scholarly journals SCALE: modeling allele-specific gene expression by single-cell RNA sequencing

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Yuchao Jiang ◽  
Nancy R. Zhang ◽  
Mingyao Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Scale Modeling ◽  
Single Cell Rna Sequencing ◽  
Allele Specific

scholarly journals Modeling allele-specific gene expression by single-cell RNA sequencing

2017 ◽  
Author(s):  
Yuchao Jiang ◽  
Nancy R Zhang ◽  
Mingyao Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Burst Frequency ◽  
Specific Expression ◽  
Genome Wide ◽  
Single Cell Rna Sequencing ◽  
Allelic Differences ◽  
Allele Specific

AbstractAllele-specific expression is traditionally studied by bulk RNA sequencing, which measures average expression across cells. Single-cell RNA sequencing (scRNA-seq) allows the comparison of expression distribution between the two alleles of a diploid organism and thus the characterization of allele-specific bursting. We propose SCALE to analyze genome-wide allele-specific bursting, with adjustment of technical variability. SCALE detects genes exhibiting allelic differences in bursting parameters, and genes whose alleles burst non-independently. We apply SCALE to mouse blastocyst and human fibroblast cells and find that, globally, cis control in gene expression overwhelmingly manifests as differences in burst frequency.

scholarly journals Single-cell RNA Sequencing Reveals Transcriptional Signatures and Cell-cell Crosstalk in Patients With Hypertensive Nephropathy

2021 ◽  
Author(s):  
Rong Tang ◽  
Wei Lin ◽  
Chanjuan Shen ◽  
Ting Meng ◽  
Joshua D Ooi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Signaling Pathways ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
Cell Crosstalk ◽  
Cell Cell

Abstract BackgroundHypertensive nephropathy (HTN) is one of the leading causes of end-stage renal disease, yet the precise mechanisms and cell-specific gene expression changes are still unknown. This study used single-cell RNA sequencing (scRNA-seq) to explore novel molecular mechanisms and gene targets for HTN for the first time. Methods: The gene expression profiles of renal biopsy samples obtained from HTN patients and healthy living donor controls were determined by scRNA-seq technology. Distinct cell clusters, differential gene expression, cell-cell interaction and potential signaling pathways involved in HTN were determined. Results18 distinct cell clusters were identified in kidney from HTN and control subjects. Endothelial cells overexpressed LRG1 , a pleiotropic factor linked to apoptosis and inflammation, providing a potential novel molecular target. HTN endothelium also overexpressed genes linked to cellular adhesion, extracellular matrix accumulation and inflammation. In HTN patients, mesangial cells highly expressed proliferation related signatures ( MGST1 , TMSB10, EPS8 and IER2 ) not detected in renal diseases before. The upregulated genes in tubules of HTN were mainly participating in inflammatory signatures including IFN-γ signature, IL-17 signaling and TLR signaling. Specific gene expression of kidney-resident CD8 + T cells exhibited a proinflammatory, chemotactic and cytotoxic phenotype. Furthermore, receptor-ligand interaction analysis indicated cell-cell crosstalk in kidney contributes to recruitment and infiltration of inflammatory cells into kidneys, and fibrotic process in hypertensive renal injury. ConclusionsIn summary, our data identifies a distinct cell-specific gene expression profile, pathogenic signaling pathways and potential cell-cell communications in the pathogenesis of HTN. These findings will provide a promising novel landscape for mechanisms and treatment of HTN.

scholarly journals PAC: Highly accurate quantification of allelic gene expression for population and disease genetics

2021 ◽  
Author(s):  
Anna Saukkonen ◽  
Helena Kilpinen ◽  
Alan Hodgkinson
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Read Alignment ◽  
Specific Gene Expression ◽  
Powerful Approach ◽  
Allelic Gene ◽  
Allele Specific ◽  
Accurate Quantification

Analysis of allele-specific gene expression (ASE) is a powerful approach for studying gene regulation. However, detection of ASE events relies on accurate alignment of RNA-sequencing reads, where challenges still remain. We have developed PAC, a method that combines multiple steps to improve the quantification of allelic reads, including personalised (i.e. diploid) read alignment with improved allocation of multi-mapping reads. We show that PAC outperforms standard alignment approaches for ASE detection in both accuracy and in the number of sites it can reliably quantify.

scholarly journals Recovering genomes and phenotypes using allele-specific gene expression

2020 ◽  
Author(s):  
Gamze Gürsoy ◽  
Nancy Lu ◽  
Sarah Wagner ◽  
Mark Gerstein
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Recent Increase ◽  
Variant Information ◽  
Allele Specific ◽  
The Individual

AbstractWith the recent increase in RNA sequencing efforts using large cohorts of individuals, studying allele-specific gene expression is becoming increasingly important. Here, we report that, despite not containing explicit variant information, a list of allele-specific gene names of an individual is enough to recover key variants and link the individual back to their genome or phenotype. This creates a privacy conundrum.

scholarly journals SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

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