scholarly journals Single-Cell RNA Sequencing Unveils Unique Transcriptomic Signatures of Organ-Specific Endothelial Cells

Circulation ◽  
2020 ◽  
Vol 142 (19) ◽  
pp. 1848-1862 ◽  
Author(s):  
David T. Paik ◽  
Lei Tian ◽  
Ian M. Williams ◽  
Siyeon Rhee ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Receptor Expression ◽  
Transcriptional Networks ◽  
Sequencing Data ◽  
Tissue Specific ◽  
Functional Relationships ◽  
Single Cell Rna Sequencing

Background: Endothelial cells (ECs) display considerable functional heterogeneity depending on the vessel and tissue in which they are located. Whereas these functional differences are presumably imprinted in the transcriptome, the pathways and networks that sustain EC heterogeneity have not been fully delineated. Methods: To investigate the transcriptomic basis of EC specificity, we analyzed single-cell RNA sequencing data from tissue-specific mouse ECs generated by the Tabula Muris consortium. We used a number of bioinformatics tools to uncover markers and sources of EC heterogeneity from single-cell RNA sequencing data. Results: We found a strong correlation between tissue-specific EC transcriptomic measurements generated by either single-cell RNA sequencing or bulk RNA sequencing, thus validating the approach. Using a graph-based clustering algorithm, we found that certain tissue-specific ECs cluster strongly by tissue (eg, liver, brain), whereas others (ie, adipose, heart) have considerable transcriptomic overlap with ECs from other tissues. We identified novel markers of tissue-specific ECs and signaling pathways that may be involved in maintaining their identity. Sex was a considerable source of heterogeneity in the endothelial transcriptome and we discovered Lars2 to be a gene that is highly enriched in ECs from male mice. We found that markers of heart and lung ECs in mice were conserved in human fetal heart and lung ECs. We identified potential angiocrine interactions between tissue-specific ECs and other cell types by analyzing ligand and receptor expression patterns. Conclusions: We used single-cell RNA sequencing data generated by the Tabula Muris consortium to uncover transcriptional networks that maintain tissue-specific EC identity and to identify novel angiocrine and functional relationships between tissue-specific ECs.

scholarly journals Probabilistic index models for testing differential expression in single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Effect Size ◽  
Expression Patterns ◽  
Simulation Studies ◽  
Sequencing Data ◽  
Rank Tests ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing (scRNA-seq) technologies profile gene expression patterns in individual cells. It is often of interest to test for differential expression (DE) between conditions, e.g. treatment vs control or between cell types. Simulation studies have shown that non-parametric tests, such as the Wilcoxon-rank sum test, can robustly detect significant DE, with better performance than many parametric tools specifically developed for scRNA-seq data analysis. However, these rank tests cannot be used for complex experimental designs involving multiple groups, multiple factors and confounding variables. Further, rank based tests do not provide an interpretable measure of the effect size. We propose a semi-parametric approach based on probabilistic index models (PIM) that form a flexible class of models that generalize classical rank tests. Our method does not rely on strong distributional assumptions and it allows accounting for confounding factors. Moreover, it allows for the estimation of the effect size in terms of a probabilistic index. Real data analysis demonstrate that PIM is capable of identifying biologically meaningful DE. Our simulation studies also show that DE tests succeed well in controlling the false discovery rate at its nominal level, while maintaining good sensitivity as compared to competing methods.

scholarly journals A monotonicity-based gene clustering algorithm for enhancing clarity in single-cell RNA sequencing data

2020 ◽  
Author(s):  
Victor Wang ◽  
Pietro Antonio Cicalese ◽  
Chandra Mohan
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Expression Patterns ◽  
Gene Clustering ◽  
Sequencing Data ◽  
Technical Noise ◽  
Cell Clustering ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing (scRNA-seq) technologies and analysis tools have allowed for meaningful insight into the roles and relationships of cells. However, high dimensionality, frequent dropout values, and technical noise remain prevalent challenges for scRNA-seq data, obscuring the already complex expression patterns. To address several shortcomings in commonly used distance metrics, we present a monotonicity-based distance metric designed to enhance the clarity of scRNA-seq data. We apply our metric in a gene clustering algorithm, which we run on several biological datasets. We compare our results to those generated by popular clustering algorithms to demonstrate that our algorithm has substantial ability to improve the accuracy of subsequent cell clustering.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single cell RNA sequencing

2020 ◽  
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Local Community ◽  
Expression Patterns ◽  
Imputation Method ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data therefore need to be carefully processed before in-depth analysis. Here we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local community of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single cell sequencing), on six datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise.

scholarly journals Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data

2019 ◽  
Author(s):  
Koki Tsuyuzaki ◽  
Manabu Ishii ◽  
Itoshi Nikaido
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cell Interaction ◽  
Receptor Expression ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Single Cell Rna Sequencing ◽  
Ligand Expression ◽  
Cell Cell

AbstractComplex biological systems can be described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing technologies have enabled the detection of CCIs and related ligand-receptor (L-R) gene expression simultaneously. However, previous data analysis methods have focused on only one-to-one CCIs between two cell types. To also detect many-to-many CCIs, we proposescTensor, a novel method for extracting representative triadic relationships (hypergraphs), which include (i) ligand-expression, (ii) receptor-expression, and (iii) L-R pairs. When applied to simulated and empirical datasets,scTensorwas able to detect some hypergraphs including paracrine/autocrine CCI patterns, which cannot be detected by previous methods.

scholarly journals Isoform-level gene expression patterns in single-cell RNA-sequencing data

2018 ◽  
Vol 34 (14) ◽  
pp. 2392-2400 ◽  
Author(s):  
Trung Nghia Vu ◽  
Quin F Wills ◽  
Krishna R Kalari ◽  
Nifang Niu ◽  
Liewei Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

scholarly journals FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms

2020 ◽  
Author(s):  
Jingsi Ming ◽  
Zhixiang Lin ◽  
Xiang Wan ◽  
Can Yang ◽  
Angela Ruohao Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Biology ◽  
Developmental Trajectories ◽  
Expression Patterns ◽  
Cell Types ◽  
Original Structure ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing (scRNA-seq) has now been used extensively to discover novel cell types and reconstruct developmental trajectories by measuring mRNA expression patterns of individual cells. However, datasets collected using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms, are difficult to compare because of their heterogeneity. Each platform has unique advantages, and integration of these datasets would provide deeper insights into cell biology and gene regulation. Through comprehensive data exploration, we found that accurate integration is often hampered by differences in cell-type compositions. Herein we describe FIRM, an algorithm that addresses this problem and achieves efficient and accurate integration of heterogeneous scRNA-seq datasets across multiple platforms. We applied FIRM to numerous scRNA-seq datasets generated using SS2 and 10X from mouse, mouse lemur, and human, comparing its performance in dataset integration with other state-of-the-art methods. The integrated datasets generated using FIRM show accurate mixing of shared cell type identities and superior preservation of original structure for each dataset. FIRM not only generates robust integrated datasets for downstream analysis, but is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a versatile and indispensable tool for scRNA-seq analysis.

scholarly journals Canonical and early lineage-specific stem cell types identified in planarian SirNeoblasts

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Kaimeng Niu ◽  
Hao Xu ◽  
Yuanyi Zhou Xiong ◽  
Yun Zhao ◽  
Chong Gao ◽  
...  
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Expression Patterns ◽  
Cell Subpopulation ◽  
Functional Study ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background The pluripotent stem cells in planarians, a model for tissue and cellular regeneration, remain further identification. We recently developed a method to enrich piwi-1+ cells in Schmidtea mediterranea, by staining cells with SiR-DNA and Cell Tracker Green, named SirNeoblasts that permits their propagation and subsequent functional study in vivo. Since traditional enrichment for planarian neoblasts by Hoechst 33342 staining generates X1 cells, blocking the cell cycle and inducing cytotoxicity, this method by SiR-DNA and Cell Tracker Green represents a complementary technological advance for functional investigation of cell fate and regeneration. However, the similarities in heterogeneity of cell subtypes between SirNeoblasts and X1 remain unknown. Results In this work, we performed single cell RNA sequencing of SirNeoblasts for comparison with differential expression patterns in a publicly available X1 single cell RNA sequencing data. We found first that all of the lineage-specific progenitor cells in X1 were present in comparable proportions in SirNeoblasts. In addition, SirNeoblasts contain an early muscle progenitor that is unreported in X1. Analysis of new markers for putative pluripotent stem cells identified here, with subsequent sub-clustering analysis, revealed earlier lineages of epidermal, muscular, intestinal, and pharyngeal progenitors than have been observed in X1. Using the gcm as a marker, we also identified a cell subpopulation resided in previously identified tgs-1+ neoblasts. Knockdown of gcm impaired the neoblast repopulation, suggesting a function of gcm in neoblasts. Conclusions In summary, the use of SirNeoblasts will enable broad experimental advances in regeneration and cell fate specification, given the possibility for propagation and transplantation of recombinant and mutagenized pluripotent stem cells that are not previously afforded to this rapid and versatile model system.

scholarly journals Isoform-level gene expression patterns in single-cell RNA-sequencing data

2016 ◽  
Author(s):  
Trung Nghia Vu ◽  
Quin F Wills ◽  
Krishna R Kalari ◽  
Nifang Niu ◽  
Liewei Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Pattern Analysis ◽  
Single Cells ◽  
Expression Patterns ◽  
Cell Populations ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Differential Pattern

RNA-sequencing of single-cells enables characterization of transcriptional heterogeneity in seemingly homogenous cell populations. In this study we propose and apply a novel method, ISOform-Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of pairs of isoforms from the same gene in single-cell isoform-level expression data. We define six principal patterns of isoform expression relationships and introduce the concept of differential pattern analysis. We applied ISOP for analysis of single-cell RNA-sequencing data from a breast cancer cell line, with replication in two independent datasets. In the primary dataset we detected and assigned pattern type of 16562 isoform-pairs from 4929 genes. Our results showed that 78% of the isoform pairs displayed a mutually exclusive expression pattern, 14% of the isoform pairs displayed bimodal isoform preference and 8% isoform pairs displayed isoform preference. 26% of the isoform-pair patterns were significant, while remaining isoform-pair patterns can be understood as effects of transcriptional bursting, drop-out and biological heterogeneity. 32% of genes discovered through differential pattern analysis were novel and not detected by differential expression analysis. ISOP provides a novel approach for characterization of isoform-level expression in single-cell populations. Our results reveal a common occurrence of isoform-level preference, commitment and heterogeneity in single-cell populations.

scholarly journals Analysis of single-cell RNA-sequencing data to identify quiescent and proliferating neural cell populations in Glioblastoma

2021 ◽  
Author(s):  
Rajeev Vikram ◽  
Wen□Cheng Chou ◽  
Pei-Ei Wu ◽  
Wei-Ting Chen ◽  
Chen-Yang Shen
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
High Grade ◽  
Sequencing Data ◽  
Quiescent Cells ◽  
Single Cell Rna Sequencing ◽  
Tumorigenic Cells

ABSTRACTBackgroundDiffuse Glioblastoma (GBM) has high mortality and remains one of the most challenging type of cancer to treat. Identifying and characterizing the cells populations driving tumor growth and therapy resistance has been particularly difficult owing to marked inter and intra tumoral heterogeneity observed in these tumors. These tumorigenic populations contain long lived cells associated with latency, immune evasion and metastasis.MethodsHere, we analyzed the single-cell RNA-sequencing data of high grade glioblastomas from four different studies using integrated analysis of gene expression patterns, cell cycle stages and copy number variation to identify gene expression signatures associated with quiescent and cycling neuronal tumorigenic cells.ResultsThe results show that while cycling and quiescent cells are present in GBM of all age groups, they exist in a much larger proportion in pediatric glioblastomas. These cells show similarities in their expression patterns of a number of pluripotency and proliferation related genes. Upon unbiased clustering, these cells explicitly clustered on their cell cycle stage. Quiescent cells in both the groups specifically overexpressed a number of genes for ribosomal protein, while the cycling cells were enriched in the expression of high-mobility group and heterogeneous nuclear ribonucleoprotein group genes. A number of well-known markers of quiescence and proliferation in neurogenesis showed preferential expression in the quiescent and cycling populations identified in our analysis. Through our analysis, we identify ribosomal proteins as key constituents of quiescence in glioblastoma stem cells.ConclusionsThis study identifies gene signatures common to adult and pediatric glioblastoma quiescent and cycling stem cell niches. Further research elucidating their role in controlling quiescence and proliferation in tumorigenic cells in high grade glioblastoma will open avenues in more effective treatment strategies for glioblastoma patients.

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