scholarly journals scRegulocity: Detection of local RNA velocity patterns in embeddings of single cell RNA-Seq data

2021 ◽  
Author(s):  
Akdes Serin Harmanci ◽  
Arif O Harmanci ◽  
Xiaobo Zhou ◽  
Benjamin Deneen ◽  
Ganesh Rao ◽  
...  
Keyword(s):  
Single Cell ◽  
Velocity Estimation ◽  
Rna Seq ◽  
Cell Level ◽  
Regulatory Changes ◽  
Transcriptional Dynamics ◽  
Biological Phenomena ◽  
Switching Patterns ◽  
Dynamic Cell ◽  
Local Patterns

Single cell RNA-sequencing has revolutionized transcriptome analysis. ScRNA-seq provides a massive resource for studying biological phenomena at single cell level. One of the most important applications of scRNA-seq is the inference of dynamic cell states through modeling of transcriptional dynamics. Understanding the full transcriptional dynamics using the concept named RNA Velocity enables us to identify cell states, regimes of regulatory changes in cell states, and putative drivers within these states. We present scRegulocity that integrates RNA-velocity estimates with locality information from cell embedding coordinates. scRegulocity focuses on velocity switching patterns, local patterns where velocity of nearby cells change abruptly. These different transcriptional dynamics patterns can be indicative of transitioning cell states. scRegulocity annotates these patterns with genes and enriched pathways and also analyzes and visualizes the velocity switching patterns at the regulatory network level. scRegulocity also combines velocity estimation, pattern detection and visualization steps.

scholarly journals SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells

2021 ◽  
Vol 7 (8) ◽  
pp. eabe3610
Author(s):  
Conor J. Kearney ◽  
Stephin J. Vervoort ◽  
Kelly M. Ramsbottom ◽  
Izabela Todorovski ◽  
Emily J. Lelliott ◽  
...  
Keyword(s):  
Single Cell ◽  
Posttranslational Modification ◽  
Cellular Differentiation ◽  
Single Cells ◽  
Simultaneous Detection ◽  
Surface Protein ◽  
Rna Seq ◽  
Cell Level ◽  
Simultaneous Integration ◽  
Unique Surface

Multimodal single-cell RNA sequencing enables the precise mapping of transcriptional and phenotypic features of cellular differentiation states but does not allow for simultaneous integration of critical posttranslational modification data. Here, we describe SUrface-protein Glycan And RNA-seq (SUGAR-seq), a method that enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Integrated SUGAR-seq and glycoproteome analysis identified tumor-infiltrating T cells with unique surface glycan properties that report their epigenetic and functional state.

scholarly journals Single-cell transcriptional dynamics of flavivirus infection

2017 ◽  
Author(s):  
Fabio Zanini ◽  
Szu-Yuan Pu ◽  
Elena Bekerman ◽  
Shirit Einav ◽  
Stephen R. Quake
Keyword(s):  
Single Cell ◽  
Membrane Trafficking ◽  
Viral Infections ◽  
Cultured Cells ◽  
Complex Dynamics ◽  
Single Cell Level ◽  
Cell Level ◽  
Transcriptional Dynamics ◽  
High Degree ◽  
Virus Abundance

ABSTRACTDengue and Zika viral infections affect millions of people annually and can be complicated by hemorrhage or neurological manifestations, respectively. However, a thorough understanding of the host response to these viruses is lacking, partly because conventional approaches ignore heterogeneity in virus abundance across cells. We present viscRNA-Seq (virus-inclusive single cell RNA-Seq), an approach to probe the host transcriptome together with intracellular viral RNA at the single cell level. We applied viscRNA-Seq to monitor dengue and Zika virus infection in cultured cells and discovered extreme heterogeneity in virus abundance. We exploited this variation to identify host factors that show complex dynamics and a high degree of specificity for either virus, including proteins involved in the endoplasmic reticulum translocon, signal peptide processing, and membrane trafficking. We validated the viscRNA-Seq hits and discovered novel proviral and antiviral factors. viscRNA-Seq is a powerful approach to assess the genome-wide virus-host dynamics at single cell level.

scholarly journals scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

2019 ◽  
Author(s):  
Congting Ye ◽  
Qian Zhou ◽  
Xiaohui Wu ◽  
Chen Yu ◽  
Guoli Ji ◽  
...  
Keyword(s):  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Rna Seq ◽  
Computational Tool ◽  
Cell Level ◽  
Wilcoxon Rank Sum Test ◽  
Transcriptional Regulatory ◽  
Cell Groups

Abstract Motivation Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3′ enriched strategy in library construction, the most commonly used scRNA-seq protocol—10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data. Availability and implementation The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Integrated profiling of single cell epigenomic and transcriptomic landscape of Parkinson’s disease mouse brain

2020 ◽  
Author(s):  
Jixing Zhong ◽  
Gen Tang ◽  
Jiacheng Zhu ◽  
Xin Qiu ◽  
Weiying Wu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Single Cell ◽  
Early Stage ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Distinct Cell ◽  
Single Nucleus

AbstractParkinson’s disease (PD) is a neurodegenerative disease leading to the impairment of execution of movement. PD pathogenesis has been largely investigated, but either restricted in bulk level or at certain cell types, which failed to capture cellular heterogeneity and intrinsic interplays among distinct cell types. To overcome this, we applied single-nucleus RNA-seq and single cell ATAC-seq on cerebellum, midbrain and striatum of PD mouse and matched control. With 74,493 cells in total, we comprehensively depicted the dysfunctions under PD pathology covering proteostasis, neuroinflammation, calcium homeostasis and extracellular neurotransmitter homeostasis. Besides, by multi-omics approach, we identified putative biomarkers for early stage of PD, based on the relationships between transcriptomic and epigenetic profiles. We located certain cell types that primarily contribute to PD early pathology, narrowing the gap between genotypes and phenotypes. Taken together, our study provides a valuable resource to dissect the molecular mechanism of PD pathogenesis at single cell level, which could facilitate the development of novel methods regarding diagnosis, monitoring and practical therapies against PD at early stage.

scholarly journals scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

2021 ◽  
Author(s):  
Sheng Zhu ◽  
Qiwei Lian ◽  
Wenbin Ye ◽  
Wei Qin ◽  
Zhe Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Eukaryotic Gene ◽  
User Friendly ◽  
Different Cell Types

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

scholarly journals Cross-Tissue Characterization of Heterogeneities of Mesenchymal Stem Cells and Their Differentiation Potentials

2021 ◽  
Vol 9 ◽  
Author(s):  
Wenhong Hou ◽  
Li Duan ◽  
Changyuan Huang ◽  
Xingfu Li ◽  
Xiao Xu ◽  
...  
Keyword(s):  
Single Cell ◽  
Umbilical Cord ◽  
Stromal Cells ◽  
Tissue Characterization ◽  
Single Cell Level ◽  
Rna Seq ◽  
Cell Level ◽  
Cell Sources ◽  
Cell Subpopulations

Mesenchymal stem/stromal cells (MSCs) are promising cell sources for regenerative medicine and the treatment of autoimmune disorders. Comparing MSCs from different tissues at the single-cell level is fundamental for optimizing clinical applications. Here we analyzed single-cell RNA-seq data of MSCs from four tissues, namely umbilical cord, bone marrow, synovial tissue, and adipose tissue. We identified three major cell subpopulations, namely osteo-MSCs, chondro-MSCs, and adipo/myo-MSCs, across all MSC samples. MSCs from the umbilical cord exhibited the highest immunosuppression, potentially indicating it is the best immune modulator for autoimmune diseases. MSC subpopulations, with different subtypes and tissue sources, showed pronounced differences in differentiation potentials. After we compared the cell subpopulations and cell status pre-and-post chondrogenesis induction, osteogenesis induction, and adipogenesis induction, respectively, we found MSC subpopulations expanded and differentiated when their subtypes consist with induction directions, while the other subpopulations shrank. We identified the genes and transcription factors underlying each induction at the single-cell level and subpopulation level, providing better targets for improving induction efficiency.

scholarly journals Single-cell RNA-seq reveals a concomitant delay in differentiation and cell cycle of aged hematopoietic stem cell

2020 ◽  
Author(s):  
Léonard Hérault ◽  
Mathilde Poplineau ◽  
Adrien Mazuel ◽  
Nadine Platet ◽  
Élisabeth Remy ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Cycle Regulators ◽  
Potential Mechanism ◽  
Rna Seq ◽  
Cell Level ◽  
Hematopoietic Stem ◽  
Undifferentiated State ◽  
Reference Map

ABSTRACTHematopoietic stem cells (HSCs) are the guarantor of the proper functioning of hematopoiesis due to their incredible diversity of potential. During aging the heterogeneity of mouse HSCs evolves, which contributes to the deterioration of the immune system. Here we address the transcriptional plasticity of HSC upon aging at the single-cell resolution. Through the analysis of 15,000 young and aged transcriptomes, we reveal 15 clusters of HSCs unveiling rare and specific HSC abilities that change with age. Pseudotime ordering complemented with regulon analysis showed that the consecutive differentiation states of HSC are delayed upon aging. By analysing cell cycle at the single cell level we highlight an imbalance of cell cycle regulators of very immature aged HSC that may contribute to their accumulation in an undifferentiated state.Our results therefore establish a reference map of young and old mouse HSC differentiation and reveal a potential mechanism that delay aged HSC differentiation.

scholarly journals Meta-Transcriptome Detector (MTD): a novel pipeline for metatranscriptome analysis of bulk and single-cell RNAseq data

2021 ◽  
Author(s):  
Fei Wu ◽  
Yaozhong Liu ◽  
Binhua Ling
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Host Cells ◽  
Host Responses ◽  
Rna Seq ◽  
Cell Level ◽  
Gene Expressions ◽  
Rnaseq Data ◽  
Software Program ◽  
User Friendly

RNA-seq data contains not only host transcriptomes but also non-host information that comprises transcripts from active microbiota in the host cells. Therefore, metatranscriptomics can reveal gene expression of the entire microbial community in a given sample. However, there is no single tool that can simultaneously analyze host-microbiota interactions and to quantify microbiome at the single-cell level, particularly for users with limited expertise of bioinformatics. Here, we developed a novel software program that can comprehensively and synergistically analyze gene expression of the host and microbiome as well as their association using bulk and single-cell RNA-seq data. Our pipeline, named Meta-Transcriptome Detector (MTD), can identify and quantify microbiome extensively, including viruses, bacteria, protozoa, fungi, plasmids, and vectors. MTD is easy to install and is user-friendly. This novel software program empowers researchers to study the interactions between microbiota and the host by analyzing gene expressions and pathways, which provides further insights into host responses to microorganisms.

scholarly journals scCancer: a package for automated processing of single cell RNA-seq data in cancer

2019 ◽  
Author(s):  
Wenbo Guo ◽  
Dongfang Wang ◽  
Shicheng Wang ◽  
Yiran Shan ◽  
Jin Gu
Keyword(s):  
Single Cell ◽  
Learning Algorithm ◽  
R Package ◽  
Rna Seq ◽  
Cell Level ◽  
Quality Control Metrics ◽  
Automated Processing ◽  
Cellular Phenotypes ◽  
User Friendly ◽  
Processing Steps

AbstractSummaryMolecular heterogeneities bring great challenges for cancer diagnosis and treatment. Recent advance in single cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer transcriptomic heterogeneities at single cell level. Here, we develop an R package named scCancer which focuses on processing and analyzing scRNA-seq data for cancer research. Except basic data processing steps, this package takes several special considerations for cancer-specific features. Firstly, the package introduced comprehensive quality control metrics. Secondly, it used a data-driven machine learning algorithm to accurately identify major cancer microenvironment cell populations. Thirdly, it estimated a malignancy score to classify malignant (cancerous) and non-malignant cells. Then, it analyzed intra-tumor heterogeneities by key cellular phenotypes (such as cell cycle and stemness) and gene signatures. Finally, a user-friendly graphic report was generated for all the analyses.Availabilityhttp://lifeome.net/software/sccancer/[email protected]

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