scholarly journals Human embryoid bodies as a novel system for genomic studies of functionally diverse cell types

2021 ◽  
Author(s):  
Katherine Rhodes ◽  
Kenneth A Barr ◽  
Joshua M Popp ◽  
Benjamin J Strober ◽  
Alexis Battle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Embryoid Bodies ◽  
Regulatory Processes ◽  
Study Gene Expression ◽  
Genomic Studies ◽  
Functionally Diverse ◽  
Affect Gene Expression

Most disease-associated loci, though located in putatively regulatory regions, have not yet been confirmed to affect gene expression. One reason for this could be that we have not examined gene expression in the most relevant cell types or conditions. Indeed, even large-scale efforts to study gene expression broadly across tissues are limited by the necessity of obtaining human samples post-mortem, and almost exclusively from adults. Thus, there is an acute need to expand gene regulatory studies in humans to the most relevant cell types, tissues, and states. We propose that embryoid bodies (EBs), which are organoids that contain a multitude of cell types in dynamic states, can provide an answer. Single cell RNA-sequencing now provides a way to interrogate developmental trajectories in EBs and enhance the potential to uncover dynamic regulatory processes that would be missed in studies of static adult tissue. Here, we examined the properties of the EB model for the purpose mapping inter-individual regulatory differences in a large variety of cell types.

scholarly journals Gene Expression Imputation with Generative Adversarial Imputation Nets

2020 ◽  
Author(s):  
Ramon Viñas ◽  
Tiago Azevedo ◽  
Eric R. Gamazon ◽  
Pietro Liò
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Biological Significance ◽  
Predictive Performance ◽  
Cost Effective ◽  
Rna Seq ◽  
Comprehensive Collection ◽  
Genomic Studies ◽  
Biological Discovery ◽  
Cancer Types

AbstractA question of fundamental biological significance is to what extent the expression of a subset of genes can be used to recover the full transcriptome, with important implications for biological discovery and clinical application. To address this challenge, we present GAIN-GTEx, a method for gene expression imputation based on Generative Adversarial Imputation Networks. In order to increase the applicability of our approach, we leverage data from GTEx v8, a reference resource that has generated a comprehensive collection of transcriptomes from a diverse set of human tissues. We compare our model to several standard and state-of-the-art imputation methods and show that GAIN-GTEx is significantly superior in terms of predictive performance and runtime. Furthermore, our results indicate strong generalisation on RNA-Seq data from 3 cancer types across varying levels of missingness. Our work can facilitate a cost-effective integration of large-scale RNA biorepositories into genomic studies of disease, with high applicability across diverse tissue types.

scholarly journals A comprehensive annotation and differential expression analysis of short and long non-coding RNAs in 16 bat genomes

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Nelly F Mostajo ◽  
Marie Lataretu ◽  
Sebastian Krautwurst ◽  
Florian Mock ◽  
Daniel Desirò ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Large Scale ◽  
Viral Infections ◽  
Gene Expression Regulation ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Regulatory Processes ◽  
Genome Annotations ◽  
Host Virus Interactions

Abstract Although bats are increasingly becoming the focus of scientific studies due to their unique properties, these exceptional animals are still among the least studied mammals. Assembly quality and completeness of bat genomes vary a lot and especially non-coding RNA (ncRNA) annotations are incomplete or simply missing. Accordingly, standard bioinformatics pipelines for gene expression analysis often ignore ncRNAs such as microRNAs or long antisense RNAs. The main cause of this problem is the use of incomplete genome annotations. We present a complete screening for ncRNAs within 16 bat genomes. NcRNAs affect a remarkable variety of vital biological functions, including gene expression regulation, RNA processing, RNA interference and, as recently described, regulatory processes in viral infections. Within all investigated bat assemblies, we annotated 667 ncRNA families including 162 snoRNAs and 193 miRNAs as well as rRNAs, tRNAs, several snRNAs and lncRNAs, and other structural ncRNA elements. We validated our ncRNA candidates by six RNA-Seq data sets and show significant expression patterns that have never been described before in a bat species on such a large scale. Our annotations will be usable as a resource (rna.uni-jena.de/supplements/bats) for deeper studying of bat evolution, ncRNAs repertoire, gene expression and regulation, ecology and important host–virus interactions.

scholarly journals scAIDE: clustering of large-scale single-cell RNA-seq data reveals putative and rare cell types

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Kaikun Xie ◽  
Yu Huang ◽  
Feng Zeng ◽  
Zehua Liu ◽  
Ting Chen
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Random Projection ◽  
Good Representation ◽  
Rna Seq ◽  
Unsupervised Deep Learning ◽  
High Level ◽  
Computational Resources

Abstract Recent advancements in both single-cell RNA-sequencing technology and computational resources facilitate the study of cell types on global populations. Up to millions of cells can now be sequenced in one experiment; thus, accurate and efficient computational methods are needed to provide clustering and post-analysis of assigning putative and rare cell types. Here, we present a novel unsupervised deep learning clustering framework that is robust and highly scalable. To overcome the high level of noise, scAIDE first incorporates an autoencoder-imputation network with a distance-preserved embedding network (AIDE) to learn a good representation of data, and then applies a random projection hashing based k-means algorithm to accommodate the detection of rare cell types. We analyzed a 1.3 million neural cell dataset within 30 min, obtaining 64 clusters which were mapped to 19 putative cell types. In particular, we further identified three different neural stem cell developmental trajectories in these clusters. We also classified two subpopulations of malignant cells in a small glioblastoma dataset using scAIDE. We anticipate that scAIDE would provide a more in-depth understanding of cell development and diseases.

scholarly journals Gene expression profiling of gilthead sea bream during early development and detection of stress-related genes by the application of cDNA microarray technology

2005 ◽  
Vol 23 (2) ◽  
pp. 182-191 ◽  
Author(s):  
Elena Sarropoulou ◽  
Georgios Kotoulas ◽  
Deborah M. Power ◽  
Robert Geisler
Keyword(s):  
Gene Expression ◽  
Early Development ◽  
Cdna Microarray ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Expression Profiles ◽  
Gilthead Sea Bream ◽  
Sea Bream ◽  
Regulatory Pathways ◽  
Study Gene Expression

Large-scale gene expression studies were performed for one of the main European aquaculture species, the gilthead sea bream Sparus auratus L. For this purpose, a cDNA microarray containing 10,176 clones from a cDNA library of mixed embryonic and larval stages was constructed. In addition to its importance for aquaculture, the taxonomic position and the relatively small genome size of sea bream makes it a prospective model for evolutionary biology and comparative genomics. However, so far, no large-scale analysis of gene expression exists for this species. In the present study, gene expression was analyzed in gilthead sea bream during early development, a significant period in the determination of quantitative traits and therefore of considerable interest for aquaculture. Synexpression groups expressed primarily early and late in development were determined and were composed of both known and novel genes. Furthermore, it was possible to identify stress response genes induced by cortisol injections using the cDNA microarray generated. The creation of gene expression profiles for sea bream by microarray hybridization will accelerate identification of candidate genes involved in multifactorial traits and certain regulatory pathways and will also contribute to a better understanding of the genetic background of fish physiology, which may help to improve aquaculture practices.

scholarly journals Unraveling patterns of disrupted gene expression across a complex tissue

2021 ◽  
Author(s):  
Kelsie E Hunnicutt ◽  
Jeffrey M Good ◽  
Erica L Larson
Keyword(s):  
Gene Expression ◽  
Evolutionary Biology ◽  
Large Scale ◽  
Expression Profiles ◽  
Cell Types ◽  
Tissue Expression ◽  
Network Evolution ◽  
F1 Hybrids ◽  
Cellular Composition ◽  
Mus Musculus Musculus

Whole tissue RNASeq is the standard approach for studying gene expression divergence in evolutionary biology and provides a snapshot of the comprehensive transcriptome for a given tissue. However, whole tissues consist of diverse cell types differing in expression profiles, and the cellular composition of these tissues can evolve across species. Here, we investigate the effects of different cellular composition on whole tissue expression profiles. We compared gene expression from whole testes and enriched spermatogenesis populations in two species of house mice, Mus musculus musculus and M. m. domesticus, and their sterile and fertile F1 hybrids, which differ in both cellular composition and regulatory dynamics. We found that cellular composition differences skewed expression profiles and differential gene expression in whole testes samples. Importantly, both approaches were able to detect large-scale patterns such as disrupted X chromosome expression although whole testes sampling resulted in decreased power to detect differentially expressed genes. We encourage researchers to account for histology in RNASeq and consider methods that reduce sample complexity whenever feasible. Ultimately, we show that differences in cellular composition between tissues can modify expression profiles, potentially altering inferred gene ontological processes, insights into gene network evolution, and processes governing gene expression evolution.

scholarly journals Dynamics of gene expression in single root cells ofA. thaliana

2018 ◽  
Author(s):  
Ken Jean-Baptiste ◽  
José L. McFaline-Figueroa ◽  
Cristina M. Alexandre ◽  
Michael W. Dorrity ◽  
Lauren Saunders ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Cell Type ◽  
Specific Expression ◽  
Root Cells ◽  
Cell Lineages ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTSingle-cell RNA-seq can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach toA. thalianaroot cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single-cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single-cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution.

scholarly journals The single-cell eQTLGen consortium

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
MGP van der Wijst ◽  
DH de Vries ◽  
HE Groot ◽  
G Trynka ◽  
CC Hon ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Sequencing Data ◽  
Scale Population ◽  
Trait Locus ◽  
Different Cell Types ◽  
Affect Gene Expression

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

scholarly journals Bulk and Single-Cell Transcriptomics Identify Tobacco-Use Disparity in Lung Gene Expression of ACE2, the Receptor of 2019-nCov

2020 ◽  
Author(s):  
Guoshuai Cai
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Cell Types ◽  
Smoking History ◽  
Normal Lung ◽  
Specific Cell ◽  
Susceptible Population ◽  
Former Smokers ◽  
Ace2 Gene

In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed four large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn’t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker’s lung compared to non-smoker’s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

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.

scholarly journals Nuclear RNA-seq of single neurons reveals molecular signatures of activation

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Benjamin Lacar ◽  
Sara B. Linker ◽  
Baptiste N. Jaeger ◽  
Suguna Rani Krishnaswami ◽  
Jerika J. Barron ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Granule Cells ◽  
Mapk Pathway ◽  
Cell Types ◽  
Brain Regions ◽  
Activation Patterns ◽  
Environment Exposure

Abstract Single-cell sequencing methods have emerged as powerful tools for identification of heterogeneous cell types within defined brain regions. Application of single-cell techniques to study the transcriptome of activated neurons can offer insight into molecular dynamics associated with differential neuronal responses to a given experience. Through evaluation of common whole-cell and single-nuclei RNA-sequencing (snRNA-seq) methods, here we show that snRNA-seq faithfully recapitulates transcriptional patterns associated with experience-driven induction of activity, including immediate early genes (IEGs) such as Fos, Arc and Egr1. SnRNA-seq of mouse dentate granule cells reveals large-scale changes in the activated neuronal transcriptome after brief novel environment exposure, including induction of MAPK pathway genes. In addition, we observe a continuum of activation states, revealing a pseudotemporal pattern of activation from gene expression alone. In summary, snRNA-seq of activated neurons enables the examination of gene expression beyond IEGs, allowing for novel insights into neuronal activation patterns in vivo.

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