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

2021 ◽  
Author(s):  
Reem Elorbany ◽  
Joshua M Popp ◽  
Katherine Rhodes ◽  
Benjamin J Strober ◽  
Kenneth Barr ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Specific Gene ◽  
Specific Cell ◽  
Cardiomyocyte Differentiation ◽  
Cell Type ◽  
Dynamic Effects ◽  
Regulatory Changes ◽  
Gene Regulatory ◽  
Cell Data

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.

Single-cell RNA-seq reveals the transcriptional landscape in ischemic stroke

2021 ◽  
pp. 0271678X2110267
Author(s):  
Kai Zheng ◽  
Lingmin Lin ◽  
Wei Jiang ◽  
Lin Chen ◽  
Xiyue Zhang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Single Cell ◽  
Expression Patterns ◽  
Cellular Heterogeneity ◽  
Ependymal Cells ◽  
Specific Gene ◽  
Specific Cell ◽  
Cell Type ◽  
Cns Inflammation ◽  
Cell Type Specific

Ischemic stroke (IS) is a detrimental neurological disease with limited treatments options. It has been challenging to define the roles of brain cell subsets in IS onset and progression due to cellular heterogeneity in the CNS. Here, we employed single-cell RNA sequencing (scRNA-seq) to comprehensively map the cell populations in the mouse model of MCAO (middle cerebral artery occlusion). We identified 17 principal brain clusters with cell-type specific gene expression patterns as well as specific cell subpopulations and their functions in various pathways. The CNS inflammation triggered upregulation of key cell type-specific genes unpublished before. Notably, microglia displayed a cell differentiation diversity after stroke among its five distinct subtypes. Importantly, we found the potential trajectory branches of the monocytes/macrophage’s subsets. Finally, we also identified distinct subclusters among brain vasculature cells, ependymal cells and other glia cells. Overall, scRNA-seq revealed the precise transcriptional changes during neuroinflammation at the single-cell level, opening up a new field for exploration of the disease mechanisms and drug discovery in stroke based on the cell-subtype specific molecules.

scholarly journals Implication of specific retinal cell-type involvement and gene expression changes in AMD progression using integrative analysis of single-cell and bulk RNA-seq profiling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yafei Lyu ◽  
Randy Zauhar ◽  
Nicholas Dana ◽  
Christianne E. Strang ◽  
Jian Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Age Related Macular Degeneration ◽  
Specific Gene ◽  
Cell Type ◽  
Adult Human ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Cell Data

AbstractAge‐related macular degeneration (AMD) is a blinding eye disease with no unifying theme for its etiology. We used single-cell RNA sequencing to analyze the transcriptomes of ~ 93,000 cells from the macula and peripheral retina from two adult human donors and bulk RNA sequencing from fifteen adult human donors with and without AMD. Analysis of our single-cell data identified 267 cell-type-specific genes. Comparison of macula and peripheral retinal regions found no cell-type differences but did identify 50 differentially expressed genes (DEGs) with about 1/3 expressed in cones. Integration of our single-cell data with bulk RNA sequencing data from normal and AMD donors showed compositional changes more pronounced in macula in rods, microglia, endothelium, Müller glia, and astrocytes in the transition from normal to advanced AMD. KEGG pathway analysis of our normal vs. advanced AMD eyes identified enrichment in complement and coagulation pathways, antigen presentation, tissue remodeling, and signaling pathways including PI3K-Akt, NOD-like, Toll-like, and Rap1. These results showcase the use of single-cell RNA sequencing to infer cell-type compositional and cell-type-specific gene expression changes in intact bulk tissue and provide a foundation for investigating molecular mechanisms of retinal disease that lead to new therapeutic targets.

scholarly journals scTenifoldKnk: a machine learning workflow performing virtual knockout experiments on single-cell gene regulatory networks

2021 ◽  
Author(s):  
Daniel Osorio ◽  
Yan Zhong ◽  
Guanxun Li ◽  
Qian Xu ◽  
Andrew E. Hillhouse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Gene Function ◽  
Specific Gene ◽  
Cell Type ◽  
Cell Technology ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
The Impact ◽  
Cell Gene

Gene knockout (KO) experiments are a proven approach for studying gene function. A typical KO experiment usually involves the phenotypic characterization of KO organisms. The recent advent of single-cell technology has greatly boosted the resolution of cellular phenotyping, providing unprecedented insights into cell-type-specific gene function. However, the use of single-cell technology in large-scale, systematic KO experiments is prohibitive due to the vast resources required. Here we present scTenifoldKnk, a machine learning workflow that performs virtual KO experiments using single-cell RNA sequencing (scRNA-seq) data. scTenifoldKnk first uses data from wild-type (WT) samples to construct a single-cell gene regulatory network (scGRN). Then, a gene is knocked out from the constructed scGRN by setting weights of the gene's outward edges to zeros. ScTenifoldKnk then compares this "pseudo-KO" scGRN with the original scGRN to identify differentially regulated (DR) genes. These DR genes, also called virtual-KO perturbed genes, are used to assess the impact of the gene KO and reveal the gene's function in analyzed cells. Using existing data sets, we demonstrate that the scTenifoldKnk analysis recapitulates the main findings of three real-animal KO experiments and confirms the functions of genes underlying three Mendelian diseases. We show the power of scTenifoldKnk as a predictive method to successfully predict the outcomes of two KO experiments that involve intestinal enterocytes in Ahr-/- mice and pancreatic islet cells in Malat1-/- mice, respectively. Finally, we demonstrate the use of scTenifoldKnk to perform systematic KO analyses, in which a large number of genes are virtually deleted, allowing gene functions to be revealed in a cell type-specific manner.

scholarly journals IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq

2020 ◽  
Vol 48 (W1) ◽  
pp. W275-W286 ◽  
Author(s):  
Anjun Ma ◽  
Cankun Wang ◽  
Yuzhou Chang ◽  
Faith H Brennan ◽  
Adam McDermaid ◽  
...  
Keyword(s):  
Single Cell ◽  
Target Genes ◽  
Complex Diseases ◽  
Specific Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Cell Type ◽  
Cell Type Specific ◽  
Gene Regulatory ◽  
Human And Mouse

Abstract A group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development. IRIS3 is freely accessible from https://bmbl.bmi.osumc.edu/iris3/ with no login requirement.

scholarly journals HMGA2 Promotes Colorectal Cancer Angiogenesis via Dual Regulation of Sema3A and VEGFA

2021 ◽  
Author(s):  
Cancan Wang ◽  
Yi Long ◽  
Miaomiao Tao ◽  
Hongbo Ma ◽  
Yanyan Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Epithelial Cells ◽  
Single Cell ◽  
Transcriptional Activity ◽  
Specific Cell ◽  
Cell Type ◽  
Invasion And Metastasis ◽  
Mesenchymal Tumors ◽  
Online Tools ◽  
Cell Data

Background: HMGA2 encodes a small non histone chromatin-associated protein that has no intrinsic transcriptional activity, but can modulate transcription by altering the chromatin architecture. HMGA2 was found overexpressed in a variety of epithelial and mesenchymal tumors and promoted invasion and metastasis in most malignant epithelial tumors. A recent study showed that P53 inhibited CRC progression by targeting HMGA2. However, the mechanism by which HMGA2 affect angiogenesis in CRC has not been clarified. Methods: The expression of HMGA2 was analyzed by IHC, WB and bio infomatic analysis. Cbioportal and mexpress online tools were applied to explore the CNV and methylation of HMGA2 in CRC patients. Single cell data from GEO was used to examine the specific cell type that contribute to the high HMGA2 expression in CRC. Lentivirus was used to knock down HMGA2 in CRC cells and HUVECs was used to study angiogenesis. Results: In the current study, we first detected the expression pattern of HMGA2 in CRC patients and evaluated its clinical values and CNV amplification could possibly contribute to the up regulation of HMGA2 in CRC patients. By analyzing CRC single cell data we found that HMGA2 was specifically up regulated in the colorectal epithelial cells. Furthermore, knocking down of HMGA2 suppresses angiogenesis via dual regulation of VEGF-A and SEMA3A in CRC through inactivating VEGRR2 pathway in HUVECs. Conclusions: HMGA2 might be a promising prognostic marker and target for treating advanced CRC patients.

scholarly journals Cell-Type-Specific Gene Regulatory Networks Underlying Murine Neonatal Heart Regeneration at Single-Cell Resolution

Cell Reports ◽  
2020 ◽  
Vol 33 (10) ◽  
pp. 108472
Author(s):  
Zhaoning Wang ◽  
Miao Cui ◽  
Akansha M. Shah ◽  
Wei Tan ◽  
Ning Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Heart Regeneration ◽  
Cell Type ◽  
Neonatal Heart ◽  
Cell Type Specific ◽  
Gene Regulatory

scholarly journals Cell-Type-Specific Gene Regulatory Networks Underlying Murine Neonatal Heart Regeneration at Single-Cell Resolution

Cell Reports ◽  
2021 ◽  
Vol 35 (8) ◽  
pp. 109211
Author(s):  
Zhaoning Wang ◽  
Miao Cui ◽  
Akansha M. Shah ◽  
Wei Tan ◽  
Ning Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Heart Regeneration ◽  
Cell Type ◽  
Neonatal Heart ◽  
Cell Type Specific ◽  
Gene Regulatory

scholarly journals The three-dimensional chromatin structure of the major human pancreatic cell types reveals lineage-specific regulatory architecture of T2D risk

2021 ◽  
Author(s):  
Su Chun ◽  
Long Gao ◽  
Catherine L May ◽  
James A Pippin ◽  
Keith Boehm ◽  
...  
Keyword(s):  
Single Cell ◽  
Target Genes ◽  
Genetic Diseases ◽  
Three Dimensional ◽  
Specific Gene ◽  
Cell Type ◽  
Pancreatic Cell ◽  
Regulatory Architecture ◽  
Cell Type Specific ◽  
Gene Regulatory

Three-dimensional (3D) chromatin organization maps help to dissect cell type-specific gene regulatory programs. Furthermore, 3D chromatin maps have contributed to elucidating the pathogenesis of complex genetic diseases by connecting distal regulatory regions and genetic risk variants to their respective target genes. To understand the cell type-specific regulatory architecture of diabetes risk, we generated transcriptomic and 3D epigenomic profiles of human pancreatic acinar, alpha, and beta cells using single-cell RNA-seq, single-cell ATAC-seq, and high-resolution Hi-C of sorted cells. Comparisons of these profiles revealed differential A/B (open/closed) chromatin compartmentalization, chromatin looping, and control of cell type-specific gene regulatory programs. We identified a total of 1,094 putative causal-variant-target-gene pairs at 129 type 2 diabetes GWAS signals using pancreatic 3D chromatin maps. We found that the connections between candidate causal variants and their putative target effector genes are cell-type stratified and emphasize previously underappreciated roles for alpha and acinar cells in diabetes pathogenesis

scholarly journals A United Statistical Framework for Single Cell and Bulk Sequencing Data

2017 ◽  
Author(s):  
Lingxue Zhu ◽  
Jing Lei ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Accurate Estimation ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific ◽  
Different Cell Types ◽  
Cell Data

Recent advances in technology have enabled the measurement of RNA levels for individual cells. Compared to traditional tissue-level bulk RNA-seq data, single cell sequencing yields valuable insights about gene expression profiles for different cell types, which is potentially critical for understanding many complex human diseases. However, developing quantitative tools for such data remains challenging because of high levels of technical noise, especially the “dropout” events. A “dropout” happens when the RNA for a gene fails to be amplified prior to sequencing, producing a “false” zero in the observed data. In this paper, we propose a Unified RNA-Sequencing Model (URSM) for both single cell and bulk RNA-seq data, formulated as a hierarchical model. URSM borrows the strength from both data sources and carefully models the dropouts in single cell data, leading to a more accurate estimation of cell type specific gene expression profile. In addition, URSM naturally provides inference on the dropout entries in single cell data that need to be imputed for downstream analyses, as well as the mixing proportions of different cell types in bulk samples. We adopt an empirical Bayes approach, where parameters are estimated using the EM algorithm and approximate inference is obtained by Gibbs sampling. Simulation results illustrate that URSM outperforms existing approaches both in correcting for dropouts in single cell data, as well as in deconvolving bulk samples. We also demonstrate an application to gene expression data on fetal brains, where our model successfully imputes the dropout genes and reveals cell type specific expression patterns.

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