scholarly journals Generation of guard cell RNA-seq transcriptomes during progressive drought and recovery using an adapted INTACT protocol for Arabidopsis thaliana shoot tissue

2021 ◽  
Author(s):  
Anna van Weringh ◽  
Asher Pasha ◽  
Eddi Esteban ◽  
Paul J. Gamueda ◽  
Nicholas J. Provart
Keyword(s):  
Gene Expression ◽  
Arabidopsis Thaliana ◽  
Drought Stress ◽  
Guard Cell ◽  
Crop Production ◽  
Leaf Tissue ◽  
Cell Types ◽  
Severe Drought ◽  
Data Sets ◽  
Rna Seq

Drought is an important environmental stress that limits crop production. Guard cells (GC) act to control the rate of water loss. To better understand how GCs change their gene expression during a progressive drought we generated guard cell-specific RNA-seq transcriptomes during mild, moderate, and severe drought stress. We additionally sampled re-watered plants that had experienced severe drought stress. These transcriptomes were generated using the INTACT system to capture the RNA from GC nuclei. We optimized the INTACT protocol for Arabidopsis thaliana leaf tissue, incorporating fixation to preserve RNA during nuclear isolation. To be able to identify gene expression changes unique to GCs, we additionally generated transcriptomes from all cell types, using a 35S viral promoter to capture the nuclei of all cell types in leaves. These data sets highlight shared and unique gene expression changes between GCs and the bulk leaf tissue. The timing of gene expression changes is different between GCs and other cell types: we found that only GCs had detectable gene expression changes at the earliest drought time point. The drought responsive GC and leaf RNA-seq transcriptomes are available in the Arabidopsis ePlant at the Bio-Analytic Resource for Plant Biology website.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

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 LTMG: A novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

2018 ◽  
Author(s):  
Changlin Wan ◽  
Wennan Chang ◽  
Yu Zhang ◽  
Fenil Shah ◽  
Xiaoyu Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
R Package ◽  
Data Sets ◽  
Rna Seq ◽  
Cell Functions ◽  
Transcriptional Regulatory ◽  
A Cell

ABSTRACTA key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a gene’s diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

scholarly journals LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

2019 ◽  
Vol 47 (18) ◽  
pp. e111-e111 ◽  
Author(s):  
Changlin Wan ◽  
Wennan Chang ◽  
Yu Zhang ◽  
Fenil Shah ◽  
Xiaoyu Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
R Package ◽  
Data Sets ◽  
Rna Seq ◽  
Mrna Metabolism ◽  
Transcriptional Regulatory ◽  
User Friendly

Abstract A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

scholarly journals RNA-Seq Data-Mining Allows the Discovery of Two Long Non-Coding RNA Biomarkers of Viral Infection in Humans

2020 ◽  
Vol 21 (8) ◽  
pp. 2748 ◽  
Author(s):  
Ruth Barral-Arca ◽  
Alberto Gómez-Carballa ◽  
Miriam Cebey-López ◽  
María José Currás-Tuala ◽  
Sara Pischedda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Viral Infections ◽  
Umbilical Vein ◽  
Cell Types ◽  
Dermal Fibroblasts ◽  
Learning Approaches ◽  
Rna Seq ◽  
Wide Range ◽  
Healthy Control ◽  
Umbilical Vein Endothelial Cells

There is a growing interest in unraveling gene expression mechanisms leading to viral host invasion and infection progression. Current findings reveal that long non-coding RNAs (lncRNAs) are implicated in the regulation of the immune system by influencing gene expression through a wide range of mechanisms. By mining whole-transcriptome shotgun sequencing (RNA-seq) data using machine learning approaches, we detected two lncRNAs (ENSG00000254680 and ENSG00000273149) that are downregulated in a wide range of viral infections and different cell types, including blood monocluclear cells, umbilical vein endothelial cells, and dermal fibroblasts. The efficiency of these two lncRNAs was positively validated in different viral phenotypic scenarios. These two lncRNAs showed a strong downregulation in virus-infected patients when compared to healthy control transcriptomes, indicating that these biomarkers are promising targets for infection diagnosis. To the best of our knowledge, this is the very first study using host lncRNAs biomarkers for the diagnosis of human viral infections.

scholarly journals Pharmacologically targeting tribbles gene expression in colorectal cancer

2021 ◽  
Vol 31 (Supplement_2) ◽  
Author(s):  
Victor Yassuda ◽  
Ana Luísa De Sousa-Coelho
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cell Types ◽  
Disease Recurrence ◽  
Rosemary Extract ◽  
Acid Oxidation ◽  
Data Sets ◽  
Compensatory Mechanism ◽  
Dependent Manner ◽  
Pharmacological Potential

Abstract Background TRIB1, TRIB2 and TRIB3 belong to the mammalian Tribbles family of pseudokinases proteins. Several studies reported Tribbles oncogenic role in different types of cancer, including colorectal cancer (CRC). Though current CRC treatment can be curative, patients are in risk of disease recurrence, meaning novel pharmacological targets and strategies are required. Our goal was to analyze Tribbles gene expression in CRC in response to different drugs. Methods Tribbles transcript levels were obtained from GEO profiles database (NCBI). Gene data sets (GDS) were selected based on experimental drug treatment description. Statistical analysis was performed at GraphPadPrism. Results Compared to non-treated control, TRIB2 expression was ∼2-fold increased in colorectal adenocarcinoma samples from patients treated with cyclooxygenase-2 inhibitor celecoxib (GDS3384), though not statistically significant (P < 0.1). TRIB1 was unaltered and data for TRIB3 was not available. By contrast, all Tribbles showed differential expression after treatment of SW620 colon cancer cells with supercritical rosemary extract in progressive increasing doses (0, 30, 60, 100 μg/mL) (P < 0.01;GDS5416). While both TRIB1 and TRIB3 were moderately increased in a dose-dependent manner (∼18% and 13%, respectively), TRIB2 was maximally down-regulated by ∼15% after 60 μg/mL. Conclusions Although celecoxib exhibits antiproliferative effects in different cancer cell types, TRIB2 gene expression showed a trend to be induced after treatment, in contrast to several genes involved in fatty acid oxidation that were down-regulated, which could result from a compensatory mechanism based on a metabolic shift. Since TRIB1/TRIB3 and TRIB2 were oppositely modulated in response to rosemary extract, additional studies are needed to validate its specific pharmacological potential interest for CRC treatment.

scholarly journals Gene Expression Does Not Support the Developmental Hourglass Model in Three Animals with Spiralian Development

2019 ◽  
Vol 36 (7) ◽  
pp. 1373-1383 ◽  
Author(s):  
Longjun Wu ◽  
Kailey E Ferger ◽  
J David Lambert
Keyword(s):  
Gene Expression ◽  
Large Fraction ◽  
Molecular Data ◽  
Development Stage ◽  
Data Sets ◽  
Rna Seq ◽  
Developmental Evolution ◽  
Phylotypic Stage ◽  
Hourglass Model ◽  
Almost All

Abstract It has been proposed that animals have a pattern of developmental evolution resembling an hourglass because the most conserved development stage—often called the phylotypic stage—is always in midembryonic development. Although the topic has been debated for decades, recent studies using molecular data such as RNA-seq gene expression data sets have largely supported the existence of periods of relative evolutionary conservation in middevelopment, consistent with the phylotypic stage and the hourglass concepts. However, so far this approach has only been applied to a limited number of taxa across the tree of life. Here, using established phylotranscriptomic approaches, we found a surprising reverse hourglass pattern in two molluscs and a polychaete annelid, representatives of the Spiralia, an understudied group that contains a large fraction of metazoan body plan diversity. These results suggest that spiralians have a divergent midembryonic stage, with more conserved early and late development, which is the inverse of the pattern seen in almost all other organisms where these phylotranscriptomic approaches have been reported. We discuss our findings in light of proposed reasons for the phylotypic stage and hourglass model in other systems.

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 Integration of GWAS Summary Statistics and Gene Expression Reveals Target Cell Types Underlying Kidney Function Traits

2020 ◽  
Vol 31 (10) ◽  
pp. 2326-2340 ◽  
Author(s):  
Yong Li ◽  
Stefan Haug ◽  
Pascal Schlosser ◽  
Alexander Teumer ◽  
Adrienne Tin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Function ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Rna Seq ◽  
Cell Type ◽  
Gene Set Enrichment ◽  
Gene Set

BackgroundGenetic variants identified in genome-wide association studies (GWAS) are often not specific enough to reveal complex underlying physiology. By integrating RNA-seq data and GWAS summary statistics, novel computational methods allow unbiased identification of trait-relevant tissues and cell types.MethodsThe CKDGen consortium provided GWAS summary data for eGFR, urinary albumin-creatinine ratio (UACR), BUN, and serum urate. Genotype-Tissue Expression Project (GTEx) RNA-seq data were used to construct the top 10% specifically expressed genes for each of 53 tissues followed by linkage disequilibrium (LD) score–based enrichment testing for each trait. Similar procedures were performed for five kidney single-cell RNA-seq datasets from humans and mice and for a microdissected tubule RNA-seq dataset from rat. Gene set enrichment analyses were also conducted for genes implicated in Mendelian kidney diseases.ResultsAcross 53 tissues, genes in kidney function–associated GWAS loci were enriched in kidney (P=9.1E-8 for eGFR; P=1.2E-5 for urate) and liver (P=6.8·10-5 for eGFR). In the kidney, proximal tubule was enriched in humans (P=8.5E-5 for eGFR; P=7.8E-6 for urate) and mice (P=0.0003 for eGFR; P=0.0002 for urate) and confirmed as the primary cell type in microdissected tubules and organoids. Gene set enrichment analysis supported this and showed enrichment of genes implicated in monogenic glomerular diseases in podocytes. A systematic approach generated a comprehensive list of GWAS genes prioritized by cell type–specific expression.ConclusionsIntegration of GWAS statistics of kidney function traits and gene expression data identified relevant tissues and cell types, as a basis for further mechanistic studies to understand GWAS loci.

scholarly journals Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Anna Pačínková ◽  
Vlad Popovici
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Colon Cancer ◽  
Microsatellite Instability ◽  
Gene Expression Signature ◽  
Data Sets ◽  
Rna Seq ◽  
Cancer Data ◽  
Expression Signature ◽  
Endometrial Cancers

The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 – 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 – 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 – 0.94) and AUC = 0.83, 95% CI = (0.69 – 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 – 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.

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