scholarly journals scSTATseq: Diminishing Technical Dropout Enables Core Transcriptome Recovery and Comprehensive Single-cell Trajectory Mapping

2020 ◽  
Author(s):  
Zihan Zheng ◽  
Xiangyu Tang ◽  
Xin Qiu ◽  
Hao Xu ◽  
Haiyang Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Osteoclast Differentiation ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Large Numbers ◽  
Single Cell Rna Sequencing ◽  
Trajectory Mapping ◽  
Cell Trajectory ◽  
Tn5 Transposase

AbstractThe advent of single-cell RNA sequencing has provided illuminating information on complex systems. However, large numbers of genes tend to be scarcely detected in common scRNAseq approaches due to technical dropout. Although bioinformatics approaches have been developed to approximate true expression profiles, assess the dropout events on single-cell transcriptomes is still consequently challenging. In this report, we present a new plate-based method for scRNAseq that relies on Tn5 transposase to tagment cDNA following second strand synthesis. By utilizing pre-amplification tagmentation step, scSTATseq libraries are insulated against technical dropout, allowing for detailed analysis of gene-gene co-expression relationships and mapping of pathway trajectories. The entire scSTATseq library construction workflow can be completed in 7 hours, and recover transcriptome information on up to 8,000 protein-coding genes. Investigation of osteoclast differentiation using this workflow allowed us to identify novel markers of interest such as Rab15. Overall, scSTATseq is an efficient and economical method for scRNAseq that compares favorably with existing workflows.

scholarly journals Super-cells untangle large and complex single-cell transcriptome networks

2021 ◽  
Author(s):  
Mariia Bilous ◽  
Loc Tran ◽  
Chiara Cianciaruso ◽  
Santiago J Carmona ◽  
Mikael J Pittet ◽  
...  
Keyword(s):  
Single Cell ◽  
Coarse Graining ◽  
Cell Populations ◽  
Cell Type ◽  
Large Numbers ◽  
Single Cell Rna Sequencing ◽  
Gene Correlation ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Here we develop a network-based coarse-graining framework where highly similar cells are merged into super-cells. We demonstrate that super-cells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, super-cells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

Localization of migraine susceptibility genes in human brain by single-cell RNA sequencing

Cephalalgia ◽  
2018 ◽  
Vol 38 (13) ◽  
pp. 1976-1983 ◽  
Author(s):  
William Renthal
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Cell Types ◽  
Susceptibility Genes ◽  
Brain Cell ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Brain Cell Types

Background Migraine is a debilitating disorder characterized by severe headaches and associated neurological symptoms. A key challenge to understanding migraine has been the cellular complexity of the human brain and the multiple cell types implicated in its pathophysiology. The present study leverages recent advances in single-cell transcriptomics to localize the specific human brain cell types in which putative migraine susceptibility genes are expressed. Methods The cell-type specific expression of both familial and common migraine-associated genes was determined bioinformatically using data from 2,039 individual human brain cells across two published single-cell RNA sequencing datasets. Enrichment of migraine-associated genes was determined for each brain cell type. Results Analysis of single-brain cell RNA sequencing data from five major subtypes of cells in the human cortex (neurons, oligodendrocytes, astrocytes, microglia, and endothelial cells) indicates that over 40% of known migraine-associated genes are enriched in the expression profiles of a specific brain cell type. Further analysis of neuronal migraine-associated genes demonstrated that approximately 70% were significantly enriched in inhibitory neurons and 30% in excitatory neurons. Conclusions This study takes the next step in understanding the human brain cell types in which putative migraine susceptibility genes are expressed. Both familial and common migraine may arise from dysfunction of discrete cell types within the neurovascular unit, and localization of the affected cell type(s) in an individual patient may provide insight into to their susceptibility to migraine.

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Growth Stages ◽  
High Throughput Analysis ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

scholarly journals Overlapping protein-coding genes in human genome and their coincidental expression in tissues

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Chao-Hsin Chen ◽  
Chao-Yu Pan ◽  
Wen-chang Lin
Keyword(s):  
Human Genome ◽  
Expression Profiles ◽  
Tissue Expression ◽  
Human Protein ◽  
Clear Understanding ◽  
Overlapping Genes ◽  
Genome Sequences ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Overlapping Gene

Abstract The completion of human genome sequences and the advancement of next-generation sequencing technologies have engendered a clear understanding of all human genes. Overlapping genes are usually observed in compact genomes, such as those of bacteria and viruses. Notably, overlapping protein-coding genes do exist in human genome sequences. Accordingly, we used the current Ensembl gene annotations to identify overlapping human protein-coding genes. We analysed 19,200 well-annotated protein-coding genes and determined that 4,951 protein-coding genes overlapped with their adjacent genes. Approximately a quarter of all human protein-coding genes were overlapping genes. We observed different clusters of overlapping protein-coding genes, ranging from two genes (paired overlapping genes) to 22 genes. We also divided the paired overlapping protein-coding gene groups into four subtypes. We found that the divergent overlapping gene subtype had a stronger expression association than did the subtypes of 5ʹ-tandem overlapping and 3ʹ-tandem overlapping genes. The majority of paired overlapping genes exhibited comparable coincidental tissue expression profiles; however, a few overlapping gene pairs displayed distinctive tissue expression association patterns. In summary, we have carefully examined the genomic features and distributions about human overlapping protein-coding genes and found coincidental expression in tissues for most overlapping protein-coding genes.

scholarly journals LncExpDB: an expression database of human long non-coding RNAs

2020 ◽  
Vol 49 (D1) ◽  
pp. D962-D968 ◽  
Author(s):  
Zhao Li ◽  
Lin Liu ◽  
Shuai Jiang ◽  
Qianpeng Li ◽  
Changrui Feng ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Biological Functions ◽  
Protein Coding ◽  
Web Interfaces ◽  
Functional Studies ◽  
Protein Coding Genes ◽  
Genes Expression ◽  
Wide Range ◽  
Non Coding Rnas ◽  
User Friendly

Abstract Expression profiles of long non-coding RNAs (lncRNAs) across diverse biological conditions provide significant insights into their biological functions, interacting targets as well as transcriptional reliability. However, there lacks a comprehensive resource that systematically characterizes the expression landscape of human lncRNAs by integrating their expression profiles across a wide range of biological conditions. Here, we present LncExpDB (https://bigd.big.ac.cn/lncexpdb), an expression database of human lncRNAs that is devoted to providing comprehensive expression profiles of lncRNA genes, exploring their expression features and capacities, identifying featured genes with potentially important functions, and building interactions with protein-coding genes across various biological contexts/conditions. Based on comprehensive integration and stringent curation, LncExpDB currently houses expression profiles of 101 293 high-quality human lncRNA genes derived from 1977 samples of 337 biological conditions across nine biological contexts. Consequently, LncExpDB estimates lncRNA genes’ expression reliability and capacities, identifies 25 191 featured genes, and further obtains 28 443 865 lncRNA-mRNA interactions. Moreover, user-friendly web interfaces enable interactive visualization of expression profiles across various conditions and easy exploration of featured lncRNAs and their interacting partners in specific contexts. Collectively, LncExpDB features comprehensive integration and curation of lncRNA expression profiles and thus will serve as a fundamental resource for functional studies on human lncRNAs.

scholarly journals A map of tumor–host interactions in glioma at single-cell resolution

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Francesca Pia Caruso ◽  
Luciano Garofano ◽  
Fulvio D'Angelo ◽  
Kai Yu ◽  
Fuchou Tang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cross Talk ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Data ◽  
Host Interaction ◽  
Receptor Interactions ◽  
Single Cell Rna Sequencing

ABSTRACT Background Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets. Results We present a novel method, single-cell Tumor–Host Interaction tool (scTHI), to identify significantly activated ligand–receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand–receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma. Conclusions Our results provide a complete map of the active tumor–host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

scholarly journals Characterization of Diabetic and Non-Diabetic Foot Ulcers Using Single-Cell RNA-Sequencing

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 815
Author(s):  
Michael Januszyk ◽  
Kellen Chen ◽  
Dominic Henn ◽  
Deshka S. Foster ◽  
Mimi R. Borrelli ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Chronic Wounds ◽  
Expression Profiles ◽  
Foot Ulcers ◽  
Cold Ischemia Time ◽  
Cold Ischemia ◽  
Ischemia Time ◽  
Extracellular Matrix Components ◽  
Single Cell Rna Sequencing

Background: Recent advances in high-throughput single-cell sequencing technologies have led to their increasingly widespread adoption for clinical applications. However, challenges associated with tissue viability, cell yield, and delayed time-to-capture have created unique obstacles for data processing. Chronic wounds, in particular, represent some of the most difficult target specimens, due to the significant amount of fibrinous debris, extracellular matrix components, and non-viable cells inherent in tissue routinely obtained from debridement. Methods: Here, we examined the feasibility of single cell RNA sequencing (scRNA-seq) analysis to evaluate human chronic wound samples acquired in the clinic, subjected to prolonged cold ischemia time, and processed without FACS sorting. Wound tissue from human diabetic and non-diabetic plantar foot ulcers were evaluated using an optimized 10X Genomics scRNA-seq platform and analyzed using a modified data pipeline designed for low-yield specimens. Cell subtypes were identified informatically and their distributions and transcriptional programs were compared between diabetic and non-diabetic tissue. Results: 139,000 diabetic and non-diabetic wound cells were delivered for 10X capture after either 90 or 180 min of cold ischemia time. cDNA library concentrations were 858.7 and 364.7 pg/µL, respectively, prior to sequencing. Among all barcoded fragments, we found that 83.5% successfully aligned to the human transcriptome and 68% met the minimum cell viability threshold. The average mitochondrial mRNA fraction was 8.5% for diabetic cells and 6.6% for non-diabetic cells, correlating with differences in cold ischemia time. A total of 384 individual cells were of sufficient quality for subsequent analyses; from this cell pool, we identified transcriptionally-distinct cell clusters whose gene expression profiles corresponded to fibroblasts, keratinocytes, neutrophils, monocytes, and endothelial cells. Fibroblast subpopulations with differing fibrotic potentials were identified, and their distributions were found to be altered in diabetic vs. non-diabetic cells. Conclusions: scRNA-seq of clinical wound samples can be achieved using minor modifications to standard processing protocols and data analysis methods. This simple approach can capture widespread transcriptional differences between diabetic and non-diabetic tissue obtained from matched wound locations.

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

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-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 subnetwork 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 synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Reclassification of classic T cell subsets and identification of novel cell types by single-cell RNA sequencing

2020 ◽  
Author(s):  
Xiangru Shen ◽  
Xuefei Wang ◽  
Shan Chen ◽  
Hongyi Liu ◽  
Ni Hong ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
T Cell Activation ◽  
Cell Activation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
T Cell Subsets ◽  
Single Cell Rna Sequencing

Abstract Single cell RNA sequencing (scRNA-seq) clusters cells using genome-wide gene expression profiles. The relationship between scRNA-seq Clustered-Populations (scCPops) and cell surface marker-defined classic T cell subsets is unclear. Here, we interrogated 6 bead-enriched T cell subsets with 62,235 single cell transcriptomes and re-grouped them into 9 scCPops. Bead-enriched CD4 Naïve, CD8 Naïve and CD4 memory were mainly clustered into their scCPop counterparts, while the other T cell subsets were clustered into multiple scCPops including unexpected mucosal-associated invariant T cell and natural killer T cell. Most interestingly, we discovered a new T cell type that highly expressed Interferon Signaling Associated Genes (ISAGs), namely IFNhi T. We further enriched IFNhi T for scRNA-seq analyses. IFNhi T cluster disappeared on tSNE after removing ISAGs, and IFNhi T cluster showed up by tSNE analyses of ISAGs alone, indicating ISAGs are the major contributor of IFNhi T cluster. BST2+ cells and BST2- cells showing different efficiencies of T cell activation indicates high ISAGs may contribute to quick immune responses.

scholarly journals Re-investigation of classic T cell subsets and identification of novel cell subpopulations by single-cell RNA sequencing

2021 ◽  
Author(s):  
Xuefei Wang ◽  
Xiangru Shen ◽  
Shan Chen ◽  
Hongyi Liu ◽  
Ni Hong ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Cell Surface ◽  
Single Cell ◽  
Rna Sequencing ◽  
T Cell Activation ◽  
Expression Profiles ◽  
T Cell Subsets ◽  
Cell Subpopulation ◽  
Single Cell Rna Sequencing

AbstractClassic T cell subsets are defined by a small set of cell surface markers, while single cell RNA sequencing (scRNA-seq) clusters cells using genome-wide gene expression profiles. The relationship between scRNA-seq Clustered-Populations (scCPops) and cell surface marker-defined classic T cell subsets remain unclear. Here, we interrogated 6 bead-enriched T cell subsets with 62,235 single cell transcriptomes and re-grouped them into 9 scCPops. Bead-enriched CD4 Naïve and CD8 Naïve were mainly clustered into their scCPop counterparts, while cells from the other T cell subsets were assigned to multiple scCPops including mucosal-associated invariant T cells and natural killer T cells. The multiple T cell subsets that form a single scCPop exhibited similar expression pattern, but not vice versa, indicating scCPops are much homogeneous cell populations with similar cell states. Interestingly, we discovered and named IFNhi T, a new T cell subpopulation that highly expressed Interferon Signaling Associated Genes (ISAGs). We further enriched IFNhi T by FACS sorting of BST2 for scRNA-seq analyses. IFNhi T cluster disappeared on tSNE plot after removing ISAGs, while IFNhi T cluster showed up by tSNE analyses of ISAGs alone, indicating ISAGs are the major contributor of IFNhi T cluster. BST2+ T cells and BST2− T cells showing different efficiencies of T cell activation indicates high level of ISAGs may contribute to quick immune responses.

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