EPCO-33. CELL-TYPE PROPORTION DECONVOLUTION OF PEDIATRIC CENTRAL NERVOUS SYSTEM TUMORS FROM SINGLE NUCLEI RNA-seq UNCOVERS UNDERLYING TRANSCRIPTOMIC CHANGES FROM BULK TUMOR RNA-seq COMPARED TO NORMAL BRAIN

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi9-vi9
Author(s):  
Min Kyung Lee ◽  
Nasim Azizgolshani ◽  
Fred Kolling ◽  
Lananh Nguyen ◽  
George Zanazzi ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Rna Sequencing ◽  
Differentially Expressed ◽  
Normal Brain ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Samples ◽  
Pediatric Brain ◽  
Bulk Tissue ◽  
Unadjusted Analysis

Abstract Identifying transcriptomic alterations in pediatric central nervous system (pCNS) tumors often relies on transcriptomic profiles from bulk tissue RNA-sequencing that can be confounded by varying cell type proportions across tumor and normal brain tissues. We utilized single nuclei RNA-sequencing (snRNA-seq) and bulk RNA-seq in 33 pCNS tumors and 3 non-diseased pediatric brain tissue samples collected from the Norris Cotton Cancer Center to identify variation in gene expression in bulk tissue attributed to overrepresentation of specific cell-type populations when determining differentially expressed genes comparing pCNS tumors to normal pediatric brain tissues. snRNA-seq of 43,515 nuclei (mean = 1,209 nuclei/sample) revealed large proportions of astrocytes (median = 0.45, range = 0.24–0.93) and oligodendrocytes (median = 0.37, range = 0.00–0.66) in pCNS tumors. Compared to normal pediatric brain, proportions of astrocytes were significantly higher (P = 9.2E-03) and neurons were significantly lower (P = 9.4E-03) in pCNS tumors. Differential expression analyses comparing bulk RNA-sequencing data from pCNS tumors to normal pediatric brain identified 902 additional differentially expressed genes (# DE genes = 1,802) when adjusting for astrocyte and neuron proportions compared with unadjusted analysis (# DE genes = 900). In cell-type proportion unadjusted analysis, top DE genes included astrocyte-specific markers, GFAP and CIITA, both of which were found to be not significantly differentially expressed in cell-type proportion adjusted analysis. Indeed, pathways enrichment analysis revealed DE genes in unadjusted models were associated with processes of the neurons and astrocytes such as interferon signaling and postsynaptic signal transmission. After adjustment for astrocyte and neuron proportions, DE genes were associated with defensins and DNA replication-related processes. Our results highlight new potential biological pathways essential in pCNS tumors and indicate the significance of the distribution of varying cell types in tissue samples when conducting studies to investigate transcriptomic alterations in bulk tissue of pCNS tumors.

Identification of Differentially Expressed and Spliced Genes with RNA Sequencing Analysis of CLL Specimens

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 4582-4582
Author(s):  
Wei Liao ◽  
Gwen Jordaan ◽  
Artur Jaroszewicz ◽  
Matteo Pellegrini ◽  
Sanjai Sharma
Keyword(s):  
B Cells ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Fold Change ◽  
Differentially Expressed ◽  
Pcr Analysis ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
Mrna Isoforms ◽  
Core Set

Abstract Abstract 4582 High throughput sequencing of cellular mRNA provides a comprehensive analysis of the transcriptome. Besides identifying differentially expressed genes in different cell types, it also provides information of mRNA isoforms and splicing alterations. We have analyzed two CLL specimens and a normal peripheral blood B cells mRNA by this approach and performed data analysis to identify differentially expressed and spliced genes. The result showed CLLs specimens express approximately 40% more transcripts compared to normal B cells. The FPKM data (fragment per kilobase of exon per million) revealed a higher transcript expression on chromosome 12 in CLL#1 indicating the presence of trisomy 12, which was confirmed by fluorescent in-situ hybridization assay. With a two-fold change in FPKM as a cutoff and a p value cutoff of 0.05 as compared to the normal B cell control, 415 genes and 174 genes in CLL#1 and 676 and 235 genes in CLL#2 were up and downregulated or differentially expressed. In these two CLL specimens, 45% to 75% of differentially expressed genes are common to both the CLL specimens indicating that genetically disparate CLL specimens have a high percentage of a core set of genes that are potentially important for CLL biology. Selected differentially expressed genes with increased expression (selectin P ligand, SELPLG, and adhesion molecule interacts with CXADR antigen 1, AMICA) and decreased (Fos, Jun, CD69 and Rhob) expression based on the FPKM from RNA-sequencing data were also analyzed in additional CLL specimens by real time PCR analysis. The expression data from RNA-seq closely matches the fold-change in expression as measured by RT-PCR analysis and confirms the validity of the RNA-seq analysis. Interestingly, Fos was identified as one of the most downregulated gene in CLL. Using the Cufflinks and Cuffdiff software, the splicing patterns of genes in CLL specimens and normal B cells were analyzed. Approximately, 1100 to 1250 genes in the two CLL specimens were significantly differentially spliced as compared to normal B cells. In this analysis as well, there is a core set of 800 common genes which are differentially spliced in the two CLL specimens. The RNA-sequencing analysis accurately identifies differentially expressed novel genes and splicing variations that will help us understand the biology of CLL. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Deep RNA sequencing analysis of syncytialization-related genes during BeWo cell fusion

Reproduction ◽  
2017 ◽  
Vol 153 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Ru Zheng ◽  
Yue Li ◽  
Huiying Sun ◽  
Xiaoyin Lu ◽  
Bao-Fa Sun ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Cell Fate ◽  
Cell Fusion ◽  
Mapk Pathway ◽  
Differentially Expressed ◽  
Cell Junction ◽  
Bewo Cell ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
Bewo Cells

The syncytiotrophoblast (STB) plays a key role in maintaining the function of the placenta during human pregnancy. However, the molecular network that orchestrates STB development remains elusive. The aim of this study was to obtain broad and deep insight into human STB formation via transcriptomics. We adopted RNA sequencing (RNA-Seq) to investigate genes and isoforms involved in forskolin (FSK)-induced fusion of BeWo cells. BeWo cells were treated with 50 μM FSK or dimethyl sulfoxide (DMSO) as a vehicle control for 24 and 48 h, and the mRNAs at 0, 24 and 48 h were sequenced. We detected 28,633 expressed genes and identified 1902 differentially expressed genes (DEGs) after FSK treatment for 24 and 48 h. Among the 1902 DEGs, 461 were increased and 395 were decreased at 24 h, whereas 879 were upregulated and 763 were downregulated at 48 h. When the 856 DEGs identified at 24 h were traced individually at 48 h, they separated into 6 dynamic patterns via a K-means algorithm, and most were enriched in down–even and up–even patterns. Moreover, the gene ontology (GO) terms syncytium formation, cell junction assembly, cell fate commitment, calcium ion transport, regulation of epithelial cell differentiation and cell morphogenesis involved in differentiation were clustered, and the MAPK pathway was most significantly regulated. Analyses of alternative splicing isoforms detected 123,200 isoforms, of which 1376 were differentially expressed. The present deep analysis of the RNA-Seq data of BeWo cell fusion provides important clues for understanding the mechanisms underlying human STB formation.

scholarly journals rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database

2021 ◽  
Author(s):  
Daniel Osorio ◽  
Marieke Lydia Kuijjer ◽  
James J. Cai
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
R Package ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Experiment ◽  
Tissue Samples ◽  
Molecular Phenotypes ◽  
Public Datasets

Motivation: Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. Results: Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes' unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability and Implementation: rPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

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 FIN-Seq: transcriptional profiling of specific cell types from frozen archived tissue of the human central nervous system

2019 ◽  
Author(s):  
Ryoji Amamoto ◽  
Emanuela Zuccaro ◽  
Nathan C Curry ◽  
Sonia Khurana ◽  
Hsu-Hsin Chen ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Rna Sequencing ◽  
Human Tissue ◽  
Transcriptional Profiling ◽  
Cell Types ◽  
Specific Cell ◽  
Human Central Nervous System ◽  
Tissue Samples ◽  
Rna Profiling

Abstract Thousands of frozen, archived tissue samples from the human central nervous system (CNS) are currently available in brain banks. As recent developments in RNA sequencing technologies are beginning to elucidate the cellular diversity present within the human CNS, it is becoming clear that an understanding of this diversity would greatly benefit from deeper transcriptional analyses. Single cell and single nucleus RNA profiling provide one avenue to decipher this heterogeneity. An alternative, complementary approach is to profile isolated, pre-defined cell types and use methods that can be applied to many archived human tissue samples that have been stored long-term. Here, we developed FIN-Seq (Frozen Immunolabeled Nuclei Sequencing), a method that accomplishes these goals. FIN-Seq uses immunohistochemical isolation of nuclei of specific cell types from frozen human tissue, followed by bulk RNA-Sequencing. We applied this method to frozen postmortem samples of human cerebral cortex and retina and were able to identify transcripts, including low abundance transcripts, in specific cell types.

scholarly journals SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

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 Comprehensive MicroRNA Expression Profile of the Mammary Gland in Lactating Dairy Cows With Extremely Different Milk Protein and Fat Percentages

2020 ◽  
Vol 11 ◽  
Author(s):  
Xiaogang Cui ◽  
Shengli Zhang ◽  
Qin Zhang ◽  
Xiangyu Guo ◽  
Changxin Wu ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Small Rna ◽  
Milk Protein ◽  
Milk Composition ◽  
Differentially Expressed ◽  
Small Rna Sequencing ◽  
Rna Seq

A total of 31 differentially expressed genes in the mammary glands were identified in our previous study using RNA sequencing (RNA-Seq), for lactating cows with extremely high and low milk protein and fat percentages. To determine the regulation of milk composition traits, we herein investigated the expression profiles of microRNA (miRNA) using small RNA sequencing based on the same samples as in the previous RNA-Seq experiment. A total of 497 known miRNAs (miRBase, release 22.1) and 49 novel miRNAs among the reads were identified. Among these miRNAs, 71 were found differentially expressed between the high and low groups (p < 0.05, q < 0.05). Furthermore, 21 of the differentially expressed genes reported in our previous RNA-Seq study were predicted as target genes for some of the 71 miRNAs. Gene ontology and KEGG pathway analyses showed that these targets were enriched for functions such as metabolism of protein and fat, and development of mammary gland, which indicating the critical role of these miRNAs in regulating the formation of milk protein and fat. With dual luciferase report assay, we further validated the regulatory role of 7 differentially expressed miRNAs through interaction with the specific sequences in 3′UTR of the targets. In conclusion, the current study investigated the complexity of the mammary gland transcriptome in dairy cattle using small RNA-seq. Comprehensive analysis of differential miRNAs expression and the data from previous study RNA-seq provided the opportunity to identify the key candidate genes for milk composition traits.

scholarly journals Identification of differentially expressed lncRNAs and mRNAs in luminal-B breast cancer by RNA-sequencing

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Cheng-Liang Yuan ◽  
Xiang-Mei Jiang ◽  
Ying Yi ◽  
Jian-Fei E ◽  
Nai-Dan Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Rna Sequencing ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Cancer Tissue ◽  
Tissue Samples ◽  
Luminal B ◽  
Ppi Networks ◽  
Tumor Tissues ◽  
Cancer Tumor

Abstract Background Luminal B cancers show much worse outcomes compared to luminal A. This present study aims to screen key lncRNAs and mRNAs correlated with luminal-B breast cancer. Methods Luminal-B breast cancer tissue samples and adjacent tissue samples were obtained from 4 patients with luminal-B breast cancer. To obtain differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) between luminal-B breast cancer tumor tissues and adjacent tissues, RNA-sequencing and bioinformatics analysis were performed. Functional annotation of DEmRNAs and protein-protein interaction networks (PPI) construction were performed. DEmRNAs transcribed within a 100 kb window up- or down-stream of DElncRNAs were searched, which were defined as cis nearby-targeted DEmRNAs of DElncRNAs. DElncRNA-DEmRNA co-expression networks were performed. The mRNA and lncRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database to validate the expression patterns of selected DEmRNAs and DElncRNAs. Results A total of 1178 DEmRNAs and 273 DElncRNAs between luminal-B breast cancer tumor tissues and adjacent tissues were obtained. Hematopoietic cell lineage, Cytokine-cytokine receptor interaction, Cell adhesion molecules (CAMs) and Primary immunodeficiency were significantly enriched KEGG pathways in luminal-B breast cancer. FN1, EGFR, JAK3, TUBB3 and PTPRC were five hub proteins of the PPI networks. A total of 99 DElncRNAs-nearby-targeted DEmRNA pairs and 1878 DElncRNA-DEmRNA co-expression pairs were obtained. Gene expression results validated in TCGA database were consistent with our RNA-sequencing results, generally. Conclusion This study determined key genes and lncRNAs involved in luminal-B breast cancer, which expected to present a new avenue for the diagnosis and treatment of luminal-B breast cancer.

scholarly journals Building an RNA Sequencing Transcriptome of the Central Nervous System

2016 ◽  
Vol 22 (6) ◽  
pp. 579-592 ◽  
Author(s):  
Xiaomin Dong ◽  
Yanan You ◽  
Jia Qian Wu
Keyword(s):  
Gene Expression ◽  
Central Nervous System ◽  
Nervous System ◽  
Rna Sequencing ◽  
Large Scale ◽  
Expression Profiles ◽  
Cell Types ◽  
Specific Cell ◽  
Rna Seq ◽  
The Central Nervous System

The composition and function of the central nervous system (CNS) is extremely complex. In addition to hundreds of subtypes of neurons, other cell types, including glia (astrocytes, oligodendrocytes, and microglia) and vascular cells (endothelial cells and pericytes) also play important roles in CNS function. Such heterogeneity makes the study of gene transcription in CNS challenging. Transcriptomic studies, namely the analyses of the expression levels and structures of all genes, are essential for interpreting the functional elements and understanding the molecular constituents of the CNS. Microarray has been a predominant method for large-scale gene expression profiling in the past. However, RNA-sequencing (RNA-Seq) technology developed in recent years has many advantages over microarrays, and has enabled building more quantitative, accurate, and comprehensive transcriptomes of the CNS and other systems. The discovery of novel genes, diverse alternative splicing events, and noncoding RNAs has remarkably expanded the complexity of gene expression profiles and will help us to understand intricate neural circuits. Here, we discuss the procedures and advantages of RNA-Seq technology in mammalian CNS transcriptome construction, and review the approaches of sample collection as well as recent progress in building RNA-Seq-based transcriptomes from tissue samples and specific cell types.

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