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 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 scHLAcount: Allele-specific HLA expression from single-cell gene expression data

2019 ◽  
Author(s):  
Charlotte A. Darby ◽  
Michael J. T. Stubbington ◽  
Patrick J. Marks ◽  
Álvaro Martínez Barrio ◽  
Ian T. Fiddes
Keyword(s):  
Single Cells ◽  
Expression Patterns ◽  
Allelic Expression ◽  
Loss Of Function ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Type Specificity ◽  
Specific Expression ◽  
Hla Genes ◽  
Allele Specific

AbstractStudies in bulk RNA sequencing data suggest cell-type and allele-specific expression of the human leukocyte antigen (HLA) genes. These loci are extremely diverse and they function as part of the major histocompatibility complex (MHC) which is responsible for antigen presentation. Mutation and or misregulation of expression of HLA genes has implications in diseases, especially cancer. Immune responses to tumor cells can be evaded through HLA loss of function. However, bulk RNA-seq does not fully disentangle cell type specificity and allelic expression. Here we present scHLAcount, a workflow for computing allele-specific molecule counts of the HLA genes in single cells an individualized reference. We demonstrate that scHLAcount can be used to find cell-type specific allelic expression of HLA genes in blood cells, and detect different allelic expression patterns between tumor and normal cells in patient biopsies. scHLAcount is available at https://github.com/10XGenomics/scHLAcount.

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 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 Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci

2019 ◽  
Author(s):  
Maria Gutierrez-Arcelus ◽  
Yuriy Baglaenko ◽  
Jatin Arora ◽  
Susan Hannes ◽  
Yang Luo ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
T Cell Activation ◽  
Time Course ◽  
Regulatory Elements ◽  
Dependent Manner ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific

AbstractUnderstanding how genetic regulatory variation affects gene expression in different T cell states is essential to deciphering autoimmunity. We conducted a high-resolution RNA-seq time course analysis of stimulated memory CD4+T cells from 24 healthy individuals. We identified 186 genes with dynamic allele-specific expression, where the balance of alleles changes over time. These genes were four fold enriched in autoimmune loci. We found pervasive dynamic regulatory effects within six HLA genes, particularly for a major autoimmune risk gene,HLA-DQB1. EachHLA-DQB1allele had one of three distinct transcriptional regulatory programs. Using CRISPR/Cas9 genomic editing we demonstrated that a single nucleotide variant at the promoter is causal for T cell-specific control ofHLA-DQB1expression. Our study in CD4+T cells shows that genetic variation incisregulatory elements may affect gene expression in a lymphocyte activation status-dependent manner contributing to the inter-individual complexity of immune responses.

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 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 Allele-specific expression of Parkinson’s disease susceptibility genes in human brain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margrete Langmyhr ◽  
Sandra Pilar Henriksen ◽  
Chiara Cappelletti ◽  
Wilma D. J. van de Berg ◽  
Lasse Pihlstrøm ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Whole Blood ◽  
Allelic Expression ◽  
Genome Wide Association Studies ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Specific Effects ◽  
Allele Specific

AbstractGenome-wide association studies have identified genetic variation in genomic loci associated with susceptibility to Parkinson’s disease (PD), the most common neurodegenerative movement disorder worldwide. We used allelic expression profiling of genes located within PD-associated loci to identify cis-regulatory variation affecting gene expression. DNA and RNA were extracted from post-mortem superior frontal gyrus tissue and whole blood samples from PD patients and controls. The relative allelic expression of transcribed SNPs in 12 GWAS risk genes was analysed by real-time qPCR. Allele-specific expression was identified for 9 out of 12 genes tested (GBA, TMEM175, RAB7L1, NUCKS1, MCCC1, BCKDK, ZNF646, LZTS3, and WDHD1) in brain tissue samples. Three genes (GPNMB, STK39 and SIPA1L2) did not show significant allele-specific effects. Allele-specific effects were confirmed in whole blood for three genes (BCKDK, LZTS3 and MCCC1), whereas two genes (RAB7L1 and NUCKS1) showed brain-specific allelic expression. Our study supports the hypothesis that changes to the cis-regulation of gene expression is a major mechanism behind a large proportion of genetic associations in PD. Interestingly, allele-specific expression was also observed for coding variants believed to be causal variants (GBA and TMEM175), indicating that splicing and other regulatory mechanisms may be involved in disease development.

scholarly journals ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes

2020 ◽  
Author(s):  
Kenneth H. Hu ◽  
John P. Eichorst ◽  
Chris S. McGinnis ◽  
David M. Patterson ◽  
Eric D. Chow ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Real Time ◽  
Dimensional Space ◽  
Single Cells ◽  
Expression Patterns ◽  
Live Cells ◽  
Glass Substrates ◽  
Complete Mapping

ABSTRACTSpatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

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