scholarly journals Extensive cellular heterogeneity of X inactivation revealed by single-cell allele-specific expression in human fibroblasts

2018 ◽  
Author(s):  
Marco Garieri ◽  
Georgios Stamoulis ◽  
Emilie Falconnet ◽  
Pascale Ribaux ◽  
Christelle Borel ◽  
...  
Keyword(s):  
Single Cell ◽  
X Chromosome ◽  
Expression Profiles ◽  
Single Cells ◽  
Human Fibroblasts ◽  
Cellular Heterogeneity ◽  
Specific Expression ◽  
Primary Fibroblast ◽  
Allele Specific Expression ◽  
Allele Specific

ABSTRACTIn eutherian mammals, X chromosome inactivation (XCI) provides a dosage compensation mechanism where in each female cell one of the two X chromosomes is randomly silenced. However, some genes on the inactive X chromosome and outside the pseudoautosomal regions escape from XCI and are expressed from both alleles (escapees). Given the relevance of the escapees in biology and medicine, we investigated XCI at an unprecedented single-cell resolution. We combined deep single-cell RNA sequencing with whole genome sequencing to examine allelic specific expression (ASE) in 935 primary fibroblast and 48 lymphoblastoid single cells from five female individuals. In this framework we integrated an original method to identify and exclude doublets of cells. We have identified 55 genes as escapees including 5 novel escapee genes. Moreover, we observed that all genes exhibit a variable propensity to escape XCI in each cell and cell type, and that each cell displays a distinct expression profile of the escapee genes. We devised a novel metric, the Inactivation Score (IS), defined as the mean of the allelic expression profiles of the escapees per cell, and discovered a heterogeneous and continuous degree of cellular XCI with extremes represented by “inactive” cells, i.e., exclusively expressing the escaping genes from the active X chromosome, and “escaping” cells, expressing the escapees from both alleles. Intriguingly we found that XIST is the major genetic determinant of IS, and that XIST expression, higher in G0 phase, is negatively correlated with the expression of escapees, inactivated and pseudoautosomal genes. In this study we use single-cell allele specific expression to identify novel escapees in different tissues and provide evidence of an unexpected cellular heterogeneity of XCI driven by a possible regulatory activity of XIST.

scholarly journals Extensive cellular heterogeneity of X inactivation revealed by single-cell allele-specific expression in human fibroblasts

2018 ◽  
Vol 115 (51) ◽  
pp. 13015-13020 ◽  
Author(s):  
Marco Garieri ◽  
Georgios Stamoulis ◽  
Xavier Blanc ◽  
Emilie Falconnet ◽  
Pascale Ribaux ◽  
...  
Keyword(s):  
Single Cell ◽  
X Chromosome ◽  
Expression Profiles ◽  
Single Cells ◽  
Human Fibroblasts ◽  
Cellular Heterogeneity ◽  
Specific Expression ◽  
Primary Fibroblast ◽  
Allele Specific Expression ◽  
Allele Specific

X-chromosome inactivation (XCI) provides a dosage compensation mechanism where, in each female cell, one of the two X chromosomes is randomly silenced. However, some genes on the inactive X chromosome and outside the pseudoautosomal regions escape from XCI and are expressed from both alleles (escapees). We investigated XCI at single-cell resolution combining deep single-cell RNA sequencing with whole-genome sequencing to examine allelic-specific expression in 935 primary fibroblast and 48 lymphoblastoid single cells from five female individuals. In this framework we integrated an original method to identify and exclude doublets of cells. In fibroblast cells, we have identified 55 genes as escapees including five undescribed escapee genes. Moreover, we observed that all genes exhibit a variable propensity to escape XCI in each cell and cell type and that each cell displays a distinct expression profile of the escapee genes. A metric, the Inactivation Score—defined as the mean of the allelic expression profiles of the escapees per cell—enables us to discover a heterogeneous and continuous degree of cellular XCI with extremes represented by “inactive” cells, i.e., cells exclusively expressing the escaping genes from the active X chromosome and “escaping” cells expressing the escapees from both alleles. We found that this effect is associated with cell-cycle phases and, independently, with the XIST expression level, which is higher in the quiescent phase (G0). Single-cell allele-specific expression is a powerful tool to identify novel escapees in different tissues and provide evidence of an unexpected cellular heterogeneity of XCI.

scholarly journals A Bayesian mixture model for the analysis of allelic expression in single cells

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Kwangbom Choi ◽  
Narayanan Raghupathy ◽  
Gary A. Churchill
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Mixture Model ◽  
Time Course ◽  
Single Cells ◽  
Expression Patterns ◽  
Allelic Expression ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific

AbstractAllele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. However, low read coverage and high biological variability present challenges for analyzing ASE. We demonstrate that discarding multi-mapping reads leads to higher variability in estimates of allelic proportions, an increased frequency of sampling zeros, and can lead to spurious findings of dynamic and monoallelic gene expression. Here, we report a method for ASE analysis from single-cell RNA-Seq data that accurately classifies allelic expression states and improves estimation of allelic proportions by pooling information across cells. We further demonstrate that combining information across cells using a hierarchical mixture model reduces sampling variability without sacrificing cell-to-cell heterogeneity. We applied our approach to re-evaluate the statistical independence of allelic bursting and track changes in the allele-specific expression patterns of cells sampled over a developmental time course.

scholarly journals Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 240 ◽  
Author(s):  
Prashant N. M. ◽  
Hongyu Liu ◽  
Pavlos Bousounis ◽  
Liam Spurr ◽  
Nawaf Alomran ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Sequencing Data ◽  
Specific Expression ◽  
Single Nucleotide ◽  
Healthy Donors ◽  
Allele Expression ◽  
Single Cell Rna Sequencing ◽  
Allele Specific

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.

scholarly journals scBASE: A Bayesian mixture model for the analysis of allelic expression in single cells

2018 ◽  
Author(s):  
Kwangbom Choi ◽  
Narayanan Raghupathy ◽  
Gary A. Churchill
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Allelic Expression ◽  
Biological Variability ◽  
Rna Seq ◽  
Dynamic Features ◽  
Specific Expression ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Allele Specific

Allele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. However, low read coverage and high biological variability present challenges for analyzing ASE. We propose a new method for ASE analysis from single cell RNA-Seq data that accurately classifies allelic expression states and improves estimation of allelic proportions by pooling information across cells.

scholarly journals Sensei: How many samples to tell evolution in single-cell studies?

2020 ◽  
Author(s):  
Shaoheng Liang ◽  
Jason Willis ◽  
Jinzhuang Dou ◽  
Vakul Mohanty ◽  
Yuefan Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Expression Profiles ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Controlled Study ◽  
Cancer Evolution ◽  
Cell Type ◽  
Specific Expression ◽  
Mathematical Accuracy

1AbstractCellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

scholarly journals Sensei: how many samples to tell a change in cell type abundance?

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Shaoheng Liang ◽  
Jason Willis ◽  
Jinzhuang Dou ◽  
Vakul Mohanty ◽  
Yuefan Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Expression Profiles ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Controlled Study ◽  
Cancer Evolution ◽  
Cell Type ◽  
Specific Expression ◽  
Mathematical Accuracy

AbstractCellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

239 ALLELE-SPECIFIC EXPRESSION OF X CHROMOSOME-LINKED GENE MAO-A DURING PRE-IMPLANTATION DEVELOPMENT IN BOVINE EMBRYO

2010 ◽  
Vol 22 (1) ◽  
pp. 277
Author(s):  
A. R. Ferreira ◽  
G. M. Machado ◽  
T. O. Diesel ◽  
J. O. Carvalho ◽  
R. Rumpf ◽  
...  
Keyword(s):  
X Chromosome ◽  
Bos Taurus ◽  
Paternal Allele ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
X Chromosomes ◽  
Linked Gene ◽  
Mao A ◽  
Allele Specific

The in vitro embryo culture might affect epigenetic mechanisms, which are involved in controlling the expression of genes related to embryonic development and inactivation of X chromosome. Female mammals have 2 X chromosomes, and males have only 1. This has led to a particular mechanism of evolution of dosage compensation, called X-chromosome inactivation, an important epigenetic event that must occur in all mammalian female embryos. During embryogenesis, at the late blastocyst development (Xue F et al. 2002 Nature Genet. 31, 216–220), 1 of the 2 X chromosomes is randomly inactivated in each cell of the inner cell mass and preferentially X paternal in trophoblast. The aim of this study was to characterize the allele-specific expression of the X chromosome-linked gene monoamine oxidase type A (MAO-A) during in vitro pre-implantation embryo development in bovine. For phenotyping of the MAO-A gene, the RT-PCR restriction fragment length polymorphism technique was used. Primers were designed flanking a single nucleotide polymorphism and the sequence of forward inner primer creating a site of restriction to the RsaI enzyme, thus allowing the detection of allele-specific expression (Bos taurus Taurus × Bos taurus indicus). Oocytes were aspirated from 9 Nelore heifers homozygous for theA allele previously genotyped. The oocytes were selected, matured in vitro, and inseminated with X-sorted sperm from a Holstein bull homozygous for the G allele. Two pools of 10 heterozygous in vitro embryos of each developmental stage, 4-cell [44 h post-insemination (p.i.)], 8- to 16-cell (72 h p.i.), morula (144 h p.i.), blastocyst (156 p.i.), and expanded blastocyst (168 h p.i.), were produced and frozen until RNA extraction. Total RNA was extracted using Invisorb® Spin Cell RNA Mini Kit (Invitek, Berlin, Germany) according to the manufacturer’s protocol, and residual genomic DNA was removed with DNase I treatment. cDNA was done using Oligo dT primers (Invitrogen) and superscript III reverse transcriptase (Invitrogen). Nested PCR for each pool was performed and then the amplicons were digested with 10 U of RsaI enzyme (Promega, Madison, WI, USA). The products were separated by electrophoresis on a 3% agarose gel stained with ethidium bromide. The results showed that both alleles were expressionally represented in the 4-cell, 8- to 16-cell, and expanded blastocyst stages, with the X paternal allele disappearing in morula and blastocyst. We can conclude that both, maternal and paternal X chromosomes, are activated in the 2 earliest stages, inactivated in the morula and blastocyst stages, and reactivated in the expanded blastocyst stage. This research was supported by Embrapa Genetic Resources and Biotechnology and the Brazilian National Council for Scientific and Technological Development (CNPq).

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