generated—e.g., cytokine signaling pathways were enriched in HVGs identified in LCLs, collagen formation in LAECs, and keratinization in DFs. We repeated the same analysis with scRNA-seq data from induced pluripotent stem cells (iPSCs) and identified only 79 HVGs with no statistically significant enriched functions; the overall scEV in iPSCs was of negligible magnitude. Our results support the “variation is function” hypothesis, arguing that scEV is required for cell type-specific, higher-level system function. Thus, quantifying and characterizing scEV are of importance for our understating of normal and pathological cellular processes.

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

PATH-26. EVALUATING THE DISTRIBUTION AND PROGNOSIS OF IMMUNE CELL SIGNATURES OF THE TUMOR MICRO-ENVIRONMENT IN DIFFUSE GLIOMAS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi120-vi120
Author(s):  
Bharati Mehani ◽  
Saleembhasha Asanigari ◽  
Hye-Jung Chung ◽  
Kenneth Aldape
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Immune Cell ◽  
Tumor Grade ◽  
Cell Types ◽  
Idh Mutation ◽  
Mutation Status ◽  
Cluster Analyses ◽  
Diffuse Gliomas ◽  
Cell Expression

Abstract The tumor micro-environment (TME) plays an important role in the biology of cancer, including gliomas. Single cell studies have highlighted the role of specific TME components in gliomas, and the methods to deconvolve bulk profiling data may serve to complement these studies on clinically annotated tumors. In this study, we estimated cell type proportions in 3 large glioma datasets (TCGA, CGGA-325, CGGA-693) using CIBERSORTx. Using a signature matrix comprising 22 immune cell types, we identified IDH mutation status-specific immune cell distributions and found that the proportions of 10 cell types were significantly different between IDHmut and IDHwt tumors across the 3 datasets. Looking further within IDHmut tumors, we found that monocytes were enriched in 1p/19q non-co-deleted tumors across the 3 glioma datasets, consistent with prior single cell studies. We then examined estimated gene expression among immune cell types relative to IDH mutation status and found clear separation of gene expression in 15 of 22 cell types in all 3 datasets. When we applied these 22 gene expression signatures in each tumor sample onto cluster-of-cluster analyses to identify tumor groups with distinct immune signature patterns, we found that samples were distributed largely according to the IDH status in all 3 datasets, confirming that immune cell expression is distinct based on IDH status. Among IDH-specific groups, cluster-of-cluster analyses showed that immune cell-based cluster groups had distinct survival outcomes, and that IDHwt samples were distributed significantly based on tumor grades as well as based on EGFR overexpression. Among IDHmut tumors, the distributions of tumor grade and 1p/19q co-deletion status were significantly different in the immune-based clusters in 2 of the 3 datasets examined. Overall, these results highlight the biological and clinical significance of the immune cell environment in gliomas, including distinctions based on IDH mutation status as well as prognosis within IDH-specific groups.

scholarly journals Functional Inference of Gene Regulation using Single-Cell Multi-Omics

2021 ◽  
Author(s):  
Vinay K Kartha ◽  
Fabiana M Duarte ◽  
Yan Hu ◽  
Sai Ma ◽  
Jennifer G Chew ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Regulatory Networks ◽  
Immunological Response ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Human Blood Cells ◽  
Functional Inference ◽  
Omic Data

Cells require coordinated control over gene expression when responding to environmental stimuli. Here, we apply scATAC-seq and scRNA-seq in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis -regulatory landscape of immunological response across cell types, stimuli and time. Advancing tools to integrate multi-omic data, we develop FigR - a framework to computationally pair scATAC-seq with scRNA-seq cells, connect distal cis -regulatory elements to genes, and infer gene regulatory networks (GRNs) to identify candidate TF regulators. Utilizing these paired multi-omic data, we define Domains of Regulatory Chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility prior to production of gene expression at time scales of minutes. Further, the construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables the elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

scholarly journals Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Tianyuan Lu ◽  
Jessica C. Mar
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Regulatory Networks ◽  
Cell Types ◽  
Marker Genes ◽  
Biological Functions ◽  
Sex Dimorphism ◽  
Cell Type ◽  
Transcriptional Regulatory ◽  
Single Cell Rna Sequencing

Abstract Background It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level. Methods Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism. Results For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF. Conclusions We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

scholarly journals Defining developmental diversification of diencephalon neurons through single-cell gene expression profiling

2018 ◽  
Author(s):  
Qiuxia Guo ◽  
James Y. H. Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Regulatory Networks ◽  
Neuronal Cell ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Form Complex ◽  
Molecular Features ◽  
Close Relationship ◽  
Cell Groups

ABSTRACTThe embryonic diencephalon gives rise to diverse neuronal cell types, which form complex integration centers and intricate relay stations of the vertebrate forebrain. Prior anecdotal gene expression studies suggest several developmental compartments within the developing diencephalon. In the current study, we utilized single-cell RNA sequencing to profile transcriptomes of dissociated cells from the diencephalon of E12.5 mouse embryos. Through analysis of unbiased transcriptional data, we identified the divergence of different progenitors, intermediate progenitors, and emerging neuronal cell types. After mapping the identified cell groups to their spatial origins, we were able to characterize the molecular features across different cell types and cell states, arising from various diencephalic compartments. Furthermore, we reconstructed the developmental trajectory of different cell lineages within the diencephalon. This allowed the identification of the genetic cascades and gene regulatory networks underlying the progression of the cell cycle, neurogenesis, and cellular diversification. The analysis provides new insights into the molecular mechanism underlying the specification and amplification of thalamic progenitor cells. In addition, the single-cell-resolved trajectories not only confirm a close relationship between the rostral thalamus and prethalamus, but also uncover an unexpected close relationship between the caudal thalamus, epithalamus and rostral pretectum. Our data provide a useful resource for the systematic study of cell heterogeneity and differentiation kinetics within the developing diencephalon.

scholarly journals Comprehensive analysis of retinal development at single cell resolution identifies NFI factors as essential for mitotic exit and specification of late-born cells

2018 ◽  
Author(s):  
Brian S. Clark ◽  
Genevieve L. Stein-O’Brien ◽  
Fion Shiau ◽  
Gabrielle H. Cannon ◽  
Emily Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Developmental Stages ◽  
Cell Types ◽  
Developmental Competence ◽  
Cellular Heterogeneity ◽  
Retinal Cell ◽  
Control Of Gene Expression

SUMMARYPrecise temporal control of gene expression in neuronal progenitors is necessary for correct regulation of neurogenesis and cell fate specification. However, the extensive cellular heterogeneity of the developing CNS has posed a major obstacle to identifying the gene regulatory networks that control these processes. To address this, we used single cell RNA-sequencing to profile ten developmental stages encompassing the full course of retinal neurogenesis. This allowed us to comprehensively characterize changes in gene expression that occur during initiation of neurogenesis, changes in developmental competence, and specification and differentiation of each of the major retinal cell types. These data identify transitions in gene expression between early and late-stage retinal progenitors, as well as a classification of neurogenic progenitors. We identify here the NFI family of transcription factors (Nfia, Nfib, and Nfix) as genes with enriched expression within late RPCs, and show they are regulators of bipolar interneuron and Müller glia specification and the control of proliferative quiescence.

scholarly journals Single cell transcriptomics of the developing zebrafish lens and identification of putative controllers of lens development

2020 ◽  
Author(s):  
Dylan Farnsworth ◽  
Mason Posner ◽  
Adam Miller
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Regulatory Networks ◽  
Rna Binding ◽  
Rapid Development ◽  
Cell Types ◽  
Model System ◽  
Lens Development ◽  
Functional Studies ◽  
Open Questions

AbstractThe vertebrate lens is a valuable model system for investigating the gene expression changes that coordinate tissue differentiation due to its inclusion of two spatially separated cell types, the outer epithelial cells and the deeper denucleated fiber cells that they support. Zebrafish are a useful model system for studying lens development given the organ’s rapid development in the first several days of life in an accessible, transparent embryo. While we have strong foundational knowledge of the diverse lens crystallin proteins and the basic gene regulatory networks controlling lens development, no study has detailed gene expression in a vertebrate lens at single cell resolution. Here we report an atlas of lens gene expression in zebrafish embryos at single cell resolution through five days of development, identifying a number of novel regulators of lens development as potential targets for future functional studies. Our temporospatial expression data address open questions about the function of α-crystallins during lens development and provides the first detailed view of β- and γ-crystallin expression in and outside the lens. We describe subfunctionalization in transcription factor genes that occur as paralog pairs in the zebrafish. Finally, we examine the expression dynamics of cytoskeletal, RNA-binding, and transcription factors genes, identifying a number of novel patterns. Overall these data provide a foundation for identifying and characterizing lens developmental regulatory mechanisms and revealing targets for future functional studies with potential therapeutic impact.

scholarly journals Modeling bi-modality improves characterization of cell cycle on gene expression in single cells

2014 ◽  
Author(s):  
Lucas Dennis ◽  
Andrew McDavid ◽  
Patrick Danaher ◽  
Greg Finak ◽  
Michael Krouse ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Single Cells ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Expression Variability ◽  
Cell Cycle Genes ◽  
Cell Expression ◽  
Cell Gene

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.

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