scholarly journals Cell Type-Specific Gene Expression Analyses by RNA Sequencing Reveal Local High Nitrate-Triggered Lateral Root Initiation in Shoot-Borne Roots of Maize by Modulating Auxin-Related Cell Cycle Regulation

2015 ◽  
Vol 169 (1) ◽  
pp. 690-704 ◽  
Author(s):  
Peng Yu ◽  
Kai Eggert ◽  
Nicolaus von Wirén ◽  
Chunjian Li ◽  
Frank Hochholdinger
Keyword(s):  
Lateral Root ◽  
Cell Cycle Regulation ◽  
Specific Gene ◽  
Root Initiation ◽  
Cell Type ◽  
Lateral Root Initiation ◽  
Related Cell ◽  
Gene Expression Analyses ◽  
Cell Type Specific ◽  
Cycle Regulation

scholarly journals Non-invasive diagnosis of early-onset coronary artery disease based on cell type-specific gene expression analyses

2018 ◽  
Vol 108 ◽  
pp. 1115-1122 ◽  
Author(s):  
Shayan Ziaee ◽  
Mohammad Ali Boroumand ◽  
Rasoul Salehi ◽  
Saeed Sadeghian ◽  
Maryam Hosseindokht ◽  
...  
Keyword(s):  
Gene Expression ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Onset ◽  
Specific Gene ◽  
Cell Type ◽  
Non Invasive ◽  
Gene Expression Analyses ◽  
Cell Type Specific ◽  
Artery Disease

scholarly journals Moving beyond neurons: the role of cell type-specific gene regulation in Parkinson’s disease heritability

2018 ◽  
Author(s):  
Regina H Reynolds ◽  
Juan Botía ◽  
Mike A Nalls ◽  
John Hardy ◽  
Sarah A Gagliano ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cell Types ◽  
Brain Regions ◽  
Brain Cell ◽  
Specific Gene ◽  
Specific Cell ◽  
Cell Type ◽  
Related Cell ◽  
Cell Type Specific

AbstractParkinson’s disease (PD), with its characteristic loss of nigrostriatal dopaminergic neurons and deposition of α-synuclein in neurons, is often considered a neuronal disorder. However, in recent years substantial evidence has emerged to implicate glial cell types, such as astrocytes and microglia. In this study, we used stratified LD score regression and expression-weighted cell-type enrichment together with several brain-related and cell-type-specific genomic annotations to connect human genomic PD findings to specific brain cell types. We found that PD heritability does not enrich in global and regional brain annotations or brain-related cell-type-specific annotations. Likewise, we found no enrichment of PD susceptibility genes in brain-related cell types. In contrast, we demonstrated a significant enrichment of PD heritability in a curated lysosomal gene set specifically expressed in astrocytic and microglial subtypes. Our results suggest that PD risk loci do not lie in specific cell types or individual brain regions, but rather in global cellular processes to which cell types may have varying vulnerability.

scholarly journals Comprehensive analysis of single cell ATAC-seq data with SnapATAC

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Process Data ◽  
Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

scholarly journals An analytical method for the identification of cell type-specific disease gene modules

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jinting Guan ◽  
Yiping Lin ◽  
Yang Wang ◽  
Junchao Gao ◽  
Guoli Ji
Keyword(s):  
Disease Gene ◽  
Gene Interaction ◽  
Cell Types ◽  
Autism Spectrum ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Disease ◽  
Cell Type Specific ◽  
Gene Modules ◽  
Disease Associated Genes

Abstract Background Genome-wide association studies have identified genetic variants associated with the risk of brain-related diseases, such as neurological and psychiatric disorders, while the causal variants and the specific vulnerable cell types are often needed to be studied. Many disease-associated genes are expressed in multiple cell types of human brains, while the pathologic variants affect primarily specific cell types. We hypothesize a model in which what determines the manifestation of a disease in a cell type is the presence of disease module comprised of disease-associated genes, instead of individual genes. Therefore, it is essential to identify the presence/absence of disease gene modules in cells. Methods To characterize the cell type-specificity of brain-related diseases, we construct human brain cell type-specific gene interaction networks integrating human brain nucleus gene expression data with a referenced tissue-specific gene interaction network. Then from the cell type-specific gene interaction networks, we identify significant cell type-specific disease gene modules by performing statistical tests. Results Between neurons and glia cells, the constructed cell type-specific gene networks and their gene functions are distinct. Then we identify cell type-specific disease gene modules associated with autism spectrum disorder and find that different gene modules are formed and distinct gene functions may be dysregulated in different cells. We also study the similarity and dissimilarity in cell type-specific disease gene modules among autism spectrum disorder, schizophrenia and bipolar disorder. The functions of neurons-specific disease gene modules are associated with synapse for all three diseases, while those in glia cells are different. To facilitate the use of our method, we develop an R package, CtsDGM, for the identification of cell type-specific disease gene modules. Conclusions The results support our hypothesis that a disease manifests itself in a cell type through forming a statistically significant disease gene module. The identification of cell type-specific disease gene modules can promote the development of more targeted biomarkers and treatments for the disease. Our method can be applied for depicting the cell type heterogeneity of a given disease, and also for studying the similarity and dissimilarity between different disorders, providing new insights into the molecular mechanisms underlying the pathogenesis and progression of diseases.

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