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 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 RNA sequencing data: hitchhiker's guide to expression analysis

2018 ◽  
Author(s):  
Koen Van Den Berge ◽  
Katharina Hembach ◽  
Charlotte Soneson ◽  
Simone Tiberi ◽  
Lieven Clement ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Large Scale ◽  
Science Studies ◽  
Rna Seq ◽  
Sequencing Data ◽  
Data Types ◽  
The Past ◽  
Long Read ◽  
Statistical Approaches

Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

scholarly journals Transcriptome diversity is a systematic source of bias in RNA-sequencing data

2021 ◽  
Author(s):  
Pablo E. García-Nieto ◽  
Ban Wang ◽  
Hunter B. Fraser
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Systematic Bias ◽  
Simple Explanation ◽  
Rna Seq ◽  
Sequencing Data ◽  
Biological Variables ◽  
Systematic Effects ◽  
Standard Practices ◽  
Transcriptome Diversity

ABSTRACTBackgroundRNA sequencing has been widely used as an essential tool to probe gene expression. While standard practices have been established to analyze RNA-seq data, it is still challenging to detect and remove artifactual signals. Several factors such as sex, age, and sequencing technology have been found to bias these estimates. Probabilistic estimation of expression residuals (PEER) has been used to account for some systematic effects, but it has remained challenging to interpret these PEER factors.ResultsHere we show that transcriptome diversity – a simple metric based on Shannon entropy – explains a large portion of variability in gene expression, and is a major factor detected by PEER. We then show that transcriptome diversity has significant associations with multiple technical and biological variables across diverse organisms and datasets. This prevalent confounding factor provides a simple explanation for a major source of systematic biases in gene expression estimates.ConclusionsOur results show that transcriptome diversity is a metric that captures a systematic bias in RNA-seq and is the strongest known factor encoded in PEER covariates.

scholarly journals SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Empirical Distribution ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Actual Distribution ◽  
Wide Range ◽  
Single Cell Rna Sequencing

SummarySPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.Availability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.Supplementary informationSupplementary data are available at bioRχiv online.

scholarly journals Assessment of long non-coding RNA expression reveals novel mediators of the lung tumour immune response

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Adam P. Sage ◽  
Kevin W. Ng ◽  
Erin A. Marshall ◽  
Greg L. Stewart ◽  
Brenda C. Minatel ◽  
...  
Keyword(s):  
Nk Cells ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Mononuclear Cells ◽  
Expression Patterns ◽  
Lung Tumour ◽  
Sequencing Data ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Abstract The tumour immune microenvironment is a crucial mediator of lung tumourigenesis, and characterizing the immune landscape of patient tumours may guide immunotherapy treatment regimens and uncover novel intervention points. We sought to identify the landscape of tumour-infiltrating immune cells in the context of long non-coding RNA (lncRNAs), known regulators of gene expression. We examined the lncRNA profiles of lung adenocarcinoma (LUAD) tumours by interrogating RNA sequencing data from microdissected and non-microdissected samples (BCCRC and TCGA). Subsequently, analysis of single-cell RNA sequencing data from lung tumours and flow-sorted healthy peripheral blood mononuclear cells identified lncRNAs in immune cells, highlighting their biological and prognostic relevance. We discovered lncRNA expression patterns indicative of regulatory relationships with immune-related protein-coding genes, including the relationship between AC008750.1 and NKG7 in NK cells. Activation of NK cells in vitro was sufficient to induce AC008750.1 expression. Finally, siRNA-mediated knockdown of AC008750.1 significantly impaired both the expression of NKG7 and the anti-tumour capacity of NK cells. We present an atlas of cancer-cell extrinsic immune cell-expressed lncRNAs, in vitro evidence for a functional role of lncRNAs in anti-tumour immune activity, which upon further exploration may reveal novel clinical utility as markers of immune infiltration.

scholarly journals Transcriptomic analysis reveals distinct resistant response by physcion and chrysophanol against cucumber powdery mildew

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1991 ◽  
Author(s):  
Yanping Li ◽  
Shilin Tian ◽  
Xiaojun Yang ◽  
Xin Wang ◽  
Yuhai Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Disease Resistance ◽  
Powdery Mildew ◽  
Differentially Expressed Genes ◽  
Expression Patterns ◽  
Defense Responses ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcriptional Change

Physcion and chrysophanol induce defense responses against powdery mildew in cucumbers. The combination of these two compounds has synergistic interaction against the disease. We performed RNA-seq on cucumber leaf samples treated with physcion and chrysophanol alone and with their combination. We generated 17.6 Gb of high-quality sequencing data (∼2 Gb per sample) and catalogued the expressions profiles of 12,293 annotated cucumber genes in each sample. We identified numerous differentially expressed genes that exhibited distinct expression patterns among the three treatments. The gene expression patterns of the Chr and Phy treatments were more similar to each other than to the Phy × Chr treatment. The Phy × Chr treatment induced the highest number of differentially expressed genes. This dramatic transcriptional change after Phy × Chr treatment leaves reflects that physcion combined with chrysophanol treatment was most closely associated with induction of disease resistance. The analysis showed that the combination treatment caused expression changes of numerous defense-related genes. These genes have known or potential roles in structural, chemical and signaling defense responses and were enriched in functional gene categories potentially responsible for cucumber resistance. These results clearly demonstrated that disease resistance in cucumber leaves was significantly influenced by the combined physcion and chrysophanol treatment. Thus, physcion and chrysophanol are appealing candidates for further investigation of the gene expression and associated regulatory mechanisms related to the defense response.

scholarly journals Transcriptional profiling of bovine muscle-derived satellite cells during differentiation in vitro by high throughput RNA sequencing

2015 ◽  
Vol 20 (3) ◽  
Author(s):  
Hui Li Tong ◽  
Hong Yan Yin ◽  
Wei Wei Zhang ◽  
Qian Hu ◽  
Shu Feng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
High Throughput ◽  
Satellite Cells ◽  
Muscle Development ◽  
Future Research ◽  
Pathway Enrichment Analysis

AbstractIn this study, we utilized high throughput RNA sequencing to obtain a comprehensive gene expression profile of muscle-derived satellite cells (MDSCs) upon induction of differentiation. MDSCs were cultured in vitro and RNA was extracted for sequencing prior to differentiation (MDSC-P), and again during the early and late differentiation (MDSC-D1, and MDSC-D3, respectively) stages. Sequence tags were assembled and analyzed by digital gene expression profile to screen for differentially expressed genes, Gene Ontology annotation, and pathway enrichment analysis. Quantitative real-time PCR was used to confirm the results of RNA sequencing. Our results indicate that certain of genes were changed during skeletal muscle cell development, cell cycle progression, and cell metabolism during differentiation of bovine MDSCs. Furthermore, we identified certain genes that could be used as novel candidates for future research of muscle development. Additionally, the sequencing results indicated that lipid metabolism might be the predominant cellular process that occurs during MDSC differentiation.

scholarly journals Transcriptomic profiling of three-dimensional cholangiocyte spheroids long term exposed to repetitive Clonorchis sinensis excretory-secretory products

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jung-Woong Kim ◽  
Junyeong Yi ◽  
Jinhong Park ◽  
Ji Hoon Jeong ◽  
Jinho Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Fluke ◽  
Expression Patterns ◽  
Clonorchis Sinensis ◽  
Three Dimensional ◽  
Global Changes ◽  
Rna Seq ◽  
Extracellular Region ◽  
Secretory Products

Abstract Background Biliary tract infection with the carcinogenic human liver fluke, Clonorchis sinensis, provokes chronic inflammation, epithelial hyperplasia, periductal fibrosis, and even cholangiocarcinoma. Complications are proportional to the intensity and duration of the infection. In addition to mechanical irritation of the biliary epithelia from worms, their excretory-secretory products (ESPs) cause chemical irritation, which leads to inflammation, proliferation, and free radical generation. Methods A three-dimensional in vitro cholangiocyte spheroid culture model was established, followed by ESP treatment. This allowed us to examine the intrinsic pathological mechanisms of clonorchiasis via the imitation of prolonged and repetitive in vivo infection. Results Microarray and RNA-Seq analysis revealed that ESP-treated cholangiocyte H69 spheroids displayed global changes in gene expression compared to untreated spheroids. In ESP-treated H69 spheroids, 185 and 63 probes were found to be significantly upregulated and downregulated, respectively, corresponding to 209 genes (p < 0.01, fold change > 2). RNA-Seq was performed for the validation of the microarray results, and the gene expression patterns in both transcriptome platforms were well matched for 209 significant genes. Gene ontology analysis demonstrated that differentially expressed genes were mainly classified into immune system processes, the extracellular region, and the extracellular matrix. Among the upregulated genes, four genes (XAF1, TRIM22, CXCL10, and BST2) were selected for confirmation using quantitative RT-PCR, resulting in 100% similar expression patterns in microarray and RNA-Seq. Conclusions These findings broaden our understanding of the pathological pathways of liver fluke-associated hepatobiliary disorders and suggest a novel therapeutic strategy for this infectious cancer. Graphic abstract

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