scholarly journals DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species

2021 ◽  
Author(s):  
Yanhui Hu ◽  
Sudhir Gopal Tattikota ◽  
Yifang Liu ◽  
Aram Comjean ◽  
Yue Gao ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Model Organisms ◽  
Marker Genes ◽  
Specific Expression ◽  
Web Based ◽  
Manual Curation ◽  
Original Analysis ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AbstractWith the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in studies involving scRNA-seq of several tissues across diverse species including Drosophila. Although a few databases exist for users to query genes of interest within the scRNA-seq studies, search tools that enable users to find orthologous genes and their cell type-specific expression patterns across species are limited. Here, we built a new search database, called DRscDB (https://www.flyrnai.org/tools/single_cell/web/) to address this need. DRscDB serves as a comprehensive repository for published scRNA-seq datasets for Drosophila and the relevant datasets from human and other model organisms. DRscDB is based on manual curation of Drosophila scRNA-seq studies of various tissue types and their corresponding analogous tissues in vertebrates including zebrafish, mouse, and human. Of note, our search database provides most of the literature-derived marker genes, thus preserving the original analysis of the published scRNA-seq datasets. DRscDB serves as a web-based user interface that allows users to mine, utilize and compare gene expression data pertaining to scRNA-seq datasets from the published literature.

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 Dynamics of gene expression in single root cells ofA. thaliana

2018 ◽  
Author(s):  
Ken Jean-Baptiste ◽  
José L. McFaline-Figueroa ◽  
Cristina M. Alexandre ◽  
Michael W. Dorrity ◽  
Lauren Saunders ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Root Cells ◽  
Cell Lineages ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTSingle-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach toA. thalianaroot cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.

scholarly journals O4-10-01: CHARACTERIZING CELL TYPE-SPECIFIC EXPRESSION SIGNATURES IN BRAIN USING SINGLE CELL RNA SEQUENCING

2019 ◽  
Vol 15 ◽  
pp. P1258-P1258 ◽  
Author(s):  
Tulsi Patel ◽  
Troy Carnwath ◽  
Laura Lewis-Tuffin ◽  
Mariet Allen ◽  
Sarah J. Lincoln ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Type ◽  
Specific Expression ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

scholarly journals Alternative Splicing Regulates the Subcellular Localization of Divalent Metal Transporter 1 Isoforms

2002 ◽  
Vol 13 (12) ◽  
pp. 4371-4387 ◽  
Author(s):  
Mitsuaki Tabuchi ◽  
Naotaka Tanaka ◽  
Junko Nishida-Kitayama ◽  
Hiroshi Ohno ◽  
Fumio Kishi
Keyword(s):  
Plasma Membrane ◽  
Iron Acquisition ◽  
Expression Patterns ◽  
Apical Plasma Membrane ◽  
Specific Expression ◽  
Divalent Metal Transporter 1 ◽  
Divalent Metal Transporter ◽  
Metal Transporter ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

Divalent metal transporter 1 (DMT1) is responsible for dietary-iron absorption from apical plasma membrane in the duodenum and iron acquisition from the transferrin cycle endosomes in peripheral tissues. Two isoforms of the DMT1 transcript generated by alternative splicing of the 3′ exons have been identified in mouse, rat, and human. These isoforms can be distinguished by the different C-terminal amino acid sequences and by the presence (DMT1A) or absence (DMT1B) of an iron response element located in the 3′ untranslated region of the mRNA. However, it has been still unknown whether the structural differences between the two DMT1 isoforms is functionally important. Here, we report that each DMT1 isoform exhibits a differential cell type–specific expression patterns and distinct subcellular localizations. DMT1A is predominantly expressed by epithelial cell lines, whereas DMT1B is expressed by the blood cell lines. In HEp-2 cells, GFP-tagged DMT1A is localized in late endosomes and lysosomes, whereas GFP-tagged DMT1B is localized in early endosomes. Using site-directed mutagenesis, a Y555XLXX sequence in the cytoplasmic tail of DMT1B has been identified as an important signal sequence for the early endosomal-targeting of DMT1B. In polarized MDCK cells, GFP-tagged DMT1A and DMT1B are localized in the apical plasma membrane and their respective specific endosomes. Disruption of the N-glycosylation sites in each of the DMT1 isoforms affects their polarized distribution into the apical plasma membrane but not their correct endosomal localization. Our data indicate that the cell type–specific expression patterns and the distinct subcellular localizations of two DMT1 isoforms may be involved in the different iron acquisition steps from the subcellular membranes in various cell types.

scholarly journals Single-Cell Regulatory Network Inference and Clustering Identifies Cell-Type Specific Expression Pattern of Transcription Factors in Mouse Sciatic Nerve

2021 ◽  
Vol 15 ◽  
Author(s):  
Mingchao Li ◽  
Qing Min ◽  
Matthew C. Banton ◽  
Xinpeng Dun
Keyword(s):  
Transcription Factors ◽  
Sciatic Nerve ◽  
Single Cell ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific Expression ◽  
Cell Type Specific ◽  
Different Cell Types

Advances in single-cell RNA sequencing technologies and bioinformatics methods allow for both the identification of cell types in a complex tissue and the large-scale gene expression profiling of various cell types in a mixture. In this report, we analyzed a single-cell RNA sequencing (scRNA-seq) dataset for the intact adult mouse sciatic nerve and examined cell-type specific transcription factor expression and activity during peripheral nerve homeostasis. In total, we identified 238 transcription factors expressed in nine different cell types of intact mouse sciatic nerve. Vascular smooth muscle cells have the lowest number of transcription factors expressed with 17 transcription factors identified. Myelinating Schwann cells (mSCs) have the highest number of transcription factors expressed, with 61 transcription factors identified. We created a cell-type specific expression map for the identified 238 transcription factors. Our results not only provide valuable information about the expression pattern of transcription factors in different cell types of adult peripheral nerves but also facilitate future studies to understand the function of key transcription factors in the peripheral nerve homeostasis and disease.

scholarly journals Epigenetic Regulation of Cell Type–Specific Expression Patterns in the Human Mammary Epithelium

PLoS Genetics ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. e1001369 ◽  
Author(s):  
Reo Maruyama ◽  
Sibgat Choudhury ◽  
Adam Kowalczyk ◽  
Marina Bessarabova ◽  
Bryan Beresford-Smith ◽  
...  
Keyword(s):  
Epigenetic Regulation ◽  
Expression Patterns ◽  
Mammary Epithelium ◽  
Cell Type ◽  
Specific Expression ◽  
Human Mammary Epithelium ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

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 Single-Cell mRNA Profiling Reveals Cell-Type-Specific Expression of Neurexin Isoforms

Neuron ◽  
2015 ◽  
Vol 87 (2) ◽  
pp. 326-340 ◽  
Author(s):  
Marc V. Fuccillo ◽  
Csaba Földy ◽  
Özgün Gökce ◽  
Patrick E. Rothwell ◽  
Gordon L. Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Type ◽  
Specific Expression ◽  
Mrna Profiling ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

scholarly journals A robust semi-supervised NMF model for single cell RNA-seq data

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10091
Author(s):  
Peng Wu ◽  
Mo An ◽  
Hai-Ren Zou ◽  
Cai-Ying Zhong ◽  
Wei Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Prior Knowledge ◽  
Clustering Analysis ◽  
Prior Information ◽  
Expression Patterns ◽  
Biological Significance ◽  
Model Organisms ◽  
Marker Genes ◽  
Specific Gene ◽  
Advanced Analysis

Background Single-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand cell function and constitutes the basis of other advanced analysis. Nonnegative Matrix Factorization (NMF) has been widely used in clustering analysis of transcriptome data and achieved good performance. However, the existing NMF model is unsupervised and ignores known gene functions in the process of clustering. Knowledges of cell markers genes (genes that only express in specific cells) in human and model organisms have been accumulated a lot, such as the Molecular Signatures Database (MSigDB), which can be used as prior information in the clustering analysis of scRNA-seq data. Because the same kind of cells is likely to have similar biological functions and specific gene expression patterns, the marker genes of cells can be utilized as prior knowledge in the clustering analysis. Methods We propose a robust and semi-supervised NMF (rssNMF) model, which introduces a new variable to absorb noises of data and incorporates marker genes as prior information into a graph regularization term. We use rssNMF to solve the clustering problem of scRNA-seq data. Results Twelve scRNA-seq datasets with true labels are used to test the model performance and the results illustrate that our model outperforms original NMF and other common methods such as KMeans and Hierarchical Clustering. Biological significance analysis shows that rssNMF can identify key subclasses and latent biological processes. To our knowledge, this study is the first method that incorporates prior knowledge into the clustering analysis of scRNA-seq data.

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