scholarly journals Functional annotation of lncRNA in high-throughput screening

2021 ◽  
Author(s):  
Chi Wai Yip ◽  
Divya M. Sivaraman ◽  
Anika V. Prabhu ◽  
Jay W. Shin
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Functional Annotation ◽  
Cell Types ◽  
Specific Expression ◽  
Functional Roles ◽  
Cellular Processes ◽  
Cell Type Specific Expression ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract Recent efforts on the characterization of long non-coding RNAs (lncRNAs) revealed their functional roles in modulating diverse cellular processes. These include pluripotency maintenance, lineage commitment, carcinogenesis, and pathogenesis of various diseases. By interacting with DNA, RNA and protein, lncRNAs mediate multifaceted mechanisms to regulate transcription, RNA processing, RNA interference and translation. Of more than 173000 discovered lncRNAs, the majority remain functionally unknown. The cell type-specific expression and localization of the lncRNA also suggest potential distinct functions of lncRNAs across different cell types. This highlights the niche of identifying functional lncRNAs in different biological processes and diseases through high-throughput (HTP) screening. This review summarizes the current work performed and perspectives on HTP screening of functional lncRNAs where different technologies, platforms, cellular responses and the downstream analyses are discussed. We hope to provide a better picture in applying different technologies to facilitate functional annotation of lncRNA efficiently.

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 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 Dissecting differential signals in high-throughput data from complex tissues

2019 ◽  
Vol 35 (20) ◽  
pp. 3898-3905 ◽  
Author(s):  
Ziyi Li ◽  
Zhijin Wu ◽  
Peng Jin ◽  
Hao Wu
Keyword(s):  
High Throughput ◽  
Cell Types ◽  
R Package ◽  
Supplementary Information ◽  
Simulation Studies ◽  
Clinical Practices ◽  
High Throughput Data ◽  
Heterogeneous Samples ◽  
Cell Type Specific ◽  
Different Cell Types

Abstract Motivation Samples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for. Results We develop a method to model the high-throughput data from mixed, heterogeneous samples, and to detect differential signals. Our method allows flexible statistical inference for detecting a variety of cell-type specific changes. Extensive simulation studies and analyses of two real datasets demonstrate the favorable performance of our proposed method compared with existing ones serving similar purpose. Availability and implementation The proposed method is implemented as an R package and is freely available on GitHub (https://github.com/ziyili20/TOAST). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Cell type–specific expression of SEPT3-homology subgroup members controls the subunit number of heteromeric septin complexes

2014 ◽  
Vol 25 (10) ◽  
pp. 1594-1607 ◽  
Author(s):  
Mikael E. Sellin ◽  
Sonja Stenmark ◽  
Martin Gullberg
Keyword(s):  
Animal Cell ◽  
Building Blocks ◽  
Cell Types ◽  
Specific Expression ◽  
Tissue Specific ◽  
Cell Type Specific Expression ◽  
A Subunit ◽  
Cell Type Specific ◽  
Genetically Manipulated ◽  
Differential Properties

Septins are filament-forming proteins important for organizing the cortex of animal and fungal cells. In mammals, 13 septin paralogues were recently shown to assemble into core heterohexamer and heterooctamer complexes, which serve as building blocks for apolar filamentous structures that differ among cell types. To determine how tissue-specific septin paralogue expression may shape core heteromer repertoires and thereby modulate properties of septin filaments, we devised protocols to analyze native septin heteromers with distinct numbers of subunits. Our evidence based on genetically manipulated human cells supports and extends recent concepts of homology subgroup–restricted assembly into distinct categories of apolar heterohexamers and heterooctamers. We also identify a category of tetramers that have a subunit composition equivalent to an octameric building block. These atypical tetramers are prevalent in lymphocytes and neural tissues, in which octamers are abundant but hexamers are rare. Our results can be explained by tissue-specific expression of SEPT3 subgroup members: SEPT3, SEPT9, and SEPT12. These serve as cognate subunits in either heterooctamers or atypical tetramers but exhibit different preferences in various tissues. The identified tissue-specific repertoires of septin heteromers provide insights into how higher-order septin structures with differential properties and stabilities may form in diverse animal cell types.

scholarly journals Adeno-Associated Virus Technologies and Methods for Targeted Neuronal Manipulation

2019 ◽  
Author(s):  
Leila Haery ◽  
Benjamin E. Deverman ◽  
Katherine Matho ◽  
Ali Cetin ◽  
Kenton Woodard ◽  
...  
Keyword(s):  
Viral Vectors ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Cell ◽  
Specific Expression ◽  
Adeno Associated Virus ◽  
Cell Type Specific Expression ◽  
Cell Type Specific ◽  
And Function ◽  
Novel Applications

AbstractCell-type-specific expression of molecular tools and sensors is critical to construct circuit diagrams and to investigate the activity and function of neurons within the nervous system. Strategies for targeted manipulation include combinations of classical genetic tools such as Cre/loxP and Flp/FRT, use of cis-regulatory elements, targeted knock-in transgenic mice, and gene delivery by AAV and other viral vectors. The combination of these complex technologies with the goal of precise neuronal targeting is a challenge in the lab. This report will discuss the theoretical and practical aspects of combining current technologies and establish best practices for achieving targeted manipulation of specific cell types. Novel applications and tools, as well as areas for development, will be envisioned and discussed.

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.

Structural Studies on Different Ligand Binding Ability of Sialoadhesin Using Molecular Modeling Techniques

2020 ◽  
Vol 5 (4) ◽  
pp. 277-282
Author(s):  
Madhumita Dandopath Patra
Keyword(s):  
Molecular Modeling ◽  
Stable Complex ◽  
Free Energies ◽  
Specific Expression ◽  
Functional Roles ◽  
Modeling Techniques ◽  
Cell Type Specific Expression ◽  
Cell Type Specific ◽  
Specific Expression Pattern ◽  
Inflammatory Macrophages

Siglecs are the major homologous subfamily of I-type lectins with an ability to recognize sialylated glycans. Siglecs are attractive therapeutic targets because of their endocytic properties, ability to modulate receptor signaling and cell-type specific expression pattern. Sialoadhesin (Sn/ Siglec-1/ CD169), a member of the Siglec family expressed on subsets of resident and inflammatory macrophages and involves in modulation of inflammation and immunity. In this work, 3-D structure of human Siglec-1 (hSiglec-1) was predicted based on X-ray crystallo-graphically determined structure of mouse Siglec-1[mSiglec-1(PDB ID: 1QFP)] using molecular modeling techniques. The structure of complexes in solution of hSiglec-1 with ligands, glycopeptide and 3′-sialyllactose were predicted using a novel docking technique comprising of repeated cycles of molecular dynamics and energy minimization. Calculation of the free energies of binding of complexes suggested that glycopeptide can form stable complex with dissociation constant value of 3.31 μM whereas complex formation of 3′-sialyllactose with the protein in aqueous medium is thermodynamically unfavorable. The structural analysis of theses complexes represent the functional recognition interactions of this protein with the bound sugar molecule and as such provide detailed information about functional roles of such sugar binding protein.

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 Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell type-specific expression

2014 ◽  
Author(s):  
Maxwell W Libbrecht ◽  
Ferhat Ay ◽  
Michael M Hoffman ◽  
David M Gilbert ◽  
Jeffrey A Bilmes ◽  
...  
Keyword(s):  
Genome Annotation ◽  
Cell Types ◽  
Computational Method ◽  
Data Sets ◽  
Chromatin Conformation ◽  
Specific Expression ◽  
Regulatory Domains ◽  
A Genome ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

The genomic neighborhood of a gene influences its activity, a behavior that is attributable in part to domain-scale regulation, in which regions of hundreds or thousands of kilobases known as domains are regulated as a unit. Previous studies using genomics assays such as chromatin immunoprecipitation (ChIP)-seq and chromatin conformation capture (3C)-based assays have identified many types of regulatory domains. However, due to the difficulty of integrating genomics data sets, the relationships among these domain types are poorly understood. Semi-automated genome annotation (SAGA) algorithms facilitate human interpretation of heterogeneous collections of genomics data by simultaneously partitioning the human genome and assigning labels to the resulting genomic segments. However, existing SAGA methods can incorporate only data sets that can be expressed as a one-dimensional vector over the genome and therefore cannot integrate inherently pairwise chromatin conformation data. We developed a new computational method, called graph-based regularization (GBR), for expressing a pairwise prior that encourages certain pairs of genomic loci to receive the same label in a genome annotation. We used GBR to exploit chromatin conformation information during genome annotation by encouraging positions that are close in 3D to occupy the same type of domain. Using this approach, we produced a comprehensive model of chromatin domains in eight human cell types, thereby revealing the relationships among known domain types. Through this model, we identified clusters of tightly-regulated genes expressed in only a small number of cell types, which we term "specific expression domains." We additionally found that a subset of domain boundaries marked by promoters and CTCF motifs are consistent between cell types even when domain activity changes. Finally, we showed that GBR can be used for the seemingly unrelated task of transferring information from well-studied cell types to less well characterized cell types during genome annotation, making it possible to produce high-quality annotations of the hundreds of cell types with limited available data.

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