scholarly journals Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Jong Kyoung Kim ◽  
Aleksandra A. Kolodziejczyk ◽  
Tomislav Ilicic ◽  
Sarah A. Teichmann ◽  
John C. Marioni
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Large Fraction ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Technical Noise ◽  
Allele Specific ◽  
High Level

Abstract Single-cell RNA-sequencing (scRNA-seq) facilitates identification of new cell types and gene regulatory networks as well as dissection of the kinetics of gene expression and patterns of allele-specific expression. However, to facilitate such analyses, separating biological variability from the high level of technical noise that affects scRNA-seq protocols is vital. Here we describe and validate a generative statistical model that accurately quantifies technical noise with the help of external RNA spike-ins. Applying our approach to investigate stochastic allele-specific expression in individual cells, we demonstrate that a large fraction of stochastic allele-specific expression can be explained by technical noise, especially for lowly and moderately expressed genes: we predict that only 17.8% of stochastic allele-specific expression patterns are attributable to biological noise with the remainder due to technical noise.

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 Allele-specific expression patterns of interleukin-2 and Pax-5 revealed by a sensitive single-cell RT-PCR analysis

2000 ◽  
Vol 10 (13) ◽  
pp. 789-792 ◽  
Author(s):  
Kristina L. Rhoades ◽  
Nandita Singh ◽  
Itamar Simon ◽  
Barbara Glidden ◽  
Howard Cedar ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Interleukin 2 ◽  
Pcr Analysis ◽  
Rt Pcr ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific

scholarly journals Accurate quantitation of allele-specific expression patterns by analysis of DNA melting

2007 ◽  
Vol 17 (7) ◽  
pp. 1093-1100 ◽  
Author(s):  
S. Jeong ◽  
Y. Hahn ◽  
Q. Rong ◽  
K. Pfeifer
Keyword(s):  
Expression Patterns ◽  
Dna Melting ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific

scholarly journals Untangling the effects of cellular composition on coexpression analysis

2019 ◽  
Author(s):  
Marjan Farahbod ◽  
Paul Pavlidis
Keyword(s):  
Regulatory Networks ◽  
Large Fraction ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Composition ◽  
Cell Type ◽  
Coexpression Analysis ◽  
The Impact ◽  
Bulk Tissue ◽  
Rna Levels

AbstractBackgroundCoexpression analysis is one of the most widely used methods in genomics, with applications to inferring regulatory networks, predicting gene function, and interpretation of transcriptome profiling studies. Most studies use data collected from bulk tissue, where the effects of cellular composition present a potential confound. However, the impact of composition on coexpression analysis have not been studied in detail. Here we examine this issue for the case of human brain RNA analysis.ResultsWe found that for most genes, differences in expression levels across cell types account for a large fraction of the variance of their measured RNA levels in brain (median R2 = 0.64). We then show that genes that have similar expression patterns across cell types will have correlated RNA levels in bulk tissue, due to the effect of variation in cellular composition. We demonstrate that much of the coexpression in the bulk tissue can be attributed to this effect. We further show how this composition-induced coexpression masks underlying intra-cell-type coexpression observed in single-cell data. Attempt to correct for composition yielded mixed results.ConclusionsThe dominant coexpression signal in brain can be attributed to cellular compositional effects, rather than intra-cell-type regulatory relationships, and this is likely to be true for other tissues. These results have important implications for the relevance and interpretation of coexpression in many applications.

scholarly journals Allele specific expression in worker reproduction genes in the bumblebee Bombus terrestris

2015 ◽  
Author(s):  
Harindra E Amarasinghe ◽  
Bradley J Toghill ◽  
Despina Nathanael ◽  
Eamonn B Mallon
Keyword(s):  
Bombus Terrestris ◽  
Expression Patterns ◽  
Worker Reproduction ◽  
Imprinted Genes ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific

Methylation has previously been associated with allele specific expression in ants. Recently, we found methylation is important in worker reproduction in the bumblebee Bombus terrestris. Here we searched for allele specific expression in twelve genes associated with worker reproduction in bees. We found allele specific expression in Ecdysone 20 monooxygenase and IMP-L2-like. Although we were unable to confirm a genetic or epigenetic cause for this allele specific expression, the expression patterns of the two genes match those predicted for imprinted genes.

scholarly journals Single-cell dissection of the human cerebrovasculature in health and disease

2021 ◽  
Author(s):  
Francisco J. Garcia ◽  
Na Sun ◽  
Hyeseung Lee ◽  
Brianna Godlewski ◽  
Kyriaki Galani ◽  
...  
Keyword(s):  
Single Cell ◽  
Ex Vivo ◽  
Expression Patterns ◽  
Cell Types ◽  
Normal Brain ◽  
Specific Expression ◽  
Fresh Tissue ◽  
Tissue Samples ◽  
Brain Physiology ◽  
Human Specific

SummaryDespite the importance of the blood-brain barrier in maintaining normal brain physiology and in understanding neurodegeneration and CNS drug delivery, human cerebrovascular cells remain poorly characterized due to their sparsity and dispersion. Here, we perform the first single-cell characterization of the human cerebrovasculature using both ex vivo fresh-tissue experimental enrichment and post mortem in silico sorting of human cortical tissue samples. We capture 31,812 cerebrovascular cells across 17 subtypes, including three distinct subtypes of perivascular fibroblasts as well as vasculature-coupled neurons and glia. We uncover human-specific expression patterns along the arteriovenous axis and determine previously uncharacterized cell type-specific markers. We use our newly discovered human-specific signatures to study changes in 3,945 cerebrovascular cells of Huntington’s disease patients, which reveal an activation of innate immune signaling in vascular and vasculature-coupled cell types and the concomitant reduction to proteins critical for maintenance of BBB integrity. Finally, our study provides a comprehensive resource molecular atlas of the human cerebrovasculature to guide future biological and therapeutic studies.

scholarly journals Allele specific expression in worker reproduction genes in the bumblebee Bombus terrestris

2015 ◽  
Author(s):  
Harindra E Amarasinghe ◽  
Bradley J Toghill ◽  
Despina Nathanael ◽  
Eamonn B Mallon
Keyword(s):  
Bombus Terrestris ◽  
Expression Patterns ◽  
Worker Reproduction ◽  
Imprinted Genes ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific

Methylation has previously been associated with allele specific expression in ants. Recently, we found methylation is important in worker reproduction in the bumblebee Bombus terrestris. Here we searched for allele specific expression in twelve genes associated with worker reproduction in bees. We found allele specific expression in Ecdysone 20 monooxygenase and IMP-L2-like. Although we were unable to confirm a genetic or epigenetic cause for this allele specific expression, the expression patterns of the two genes match those predicted for imprinted genes.

scholarly journals Detection of allele-specific expression in spatial transcriptomics with spASE

2021 ◽  
Author(s):  
Luli S. Zou ◽  
Tongtong Zhao ◽  
Dylan M. Cable ◽  
Evan Murray ◽  
Martin J. Aryee ◽  
...  
Keyword(s):  
Statistical Power ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Preferential Expression ◽  
Mouse Hippocampus ◽  
Transcriptomics Data ◽  
Allele Specific ◽  
Cell Type Specific

AbstractAllele-specific expression (ASE), or the preferential expression of one allele, can be observed in transcriptomics data from early development throughout the lifespan. However, the prevalence of spatial and cell type-specific ASE variation remains unclear. Spatial transcriptomics technologies permit the study of spatial ASE patterns genome-wide at near-single-cell resolution. However, the data are highly sparse, and confounding between cell type and spatial location present further statistical challenges. Here, we introduce spASE (https://github.com/lulizou/spase), a computational framework for detecting spatial patterns in ASE within and across cell types from spatial transcriptomics data. To tackle the challenge presented by the low signal to noise ratio due to the sparsity of the data, we implement a spatial smoothing approach that greatly improves statistical power. We generated Slide-seqV2 data from the mouse hippocampus and detected ASE in X-chromosome genes, both within and across cell type, validating our ability to recover known ASE patterns. We demonstrate that our method can also identify cell type-specific effects, which we find can explain the majority of the spatial signal for autosomal genes. The findings facilitated by our method provide new insight into the uncharacterized landscape of spatial and cell type-specific ASE in the mouse hippocampus.

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.

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