scholarly journals Single-cell transcriptome analysis of the zebrafish embryonic trunk

PLoS ONE ◽  
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

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.

scholarly journals Single-cell transcriptome analysis of the zebrafish embryonic trunk

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.

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

2019 ◽  
Vol 101 (3) ◽  
pp. 617-634 ◽  
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.

scholarly journals A single cell transcriptome atlas reveals the heterogeneity of the healthy human cornea and identifies novel markers of the corneal limbus and stroma

2021 ◽  
Author(s):  
Pere Catala ◽  
Nathalie Groen ◽  
Jasmin A Dehnen ◽  
Eduardo Soares ◽  
Arianne JH van Velthoven ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Human Cornea ◽  
Corneal Tissue ◽  
Healthy Human ◽  
Disease States ◽  
Cell Niche ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Different Cell Types

The cornea is the clear window that lets light into the eye. It is composed of five layers: epithelium, Bowman layer, stroma, Descemet membrane and endothelium. The maintenance of its structure and transparency are determined by the functions of the different cell types populating each layer. Attempts to regenerate corneal tissue and understand disease conditions requires knowledge of how cell profiles vary across this heterogeneous tissue. We performed a single cell transcriptomic profiling of 19,472 cells isolated from eight healthy donor corneas. Our analysis delineates the heterogeneity of the corneal layers by identifying cell populations and revealing cell states that contribute in preserving corneal homeostasis. We identified that the expression of CAV1, CXCL14, HOMER3 and CPVL were exclusive to the corneal epithelial limbal stem cell niche, CKS2, STMN1 and UBE2C were exclusively expressed in highly proliferative transit amplifying cells, and NNMT was exclusively expressed by stromal keratocytes. Overall, this research provides a basis to improve current primary cell expansion protocols, for future profiling of corneal disease states, to help guide pluripotent stem cells into different corneal lineages, and to understand how engineered substrates affect corneal cells to improve regenerative therapies.

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 Single Cell Transcriptome-Based Dissection of Lineage Fate Decisions in Myelopoiesis

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1395-1395
Author(s):  
Andre Olsson ◽  
H. Leighton Grimes ◽  
Virendra K Chaudhri ◽  
Philip Dexheimer ◽  
Bruce J Aronow ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Molecular Mechanisms ◽  
Conflicts Of Interest ◽  
Expression Patterns ◽  
Rna Seq ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Cell Data ◽  
Insight Into

Abstract In spite of tremendous advances in the analysis of hematopoietic progenitors and transcription factors that give rise to different lineages, molecular insight into the mechanisms that underlie cell fate choice at the level of individual cells is lacking. We utilized single-cell RNA sequencing of murine granulocyte-monocyte progenitors (GMPs) to analyze the molecular basis of cell fate choice. Over 200 libraries were generated with average read depths of 4 million per library and an expressed gene call of over 3,800 genes with FPKM >3. Our data reveal a varied but coherent spectrum of gene expression patterns in individual murine GMPs. The majority of cells could be clustered into ones expressing either granulocytic or monocytic genes, suggesting that they were primed for lineage determination. A minority of GMPs expressed a mixed-lineage pattern of genes. The single-cell data suggested an antagonistic transcription factor circuit involving Gfi1 and IRF8 that was validated with both loss- and gain-of-function experiments in GMPs. Our data highlight the utility of single cell RNA-Seq analysis to reveal molecular mechanisms controlling lineage fate decisions in hematopoiesis. Disclosures No relevant conflicts of interest to declare.

scholarly journals Single cell transcriptome research in human placenta

Reproduction ◽  
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.

scholarly journals Single-cell transcriptome analysis identifies distinct cell types and niche signaling in a primary gastric organoid model

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jiamin Chen ◽  
Billy T. Lau ◽  
Noemi Andor ◽  
Susan M. Grimes ◽  
Christine Handy ◽  
...  
Keyword(s):  
Single Cell ◽  
Transcriptome Analysis ◽  
Cell Types ◽  
Distinct Cell ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

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.

Looking at the Developing Lung in Single-cell Resolution

2020 ◽  
Author(s):  
Ivana Mižíková ◽  
Bernard Thébaud
Keyword(s):  
Single Cell ◽  
Lung Development ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cell Lineages ◽  
Heterogeneous Nature ◽  
Cell Transcriptome ◽  
Tracing Techniques ◽  
Postnatal Lung Development ◽  
Single Cell Transcriptome

Lung development is a complicated and delicate process, facilitated by spatially and temporarily coordinated crosstalk of up to 40 cell types. Developmental origin and heterogeneity of lung cell lineages in context of lung development have been a focus of research efforts for decades. Bulk RNA and protein measurements, RNA and protein labelling, and lineage tracing techniques have been traditionally employed. However, the complex and heterogeneous nature of lung tissue presents a particular challenge when identifying subtle changes in gene expression in individual cell types. Rapidly developing single-cell RNA sequencing (scRNA-seq) techniques allow for unbiased and robust assessment of complex cellular dynamics during biological processes in unprecedented ways. Discovered a decade ago, scRNA-seq has been applied in respiratory research to understand lung cellular composition and to identify novel cell types. Still, very few studies to date have addressed the single-cell transcriptome in healthy or aberrantly developing lung. In this mini-review, we discuss principal discoveries with scRNA-seq in the field of prenatal and postnatal lung development. In addition, we examine challenges and expectations, and propose future steps associated with the use of scRNA-seq to study developmental lung diseases.

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