scholarly journals Novel and known transcriptional targets of ALS/FTD protein TDP-43: Meta-analysis and interactive graphical databases

2021 ◽  
Author(s):  
Maize C Cao ◽  
Emma L Scotter
Keyword(s):  
Rna Sequencing ◽  
Rna Binding ◽  
Meta Analysis ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Differentially Expressed ◽  
Cell Type ◽  
Neuronal Nuclei ◽  
Model Cell ◽  
Cell Type Specific

TDP-43 proteinopathy is the major pathological hallmark of amyotrophic lateral sclerosis (ALS) and tau-negative frontotemporal dementia (FTD). Mounting evidence implicates a loss of normal TDP-43 function in neurodegeneration, either resultant from or independent of TDP-43 aggregation. TDP-43 knockdown is therefore a common paradigm for modelling ALS and FTD. However, because TDP-43 can interact directly with thousands of mRNA targets and regulate the function of other RNA binding proteins, the phenotype of TDP-43 depletion is likely to differ depending on the proteomic and transcriptomic profile of the model cell type. Here, we conducted a meta-analysis of publicly available RNA-sequencing datasets that utilized TDP-43 knockdown to model ALS or FTD, and validated these against RNA-sequencing data from TDP-43-immunonegative neuronal nuclei from ALS/FTD brain. We present these analyses as easy-to-use interactive graphical databases. Of 9 TDP-43-knockdown datasets identified, 4 showed significant depletion of TARDBP (human HeLa and SH-SY5Y cell lines, induced human motor neurons, and mouse striatal tissue). There was little overlap in differentially expressed genes between TDP-43-knockdown model cell types, but PFKP, RANBP1, KIAA1324, ELAVL3, and STMN2 were among the common TDP-43 targets. Of these, only STMN2 was validated as a differentially expressed gene in TDP-43-immunonegative neuronal nuclei in ALS/FTD brain. Similarly, there were few genes that showed common patterns of differential exon usage between cell types and which validated in TDP-43-immunonegative neurons, but these included well-known targets POLDIP3, RANBP1, STMN2, and UNC13A, and novel targets EXD3, CEP290, KPNA4, and MMAB. Enrichment analysis showed that TDP-43 knockdown in different cell types affected a unique range of biological pathways. Together, these data identify novel TDP-43 targets, validate known TDP-43 targets, and show that TDP-43 plays both conserved and cell-type-specific roles in the regulation of gene expression and splicing. Identification of cell-type-specific TDP-43 targets will enable sensitive mapping of cell-autonomous TDP-43 dysfunction beyond just neurons, while shared TDP-43 targets are likely to have therapeutic value across myriad cell types.

scholarly journals Functional annotation of human long noncoding RNAs using chromatin conformation data

2021 ◽  
Author(s):  
Saumya Agrawal ◽  
Tanvir Alam ◽  
Masaru Koido ◽  
Ivan V. Kulakovskiy ◽  
Jessica Severin ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Rna Binding ◽  
Functional Characterization ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Spatial Proximity ◽  
Chromatin Conformation ◽  
Cell Type ◽  
Cell Type Specific ◽  
Rna Domains

AbstractTranscription of the human genome yields mostly long non-coding RNAs (lncRNAs). Systematic functional annotation of lncRNAs is challenging due to their low expression level, cell type-specific occurrence, poor sequence conservation between orthologs, and lack of information about RNA domains. Currently, 95% of human lncRNAs have no functional characterization. Using chromatin conformation and Cap Analysis of Gene Expression (CAGE) data in 18 human cell types, we systematically located genomic regions in spatial proximity to lncRNA genes and identified functional clusters of interacting protein-coding genes, lncRNAs and enhancers. Using these clusters we provide a cell type-specific functional annotation for 7,651 out of 14,198 (53.88%) lncRNAs. LncRNAs tend to have specialized roles in the cell type in which it is first expressed, and to incorporate more general functions as its expression is acquired by multiple cell types during evolution. By analyzing RNA-binding protein and RNA-chromatin interaction data in the context of the spatial genomic interaction map, we explored mechanisms by which these lncRNAs can act.

scholarly journals Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

2020 ◽  
Author(s):  
Devanshi Patel ◽  
Xiaoling Zhang ◽  
John J. Farrell ◽  
Jaeyoon Chung ◽  
Thor D. Stein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

scholarly journals Single-cell RNA sequencing reveals cell type- and artery type-specific vascular remodelling in male spontaneously hypertensive rats

2020 ◽  
Author(s):  
Jun Cheng ◽  
Wenduo Gu ◽  
Ting Lan ◽  
Jiacheng Deng ◽  
Zhichao Ni ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Spontaneously Hypertensive Rats ◽  
Cell Types ◽  
Vascular Remodelling ◽  
Cell Type ◽  
Hypertensive Rats ◽  
Spontaneously Hypertensive ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific

Abstract Aims Hypertension is a major risk factor for cardiovascular diseases. However, vascular remodelling, a hallmark of hypertension, has not been systematically characterized yet. We described systematic vascular remodelling, especially the artery type- and cell type-specific changes, in hypertension using spontaneously hypertensive rats (SHRs). Methods and results Single-cell RNA sequencing was used to depict the cell atlas of mesenteric artery (MA) and aortic artery (AA) from SHRs. More than 20 000 cells were included in the analysis. The number of immune cells more than doubled in aortic aorta in SHRs compared to Wistar Kyoto controls, whereas an expansion of MA mesenchymal stromal cells (MSCs) was observed in SHRs. Comparison of corresponding artery types and cell types identified in integrated datasets unravels dysregulated genes specific for artery types and cell types. Intersection of dysregulated genes with curated gene sets including cytokines, growth factors, extracellular matrix (ECM), receptors, etc. revealed vascular remodelling events involving cell–cell interaction and ECM re-organization. Particularly, AA remodelling encompasses upregulated cytokine genes in smooth muscle cells, endothelial cells, and especially MSCs, whereas in MA, change of genes involving the contractile machinery and downregulation of ECM-related genes were more prominent. Macrophages and T cells within the aorta demonstrated significant dysregulation of cellular interaction with vascular cells. Conclusion Our findings provide the first cell landscape of resistant and conductive arteries in hypertensive animal models. Moreover, it also offers a systematic characterization of the dysregulated gene profiles with unbiased, artery type-specific and cell type-specific manners during hypertensive vascular remodelling.

scholarly journals Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ana J. Chucair-Elliott ◽  
Sarah R. Ocañas ◽  
David R. Stanford ◽  
Victor A. Ansere ◽  
Kyla B. Buettner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Affinity Purification ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Tissue Level ◽  
Cell Phenotype ◽  
Specific Gene ◽  
Cell Type ◽  
A Cell ◽  
Cell Type Specific

AbstractEpigenetic regulation of gene expression occurs in a cell type-specific manner. Current cell-type specific neuroepigenetic studies rely on cell sorting methods that can alter cell phenotype and introduce potential confounds. Here we demonstrate and validate a Nuclear Tagging and Translating Ribosome Affinity Purification (NuTRAP) approach for temporally controlled labeling and isolation of ribosomes and nuclei, and thus RNA and DNA, from specific central nervous system cell types. Analysis of gene expression and DNA modifications in astrocytes or microglia from the same animal demonstrates differential usage of DNA methylation and hydroxymethylation in CpG and non-CpG contexts that corresponds to cell type-specific gene expression. Application of this approach in LPS treated mice uncovers microglia-specific transcriptome and epigenome changes in inflammatory pathways that cannot be detected with tissue-level analysis. The NuTRAP model and the validation approaches presented can be applied to any brain cell type for which a cell type-specific cre is available.

scholarly journals Deep analysis of RNA N6-adenosine methylation (m6A) patterns in human cells

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Jun Wang ◽  
Liangjiang Wang
Keyword(s):  
Rna Binding ◽  
Data Transfer ◽  
Rna Binding Proteins ◽  
Consensus Sequence ◽  
Model Performance ◽  
Cell Types ◽  
Human Cells ◽  
Rna Modification ◽  
Cell Type ◽  
Cell Type Specific

Abstract N6-adenosine methylation (m6A) is the most abundant internal RNA modification in eukaryotes, and affects RNA metabolism and non-coding RNA function. Previous studies suggest that m6A modifications in mammals occur on the consensus sequence DRACH (D = A/G/U, R = A/G, H = A/C/U). However, only about 10% of such adenosines can be m6A-methylated, and the underlying sequence determinants are still unclear. Notably, the regulation of m6A modifications can be cell-type-specific. In this study, we have developed a deep learning model, called TDm6A, to predict RNA m6A modifications in human cells. For cell types with limited availability of m6A data, transfer learning may be used to enhance TDm6A model performance. We show that TDm6A can learn common and cell-type-specific motifs, some of which are associated with RNA-binding proteins previously reported to be m6A readers or anti-readers. In addition, we have used TDm6A to predict m6A sites on human long non-coding RNAs (lncRNAs) for selection of candidates with high levels of m6A modifications. The results provide new insights into m6A modifications on human protein-coding and non-coding transcripts.

scholarly journals Cell-type specific cis-eQTLs in eight brain cell-types identifies novel risk genes for human brain disorders

2021 ◽  
Author(s):  
Julien Bryois ◽  
Daniela Calini ◽  
Will Macnair ◽  
Lynette Foo ◽  
Eduard Urich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Disease Risk ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Specific Cell ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Disease Etiology ◽  
Risk Genes ◽  
Cell Type Specific

Most expression quantitative trait loci (eQTL) studies to date have been performed in heterogeneous brain tissues as opposed to specific cell types. To investigate the genetics of gene expression in adult human cell types from the central nervous system (CNS), we performed an eQTL analysis using single nuclei RNA-seq from 196 individuals in eight CNS cell types. We identified 6108 eGenes, a substantial fraction (43%, 2620 out of 6108) of which show cell-type specific effects, with strongest effects in microglia. Integration of CNS cell-type eQTLs with GWAS revealed novel relationships between expression and disease risk for neuropsychiatric and neurodegenerative diseases. For most GWAS loci, a single gene colocalized in a single cell type providing new clues into disease etiology. Our findings demonstrate substantial contrast in genetic regulation of gene expression among CNS cell types and reveal genetic mechanisms by which disease risk genes influence neurological disorders.

scholarly journals Cell Type-Specific Differentiation Between Indica and Japonica Rice Root Tip Responses to Different Environments Based on Single-Cell RNA Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhe Wang ◽  
Daofu Cheng ◽  
Chengang Fan ◽  
Cong Zhang ◽  
Chao Zhang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Rice Root ◽  
Differentially Expressed ◽  
Bzip Transcription Factor ◽  
Quiescent Center ◽  
Cell Type ◽  
Rice Varieties ◽  
Specific Differentiation ◽  
Cell Type Specific

Background: As Oryza sativa ssp. indica and Oryza sativa ssp. japonica are the two major subspecies of Asian cultivated rice, the adaptative evolution of these varieties in divergent environments is an important topic in both theoretical and practical studies. However, the cell type-specific differentiation between indica and japonica rice varieties in response to divergent habitat environments, which facilitates an understanding of the genetic basis underlying differentiation and environmental adaptation between rice subspecies at the cellular level, is little known.Methods: We analyzed a published single-cell RNA sequencing dataset to explore the differentially expressed genes between indica and japonica rice varieties in each cell type. To estimate the relationship between cell type-specific differentiation and environmental adaptation, we focused on genes in the WRKY, NAC, and BZIP transcription factor families, which are closely related to abiotic stress responses. In addition, we integrated five bulk RNA sequencing datasets obtained under conditions of abiotic stress, including cold, drought and salinity, in this study. Furthermore, we analyzed quiescent center cells in rice root tips based on orthologous markers in Arabidopsis.Results: We found differentially expressed genes between indica and japonica rice varieties with cell type-specific patterns, which were enriched in the pathways related to root development and stress reposes. Some of these genes were members of the WRKY, NAC, and BZIP transcription factor families and were differentially expressed under cold, drought or salinity stress. In addition, LOC_Os01g16810, LOC_Os01g18670, LOC_Os04g52960, and LOC_Os08g09350 may be potential markers of quiescent center cells in rice root tips.Conclusion: These results identified cell type-specific differentially expressed genes between indica-japonica rice varieties that were related to various environmental stresses and provided putative markers of quiescent center cells. This study provides new clues for understanding the development and physiology of plants during the process of adaptative divergence, in addition to identifying potential target genes for the improvement of stress tolerance in rice breeding applications.

scholarly journals scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Bobby Ranjan ◽  
Florian Schmidt ◽  
Wenjie Sun ◽  
Jinyu Park ◽  
Mohammad Amin Honardoost ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Unsupervised Clustering ◽  
Differentially Expressed ◽  
Consensus Clustering ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Abstract Background Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. Results We present scConsensus, an $${\mathbf {R}}$$ R framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. Conclusions scConsensus combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. scConsensus is implemented in $${\mathbf {R}}$$ R and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

scholarly journals Transposon-mediated, cell type-specific transcription factor recording in the mouse brain

2019 ◽  
Author(s):  
Alexander J. Cammack ◽  
Arnav Moudgil ◽  
Tomas Lagunas ◽  
Michael J. Vasek ◽  
Mark Shabsovich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Mouse Tissue ◽  
Specific Cell ◽  
Cell Type ◽  
Cell Fate Decisions ◽  
Cell Type Specific ◽  
The Brain

AbstractTranscription factors (TFs) play a central role in the regulation of gene expression, controlling everything from cell fate decisions to activity dependent gene expression. However, widely-used methods for TF profiling in vivo (e.g. ChIP-seq) yield only an aggregated picture of TF binding across all cell types present within the harvested tissue; thus, it is challenging or impossible to determine how the same TF might bind different portions of the genome in different cell types, or even to identify its binding events at all in rare cell types in a complex tissue such as the brain. Here we present a versatile methodology, FLEX Calling Cards, for the mapping of TF occupancy in specific cell types from heterogenous tissues. In this method, the TF of interest is fused to a hyperactive piggyBac transposase (hypPB), and this bipartite gene is delivered, along with donor transposons, to mouse tissue via a Cre-dependent adeno-associated virus (AAV). The fusion protein is expressed in Cre-expressing cells where it inserts transposon “Calling Cards” near to TF binding sites. These transposons permanently mark TF binding events and can be mapped using high-throughput sequencing. Alternatively, unfused hypPB interacts with and records the binding of the super enhancer (SE)-associated bromodomain protein, Brd4. To demonstrate the FLEX Calling Card method, we first show that donor transposon and transposase constructs can be efficiently delivered to the postnatal day 1 (P1) mouse brain with AAV and that insertion profiles report TF occupancy. Then, using a Cre-dependent hypPB virus, we show utility of this tool in defining cell type-specific TF profiles in multiple cell types of the brain. This approach will enable important cell type-specific studies of TF-mediated gene regulation in the brain and will provide valuable insights into brain development, homeostasis, and disease.

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