scholarly journals Single Cell Transcriptome Data Analysis Defines the Heterogeneity of Peripheral Nerve Cells in Homeostasis and Regeneration

2021 ◽  
Vol 15 ◽  
Author(s):  
Bing Chen ◽  
Matthew C. Banton ◽  
Lolita Singh ◽  
David B. Parkinson ◽  
Xin-peng Dun
Keyword(s):  
Endothelial Cells ◽  
Data Analysis ◽  
Single Cell ◽  
Schwann Cells ◽  
Peripheral Nerves ◽  
Cell Types ◽  
Transcriptome Data ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

The advances in single-cell RNA sequencing technologies and the development of bioinformatics pipelines enable us to more accurately define the heterogeneity of cell types in a selected tissue. In this report, we re-analyzed recently published single-cell RNA sequencing data sets and provide a rationale to redefine the heterogeneity of cells in both intact and injured mouse peripheral nerves. Our analysis showed that, in both intact and injured peripheral nerves, cells could be functionally classified into four categories: Schwann cells, nerve fibroblasts, immune cells, and cells associated with blood vessels. Nerve fibroblasts could be sub-clustered into epineurial, perineurial, and endoneurial fibroblasts. Identified immune cell clusters include macrophages, mast cells, natural killer cells, T and B lymphocytes as well as an unreported cluster of neutrophils. Cells associated with blood vessels include endothelial cells, vascular smooth muscle cells, and pericytes. We show that endothelial cells in the intact mouse sciatic nerve have three sub-types: epineurial, endoneurial, and lymphatic endothelial cells. Analysis of cell type-specific gene changes revealed that Schwann cells and endoneurial fibroblasts are the two most important cell types promoting peripheral nerve regeneration. Analysis of communication between these cells identified potential signals for early blood vessel regeneration, neutrophil recruitment of macrophages, and macrophages activating Schwann cells. Through this analysis, we also report appropriate marker genes for future single cell transcriptome data analysis to identify cell types in intact and injured peripheral nerves. The findings from our analysis could facilitate a better understanding of cell biology of peripheral nerves in homeostasis, regeneration, and disease.

scholarly journals Transcriptional profiling of mouse peripheral nerves to the single-cell level to build a sciatic nerve ATlas (SNAT)

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Daniel Gerber ◽  
Jorge A Pereira ◽  
Joanne Gerber ◽  
Ge Tan ◽  
Slavica Dimitrieva ◽  
...  
Keyword(s):  
Sciatic Nerve ◽  
Single Cell ◽  
Rna Sequencing ◽  
Schwann Cells ◽  
Peripheral Nerves ◽  
Transcriptional Profiling ◽  
Cell Types ◽  
Cell Transcriptome ◽  
Transcriptome Resource ◽  
Single Cell Transcriptome

Peripheral nerves are organ-like structures containing diverse cell types to optimize function. This interactive assembly includes mostly axon-associated Schwann cells, but also endothelial cells of supporting blood vessels, immune system-associated cells, barrier-forming cells of the perineurium surrounding and protecting nerve fascicles, and connective tissue-resident cells within the intra-fascicular endoneurium and inter-fascicular epineurium. We have established transcriptional profiles of mouse sciatic nerve-inhabitant cells to foster the fundamental understanding of peripheral nerves. To achieve this goal, we have combined bulk RNA sequencing of developing sciatic nerves up to the adult with focused bulk and single-cell RNA sequencing of Schwann cells throughout postnatal development, extended by single-cell transcriptome analysis of the full sciatic nerve both perinatally and in the adult. The results were merged in the transcriptome resource Sciatic Nerve ATlas (SNAT:https://www.snat.ethz.ch). We anticipate that insights gained from our multi-layered analysis will serve as valuable interactive reference point to guide future studies.

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 DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243360
Author(s):  
Johan Gustafsson ◽  
Jonathan Robinson ◽  
Juan S. Inda-Díaz ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Log Likelihood ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Average Gene ◽  
Single Cell Transcriptome

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

scholarly journals High-throughput single-cell transcriptome profiling of plant cell types

2018 ◽  
Author(s):  
Christine N. Shulse ◽  
Benjamin J. Cole ◽  
Gina M. Turco ◽  
Yiwen Zhu ◽  
Siobhan M. Brady ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Marker Genes ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Heterogeneous Tissues ◽  
Genomic Basis

AbstractSingle-cell transcriptome analysis of heterogeneous tissues can provide high-resolution windows into the genomic basis and spatiotemporal dynamics of developmental processes. Here we demonstrate the feasibility of high-throughput single-cell RNA sequencing of plant tissue using the Drop-seq approach. Profiling of >4,000 individual cells from the Arabidopsis root provides transcriptomes and marker genes for a diversity of cell types and illuminates the gene expression changes that occur across endodermis development.

scholarly journals scTPA: a web tool for single-cell transcriptome analysis of pathway activation signatures

2020 ◽  
Vol 36 (14) ◽  
pp. 4217-4219
Author(s):  
Yan Zhang ◽  
Yaru Zhang ◽  
Jun Hu ◽  
Ji Zhang ◽  
Fangjie Guo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Cell Types ◽  
Biological Pathways ◽  
Supplementary Information ◽  
Web Tool ◽  
Functional Interpretation ◽  
Pathway Activation ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Motivation At present, a fundamental challenge in single-cell RNA-sequencing data analysis is functional interpretation and annotation of cell clusters. Biological pathways in distinct cell types have different activation patterns, which facilitates the understanding of cell functions using single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptome data analysis based on prior biological pathway knowledge. Results Here, we present scTPA, a web-based platform for pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from a pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Overall, scTPA is a comprehensive tool for the identification of pathway activation signatures for the analysis of single cell heterogeneity. Availability and implementation http://sctpa.bio-data.cn/sctpa. Contact [email protected] or [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CellView: Interactive exploration of high dimensional single cell RNA-seq data

2017 ◽  
Author(s):  
Mohan T. Bolisetty ◽  
Michael L. Stitzel ◽  
Paul Robson
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Web Application ◽  
Cell Types ◽  
High Dimensional ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Measure Gene Expression

Advances in high-throughput single cell transcriptomics technologies have revolutionized the study of complex tissues. It is now possible to measure gene expression across thousands of individual cells to define cell types and states. While powerful computational and statistical frameworks are emerging to analyze these complex datasets, a gap exists between this data and a biologist’s insight. The CellView web application fills this gap by providing easy and intuitive exploration of single cell transcriptome data.

scholarly journals In search of a core cellular network in single cell transcriptome data

2021 ◽  
Author(s):  
Ming Yang ◽  
Benjamin R. Harrison ◽  
Daniel E.L. Promislow
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cellular Network ◽  
Cell Types ◽  
Specific Gene ◽  
Transcriptome Data ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractBackgroundAlong with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, remains a central question in biology. Single-cell RNA-sequencing (scRNA-seq) measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current analyses focus primarily on identifying differentially expressed genes across cells. However, patterns of co-expression between genes are probably more indicative of biological processes than are the expression of individual genes. Using single cell transcriptome data from the fly brain, here we focus on gene co-expression to search for a core cellular network.ResultsIn this study, we constructed cell type-specific gene co-expression networks using single cell transcriptome data of brains from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types in fly brains and defined this set as the core cellular network. This core is very small compared with cell type-specific gene co-expression networks and shows dense connectivity. Modules within this core are enriched for basic cellular functions, such as translation and ATP metabolic processes, and gene members of these modules have distinct evolutionary signatures.ConclusionsOverall, we demonstrated that a core cellular network exists in diverse cell types of fly brains and this core exhibits unique topological, structural, functional and evolutionary properties.

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.

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