scholarly journals Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression

2019 ◽  
Author(s):  
Anna SE Cuomo ◽  
Daniel D Seaton ◽  
Davis J McCarthy ◽  
Iker Martinez ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Cell Biology ◽  
Population Variation ◽  
Ips Cell ◽  
Cell Fate Decisions ◽  
Common Genetic Variants ◽  
Single Cell Rna Sequencing

AbstractRecent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.

scholarly journals Combining single-cell RNA-sequencing with a molecular atlas unveils new markers for Caenorhabditis elegans neuron classes

2020 ◽  
Author(s):  
Ramiro Lorenzo ◽  
Michiho Onizuka ◽  
Matthieu Defrance ◽  
Patrick Laurent
Keyword(s):  
Caenorhabditis Elegans ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Single Neuron ◽  
Genetic Manipulations ◽  
C Elegans ◽  
Single Cell Rna Sequencing ◽  
Normal Differentiation

Abstract Single-cell RNA-sequencing (scRNA-seq) of the Caenorhabditis elegans nervous system offers the unique opportunity to obtain a partial expression profile for each neuron within a known connectome. Building on recent scRNA-seq data and on a molecular atlas describing the expression pattern of ∼800 genes at the single cell resolution, we designed an iterative clustering analysis aiming to match each cell-cluster to the ∼100 anatomically defined neuron classes of C. elegans. This heuristic approach successfully assigned 97 of the 118 neuron classes to a cluster. Sixty two clusters were assigned to a single neuron class and 15 clusters grouped neuron classes sharing close molecular signatures. Pseudotime analysis revealed a maturation process occurring in some neurons (e.g. PDA) during the L2 stage. Based on the molecular profiles of all identified neurons, we predicted cell fate regulators and experimentally validated unc-86 for the normal differentiation of RMG neurons. Furthermore, we observed that different classes of genes functionally diversify sensory neurons, interneurons and motorneurons. Finally, we designed 15 new neuron class-specific promoters validated in vivo. Amongst them, 10 represent the only specific promoter reported to this day, expanding the list of neurons amenable to genetic manipulations.

scholarly journals Deciphering cell lineage specification during male sex determination with single-cell RNA sequencing

2017 ◽  
Author(s):  
Isabelle Stévant ◽  
Yasmine Neirjinck ◽  
Christelle Borel ◽  
Jessica Escoffier ◽  
Lee B. Smith ◽  
...  
Keyword(s):  
Sex Determination ◽  
Single Cell ◽  
Progenitor Cells ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Time Course ◽  
Cell Lineage ◽  
Lineage Specification ◽  
Cell Fate Decisions ◽  
Single Cell Rna Sequencing

SummaryThe gonad is a unique biological system for studying cell fate decisions. However, major questions remain regarding the identity of somatic progenitor cells and the transcriptional events driving cell differentiation. Using time course single cell RNA sequencing on XY mouse gonads during sex determination, we identified a single population of somatic progenitor cells prior sex determination. A subset of these progenitors differentiate into Sertoli cells, a process characterized by a highly dynamic genetic program consisting of sequential waves of gene expression. Another subset of multipotent cells maintains their progenitor state but undergo significant transcriptional changes that restrict their competence towards a steroidogenic fate required for the differentiation of fetal Leydig cells. These results question the dogma of the existence of two distinct somatic cell lineages at the onset of sex determination and propose a new model of lineage specification from a unique progenitor cell population.

scholarly journals Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
David S. Fischer ◽  
Meshal Ansari ◽  
Karolin I. Wagner ◽  
Sebastian Jarosch ◽  
Yiqi Huang ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Ex Vivo ◽  
Severe Disease ◽  
Human Leukocyte ◽  
Cell Receptor ◽  
Single Cell Rna Sequencing

AbstractThe in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for ‘reverse phenotyping’. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

scholarly journals Single-cell RNA sequencing of cultured human endometrial CD140b+CD146+ perivascular cells highlights the importance of in vivo microenvironment

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dandan Cao ◽  
Rachel W. S. Chan ◽  
Ernest H. Y. Ng ◽  
Kristina Gemzell-Danielsson ◽  
William S. B. Yeung
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Marker Gene ◽  
Cellular Heterogeneity ◽  
Stromal Fibroblast ◽  
Perivascular Cells ◽  
Endometrial Cells ◽  
Single Cell Rna Sequencing

Abstract Background Endometrial mesenchymal-like stromal/stem cells (eMSCs) have been proposed as adult stem cells contributing to endometrial regeneration. One set of perivascular markers (CD140b&CD146) has been widely used to enrich eMSCs. Although eMSCs are easily accessible for regenerative medicine and have long been studied, their cellular heterogeneity, relationship to primary counterpart, remains largely unclear. Methods In this study, we applied 10X genomics single-cell RNA sequencing (scRNA-seq) to cultured human CD140b+CD146+ endometrial perivascular cells (ePCs) from menstrual and secretory endometrium. We also analyzed publicly available scRNA-seq data of primary endometrium and performed transcriptome comparison between cultured ePCs and primary ePCs at single-cell level. Results Transcriptomic expression-based clustering revealed limited heterogeneity within cultured menstrual and secretory ePCs. A main subpopulation and a small stress-induced subpopulation were identified in secretory and menstrual ePCs. Cell identity analysis demonstrated the similar cellular composition in secretory and menstrual ePCs. Marker gene expression analysis showed that the main subpopulations identified from cultured secretory and menstrual ePCs simultaneously expressed genes marking mesenchymal stem cell (MSC), perivascular cell, smooth muscle cell, and stromal fibroblast. GO enrichment analysis revealed that genes upregulated in the main subpopulation enriched in actin filament organization, cellular division, etc., while genes upregulated in the small subpopulation enriched in extracellular matrix disassembly, stress response, etc. By comparing subpopulations of cultured ePCs to the publicly available primary endometrial cells, it was found that the main subpopulation identified from cultured ePCs was culture-unique which was unlike primary ePCs or primary endometrial stromal fibroblast cells. Conclusion In summary, these data for the first time provides a single-cell atlas of the cultured human CD140b+CD146+ ePCs. The identification of culture-unique relatively homogenous cell population of CD140b+CD146+ ePCs underscores the importance of in vivo microenvironment in maintaining cellular identity.

scholarly journals Single-cell RNA sequencing of human kidney

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jinling Liao ◽  
Zhenyuan Yu ◽  
Yang Chen ◽  
Mengying Bao ◽  
Chunlin Zou ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Biology ◽  
Kidney Diseases ◽  
Human Kidney ◽  
High Quality ◽  
Renal Tubular Cells ◽  
Single Cell Rna Sequencing ◽  
Normal Human ◽  
Renal Tubular

AbstractA comprehensive cellular anatomy of normal human kidney is crucial to address the cellular origins of renal disease and renal cancer. Some kidney diseases may be cell type-specific, especially renal tubular cells. To investigate the classification and transcriptomic information of the human kidney, we rapidly obtained a single-cell suspension of the kidney and conducted single-cell RNA sequencing (scRNA-seq). Here, we present the scRNA-seq data of 23,366 high-quality cells from the kidneys of three human donors. In this dataset, we show 10 clusters of normal human renal cells. Due to the high quality of single-cell transcriptomic information, proximal tubule (PT) cells were classified into three subtypes and collecting ducts cells into two subtypes. Collectively, our data provide a reliable reference for studies on renal cell biology and kidney disease.

scholarly journals A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Mehmet Tekman ◽  
Bérénice Batut ◽  
Alexander Ostrovsky ◽  
Christophe Antoniewski ◽  
Dave Clements ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Biology ◽  
High Performance ◽  
Learning Experience ◽  
Sequencing Analysis ◽  
Teaching Resources ◽  
Single Cell Rna Sequencing ◽  
The Galaxy ◽  
Downstream Analysis

Abstract Background The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. Results Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. Conclusions The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

scholarly journals Single-Cell RNA Sequencing Reveals Renal Endothelium Heterogeneity and Metabolic Adaptation to Water Deprivation

2019 ◽  
Vol 31 (1) ◽  
pp. 118-138 ◽  
Author(s):  
Sébastien J. Dumas ◽  
Elda Meta ◽  
Mila Borri ◽  
Jermaine Goveia ◽  
Katerina Rohlenova ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Endothelial Cell ◽  
Oxidative Phosphorylation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Water Deprivation ◽  
Metabolic Adaptation ◽  
Single Cell Rna Sequencing

BackgroundRenal endothelial cells from glomerular, cortical, and medullary kidney compartments are exposed to different microenvironmental conditions and support specific kidney processes. However, the heterogeneous phenotypes of these cells remain incompletely inventoried. Osmotic homeostasis is vitally important for regulating cell volume and function, and in mammals, osmotic equilibrium is regulated through the countercurrent system in the renal medulla, where water exchange through endothelium occurs against an osmotic pressure gradient. Dehydration exposes medullary renal endothelial cells to extreme hyperosmolarity, and how these cells adapt to and survive in this hypertonic milieu is unknown.MethodsWe inventoried renal endothelial cell heterogeneity by single-cell RNA sequencing >40,000 mouse renal endothelial cells, and studied transcriptome changes during osmotic adaptation upon water deprivation. We validated our findings by immunostaining and functionally by targeting oxidative phosphorylation in a hyperosmolarity model in vitro and in dehydrated mice in vivo.ResultsWe identified 24 renal endothelial cell phenotypes (of which eight were novel), highlighting extensive heterogeneity of these cells between and within the cortex, glomeruli, and medulla. In response to dehydration and hypertonicity, medullary renal endothelial cells upregulated the expression of genes involved in the hypoxia response, glycolysis, and—surprisingly—oxidative phosphorylation. Endothelial cells increased oxygen consumption when exposed to hyperosmolarity, whereas blocking oxidative phosphorylation compromised endothelial cell viability during hyperosmotic stress and impaired urine concentration during dehydration.ConclusionsThis study provides a high-resolution atlas of the renal endothelium and highlights extensive renal endothelial cell phenotypic heterogeneity, as well as a previously unrecognized role of oxidative phosphorylation in the metabolic adaptation of medullary renal endothelial cells to water deprivation.

scholarly journals Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

Cells ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1751 ◽  
Author(s):  
Rishikesh Kumar Gupta ◽  
Jacek Kuznicki
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Future Directions ◽  
Strong Basis ◽  
Bodily Fluids ◽  
Single Cell Rna Sequencing

The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer’s disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.

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