56 Spatial analysis of transcriptome changes in porcine endometrium on Day 14 of pregnancy

2019 ◽  
Vol 31 (1) ◽  
pp. 153
Author(s):  
S. Zeng ◽  
S. Bauersachs
Keyword(s):  
Gene Expression Regulation ◽  
Sequence Data ◽  
Differential Expression Analysis ◽  
Cell Communication ◽  
Cell Types ◽  
Endometrial Tissue ◽  
Cell Junction ◽  
Rna Seq ◽  
Tissue Samples ◽  
Rna Integrity

During the conception cycle, the embryo undergoes a series of developmental processes including cell division, cellular reorganization, and oestrogen secretion before attaching to the uterine epithelium. The uterine endometrium is complex and consists of various layers and cell types [i.e. luminal epithelium (LE), glandular epithelium (GE), blood cells (B), and stromal areas (S)]. The objective of this study was to characterise the complex transcriptome changes in porcine endometrium during the time of conceptus attachment with respect to localization in different endometrial cell types. RNA-sequencing (RNA-Seq) was conducted for LE, GE, B, and S samples isolated from endometrial tissue collected on Day 14 of pregnancy and the oestrous cycle, respectively (each group n=4), by laser capture microdissection (PALM LCM microscope, Zeiss, Jena, Germany). Total RNA was isolated (RNA integrity number>6.5) and used for the preparation of 32 RNA-seq libraries (Ovation SoLo RNA-Seq System, NuGEN Technologies, San Carlos, CA, USA). Multiplexed (barcode-tagged) libraries were run on an Illumina HiSEqn 2500 (Illumina, San Diego, CA, USA). The obtained sequence data were analysed with a RNA-Seq data analysis pipeline on a local Galaxy server installation. The resulting read counts were used for statistical analysis in EdgeR to identify differentially expressed genes (DEG). Furthermore, an RNA-seq dataset for complete Day 14 endometrial tissue samples from a previous study was analysed using the same pipeline. A total of 14297 genes were detectable in complete endometria, and 12000, 11903, 11094, and 11933 genes in LE, GE, B, and S, respectively. Differential expression analysis was performed between the pregnant and the cyclic nonpregnant group for each cell type and the complete tissue. The highest number of DEG was found for LE (1410) when compared with GE, B, and S (800, 1216, and 384, respectively). In total, 3262 DEG were obtained for the complete tissue between pregnant and nonpregnant gilts. The DEG were assigned to Gene Ontology (GO) terms to characterise overrepresented functional categories and pathways specific for the individual endometrial compartments. The GO classification revealed that most DEG in LE were involved in cell communication, such as ‘extracellular exosome’, ‘extracellular vesicle’, ‘homeostatic process’, whereas the ‘response to organic substance’ and ‘regulation of cell migration’ categories were enriched in GE. In blood vessels, categories such as ‘membrane-bounded vesicle’, ‘cell junction’, ‘cell development’, ‘cell adhesion’ and ‘blood vessel morphogenesis’ were found as overrepresented, whereas in stromal regions, most DEG were assigned to ‘cell communication’ and ‘secretion’. These results confirmed the hypothesis that conceptus signals induce specific transcriptomic regulations in the endometrial compartments/cell types related to their functions during recognition of pregnancy adding a new level of spatial gene expression regulation to endometrial transcriptome analysis.

scholarly journals Identifying inaccuracies in gene expression estimates from unstranded RNA-seq data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mikhail Pomaznoy ◽  
Ashu Sethi ◽  
Jason Greenbaum ◽  
Bjoern Peters
Keyword(s):  
Gene Expression ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Library Preparation ◽  
Rna Seq ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Machine Learning Model ◽  
Specific Manner ◽  
Library Preparation Protocol

Abstract RNA-seq methods are widely utilized for transcriptomic profiling of biological samples. However, there are known caveats of this technology which can skew the gene expression estimates. Specifically, if the library preparation protocol does not retain RNA strand information then some genes can be erroneously quantitated. Although strand-specific protocols have been established, a significant portion of RNA-seq data is generated in non-strand-specific manner. We used a comprehensive stranded RNA-seq dataset of 15 blood cell types to identify genes for which expression would be erroneously estimated if strand information was not available. We found that about 10% of all genes and 2.5% of protein coding genes have a two-fold or higher difference in estimated expression when strand information of the reads was ignored. We used parameters of read alignments of these genes to construct a machine learning model that can identify which genes in an unstranded dataset might have incorrect expression estimates and which ones do not. We also show that differential expression analysis of genes with biased expression estimates in unstranded read data can be recovered by limiting the reads considered to those which span exonic boundaries. The resulting approach is implemented as a package available at https://github.com/mikpom/uslcount.

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 RNA-seq assembler artifacts can bias expression counts and differential expression analysis - case study on the chickpea transcriptome emphasizes importance of freely accessible data for reproducibility

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2394 ◽  
Author(s):  
Sandeep Chakraborty
Keyword(s):  
Differential Expression ◽  
Sequence Data ◽  
Differential Expression Analysis ◽  
Open Reading Frames ◽  
Ribulose Bisphosphate Carboxylase ◽  
Rna Seq ◽  
Bisphosphate Carboxylase ◽  
Intermediate Data

The unprecedented volume of genomic and transcriptomic data analyzed by software pipelines makes verification of inferences based on such data, albeit theoretically possible, a challenging proposition. The availability of intermediate data can immensely aid re-validation efforts. One such example is the transcriptome, assembled from raw RNA-seq reads, which is frequently used for annotation and quantification of genes transcribed. The quality of the assembled transcripts influences the accuracy of inferences based on them. Here the publicly available transcriptome from Cicer arietinum (ICC4958; Desi chickpea, http://www.nipgr.res.in/ctdb.html)1 was analyzed using YeATS2. This revealed that a majority of the highly expressed transcripts (HET) encoded multiple genes, strongly indicating that the counts may have been biased by the merging of different transcripts. TC00004 is ranked in the top five HET for all five tissues analyzed here, and encodes both a retinoblastoma-binding-like protein (E-value=0) and a senescence-associated protein (E-value= 5e-108). Fragmented transcripts are another source of error. The ribulose bisphosphate carboxylase small chain (RBCSC) protein is split into two transcripts with an overlapping amino acid sequence "ASNGGRVHC", TC13991 and TC23009, with length 201 and 332 nucleotides and expression counts 17.90 and 1403.8, respectively. The huge difference in counts indicates an erroneous normalization algorithm in determining counts. It is well known that RBCSC is highly expressed and expectedly TC23009 ranks fifth among HETs in the shoot. Furthermore, some transcripts are split into open reading frames that map to the same protein, although this should not have any significant bearing on the counts. It is proposed that studies analyzing differential expression based on the transcriptome should consider these artifacts, and providing intermediate assembled transcriptomes should be mandatory, possibly with a link to the raw sequence data (Bioproject).

Cell type-specific endometrial transcriptome changes during initial recognition of pregnancy in the mare

2019 ◽  
Vol 31 (3) ◽  
pp. 496 ◽  
Author(s):  
Iside Scaravaggi ◽  
Nicole Borel ◽  
Rebekka Romer ◽  
Isabel Imboden ◽  
Susanne E. Ulbrich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Information ◽  
Gene Expression Regulation ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Expression Of Genes ◽  
Prostaglandin F ◽  
Cell Type Specific ◽  
Gene Expression Studies

Previous endometrial gene expression studies during the time of conceptus migration did not provide final conclusions on the mechanisms of maternal recognition of pregnancy (MRP) in the mare. This called for a cell type-specific endometrial gene expression analysis in response to embryo signals to improve the understanding of gene expression regulation in the context of MRP. Laser capture microdissection was used to collect luminal epithelium (LE), glandular epithelium and stroma from endometrial biopsies from Day 12 of pregnancy and Day 12 of the oestrous cycle. RNA sequencing (RNA-Seq) showed greater expression differences between cell types than between pregnant and cyclic states; differences between the pregnant and cyclic states were mainly found in LE. Comparison with a previous RNA-Seq dataset for whole biopsy samples revealed the specific origin of gene expression differences. Furthermore, genes specifically differentially expressed (DE) in one cell type were found that were not detectable as DE in biopsies. Overall, this study revealed spatial information about endometrial gene expression during the phase of initial MRP. The conceptus induced changes in the expression of genes involved in blood vessel development, specific spatial regulation of the immune system, growth factors, regulation of prostaglandin synthesis, transport prostaglandin receptors, specifically prostaglandin F receptor (PTGFR) in the context of prevention of luteolysis.

scholarly journals intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Yingying Cao ◽  
Simo Kitanovski ◽  
Daniel Hoffmann
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
High Throughput Sequencing ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
R Package ◽  
Integrative Approach ◽  
Integrated Analysis ◽  
Data Sets ◽  
Rna Seq

Abstract Background RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone modifications which can be assessed by ChIP-Seq (Chromatin Immuno-Precipitation Sequencing). As more and more biological samples are analyzed by the combination of ChIP-Seq and RNA-Seq, the integrated analysis of the corresponding data sets becomes, theoretically, a unique option to study gene regulation. However, technically such analyses are still in their infancy. Results Here we introduce intePareto, a computational tool for the integrative analysis of RNA-Seq and ChIP-Seq data. With intePareto we match RNA-Seq and ChIP-Seq data at the level of genes, perform differential expression analysis between biological conditions, and prioritize genes with consistent changes in RNA-Seq and ChIP-Seq data using Pareto optimization. Conclusion intePareto facilitates comprehensive understanding of high dimensional transcriptomic and epigenomic data. Its superiority to a naive differential gene expression analysis with RNA-Seq and available integrative approach is demonstrated by analyzing a public dataset.

scholarly journals RNA-seq assembler artifacts can bias expression counts and differential expression analysis - application of YeATS on the chickpea transcriptome

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2394
Author(s):  
Sandeep Chakraborty
Keyword(s):  
Differential Expression ◽  
Sequence Data ◽  
Differential Expression Analysis ◽  
Open Reading Frames ◽  
Ribulose Bisphosphate Carboxylase ◽  
Rna Seq ◽  
Bisphosphate Carboxylase ◽  
Intermediate Data ◽  
Analysis Application

Background: The unprecedented volume of genomic and transcriptomic data analyzed by software pipelines makes verification of inferences based on such data, albeit theoretically possible, a challenging proposition. The availability of intermediate data can immensely aid re-validation efforts. One such example is the transcriptome, assembled from raw RNA-seq reads, which is frequently used for annotation and quantification of genes transcribed. The quality of the assembled transcripts influences the accuracy of inferences based on them. Method: Here the publicly available transcriptome from Cicer arietinum (ICC4958; Desi chickpea, http://www.nipgr.res.in/ctdb.html) was analyzed using YeATS. Results and Conclusion: The analysis revealed that a majority of the highly expressed transcripts (HET) encoded multiple genes, strongly indicating that the counts may have been biased by the merging of different transcripts. TC00004 is ranked in the top five HET for all five tissues analyzed here, and encodes both a retinoblastoma-binding-like protein (E-value=0) and a senescence-associated protein (E-value= 5e-108). Fragmented transcripts are another source of error. The ribulose bisphosphate carboxylase small chain (RBCSC) protein is split into two transcripts with an overlapping amino acid sequence ”ASNGGRVHC”, TC13991 and TC23009, with length 201 and 332 nucleotides and expression counts 17.90 and 1403.8, respectively. The huge difference in counts indicates an erroneous normalization algorithm in determining counts. It is well known that RBCSC is highly expressed and expectedly TC23009 ranks fifth among HETs in the shoot. Furthermore, some transcripts are split into open reading frames that map to the same protein, although this should not have any significant bearing on the counts. It is proposed that studies analyzing differential expression based on the transcriptome should consider these artifacts, and providing intermediate assembled transcriptomes should be mandatory, possibly with a link to the raw sequence data (Bioproject).

scholarly journals Cell Type Aware analysis of RNA-seq data (CARseq) reveals difference and similarities of the molecular mechanisms of Schizophrenia and Autism

2020 ◽  
Author(s):  
Chong Jin ◽  
Mengjie Chen ◽  
Danyu Lin ◽  
Wei Sun
Keyword(s):  
Differential Expression ◽  
Molecular Mechanisms ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Tissue Samples ◽  
Cell Type Composition ◽  
Type Composition ◽  
Cell Type Specific Expression

AbstractMost tissue samples are composed of different cell types. Differential expression analysis without accounting for cell type composition cannot separate the changes due to cell type composition or cell type-specific expression. We propose a new framework to address these limitations: Cell Type Aware analysis of RNA-seq (CARseq). After evaluating its performance in simulations, we apply CARseq to compare gene expression of schizophrenia/autism subjects versus controls. Our results show that these two neurodevelopmental disorders differ from each other in terms of cell type composition changes and differential expression associated with different types of neurotransmitter receptors. We also discover overlapping signals of differential expression in microglia, supporting the two diseases’ similarity through immune regulation.

scholarly journals Spatial organization of endometrial gene expression at the onset of embryo attachment in pigs

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Shuqin Zeng ◽  
Susanne E. Ulbrich ◽  
Stefan Bauersachs
Keyword(s):  
Gene Expression ◽  
Blood Vessels ◽  
Growth Regulation ◽  
Spatial Organization ◽  
Interleukin 1 ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Cell Junction ◽  
Endometrial Cell ◽  
Filamentous Form

Abstract Background During the preimplantation phase in the pig, the conceptus trophoblast elongates into a filamentous form and secretes estrogens, interleukin 1 beta 2, interferons, and other signaling molecules before attaching to the uterine epithelium. The processes in the uterine endometrium in response to conceptus signaling are complex. Thus, the objective of this study was to characterize transcriptome changes in porcine endometrium during the time of conceptus attachment considering the specific localization in different endometrial cell types. Results Low-input RNA-sequencing was conducted for the main endometrial compartments, luminal epithelium (LE), glandular epithelium (GE), blood vessels (BV), and stroma. Samples were isolated from endometria collected on Day 14 of pregnancy and the estrous cycle (each group n = 4) by laser capture microdissection. The expression of 12,000, 11,903, 11,094, and 11,933 genes was detectable in LE, GE, BV, and stroma, respectively. Differential expression analysis was performed between the pregnant and cyclic group for each cell type as well as for a corresponding dataset for complete endometrium tissue samples. The highest number of differentially expressed genes (DEGs) was found for LE (1410) compared to GE, BV, and stroma (800, 1216, and 384). For the complete tissue, 3262 DEGs were obtained. The DEGs were assigned to Gene Ontology (GO) terms to find overrepresented functional categories and pathways specific for the individual endometrial compartments. GO classification revealed that DEGs in LE were involved in ‘biosynthetic processes’, ‘related to ion transport’, and ‘apoptotic processes’, whereas ‘cell migration’, ‘cell growth’, ‘signaling’, and ‘metabolic/biosynthetic processes’ categories were enriched for GE. For blood vessels, categories such as ‘focal adhesion’, ‘actin cytoskeleton’, ‘cell junction’, ‘cell differentiation and development’ were found as overrepresented, while for stromal samples, most DEGs were assigned to ‘extracellular matrix’, ‘gap junction’, and ‘ER to Golgi vesicles’. Conclusions The localization of differential gene expression to different endometrial cell types provided a significantly improved view on the regulation of biological processes involved in conceptus implantation, such as the control of uterine fluid secretion, trophoblast attachment, growth regulation by Wnt signaling and other signaling pathways, as well as the modulation of the maternal immune system.

scholarly journals Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data

2019 ◽  
Vol 21 (1) ◽  
pp. 293 ◽  
Author(s):  
Giulio Ferrero ◽  
Nicola Licheri ◽  
Lucia Coscujuela Tarrero ◽  
Carlo De Intinis ◽  
Valentina Miano ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Gene Expression Regulation ◽  
Differential Expression Analysis ◽  
Model Organisms ◽  
Complete Analysis ◽  
Rna Seq ◽  
Sequence Reconstruction ◽  
Reproducible Analysis ◽  
User Friendly

Recent improvements in cost-effectiveness of high-throughput technologies has allowed RNA sequencing of total transcriptomes suitable for evaluating the expression and regulation of circRNAs, a relatively novel class of transcript isoforms with suggested roles in transcriptional and post-transcriptional gene expression regulation, as well as their possible use as biomarkers, due to their deregulation in various human diseases. A limited number of integrated workflows exists for prediction, characterization, and differential expression analysis of circRNAs, none of them complying with computational reproducibility requirements. We developed Docker4Circ for the complete analysis of circRNAs from RNA-Seq data. Docker4Circ runs a comprehensive analysis of circRNAs in human and model organisms, including: circRNAs prediction; classification and annotation using six public databases; back-splice sequence reconstruction; internal alternative splicing of circularizing exons; alignment-free circRNAs quantification from RNA-Seq reads; and differential expression analysis. Docker4Circ makes circRNAs analysis easier and more accessible thanks to: (i) its R interface; (ii) encapsulation of computational tasks into docker images; (iii) user-friendly Java GUI Interface availability; and (iv) no need of advanced bash scripting skills for correct use. Furthermore, Docker4Circ ensures a reproducible analysis since all its tasks are embedded into a docker image following the guidelines provided by Reproducible Bioinformatics Project.

scholarly journals SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data

2021 ◽  
Vol 17 (6) ◽  
pp. e1009118
Author(s):  
Jing Qi ◽  
Yang Zhou ◽  
Zicen Zhao ◽  
Shuilin Jin
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Level ◽  
Gene Level ◽  
Level Information ◽  
Downstream Analysis ◽  
Gene Expression Levels

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.

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