scholarly journals Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications

2018 ◽  
Author(s):  
Koen Van den Berge ◽  
Fanny Perraudeau ◽  
Charlotte Soneson ◽  
Michael I. Love ◽  
Davide Risso ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Transcriptome Sequencing ◽  
Negative Binomial ◽  
Rna Seq ◽  
Zero Inflation ◽  
Weighting Strategy ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Zero Counts

AbstractDropout events in single-cell transcriptome sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial (ZINB) model, that identifies excess zero counts and generates gene and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

scholarly journals zingeR: unlocking RNA-seq tools for zero-inflation and single cell applications

2017 ◽  
Author(s):  
Koen Van den Berge ◽  
Charlotte Soneson ◽  
Michael I. Love ◽  
Mark D. Robinson ◽  
Lieven Clement
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Negative Binomial ◽  
Differential Expression Analysis ◽  
Negative Binomial Model ◽  
Binomial Model ◽  
Rna Seq ◽  
Zero Inflation ◽  
Zero Counts

AbstractDropout in single cell RNA-seq (scRNA-seq) applications causes many transcripts to go undetected. It induces excess zero counts, which leads to power issues in differential expression (DE) analysis and has triggered the development of bespoke scRNA-seq DE tools that cope with zero-inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce zingeR, a zero-inflated negative binomial model that identifies excess zero counts and generates observation weights to unlock bulk RNA-seq pipelines for zero-inflation, boosting performance in scRNA-seq differential expression analysis.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

Single-Cell Transcriptome Sequencing Using Microfluidics

2021 ◽  
pp. 607-630
Author(s):  
Yu-Chih Chen ◽  
Seungwon Jung ◽  
Yehyun Choi ◽  
Euisik Yoon
Keyword(s):  
Single Cell ◽  
Transcriptome Sequencing ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

scholarly journals A single cell transcriptome atlas of the developing zebrafish hindbrain

2019 ◽  
Author(s):  
Monica Tambalo ◽  
Richard Mitter ◽  
David G. Wilkinson
Keyword(s):  
Single Cell ◽  
Neuronal Differentiation ◽  
Cell Types ◽  
Spatial Patterning ◽  
Rna Seq ◽  
Valuable Resource ◽  
Dorsoventral Axis ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractSegmentation of the vertebrate hindbrain leads to the formation of rhombomeres, each with a distinct anteroposterior identity. Specialised boundary cells form at segment borders that act as a source or regulator of neuronal differentiation. In zebrafish, there is spatial patterning of neurogenesis in which non-neurogenic zones form at bounderies and segment centres, in part mediated by Fgf20 signaling. To further understand the control of neurogenesis, we have carried out single cell RNA sequencing of the zebrafish hindbrain at three different stages of patterning. Analyses of the data reveal known and novel markers of distinct hindbrain segments, of cell types along the dorsoventral axis, and of the transition of progenitors to neuronal differentiation. We find major shifts in the transcriptome of progenitors and of differentiating cells between the different stages analysed. Supervised clustering with markers of boundary cells and segment centres, together with RNA-seq analysis of Fgf-regulated genes, has revealed new candidate regulators of cell differentiation in the hindbrain. These data provide a valuable resource for functional investigations of the patterning of neurogenesis and the transition of progenitors to neuronal differentiation.

scholarly journals Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wei-Wei Lin ◽  
Lin-Tao Xu ◽  
Yi-Sheng Chen ◽  
Ken Go ◽  
Chenyu Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Mouse Brain ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Rna Seq ◽  
Brain Vasculature ◽  
Transcriptional Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Background. The critical role of vascular health on brain function has received much attention in recent years. At the single-cell level, studies on the developmental processes of cerebral vascular growth are still relatively few. Techniques for constructing gene regulatory networks (GRNs) based on single-cell transcriptome expression data have made significant progress in recent years. Herein, we constructed a single-cell transcriptional regulatory network of mouse cerebrovascular cells. Methods. The single-cell RNA-seq dataset of mouse brain vessels was downloaded from GEO (GSE98816). This cell clustering was annotated separately using singleR and CellMarker. We then used a modified version of the SCENIC method to construct GRNs. Next, we used a mouse version of SEEK to assess whether genes in the regulon were coexpressed. Finally, regulatory module analysis was performed to complete the cell type relationship quantification. Results. Single-cell RNA-seq data were used to analyze the heterogeneity of mouse cerebrovascular cells, whereby four cell types including endothelial cells, fibroblasts, microglia, and oligodendrocytes were defined. These subpopulations of cells and marker genes together characterize the molecular profile of mouse cerebrovascular cells. Through these signatures, key transcriptional regulators that maintain cell identity were identified. Our findings identified genes like Lmo2, which play an important role in endothelial cells. The same cell type, for instance, fibroblasts, was found to have different regulatory networks, which may influence the functional characteristics of local tissues. Conclusions. In this study, a transcriptional regulatory network based on single-cell analysis was constructed. Additionally, the study identified and profiled mouse cerebrovascular cells using single-cell transcriptome data as well as defined TFs that affect the regulatory network of the mouse brain vasculature.

scholarly journals Single Cell Transcriptome Sequencing Reveals the Potential Mechanism of Heterogeneity in Immunoregulatory Function Between Mesenchymal Stromal Cells

2021 ◽  
Author(s):  
Siyu Cai ◽  
Chuiqin Fan ◽  
Lichun Xie ◽  
Huifeng Zhong ◽  
Aijia Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Mesenchymal Stromal Cells ◽  
Stromal Cells ◽  
Transcriptome Sequencing ◽  
Enzyme Linked Immunosorbent Assay ◽  
Human Umbilical Cord ◽  
Tnf Α ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Immunomodulatory Function

Abstract Background: Mesenchymal stromal cells (MSCs) could be applied for the treatment of immune-related diseases. However, some studies have found there is enormous heterogeneity in immunomodulatory function of MSCs isolated from different tissue. At present, the underlying mechanism of heterogeneity in immunoregulatory function is still unclear. Methods: In this study, the foreskin mesenchymal stromal cells (FSMSCs) and human umbilical cord mesenchymal stromal cells (HuMSCs) were isolated and cultured to the 3rd passage. Cell types were confirmed according to the standard of International Association for Cell Therapy. Then, FSMSCs and HuMSCs were co-cultured with human peripheral blood mononuclear cells (PBMC) stimulated by lipopolysaccharides (LPS) in viro respectively. And the supernatant was sampled for enzyme-linked immunosorbent assay to investigate the secretion of IL-1β, IL-6, IL-10, TNF-α and TGF-β. Finally, single cell transcriptome sequencing was performed in order to elucidate the mechanism for the difference of immunomodulatory function.Results: FSMSCs and HuMSCs are successfully identified as MSCs. When co-cultured with LPS pre-treated PBMC, FSMSCs and HuMSCs could effectively reduce the secretion of IL-1β and TNF-α. But FSMSCs were able to stimulate the PBMC to secrete more IL-10, TGF-β and IL-6. Furthermore, 4 MSCs subsets in integrated data were identified, including Proliferative MSCs , Pericyte, Immune MSCs and Progenitor Proliferative MSCs. Among them, the proportions of Immune MSCs in FSMSCs and HUMSCs were 56% and 10% respectively. Varieties of immune-related genes, gene sets and regulons were enriched in Immune MSCs. And Immune MSCs with powful transcriptional activity were found to be near to Pericyte at the degree of differentiation and closed to other cell subsets. Finally, the foreskin tissue might be an ideal source of isolating Immune MSCs when comparing the subset composition of MSCs derived from adipose tissue and bone marrow from public database. Conclusions: Immune MSCs may play a key role in the heterogeneity of immunoregulatory function. It is a new insight that Immune MSCs could be isolated and better applied for the treatment of immune-related diseases without being limited by the heterogeneity of immunomodulatory function derived from different tissues.

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