scholarly journals A cell type-specific expression signature predicts haploinsufficient autism-susceptibility genes

2016 ◽  
Author(s):  
Chaolin Zhang ◽  
Yufeng Shen
Keyword(s):  
Cortical Neurons ◽  
Expression Profiles ◽  
Susceptibility Genes ◽  
Large Sample Size ◽  
Cell Type ◽  
Specific Expression ◽  
Expression Signature ◽  
Cell Type Specific Expression ◽  
Cell Type Specific ◽  
Autism Susceptibility

AbstractRecent studies have identified many genes with rare de novo mutations in autism, but a limited number of these have been conclusively established as disease-susceptibility genes due to lack of recurrence and confounding background mutations. Such extreme genetic heterogeneity severely limits recurrence-based statistical power even in studies with a large sample size. In addition, the cellular contexts in which these genomic lesions confer disease risks remain poorly understood. Here we investigate the use of cell-type specific expression profiles to differentiate mutations in autism patients or unaffected siblings. Using 24 distinct cell types isolated from the mouse central nervous system, we identified an expression signature shared by genes with likely gene disrupting (LGD) mutations detected by exome-sequencing in autism cases. The signature reflects haploinsufficiency of risk genes enriched in transcriptional and post-transcriptional regulators, with the strongest positive associations with specific types of neurons in different brain regions, including cortical neurons, cerebellar granule cells, and striatal medium spiny neurons. Based on this signature, we assigned a D score to all human genes to prioritize candidate autism-susceptibility genes. When applied to genes with only a single LGD mutation in cases, the D score achieved a precision of 40% as compared to the 15% baseline with a minimal loss in sensitivity. Further improvement was made by combining D score and mutation intolerance metrics from ExAC which were derived from orthogonal data sources. The ensemble model achieved precision of 60% and predicted 117 high-priority candidates. These prioritized lists can facilitate identification of additional autism-susceptibility genes.

scholarly journals PRISM: Recovering cell type specific expression profiles from composite RNA-seq data

2019 ◽  
Author(s):  
Antti Häkkinen ◽  
Kaiyang Zhang ◽  
Amjad Alkodsi ◽  
Noora Andersson ◽  
Erdogan Pekcan Erkan ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Expression Profiles ◽  
Cell Type ◽  
Specific Expression ◽  
Patient Response ◽  
Statistical Framework ◽  
Sample Composition ◽  
Cell Type Specific Expression ◽  
Treatment Naïve ◽  
Cell Type Specific

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

scholarly journals A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

2020 ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Jubao Duan ◽  
Kai Wang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Complex Diseases ◽  
Specific Gene ◽  
Cellular Composition ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AbstractThe importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, the vast majority of gene expression studies are conducted on bulk tissues, necessitating computational approaches to infer biological insights on cell type-specific contribution to diseases. Several computational methods are available for cell type deconvolution (that is, inference of cellular composition) from bulk RNA-Seq data, but cannot impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq (scRNA-seq) and population-wide expression profiles, it can be a computationally tractable and identifiable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations by employing genome-wide tissue-wise expression signatures from GTEx to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations, and uses a multi-variate stochastic search algorithm to estimate the expression level of each gene in each cell type. Extensive analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease, and type 2 diabetes validated efficiency of CellR, while revealing how specific cell types contribute to different diseases. We conducted numerical simulations on human cerebellum to generate pseudo-bulk RNA-seq data and demonstrated its efficiency in inferring cell-specific expression profiles. Moreover, we inferred cell-specific expression levels from bulk RNA-seq data on schizophrenia and computed differentially expressed genes within certain cell types. Using predicted gene expression profile on excitatory neurons, we were able to reproduce our recently published findings on TCF4 being a master regulator in schizophrenia and showed how this gene and its targets are enriched in excitatory neurons. In summary, CellR compares favorably (both accuracy and stability of inference) against competing approaches on inferring cellular composition from bulk RNA-seq data, but also allows direct imputation of cell type-specific gene expression, opening new doors to re-analyze gene expression data on bulk tissues in complex diseases.

scholarly journals Cell-Type Specific Analysis of Selenium-Related Genes in Brain

Antioxidants ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 120
Author(s):  
Alexandru R. Sasuclark ◽  
Vedbar S. Khadka ◽  
Matthew W. Pitts
Keyword(s):  
Neural Development ◽  
Cortical Neurons ◽  
Cell Types ◽  
Inhibitory Neurons ◽  
Brain Atlas ◽  
Cell Type ◽  
Specific Expression ◽  
Rnaseq Data ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

Selenoproteins are a unique class of proteins that play key roles in redox signaling in the brain. This unique organ is comprised of a wide variety of cell types that includes excitatory neurons, inhibitory neurons, astrocytes, microglia, and oligodendrocytes. Whereas selenoproteins are known to be required for neural development and function, the cell-type specific expression of selenoproteins and selenium-related machinery has yet to be systematically investigated. Due to advances in sequencing technology and investment from the National Institutes of Health (NIH)-sponsored BRAIN initiative, RNA sequencing (RNAseq) data from thousands of cortical neurons can now be freely accessed and searched using the online RNAseq data navigator at the Allen Brain Atlas. Hence, we utilized this newly developed tool to perform a comprehensive analysis of the cell-type specific expression of selenium-related genes in brain. Select proteins of interest were further verified by means of multi-label immunofluorescent labeling of mouse brain sections. Of potential significance to neural selenium homeostasis, we report co-expression of selenoprotein P (SELENOP) and selenium binding protein 1 (SELENBP1) within astrocytes. These findings raise the intriguing possibility that SELENBP1 may negatively regulate astrocytic SELENOP synthesis and thereby limit downstream Se supply to neurons.

scholarly journals Integrative Analysis Extracts a Core ceRNA Network of the Fetal Hippocampus With Down Syndrome

2020 ◽  
Vol 11 ◽  
Author(s):  
Shengran Wang ◽  
Xia Tang ◽  
Litao Qin ◽  
Weili Shi ◽  
Shasha Bian ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cell Type ◽  
Specific Expression ◽  
Cerna Network ◽  
Mrna Targets ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

Accumulating evidence suggests that circular RNAs (circRNAs)—miRNA–mRNA ceRNA regulatory network—may play an important role in neurological disorders, such as Alzheimer’s disease (AD). Interestingly, neuropathological changes that closely resemble AD have been found in nearly all Down syndrome (DS) cases > 35 years. However, few studies have reported circRNA transcriptional profiling in DS cases, which is caused by a chromosomal aberration of trisomy 21. Here, we characterized the expression profiles of circRNAs in the fetal hippocampus of DS patients (n = 8) and controls (n = 6) by using microarray. MiRNA, mRNA expression profiling of DS from our previous study and scRNA-seq data describing normal fetal hippocampus development (GEO) were also integrated into the analysis. The similarity between circRNAs/genes with traits/cell-types was calculated by weighted correlation network analysis (WGCNA). miRanda and miRWalk2 were used to predict ceRNA network interactions. We identified a total of 7,078 significantly differentially expressed (DE) circRNAs, including 2,637 upregulated and 4,441 downregulated genes, respectively. WGCNA obtained 15 hub circRNAs and 6 modules with cell type–specific expression patterns among scRNA-seq data. Finally, a core ceRNA network was constructed by 14 hub circRNAs, 17 DE miRNA targets and 245 DE mRNA targets with a cell type–specific expression pattern annotation. Known functional molecules in DS or neurodegeneration (e.g., miR-138, OLIG1, and TPM2) were also included in this network. Our findings are the first to delineate the landscape of circRNAs in DS and the first to effectively integrate ceRNA regulation with scRNA-seq data. These data may provide a valuable resource for further research on the molecular mechanisms or therapeutic targets underlying DS neuropathy.

scholarly journals Auxiliary subunits of the CKAMP family differentially modulate AMPA receptor properties

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Paul Farrow ◽  
Konstantin Khodosevich ◽  
Yechiam Sapir ◽  
Anton Schulmann ◽  
Muhammad Aslam ◽  
...  
Keyword(s):  
Ampa Receptor ◽  
Expression Profiles ◽  
Developmental Expression ◽  
Interacting Proteins ◽  
Cell Type ◽  
Specific Expression ◽  
New Members ◽  
Auxiliary Subunits ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AMPA receptor (AMPAR) function is modulated by auxiliary subunits. Here, we report on three AMPAR interacting proteins—namely CKAMP39, CKAMP52 and CKAMP59—that, together with the previously characterized CKAMP44, constitute a novel family of auxiliary subunits distinct from other families of AMPAR interacting proteins. The new members of the CKAMP family display distinct regional and developmental expression profiles in the mouse brain. Notably, despite their structural similarities they exert diverse modulation on AMPAR gating by influencing deactivation, desensitization and recovery from desensitization, as well as glutamate and cyclothiazide potency to AMPARs. This study indicates that AMPAR function is very precisely controlled by the cell-type specific expression of the CKAMP family members.

scholarly journals A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Jubao Duan ◽  
Kai Wang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Complex Diseases ◽  
Specific Gene ◽  
Cellular Composition ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

Abstract The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.

scholarly journals A cell type‐specific expression map of NCoR1 and SMRT transcriptional co‐repressors in the mouse brain

2020 ◽  
Vol 528 (13) ◽  
pp. 2218-2238 ◽  
Author(s):  
Attilio Iemolo ◽  
Patricia Montilla‐Perez ◽  
I‐Chi Lai ◽  
Yinuo Meng ◽  
Syreeta Nolan ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
A Cell ◽  
Cell Type Specific
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