scholarly journals Comparative transcriptome profiling of the human and mouse dorsal root ganglia: An RNA-seq-based resource for pain and sensory neuroscience research

2017 ◽  
Author(s):  
Pradipta Ray ◽  
Andrew Torck ◽  
Lilyana Quigley ◽  
Andi Wangzhou ◽  
Matthew Neiman ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Rna Sequencing ◽  
Dorsal Root Ganglia ◽  
Dorsal Root ◽  
Expression Patterns ◽  
Rna Seq ◽  
Sequencing Data ◽  
Mouse Tissues ◽  
Human And Mouse ◽  
Insight Into

AbstractMolecular neurobiological insight into human nervous tissues is needed to generate next generation therapeutics for neurological disorders like chronic pain. We obtained human Dorsal Root Ganglia (DRG) samples from organ donors and performed RNA-sequencing (RNA-seq) to study the human DRG (hDRG) transcriptional landscape, systematically comparing it with publicly available data from a variety of human and orthologous mouse tissues, including mouse DRG (mDRG). We characterized the hDRG transcriptional profile in terms of tissue-restricted gene co-expression patterns and putative transcriptional regulators, and formulated an information-theoretic framework to quantify DRG enrichment. Our analyses reveal an hDRG-enriched protein-coding gene set (~140), some of which have not been described in the context of DRG or pain signaling. A majority of these show conserved enrichment in mDRG, and were mined for known drug - gene product interactions. Comparison of hDRG and tibial nerve transcriptomes suggest pervasive mRNA transport of sensory neuronal genes to axons in adult hDRG, with potential implications for mechanistic insight into chronic pain in patients. Relevant gene families and pathways were also analyzed, including transcription factors (TFs), g-protein coupled receptors (GCPRs) and ion channels. We present our work as an online, searchable repository (http://www.utdallas.edu/bbs/painneurosciencelab/DRGtranscriptome), creating a valuable resource for the community. Our analyses provide insight into DRG biology for guiding development of novel therapeutics, and a blueprint for cross-species transcriptomic analyses.SummaryWe generated RNA sequencing data from human DRG samples and comprehensively compared this transcriptome to other human tissues and a matching panel of mouse tissues. Our analysis uncovered functionally enriched genes in the human and mouse DRG with important implications for understanding sensory biology and pain drug discovery.

scholarly journals Comparison of human and mouse tissues with focus on genes with no 1-to-1 homology

2021 ◽  
Author(s):  
Jieun Jeong ◽  
Manolis Kellis
Keyword(s):  
Immune Response ◽  
Target Genes ◽  
Expression Patterns ◽  
Adaptive Immune Response ◽  
Mouse Skin ◽  
Rna Seq ◽  
Therapy Targets ◽  
Mouse Tissues ◽  
Tissues Expression ◽  
Human And Mouse

We assembled a panel of 28 tissue pairs of human and mouse with RNA-Seq data on gene expression. We focused on genes with no 1-to-1 homology, because they pose special challenges. In this way, we identified expression patterns that identify and explain differences between the two species and suggest target genes for therapeutic applications. Here we mention three examples. One pattern is observed by defining the aggregate expression of immunoglobulin genes (which have no homology) as a measure of different levels of an immune response. In Lung, we used this statistic to find genes that have significantly higher expression in low/moderate response, and thus they may be therapy targets: increasing their expression or mimicking their function with medications may help in recovery from inflammation in the lungs. Some of the observed associations are common to human and mouse; other associations involve genes involved in cell-to-cell signaling or in regeneration but were not known to be important in Lung. Second pattern is that in the Small Intestine, mouse expresses much less antimicrobial defensins, while it has much higher expression of enzymes that are found to improve adaptive immune response. Such enzymes may be tested if they improve probiotic supplements that help in gut inflammation and other diseases. Another pattern involves a many-to-many homology group of defensins that did not have a described function. In human tissues, expression of its genes was found only in a study of a disease of hair covered skin, but several of its genes are highly expressed in two tissues of our panel: mouse Skin and to a lesser degree mouse Vagina. This suggests that those genes or their homologs in other species may provide non-antibiotic medications for hair covered skin and other tissues with microbiome that includes fungi.

scholarly journals SMARTer single cell total RNA sequencing

2018 ◽  
Author(s):  
Verboom Karen ◽  
Everaert Celine ◽  
Bolduc Nathalie ◽  
Livak J. Kenneth ◽  
Yigit Nurten ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Transcript Level ◽  
Cellular Heterogeneity ◽  
Circular Rnas ◽  
Rna Seq ◽  
Sequencing Data ◽  
Total Rna ◽  
Sequencing Experiment

AbstractSingle cell RNA sequencing methods have been increasingly used to understand cellular heterogeneity. Nevertheless, most of these methods suffer from one or more limitations, such as focusing only on polyadenylated RNA, sequencing of only the 3’ end of the transcript, an exuberant fraction of reads mapping to ribosomal RNA, and the unstranded nature of the sequencing data. Here, we developed a novel single cell strand-specific total RNA library preparation method addressing all the aforementioned shortcomings. Our method was validated on a microfluidics system using three different cancer cell lines undergoing a chemical or genetic perturbation. We demonstrate that our total RNA-seq method detects an equal or higher number of genes compared to classic polyA[+] RNA-seq, including novel and non-polyadenylated genes. The obtained RNA expression patterns also recapitulate the expected biological signal. Inherent to total RNA-seq, our method is also able to detect circular RNAs. Taken together, SMARTer single cell total RNA sequencing is very well suited for any single cell sequencing experiment in which transcript level information is needed beyond polyadenylated genes.

scholarly journals RNA-sequencing data-driven dissection of human plasma cell differentiation reveals new potential transcription regulators

Leukemia ◽  
2021 ◽  
Author(s):  
Alboukadel Kassambara ◽  
Laurie Herviou ◽  
Sara Ovejero ◽  
Michel Jourdan ◽  
Coraline Thibaut ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Continuous Production ◽  
Expression Patterns ◽  
Gene Clusters ◽  
Transcriptional Networks ◽  
Pathway Enrichment Analysis ◽  
Rna Seq ◽  
Sequencing Data

AbstractPlasma cells (PCs) play an important role in the adaptive immune system through a continuous production of antibodies. We have demonstrated that PC differentiation can be modeled in vitro using complex multistep culture systems reproducing sequential differentiation process occurring in vivo. Here we present a comprehensive, temporal program of gene expression data encompassing human PC differentiation (PCD) using RNA sequencing (RNA-seq). Our results reveal 6374 differentially expressed genes classified into four temporal gene expression patterns. A stringent pathway enrichment analysis of these gene clusters highlights known pathways but also pathways largely unknown in PCD, including the heme biosynthesis and the glutathione conjugation pathways. Additionally, our analysis revealed numerous novel transcriptional networks with significant stage-specific overexpression and potential importance in PCD, including BATF2, BHLHA15/MIST1, EZH2, WHSC1/MMSET, and BLM. We have experimentally validated a potent role for BLM in regulating cell survival and proliferation during human PCD. Taken together, this RNA-seq analysis of PCD temporal stages helped identify coexpressed gene modules with associated up/downregulated transcription regulator genes that could represent major regulatory nodes for human PC maturation. These data constitute a unique resource of human PCD gene expression programs in support of future studies for understanding the underlying mechanisms that control PCD.

FC 011KIDNEYNETWORK: USING KIDNEY DERIVED GENE EXPRESSION DATA TO PREDICT AND PRIORITIZE NOVEL GENES INVOLVED IN KIDNEY DISEASE

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Disease ◽  
Candidate Gene ◽  
Exome Sequencing ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Genetic Diagnosis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Exome Sequencing Data

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

scholarly journals Sex-specific Transcriptomic Differences From Human Dorsal Root Ganglia Associated With Painful Radiculopathy

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Robert Y North ◽  
Yan Li ◽  
Pradipta Ray ◽  
Laurence D Rhines ◽  
Claudio E Tatsui ◽  
...  
Keyword(s):  
Dorsal Root Ganglia ◽  
Dorsal Root ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Epidemiological Studies ◽  
Gene Set Enrichment Analysis ◽  
Afferent Neurons ◽  
Rna Seq ◽  
Cord Injury ◽  
Medical Histories

Abstract INTRODUCTION Women are at greater risk to suffer from many chronic pain conditions, more often report painful symptoms in epidemiological studies, and demonstrate greater pain sensitivity to experimentally measured pain responses. There is growing evidence from animal models for sex-specific biological differences in nociception, particularly involving primary afferent neurons, that may contribute to these differences. However, the details and extent of sex-specific differences associated with pain in human afferent neurons has not been previously investigated. METHODS Human dorsal root ganglia (DRG) and medical histories were obtained from patients undergoing spinal surgery that necessitated sacrifice of spinal nerve roots as part of standard of care. Clinical data for presence of painful radiculopathy was obtained through retrospective review of medical records or collected at study enrollment. RNA sequencing (RNA-seq) was performed on 21 DRG from 15 patients with variable presence of radicular pain reported in a corresponding dermatome. Differential expression analysis for male w/pain (MP) vs female w/pain (FP) samples was performed with thresholds for robustly expressed autosomal genes (TPM >3.0), fold change of 2.0 or higher, with false discovery rate (FDR) <0.05. RESULTS Comparison of the MP and FP cohorts yielded 575 differentially expressed genes with 426 upregulated in MP and 149 upregulated in FP. Gene set enrichment analysis demonstrated significant differences in genes related to inflammation and immune regulation (increased MAPK and BDNF signaling in MP, increased Rhodopsin-like GPCR in FP) and differing clusters of spinal cord injury-associated genes (TLR4, AIF1, OMG, C1QB increased in FP, EGR1, NR4A1, ZFP36, BTG2, MYC in MP). CONCLUSION Utilizing RNA-seq of human DRG innervating regions of pain, this study provides the first demonstration of sex-specific differences for the biology of pain within the dorsal root ganglion in humans and implicates the immune system as a critical influence in these differences.

scholarly journals Single-Cell RNA Sequencing Unveils Unique Transcriptomic Signatures of Organ-Specific Endothelial Cells

Circulation ◽  
2020 ◽  
Vol 142 (19) ◽  
pp. 1848-1862 ◽  
Author(s):  
David T. Paik ◽  
Lei Tian ◽  
Ian M. Williams ◽  
Siyeon Rhee ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Receptor Expression ◽  
Transcriptional Networks ◽  
Sequencing Data ◽  
Tissue Specific ◽  
Functional Relationships ◽  
Single Cell Rna Sequencing

Background: Endothelial cells (ECs) display considerable functional heterogeneity depending on the vessel and tissue in which they are located. Whereas these functional differences are presumably imprinted in the transcriptome, the pathways and networks that sustain EC heterogeneity have not been fully delineated. Methods: To investigate the transcriptomic basis of EC specificity, we analyzed single-cell RNA sequencing data from tissue-specific mouse ECs generated by the Tabula Muris consortium. We used a number of bioinformatics tools to uncover markers and sources of EC heterogeneity from single-cell RNA sequencing data. Results: We found a strong correlation between tissue-specific EC transcriptomic measurements generated by either single-cell RNA sequencing or bulk RNA sequencing, thus validating the approach. Using a graph-based clustering algorithm, we found that certain tissue-specific ECs cluster strongly by tissue (eg, liver, brain), whereas others (ie, adipose, heart) have considerable transcriptomic overlap with ECs from other tissues. We identified novel markers of tissue-specific ECs and signaling pathways that may be involved in maintaining their identity. Sex was a considerable source of heterogeneity in the endothelial transcriptome and we discovered Lars2 to be a gene that is highly enriched in ECs from male mice. We found that markers of heart and lung ECs in mice were conserved in human fetal heart and lung ECs. We identified potential angiocrine interactions between tissue-specific ECs and other cell types by analyzing ligand and receptor expression patterns. Conclusions: We used single-cell RNA sequencing data generated by the Tabula Muris consortium to uncover transcriptional networks that maintain tissue-specific EC identity and to identify novel angiocrine and functional relationships between tissue-specific ECs.

scholarly journals Emerging Roles of Estrogen-Regulated Enhancer and Long Non-Coding RNAs

2020 ◽  
Vol 21 (10) ◽  
pp. 3711
Author(s):  
Melina J. Sedano ◽  
Alana L. Harrison ◽  
Mina Zilaie ◽  
Chandrima Das ◽  
Ramesh Choudhari ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Expression Patterns ◽  
Biological Significance ◽  
Rna Seq ◽  
Biological Functions ◽  
Protein Coding ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Non Coding Rnas

Genome-wide RNA sequencing has shown that only a small fraction of the human genome is transcribed into protein-coding mRNAs. While once thought to be “junk” DNA, recent findings indicate that the rest of the genome encodes many types of non-coding RNA molecules with a myriad of functions still being determined. Among the non-coding RNAs, long non-coding RNAs (lncRNA) and enhancer RNAs (eRNA) are found to be most copious. While their exact biological functions and mechanisms of action are currently unknown, technologies such as next-generation RNA sequencing (RNA-seq) and global nuclear run-on sequencing (GRO-seq) have begun deciphering their expression patterns and biological significance. In addition to their identification, it has been shown that the expression of long non-coding RNAs and enhancer RNAs can vary due to spatial, temporal, developmental, or hormonal variations. In this review, we explore newly reported information on estrogen-regulated eRNAs and lncRNAs and their associated biological functions to help outline their markedly prominent roles in estrogen-dependent signaling.

A Two-Stage Poisson Model for Testing RNA-Seq Data

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Paul L. Auer ◽  
Rebecca W Doerge
Keyword(s):  
Rna Sequencing ◽  
Statistical Approach ◽  
Poisson Model ◽  
Real Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Sequencing Technology ◽  
Two Stage ◽  
Individual Gene ◽  
Unique Nature

RNA sequencing technology is providing data of unprecedented throughput, resolution, and accuracy. Although there are many different computational tools for processing these data, there are a limited number of statistical methods for analyzing them, and even fewer that acknowledge the unique nature of individual gene transcription. We introduce a simple and powerful statistical approach, based on a two-stage Poisson model, for modeling RNA sequencing data and testing for biologically important changes in gene expression. The advantages of this approach are demonstrated through simulations and real data applications.

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