scholarly journals Current Epigenetic Insights in Kidney Development

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1281
Author(s):  
Katrina Chan ◽  
Xiaogang Li
Keyword(s):  
Single Cell ◽  
Kidney Development ◽  
Kidney Diseases ◽  
Cell Lineage ◽  
Epigenetic Mechanisms ◽  
Phenotypic Stability ◽  
Lineage Specification ◽  
Adult Kidney ◽  
Single Cell Rna Sequencing ◽  
Histone Modification Patterns

The kidney is among the best characterized developing tissues, with the genes and signaling pathways that regulate embryonic and adult kidney patterning and development having been extensively identified. It is now widely understood that DNA methylation and histone modification patterns are imprinted during embryonic development and must be maintained in adult cells for appropriate gene transcription and phenotypic stability. A compelling question then is how these epigenetic mechanisms play a role in kidney development. In this review, we describe the major genes and pathways that have been linked to epigenetic mechanisms in kidney development. We also discuss recent applications of single-cell RNA sequencing (scRNA-seq) techniques in the study of kidney development. Additionally, we summarize the techniques of single-cell epigenomics, which can potentially be used to characterize epigenomes at single-cell resolution in embryonic and adult kidneys. The combination of scRNA-seq and single-cell epigenomics will help facilitate the further understanding of early cell lineage specification at the level of epigenetic modifications in embryonic and adult kidney development, which may also be used to investigate epigenetic mechanisms in kidney diseases.

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 Deciphering Cell Lineage Specification during Male Sex Determination with Single-Cell RNA Sequencing

Cell Reports ◽  
2018 ◽  
Vol 22 (6) ◽  
pp. 1589-1599 ◽  
Author(s):  
Isabelle Stévant ◽  
Yasmine Neirijnck ◽  
Christelle Borel ◽  
Jessica Escoffier ◽  
Lee B. Smith ◽  
...  
Keyword(s):  
Sex Determination ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Lineage ◽  
Lineage Specification ◽  
Single Cell Rna Sequencing ◽  
Male Sex

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 State of the Art on Kidney Development: How Nephron Endowment at Birth Can Shape Our Susceptibility to Renal Dysfunction Later in Life

2019 ◽  
Vol 36 (S 02) ◽  
pp. S33-S36 ◽  
Author(s):  
Vassilios Fanos ◽  
Clara Gerosa ◽  
Cristina Loddo ◽  
Gavino Faa
Keyword(s):  
Birth Weight ◽  
Fetal Programming ◽  
Low Birthweight ◽  
Kidney Development ◽  
State Of The Art ◽  
Kidney Diseases ◽  
Nephron Endowment ◽  
Adult Kidney ◽  
Maternal Status

AbstractIn the present article, we discuss the following topics: (1) the fetal programming of adult kidney diseases and (2) the role of neonatologists in the regenerative renal medicine, based on the activation of resident renal SC. Here, we report the most important steps of our collaboration between neonatologists, nephrologists, and pathologists. Nephrologists should be more interested in clinical data regarding the first month of life in the womb of their adult patients, being particularly focused on birth weight and on the weeks of gestation at birth, without forgetting data regarding maternal status during gestation and neonatal asphyxia. Neonatologists should be aware that any preterm or low birthweight infant should be considered as a subject with fewer glomeruli, probably predicted to develop renal disease later in life.

Using single-cell RNA sequencing to unravel cell lineage relationships in the respiratory tract

2020 ◽  
Vol 48 (1) ◽  
pp. 327-336 ◽  
Author(s):  
L.E. Zaragosi ◽  
M. Deprez ◽  
P. Barbry
Keyword(s):  
Respiratory Tract ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Lineage ◽  
Neuroendocrine Cells ◽  
Basal Cells ◽  
Multiciliated Cells ◽  
Single Cell Rna Sequencing ◽  
Rare Cells ◽  
Genetic Labeling

The respiratory tract is lined by a pseudo-stratified epithelium from the nose to terminal bronchioles. This first line of defense of the lung against external stress includes five main cell types: basal, suprabasal, club, goblet and multiciliated cells, as well as rare cells such as ionocytes, neuroendocrine and tuft/brush cells. At homeostasis, this epithelium self-renews at low rate but is able of fast regeneration upon damage. Airway epithelial cell lineages during regeneration have been investigated in the mouse by genetic labeling, mainly after injuring the epithelium with noxious agents. From these approaches, basal cells have been identified as progenitors of club, goblet and multiciliated cells, but also of ionocytes and neuroendocrine cells. Single-cell RNA sequencing, coupled to lineage inference algorithms, has independently allowed the establishment of comprehensive pictures of cell lineage relationships in both mouse and human. In line with genetic tracing experiments in mouse trachea, studies using single-cell RNA sequencing (RNAseq) have shown that basal cells first differentiate into club cells, which in turn mature into goblet cells or differentiate into multiciliated cells. In the human airway epithelium, single-cell RNAseq has identified novel intermediate populations such as deuterosomal cells, ‘hybrid’ mucous-multiciliated cells and progenitors of rare cells. Novel differentiation dynamics, such as a transition from goblet to multiciliated cells have also been discovered. The future of cell lineage relationships in the respiratory tract now resides in the combination of genetic labeling approaches with single-cell RNAseq to establish, in a definitive manner, the hallmarks of cellular lineages in normal and pathological situations.

scholarly journals Dissecting the Global Dynamic Molecular Profiles of Human Fetal Kidney Development by Single-Cell RNA Sequencing

Cell Reports ◽  
2018 ◽  
Vol 24 (13) ◽  
pp. 3554-3567.e3 ◽  
Author(s):  
Ping Wang ◽  
Yidong Chen ◽  
Jun Yong ◽  
Yueli Cui ◽  
Rui Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Kidney Development ◽  
Fetal Kidney ◽  
Global Dynamic ◽  
Single Cell Rna Sequencing ◽  
Molecular Profiles ◽  
Human Fetal Kidney

scholarly journals Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data

BMC Genomics ◽  
2020 ◽  
Vol 21 (S9) ◽  
Author(s):  
Siamak Zamani Dadaneh ◽  
Paul de Figueiredo ◽  
Sing-Hoi Sze ◽  
Mingyuan Zhou ◽  
Xiaoning Qian
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Data Augmentation ◽  
Negative Binomial ◽  
Cell Lineage ◽  
Simulated Data ◽  
Molecular Heterogeneity ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Zero Counts

Abstract Background Single-cell RNA sequencing (scRNA-seq) is a powerful profiling technique at the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize molecular heterogeneity and shed light into the underlying cellular process to better understand development and disease mechanisms. The unique analytic challenge is to appropriately model highly over-dispersed scRNA-seq count data with prevalent dropouts (zero counts), making zero-inflated dimensionality reduction techniques popular for scRNA-seq data analyses. Employing zero-inflated distributions, however, may place extra emphasis on zero counts, leading to potential bias when identifying the latent structure of the data. Results In this paper, we propose a fully generative hierarchical gamma-negative binomial (hGNB) model of scRNA-seq data, obviating the need for explicitly modeling zero inflation. At the same time, hGNB can naturally account for covariate effects at both the gene and cell levels to identify complex latent representations of scRNA-seq data, without the need for commonly adopted pre-processing steps such as normalization. Efficient Bayesian model inference is derived by exploiting conditional conjugacy via novel data augmentation techniques. Conclusion Experimental results on both simulated data and several real-world scRNA-seq datasets suggest that hGNB is a powerful tool for cell cluster discovery as well as cell lineage inference.

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