scholarly journals Single-Cell RNA-Seq Reveals Cellular Heterogeneity of Pluripotency Transition and X Chromosome Dynamics during Early Mouse Development

Cell Reports ◽  
2019 ◽  
Vol 26 (10) ◽  
pp. 2593-2607.e3 ◽  
Author(s):  
Shangli Cheng ◽  
Yu Pei ◽  
Liqun He ◽  
Guangdun Peng ◽  
Björn Reinius ◽  
...  
Keyword(s):  
Single Cell ◽  
X Chromosome ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Mouse Development ◽  
Chromosome Dynamics ◽  
Early Mouse

scholarly journals Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos

Cell ◽  
2016 ◽  
Vol 165 (4) ◽  
pp. 1012-1026 ◽  
Author(s):  
Sophie Petropoulos ◽  
Daniel Edsgärd ◽  
Björn Reinius ◽  
Qiaolin Deng ◽  
Sarita Pauliina Panula ◽  
...  
Keyword(s):  
Single Cell ◽  
X Chromosome ◽  
Preimplantation Embryos ◽  
Rna Seq ◽  
Chromosome Dynamics ◽  
Human Preimplantation Embryos

scholarly journals Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos

Cell ◽  
2016 ◽  
Vol 167 (1) ◽  
pp. 285 ◽  
Author(s):  
Sophie Petropoulos ◽  
Daniel Edsgärd ◽  
Björn Reinius ◽  
Qiaolin Deng ◽  
Sarita Pauliina Panula ◽  
...  
Keyword(s):  
Single Cell ◽  
X Chromosome ◽  
Preimplantation Embryos ◽  
Rna Seq ◽  
Chromosome Dynamics ◽  
Human Preimplantation Embryos

scholarly journals Live imaging of X chromosome reactivation dynamics in early mouse development can discriminate naïve from primed pluripotent stem cells

Development ◽  
2016 ◽  
Vol 143 (16) ◽  
pp. 2958-2964 ◽  
Author(s):  
Shin Kobayashi ◽  
Yusuke Hosoi ◽  
Hirosuke Shiura ◽  
Kazuo Yamagata ◽  
Saori Takahashi ◽  
...  
Keyword(s):  
Stem Cells ◽  
X Chromosome ◽  
Pluripotent Stem Cells ◽  
Live Imaging ◽  
Mouse Development ◽  
Early Mouse

Detrimental effects of two active X chromosomes on early mouse development

Development ◽  
1990 ◽  
Vol 109 (1) ◽  
pp. 189-201 ◽  
Author(s):  
N. Takagi ◽  
K. Abe
Keyword(s):  
X Chromosome ◽  
Distal Segment ◽  
Translocation Chromosome ◽  
Mouse Development ◽  
X Chromosomes ◽  
Developmental Abnormalities ◽  
Inactivation Center ◽  
Normal Males ◽  
Early Mouse ◽  
Embryonic Ectoderm

Matings between female mice carrying Searle's translocation, T(X;16)16H, and normal males give rise to chromosomally unbalanced zygotes with two complete sets of autosomes, one normal X chromosome and one X16 translocation chromosome (XnX16 embryos). Since X chromosome inactivation does not occur in these embryos, probably due to the lack of the inactivation center on X16, XnX16 embryos are functionally disomic for the proximal 63% of the X chromosome and trisomic for the distal segment of chromosome 16. Developmental abnormalities found in XnX16 embryos include: (1) growth retardation detected as early as stage 9, (2) continual loss of embryonic ectoderm cells either by death or by expulsion into the proamniotic cavity, (3) underdevelopment of the ectoplacental cone throughout the course of development, (4) very limited, if any, mesoderm formation, (5) failure in early organogenesis including the embryo, amnion, chorion and yolk sac. Death occurred at 10 days p.c. Since the combination of XO and trisomy 16 does not severely affect early mouse development, it is likely that regulatory mechanisms essential for early embryogenesis do not function correctly in XnX16 embryos due to activity of the extra X chromosome segment of X16.

scholarly journals Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development

2014 ◽  
Vol 31 (7) ◽  
pp. 1060-1066 ◽  
Author(s):  
Haifen Chen ◽  
Jing Guo ◽  
Shital K. Mishra ◽  
Paul Robson ◽  
Mahesan Niranjan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Transcriptional Analysis ◽  
Mouse Development ◽  
Cell Fate Decisions ◽  
Regulatory Circuits ◽  
Early Mouse

scholarly journals scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

2019 ◽  
Author(s):  
Congting Ye ◽  
Qian Zhou ◽  
Xiaohui Wu ◽  
Chen Yu ◽  
Guoli Ji ◽  
...  
Keyword(s):  
Single Cell ◽  
Alternative Polyadenylation ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Rna Seq ◽  
Computational Tool ◽  
Cell Level ◽  
Wilcoxon Rank Sum Test ◽  
Transcriptional Regulatory ◽  
Cell Groups

Abstract Motivation Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 3′ enriched strategy in library construction, the most commonly used scRNA-seq protocol—10× Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data. Availability and implementation The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals scDoc: correcting drop-out events in single-cell RNA-seq data

2020 ◽  
Vol 36 (15) ◽  
pp. 4233-4239
Author(s):  
Di Ran ◽  
Shanshan Zhang ◽  
Nicholas Lytal ◽  
Lingling An
Keyword(s):  
Single Cell ◽  
Simulated Data ◽  
Drop Out ◽  
Cellular Heterogeneity ◽  
Supplementary Information ◽  
Cell Subpopulation ◽  
Rna Seq ◽  
Imputation Methods ◽  
Similarity Estimation ◽  
Differential Expression Detection

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) has become an important tool to unravel cellular heterogeneity, discover new cell (sub)types, and understand cell development at single-cell resolution. However, one major challenge to scRNA-seq research is the presence of ‘drop-out’ events, which usually is due to extremely low mRNA input or the stochastic nature of gene expression. In this article, we present a novel single-cell RNA-seq drop-out correction (scDoc) method, imputing drop-out events by borrowing information for the same gene from highly similar cells. Results scDoc is the first method that directly involves drop-out information to accounting for cell-to-cell similarity estimation, which is crucial in scRNA-seq drop-out imputation but has not been appropriately examined. We evaluated the performance of scDoc using both simulated data and real scRNA-seq studies. Results show that scDoc outperforms the existing imputation methods in reference to data visualization, cell subpopulation identification and differential expression detection in scRNA-seq data. Availability and implementation R code is available at https://github.com/anlingUA/scDoc. Supplementary information Supplementary data are available at Bioinformatics online.

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