A single cell transcriptome atlas of the developing zebrafish hindbrain

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.

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

PLoS ONE ◽

10.1371/journal.pone.0243360 ◽

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.

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What has single-cell RNA-seq taught us about mammalian spermatogenesis?

Biology of Reproduction ◽

10.1093/biolre/ioz088 ◽

2019 ◽

Vol 101 (3) ◽

pp. 617-634 ◽

Cited By ~ 8

Author(s):

Shinnosuke Suzuki ◽

Victoria D Diaz ◽

Brian P Hermann

Keyword(s):

Single Cell ◽

Cell Types ◽

Molecular Heterogeneity ◽

Rna Seq ◽

Technical Requirements ◽

Male Gametes ◽

The Veil ◽

Cell Transcriptome ◽

Future Utility ◽

Single Cell Transcriptome

Abstract Mammalian spermatogenesis is a complex developmental program that transforms mitotic testicular germ cells (spermatogonia) into mature male gametes (sperm) for production of offspring. For decades, it has been known that this several-weeks-long process involves a series of highly ordered and morphologically recognizable cellular changes as spermatogonia proliferate, spermatocytes undertake meiosis, and spermatids develop condensed nuclei, acrosomes, and flagella. Yet, much of the underlying molecular logic driving these processes has remained opaque because conventional characterization strategies often aggregated groups of cells to meet technical requirements or due to limited capability for cell selection. Recently, a cornucopia of single-cell transcriptome studies has begun to lift the veil on the full compendium of gene expression phenotypes and changes underlying spermatogenic development. These datasets have revealed the previously obscured molecular heterogeneity among and between varied spermatogenic cell types and are reinvigorating investigation of testicular biology. This review describes the extent of available single-cell RNA-seq profiles of spermatogenic and testicular somatic cells, how those data were produced and evaluated, their present value for advancing knowledge of spermatogenesis, and their potential future utility at both the benchtop and bedside.

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

10.1101/402966 ◽

2018 ◽

Cited By ~ 7

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.

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

Frontiers in Cellular Neuroscience ◽

10.3389/fncel.2021.624826 ◽

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.

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Single cell transcriptome research in human placenta

Reproduction ◽

10.1530/rep-20-0231 ◽

2020 ◽

Vol 160 (6) ◽

pp. R155-R167

Author(s):

Hui Li ◽

Qianhui Huang ◽

Yu Liu ◽

Lana X Garmire

Keyword(s):

Single Cell ◽

Human Placenta ◽

Pregnancy Complications ◽

Cell Types ◽

Rna Seq ◽

Structural Components ◽

The Public ◽

Cell Transcriptome ◽

Areas Of Interest ◽

Single Cell Transcriptome

Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the heterogeneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications, such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.

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

10.1101/123810 ◽

2017 ◽

Cited By ~ 6

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.

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Single-cell transcriptome analysis of the zebrafish embryonic trunk

10.1101/2021.05.07.443129 ◽

2021 ◽

Author(s):

Sanjeeva S Metikala ◽

Satish Casie Chetty ◽

Saulius Sumanas

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Cell Lineage ◽

Cell Types ◽

Rna Seq ◽

Cell Lineages ◽

Distinct Cell ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Different Cell Types

During embryonic development, cells differentiate into a variety of distinct cell types and subtypes with diverse transcriptional profiles. To date, transcriptomic signatures of different cell lineages that arise during development have been only partially characterized. Here we used single-cell RNA-seq to perform transcriptomic analysis of over 20,000 cells disaggregated from the trunk region of zebrafish embryos at the 30 hpf stage. Transcriptional signatures of 27 different cell types and subtypes were identified and annotated during this analysis. This dataset will be a useful resource for many researchers in the fields of developmental and cellular biology and facilitate the understanding of molecular mechanisms that regulate cell lineage choices during development.

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Single-cell transcriptome analysis of the zebrafish embryonic trunk

PLoS ONE ◽

10.1371/journal.pone.0254024 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254024

Author(s):

Sanjeeva Metikala ◽

Satish Casie Chetty ◽

Saulius Sumanas

Keyword(s):

Single Cell ◽

Molecular Mechanisms ◽

Cell Lineage ◽

Cell Types ◽

Rna Seq ◽

Cell Lineages ◽

Distinct Cell ◽

Cell Transcriptome ◽

Single Cell Transcriptome ◽

Different Cell Types

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

10.1101/2021.06.07.447430 ◽

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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