scholarly journals CDC20B is required for deuterosome-mediated centriole production in multiciliated cells

2017 ◽  
Author(s):  
Diego R. Revinski ◽  
Laure-Emmanuelle Zaragosi ◽  
Camille Boutin ◽  
Sandra Ruiz-Garcia ◽  
Marie Deprez ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Division ◽  
Single Cell ◽  
Rna Sequencing ◽  
Biological Function ◽  
Host Gene ◽  
Over Expression ◽  
Animal Physiology ◽  
Multiciliated Cells ◽  
Single Cell Rna Sequencing

AbstractMulticiliated cells (MCCs) harbour dozens to hundreds of motile cilia, which beat in a synchronized and directional manner, thus generating hydrodynamic forces important in animal physiology. In vertebrates, MCC differentiation critically depends on the synthesis and release of numerous centrioles by specialized structures called deuterosomes. Little is known about the composition, organization and regulation of deuterosomes. Here, single-cell RNA sequencing reveals that human deuterosome-stage MCCs are characterized by the expression of many cell cycle-related genes. We further investigated the uncharacterized vertebrate-specific cell division cycle 20B (CDC20B) gene, the host gene of microRNA-449abc. We show that the CDC20B protein associates to deuterosomes and is required for the release of centrioles and the subsequent production of cilia in mouse and Xenopus MCCs. CDC20B interacts with PLK1, which has been shown to coordinate centriole disengagement with the protease Separase in mitotic cells. Strikingly, over-expression of Separase rescued centriole disengagement and cilia production in CDC20B-deficient MCCs. This work reveals the shaping of a new biological function, deuterosome-mediated centriole production in vertebrate MCCs, by adaptation of canonical and recently evolved cell cycle-related molecules.

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 Untangling biological factors influencing trajectory inference from single cell data

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Mohammed Charrout ◽  
Marcel J T Reinders ◽  
Ahmed Mahfouz
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Rna Sequencing ◽  
The Past ◽  
Single Cell Rna Sequencing ◽  
Sources Of Variation ◽  
Biological Sources ◽  
Cellular Identity ◽  
Cell Data ◽  
Transcriptional State

Abstract Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity toward the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unraveling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajectory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell_variation.

Single-cell RNA sequencing reveals species-specific time spans of cell cycle transitions in early oogenesis

2021 ◽  
Author(s):  
Zheng-Hui Zhao ◽  
Tie-Gang Meng ◽  
Hong-Yong Zhang ◽  
Yi Hou ◽  
Heide Schatten ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Germ Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Germ Cells ◽  
Cell Cycle Regulators ◽  
Sequencing Data ◽  
Interspecies Differences ◽  
Single Cell Rna Sequencing ◽  
Female Germ Cell

Abstract Oogenesis is a highly regulated process and its basic cellular events are evolutionarily conserved. However, the time spans of oogenesis differ substantially among species. To explore these interspecies differences in oogenesis, we performed single-cell RNA-sequencing on mouse and monkey female germ cells and downloaded the single-cell RNA-sequencing data of human female germ cells. The cell cycle analyses indicate that the period and extent of cell cycle transitions are significantly different between the species. Moreover, hierarchical clustering of critical cell cycle genes and the interacting network of cell cycle regulators also exhibit distinguished patterns across species. We propose that differences in the regulation of cell cycle transitions may underlie female germ cell developmental allochrony between species. A better understanding of the cell cycle transition machinery will provide new insights into the interspecies differences in female germ cell developmental time spans.

scholarly journals A comprehensive temporal patterning gene network in Drosophila medulla neuroblasts revealed by single-cell RNA sequencing

2021 ◽  
Author(s):  
Hailun Zhu ◽  
Sihai Dave Zhao ◽  
Alokananda Ray ◽  
Yu Zhang ◽  
Xin Li
Keyword(s):  
Cell Cycle ◽  
Stem Cells ◽  
Transcription Factors ◽  
Neural Stem Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Network ◽  
Temporal Patterning ◽  
Single Cell Rna Sequencing ◽  
High Gradients

During development, neural stem cells are temporally patterned to sequentially generate a variety of neural types before exiting the cell cycle. Temporal patterning is well-studied in Drosophila, where neural stem cells called neuroblasts sequentially express cascades of Temporal Transcription Factors (TTFs) to control the birth-order dependent neural specification. However, currently known TTFs were mostly identified through candidate approaches and may not be complete. In addition, many fundamental questions remain concerning the TTF cascade initiation, progression, and termination. It is also not known why temporal progression only happens in neuroblasts but not in their differentiated progeny. In this work, we performed single-cell RNA sequencing of Drosophila medulla neuroblasts of all ages to study the temporal patterning process with single-cell resolution. Our scRNA-seq data revealed that sets of genes involved in different biological processes show high to low or low to high gradients as neuroblasts age. We also identified a list of novel TTFs, and experimentally characterized their roles in the temporal progression and neural fate specification. Our study revealed a comprehensive temporal gene network that patterns medulla neuroblasts from start to end. Furthermore, we found that the progression and termination of this temporal cascade also require transcription factors differentially expressed along the differentiation axis (neuroblasts -> -> neurons). Lola proteins function as a speed modulator of temporal progression in neuroblasts; while Nerfin-1, a factor required to suppress de-differentiation in post-mitotic neurons, acts at the final temporal stage together with the last TTF of the cascade, to promote the switch to gliogenesis and the cell cycle exit. Our comprehensive study of the medulla neuroblast temporal cascade illustrated mechanisms that might be conserved in the temporal patterning of neural stem cells.

scholarly journals Untangling biological factors influencing trajectory inference from single cell data

2020 ◽  
Author(s):  
Mohammed Charrout ◽  
Marcel J.T. Reinders ◽  
Ahmed Mahfouz
Keyword(s):  
Cell Cycle ◽  
Cell Differentiation ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cell Rna Sequencing ◽  
Small Set ◽  
Sources Of Variation ◽  
Biological Sources ◽  
Cell Data ◽  
Transcriptional State

Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity towards the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unravelling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajetory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell_variation.Significance StatementPseudotime inference is a bioinformatics tool used to characterize and understand the role and activity of genes involved in cell differentiation. To achieve this, the level of expression of thousands of genes are simultaneously used to order cells along a developmental axis. However, this may result in distorted trajectories as many genes are not necessary involved in cell differentiation, and might even provide the pseudotime inference tool with conflicting (confounding) information. Here we present a methodology for improving inference of the differentiation trajectories by restricting it to a small set of genes assumed to regulate cell differentiation.

108 Single-Cell RNA Sequencing Reveals Blastomere Heterogeneity and Early Lineage Specification Events in Bovine Embryos During Major Embryonic Genome Activation

2018 ◽  
Vol 30 (1) ◽  
pp. 193
Author(s):  
I. Lavagi ◽  
S. Krebs ◽  
K. Simmet ◽  
V. Zakhartchenko ◽  
E. Wolf ◽  
...  
Keyword(s):  
Cell Division ◽  
Single Cell ◽  
Rna Sequencing ◽  
Bovine Embryos ◽  
Lineage Specification ◽  
Embryonic Genome Activation ◽  
Embryonic Genome ◽  
Single Cell Rna Sequencing ◽  
Genome Activation ◽  
Go Terms

During early embryonic stages, gene products generated by the embryo acquire control over embryonic development. At the 8- to 16-cell stage, major embryonic genome activation (EGA) occurs in bovine embryos. Morphological observations, such as size of blastomeres and distribution of microvilli, suggest heterogeneity of individual cells already at this developmental stage. To study this heterogeneity on the transcriptome level, we performed single-cell RNA sequencing (scRNA-seq) of 161 blastomeres from 14 in vitro-produced bovine embryos at Day 2 and Day 3 post-fertilization. After removing the zona pellucida, blastomeres were mechanically separated in Ca2+- and Mg2+-free PBS, individually collected, and lysed. Complementary DNA libraries were prepared by the single cell RNA-barcoding and sequencing (SCRB-Seq) protocol. Exogenous RNA was added for quality control and cell specific barcodes and unique molecular identifiers (UMI) were used to enable pooling of libraries and to exclude PCR duplicates, respectively. After sequencing (Illumina HiSEqn 1500; 50 nt reads; Illumina Inc., San Diego, CA, USA), UMI were counted with the published Drop-seq pipeline (45,000 UMI on average per library) and cells with UMI count <2.000 were removed. Data were normalized based on UMI and non-supervised clustering analyses of single-cell data were performed (SC3 and M3Drop R packages). The transcriptome profiles of all individual cells were assigned to 6 clusters with specific sets of genes. Sorting cells according to their transcriptome profiles by the CellTree R package (Bioconductor; https://bioconductor.org/packages/release/bioc/html/cellTree.html) resulted in a linear pseudo-timeline. Furthermore, this tool identified 6 groups of genes (topics). Each of them showed an over-representation of distinct Gene Ontology (GO) terms; topic 1, “translation” and “cell division”; topic 2, GO terms involved in translation, RNA splicing and cell division; topic 3, “translation”; topic 4, “ATP synthesis coupled proton transport”; topic 5, “mitochondrial translational elongation”; topic 6, “organic hydroxyl compound transport”. Moreover, increased expression of PCDH10 (protocadherin 10) was observed in the biologically pseudo-ordered more advanced blastomeres. This gene is known to be predominantly expressed in the inner cell mass (ICM) at the blastocyst stage, suggesting that these cells might become ICM. In summary, our study reveals developmental heterogeneity and hints to early lineage specification events in bovine embryos at the time of major EGA.

scholarly journals Analysis of single-cell RNA-sequencing data to identify quiescent and proliferating neural cell populations in Glioblastoma

2021 ◽  
Author(s):  
Rajeev Vikram ◽  
Wen□Cheng Chou ◽  
Pei-Ei Wu ◽  
Wei-Ting Chen ◽  
Chen-Yang Shen
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
High Grade ◽  
Sequencing Data ◽  
Quiescent Cells ◽  
Single Cell Rna Sequencing ◽  
Tumorigenic Cells

ABSTRACTBackgroundDiffuse Glioblastoma (GBM) has high mortality and remains one of the most challenging type of cancer to treat. Identifying and characterizing the cells populations driving tumor growth and therapy resistance has been particularly difficult owing to marked inter and intra tumoral heterogeneity observed in these tumors. These tumorigenic populations contain long lived cells associated with latency, immune evasion and metastasis.MethodsHere, we analyzed the single-cell RNA-sequencing data of high grade glioblastomas from four different studies using integrated analysis of gene expression patterns, cell cycle stages and copy number variation to identify gene expression signatures associated with quiescent and cycling neuronal tumorigenic cells.ResultsThe results show that while cycling and quiescent cells are present in GBM of all age groups, they exist in a much larger proportion in pediatric glioblastomas. These cells show similarities in their expression patterns of a number of pluripotency and proliferation related genes. Upon unbiased clustering, these cells explicitly clustered on their cell cycle stage. Quiescent cells in both the groups specifically overexpressed a number of genes for ribosomal protein, while the cycling cells were enriched in the expression of high-mobility group and heterogeneous nuclear ribonucleoprotein group genes. A number of well-known markers of quiescence and proliferation in neurogenesis showed preferential expression in the quiescent and cycling populations identified in our analysis. Through our analysis, we identify ribosomal proteins as key constituents of quiescence in glioblastoma stem cells.ConclusionsThis study identifies gene signatures common to adult and pediatric glioblastoma quiescent and cycling stem cell niches. Further research elucidating their role in controlling quiescence and proliferation in tumorigenic cells in high grade glioblastoma will open avenues in more effective treatment strategies for glioblastoma patients.

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