scholarly journals Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases

2021 ◽  
Author(s):  
Niek de Klein ◽  
Ellen A. Tsai ◽  
Martijn Vochteloo ◽  
Denis Baird ◽  
Yunfeng Huang ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Network Analysis ◽  
Genome Wide Association Study ◽  
Brain Regions ◽  
Specific Gene ◽  
Eqtl Analysis ◽  
Rna Seq ◽  
Cell Type ◽  
The Brain ◽  
Unique Genes

Gaining insight into the downstream consequences of non-coding variants is an essential step towards the identification of therapeutic targets from genome-wide association study (GWAS) findings. Here we have harmonized and integrated 8,727 RNA-seq samples with accompanying genotype data from multiple brain-regions from 14 datasets. This sample size enabled us to perform both cis- and trans-expression quantitative locus (eQTL) mapping. Upon comparing the brain cortex cis-eQTLs (for 12,307 unique genes at FDR<0.05) with a large blood cis-eQTL analysis (n=31,684 samples), we observed that brain eQTLs are more tissue specific than previously assumed. We inferred the brain cell type for 1,515 cis-eQTLs by using cell type proportion information. We conducted Mendelian Randomization on 31 brain-related traits using cis-eQTLs as instruments and found 159 significant findings that also passed colocalization. Furthermore, two multiple sclerosis (MS) findings had cell type specific signals, a neuron-specific cis-eQTL for CYP24A1 and a macrophage specific cis-eQTL for CLECL1. To further interpret GWAS hits, we performed trans-eQTL analysis. We identified 2,589 trans-eQTLs (at FDR<0.05) for 373 unique SNPs, affecting 1,263 unique genes, and 21 replicated significantly using single-nucleus RNA-seq data from excitatory neurons. We also generated a brain-specific gene-coregulation network that we used to predict which genes have brain-specific functions, and to perform a novel network analysis of Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS) and Parkinson's disease (PD) GWAS data. This resulted in the identification of distinct sets of genes that show significantly enriched co-regulation with genes inside the associated GWAS loci, and which might reflect drivers of these diseases.

scholarly journals Strategies for cellular deconvolution in human brain RNA sequencing data

2020 ◽  
Author(s):  
Olukayode A. Sosina ◽  
Matthew N Tran ◽  
Kristen R Maynard ◽  
Ran Tao ◽  
Margaret A. Taub ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Size ◽  
Expression Profiles ◽  
Brain Regions ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Target User ◽  
Size Estimates

AbstractStatistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. Here we show that several existing deconvolution algorithms which estimate the RNA composition of homogenate tissue, relates to the amount of RNA attributable to each cell type, and not the cellular composition relating to the underlying fraction of cells. Incorporating “cell size” parameters into RNA-based deconvolution algorithms can successfully recover cellular fractions in homogenate brain RNA-seq data. We lastly show that using both cell sizes and cell type-specific gene expression profiles from brain regions other than the target/user-provided bulk tissue RNA-seq dataset consistently results in biased cell fractions. We report several independently constructed cell size estimates as a community resource and extend the MuSiC framework to accommodate these cell size estimates (https://github.com/xuranw/MuSiC/).

scholarly journals Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression

2019 ◽  
Vol 36 (3) ◽  
pp. 782-788 ◽  
Author(s):  
Jiebiao Wang ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Empirical Bayes ◽  
Brain Regions ◽  
Tissue Level ◽  
Supplementary Information ◽  
Specific Gene ◽  
Expression Data ◽  
Cell Type ◽  
Multiple Measurements ◽  
Cell Type Specific

Abstract Motivation Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects. Results Complementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g. multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL). Availability and implementation We implement this method as an R package MIND, hosted on https://github.com/randel/MIND. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Improved estimation of cell type-specific gene expression through deconvolution of bulk tissues with matrix completion

2021 ◽  
Author(s):  
Weixu Wang ◽  
Jun Yao ◽  
Yi Wang ◽  
Chao Zhang ◽  
Wei Tao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Matrix Completion ◽  
Superior Performance ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Gene Expression ◽  
Large Patient ◽  
Cell Type Specific

Cell type-specific gene expression (CSE) brings novel biological insights into both physiological and pathological processes compared with bulk tissue gene expression. Although fluorescence-activated cell sorting (FACS) and single-cell RNA sequencing (scRNA-seq) are two widely used techniques to detect gene expression in a cell type-specific manner, the constraints of cost and labor force make it impractical as a routine on large patient cohorts. Here, we present ENIGMA, an algorithm that deconvolutes bulk RNA-seq into cell type-specific expression matrices and cell type fraction matrices without the need of physical sorting or sequencing of single cells. ENIGMA used cell type signature matrix generated from either FACS RNA-seq or scRNA-seq as reference, and applied matrix completion technique to achieve fast and accurate deconvolution. We demonstrated the superior performance of ENIGMA to previously published algorithms (TCA, bMIND and CIBERSORTx) while requiring much less running time on both simulated and realistic datasets. To prove its value in biological discovery, we applied ENIGMA to bulk RNA-seq from arthritis patients and revealed a pseudo-differentiation trajectory that could reflect monocyte to macrophage transition. We also applied ENIGMA to bulk RNA-seq data of pancreatic islet tissue from type 2 diabetes (T2D) patients and discovered a beta cell-specific gene co-expression module related to senescence and apoptosis that possibly contributed to the pathogenesis of T2D. Together, ENIGMA provides a new framework to improve the CSE estimation by integrating FACS RNA-seq and scRNA-seq with tissue bulk RNA-seq data, and will extend our understandings about cell heterogeneity on population level with no need for experimental tissue disaggregation.

scholarly journals Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

2018 ◽  
Author(s):  
Xuran Wang ◽  
Jihwan Park ◽  
Katalin Susztak ◽  
Nancy R. Zhang ◽  
Mingyao Li
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Tissue Cell ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

scholarly journals Neurorehabilitation in Multiple Sclerosis – Resilience in Practice

2017 ◽  
Vol 12 (01) ◽  
pp. 31 ◽  
Author(s):  
Jürg Kesselring ◽  
Keyword(s):  
Multiple Sclerosis ◽  
Best Practice ◽  
Healthcare Providers ◽  
Brain Regions ◽  
Standards Of Care ◽  
Key Factors ◽  
Disease Modifying Drugs ◽  
Randomised Controlled ◽  
The Impact ◽  
The Brain

In recent years, enormous strides have been made in increasing the range and efficacy of disease-modifying drugs available for the treatment of multiple sclerosis (MS) in its early and remitting stages, and more continue to emerge. Another equally important concept of successful treatment of MS is neurorehabilitation, which must be pursued alongside these medications. Key factors that contribute to the impact of neurorehabilitation include resilience and neuroplasticity. In the former, components such as nutrition, self-belief and physical activity provide a stronger response to the disease and improved responses to treatment. Neuroplasticity is the capacity of the brain to establish new neuronal networks after lesion damage has occurred and distant brain regions assume control of lost functions. In MS, it is vital that each patient is treated by a coordinated multidisciplinary team. This enables all aspects of the disease including problems with mobility, gait, bladder/bowel disturbances, fatigue and depression to be effectively treated. It is also important that the treating team adopts current best practice and provides internationally agreed standards of care. A further vital aspect of MS management is patient engagement, in which individuals are fully involved and are encouraged to strive and put effort into meeting treatment goals. In this approach, healthcare providers become motivators and patients need less intervention and consume fewer resources. Numerous interventions that promote neurorehabilitation are available, though evidence to support their use is limited by a lack of data from large randomised controlled trials. Combining interventions that promote neurorehabilitation with newer, more effective treatments creates a promising potential to substantially improve the outlook for patients at all stages of MS.

scholarly journals Chromatin-enriched RNAs mark active and repressive cis-regulation: an analysis of nuclear RNA-seq

2019 ◽  
Author(s):  
Xiangying Sun ◽  
Zhezhen Wang ◽  
Carlos Perez-Cervantes ◽  
Alex Ruthenburg ◽  
Ivan Moskowitz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Noncoding Rna ◽  
Molecular Mechanisms ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Neighboring Gene ◽  
Cis Regulation ◽  
Nuclear Rna ◽  
Cell Type Specific

AbstractLong noncoding RNAs (lncRNAs) localize in the cell nucleus and influence gene expression through a variety of molecular mechanisms. RNA sequencing of two biochemical fractions of nuclei reveals a unique class of lncRNAs, termed chromatin-enriched nuclear RNAs (cheRNAs) that are tightly bound to chromatin and putatively function to cis-activate gene expression. Until now, a rigorous analytic pipeline for nuclear RNA-seq has been lacking. In this study, we survey four computational strategies for nuclear RNA-seq data analysis and show that a new pipeline, Tuxedo, outperforms other approaches. Tuxedo not only assembles a more complete transcriptome, but also identifies cheRNA with higher accuracy. We have used Tuxedo to analyze gold-standard K562 cell datasets and further characterize the genomic features of intergenic cheRNA (icheRNA) and their similarity to those of enhancer RNA (eRNA). Moreover, we quantify the transcriptional correlation of icheRNA and adjacent genes, and suggest that icheRNA may be the cis-acting transcriptional regulator that is more positively associated with neighboring gene expression than eRNA predicted by state-of-art method or CAGE signal. We also explore two novel genomic associations, suggesting cheRNA may have diverse functions. A possible new role of H3K9me3 modification coincident with icheRNA may be associated with active enhancer derived from ancient mobile elements, while a potential cis-repressive function of antisense cheRNA (as-cheRNA) is likely to be involved in transiently modulating cell type-specific cis-regulation.Author SummaryChromatin-enriched nuclear RNA (cheRNA) is a class of gene regulatory non-coding RNAs. CheRNA provides a powerful way to profile the nuclear transcriptional landscape, especially to profile the noncoding transcriptome. The computational framework presented here provides a reliable approach to identifying cheRNA, and for studying cell-type specific gene regulation. We found that intergenic cheRNA, including intergenic cheRNA with high levels of H3K9me3 (a mark associated with closed/repressed chromatin), may act as a transcriptional activator. In contrast, antisense cheRNA, which originates from the complementary strand of the protein-coding gene, may interact with diverse chromatin modulators to repress local transcription. With our new pipeline, one future challenge will be refining the functional mechanisms of these noncoding RNA classes through exploring their regulatory roles, which are involved in diverse molecular and cellular processes in human and other organisms.

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 Predicting Brain Regions Related to Alzheimer’s Disease Based on Global Feature

2020 ◽  
Author(s):  
Qi Wang ◽  
Siwei Chen ◽  
He Wang ◽  
Luzeng Chen ◽  
Yongan Sun ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Diffusion Tensor ◽  
The Elderly ◽  
Brain Regions ◽  
Imaging Method ◽  
Global Feature ◽  
Objective Monitoring ◽  
The Brain

AbstractAlzheimer’s disease (AD) is a common neurodegenerative disease in the elderly, early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most of the brain regions related to AD were identified based on the imaging method, which can only identify some atrophic brain regions. In this work, we used mathematical models to find out the potential brain regions related to AD. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, we set a new local feature index 2hop-connectivity to measure the correlation among different areas. And for this, we proposed a novel algorithm named 2hopRWR to measure 2hop-connectivity. At last, we proposed a new index GFS (Global Feature Score) based on global feature by combing 5 local features: degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are likely related to Alzheimer’s Disease. As a result, all the top ten brain regions in GFS scoring difference between the AD group and the non-AD group were related to AD by literature verification. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the mini-mental state examination (MMSE) scale and montreal cognitive assessment (MoCA) scale. So, we believe the GFS can also be used as a new index to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method in other areas of network analysis.

scholarly journals Unsupervised cell functional annotation for single-cell RNA-Seq

2021 ◽  
Author(s):  
Dongshunyi Li ◽  
Jun Ding ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Dimensional Space ◽  
Cell Types ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Gene Sets ◽  
Supervised Methods ◽  
Low Dimensional

One of the first steps in the analysis of single cell RNA-Sequencing data (scRNA-Seq) is the assignment of cell types. While a number of supervised methods have been developed for this, in most cases such assignment is performed by first clustering cells in low-dimensional space and then assigning cell types to different clusters. To overcome noise and to improve cell type assignments we developed UNIFAN, a neural network method that simultaneously clusters and annotates cells using known gene sets. UNIFAN combines both, low dimension representation for all genes and cell specific gene set activity scores to determine the clustering. We applied UNIFAN to human and mouse scRNA-Seq datasets from several different organs. As we show, by using knowledge on gene sets, UNIFAN greatly outperforms prior methods developed for clustering scRNA-Seq data. The gene sets assigned by UNIFAN to different clusters provide strong evidence for the cell type that is represented by this cluster making annotations easier.

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