scholarly journals Massively parallel RNA device engineering in mammalian cells with RNA-Seq

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Joy S. Xiang ◽  
Matias Kaplan ◽  
Peter Dykstra ◽  
Michaela Hinks ◽  
Maureen McKeague ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Cell Types ◽  
Massively Parallel ◽  
Rna Seq ◽  
High Activation ◽  
Conserved Sequence ◽  
Regulatory Processes ◽  
Basal Expression ◽  
Wide Range ◽  
Gene Regulatory

Abstract Synthetic RNA-based genetic devices dynamically control a wide range of gene-regulatory processes across diverse cell types. However, the limited throughput of quantitative assays in mammalian cells has hindered fast iteration and interrogation of sequence space needed to identify new RNA devices. Here we report developing a quantitative, rapid and high-throughput mammalian cell-based RNA-Seq assay to efficiently engineer RNA devices. We identify new ribozyme-based RNA devices that respond to theophylline, hypoxanthine, cyclic-di-GMP, and folinic acid from libraries of ~22,700 sequences in total. The small molecule responsive devices exhibit low basal expression and high activation ratios, significantly expanding our toolset of highly functional ribozyme switches. The large datasets obtained further provide conserved sequence and structure motifs that may be used for rationally guided design. The RNA-Seq approach offers a generally applicable strategy for developing broad classes of RNA devices, thereby advancing the engineering of genetic devices for mammalian systems.

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 Characterization of rare spindle and root cell transcriptional profiles in the stria vascularis of the adult mouse cochlea

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shoujun Gu ◽  
Rafal Olszewski ◽  
Ian Taukulis ◽  
Zheng Wei ◽  
Daniel Martin ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Adult Mouse ◽  
Stria Vascularis ◽  
Cell Types ◽  
Rna Seq ◽  
Root Cells ◽  
Transcriptional Profiles ◽  
Gene Regulatory

Abstract The stria vascularis (SV) in the cochlea generates and maintains the endocochlear potential, thereby playing a pivotal role in normal hearing. Knowing transcriptional profiles and gene regulatory networks of SV cell types establishes a basis for studying the mechanism underlying SV-related hearing loss. While we have previously characterized the expression profiles of major SV cell types in the adult mouse, transcriptional profiles of rare SV cell types remained elusive due to the limitation of cell capture in single-cell RNA-Seq. The role of these rare cell types in the homeostatic function of the adult SV remain largely undefined. In this study, we performed single-nucleus RNA-Seq on the adult mouse SV in conjunction with sample preservation treatments during the isolation steps. We distinguish rare SV cell types, including spindle cells and root cells, from other cell types, and characterize their transcriptional profiles. Furthermore, we also identify and validate novel specific markers for these rare SV cell types. Finally, we identify homeostatic gene regulatory networks within spindle and root cells, establishing a basis for understanding the functional roles of these cells in hearing. These novel findings will provide new insights for future work in SV-related hearing loss and hearing fluctuation.

scholarly journals Gene families with stochastic exclusive gene choice underlie cell adhesion in mammalian cells

2020 ◽  
Author(s):  
Mikhail Iakovlev ◽  
Simone Faravelli ◽  
Attila Becskei
Keyword(s):  
Cell Adhesion ◽  
Mammalian Cells ◽  
Gene Families ◽  
Cell Types ◽  
Cell Receptor ◽  
Odorant Receptors ◽  
Rna Seq ◽  
Receptor Isoforms ◽  
Multiple Cell ◽  
Large Repertoire

ABSTRACTExclusive stochastic gene choice combines precision with diversity. This regulation enables most T-cells to express exactly one T-cell receptor isoform chosen from a large repertoire, and to react precisely against diverse antigens. Some cells express two receptor isoforms, revealing the stochastic nature of this process. A similar regulation of odorant receptors and protocadherins enable cells to recognize odors and confer individuality to cells in neuronal interaction networks, respectively. We explored whether genes in other families are expressed exclusively by analyzing single cell RNA-seq data with a simple metric. Chromosomal segments and families are more likely to express genes concurrently than exclusively, possibly due to the evolutionary and biophysical aspects of shared regulation. Nonetheless, gene families with exclusive gene choice were detected in multiple cell types, most of them are membrane proteins involved in ion transport and cell adhesion, suggesting the coordination of these two functions. Thus, stochastic exclusive expression extends beyond the prototypical families, permitting precision in gene choice to be combined with the diversity of intercellular interactions.

scholarly journals An evolutionary-conserved Wnt3/β-catenin/Sp5 feedback loop restricts head organizer activity in Hydra

2018 ◽  
Author(s):  
Matthias C. Vogg ◽  
Leonardo Beccari ◽  
Laura Iglesias Ollé ◽  
Christine Rampon ◽  
Sophie Vriz ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Feedback Loop ◽  
Rna Seq ◽  
Temporal Gene Expression ◽  
Basal Expression ◽  
Head Formation ◽  
Organizer Activity ◽  
Head Regeneration ◽  
Expression In Mammalian Cells ◽  
Head Inhibitor

AbstractThe Hydra polyp regenerates its head by transforming the gastric tissue below the wound into a head organizer made of two antagonistic cross-reacting components. The activator, previously characterized as Wnt3, drives apical differentiation by acting locally and auto-catalytically. The uncharacterized inhibitor, produced under the control of the activator, prevents ectopic head formation. By crossing RNA-seq data obtained in a β-catenin(RNAi) screen performed in planarians and a quantitative analysis of positional and temporal gene expression in Hydra, we identified Sp5 as a transcription factor that fulfills the head inhibitor properties: a Wnt/β-catenin inducible expression, a graded apical-to-basal expression, a sustained up-regulation during head regeneration, a multi-headed phenotype when knocked-down, a repressing activity on Wnt3 expression. In mammalian cells, Hydra and zebrafish Sp5 repress Wnt3 promoter activity while Hydra Sp5 also auto-activates its expression, possibly via β-catenin and/or Tcf/Lef1 interaction. This work identifies Sp5 as a novel potent feedback loop inhibitor of Wnt/β-catenin signaling across eumetazoans.

scholarly journals Denisovan introgression has shaped the immune system of present-day Papuans

2020 ◽  
Author(s):  
Davide M. Vespasiani ◽  
Guy S. Jacobs ◽  
Nicolas Brucato ◽  
Murray P. Cox ◽  
Irene Gallego Romero
Keyword(s):  
Phenotypic Diversity ◽  
Cell Types ◽  
Sister Group ◽  
Regulatory Elements ◽  
Modern Human ◽  
Evolutionary Significance ◽  
Regulatory Processes ◽  
Wide Range ◽  
Archaic Admixture ◽  
Regulatory Effects

AbstractModern humans have substantially admixed with multiple archaic hominins. New Guineans, in particular, owe up to 5% of their genome to Denisovans, a sister group to Neanderthals, whose remains have only been identified in Siberia and Tibet. Unfortunately, the biological and evolutionary significance of these events remain poorly understood. Here we investigate the function of archaic alleles of both Denisovan and Neanderthal ancestry characterised within a previously published set of 72 genomes from individuals of Papuan genetic ancestry living in the island of New Guinea. By comparing the distribution of archaic and modern human variants, we are able to assess the consequences of archaic admixture across a multitude of different cell types and functional elements. We find that archaic alleles are often located within cis-regulatory elements and transcribed regions of the genome, suggesting that they are actively involved in a wide range of cellular regulatory processes. We identify 39,269 high-confidence Denisovan variants that fall within annotated cis-regulatory elements and have the potential to alter the affinity of multiple transcription factors to their cognate DNA motifs, highlighting a likely mechanism by which introgressed DNA can impact phenotypes in present-day humans. Additionally, we detect a consistent signal across Denisovan variants of strong involvement in immune-related processes. Lastly, we show how such regulatory effects might underlie some of the observed gene expression differences between multiple Indonesian populations carrying varying amount of Denisovan DNA. Together, these data provide support for the hypothesis that, despite their broadly deleterious nature, archaic alleles actively contribute to modern human phenotypic diversity and might have facilitated early adaptation to non-African environments.

scholarly journals SPORTS1.0: A Tool for Annotating and Profiling Non-coding RNAs Optimized for rRNA- and tRNA-derived Small RNAs

2018 ◽  
Author(s):  
Junchao Shi ◽  
Eun-A Ko ◽  
Kenton M. Sanders ◽  
Qi Chen ◽  
Tong Zhou
Keyword(s):  
Cell Types ◽  
Mouse Cell ◽  
Rapid Expansion ◽  
Rna Modification ◽  
Rna Seq ◽  
Additional Species ◽  
Wide Range ◽  
Non Coding Rnas ◽  
Nucleolar Rna ◽  
Mismatch Rate

AbstractHigh-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), Piwi-interacting RNA (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipeline optimized for rRNA- and tRNA- derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users’ input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.

scholarly journals ASAP: A web-based platform for the analysis and interactive visualization of single-cell RNA-seq data

2016 ◽  
Author(s):  
Vincent Gardeux ◽  
Fabrice David ◽  
Adrian Shajkofci ◽  
Petra C Schwalie ◽  
Bart Deplancke
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Complete Analysis ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Web Based ◽  
Wide Range

AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet, these groups often lack the expertise to handle complex scRNA-seq data sets.ResultsWe developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering, and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types.AvailabilityThe tool is freely available at http://[email protected]

scholarly journals SAME-clustering: Single-cell Aggregated Clustering via Mixture Model Ensemble

2019 ◽  
Author(s):  
Ruth Huh ◽  
Yuchen Yang ◽  
Yuchao Jiang ◽  
Yin Shen ◽  
Yun Li
Keyword(s):  
Single Cell ◽  
Mixture Model ◽  
Single Cells ◽  
Cell Types ◽  
Rna Seq ◽  
Model Ensemble ◽  
Clustering Ensemble ◽  
Number Of Clusters ◽  
Wide Range ◽  
Level Cluster

ABSTRACTClustering is an essential step in the analysis of single cell RNA-seq (scRNA-seq) data to shed light on tissue complexity including the number of cell types and transcriptomic signatures of each cell type. Due to its importance, novel methods have been developed recently for this purpose. However, different approaches generate varying estimates regarding the number of clusters and the single-cell level cluster assignments. This type of unsupervised clustering is challenging and it is often times hard to gauge which method to use because none of the existing methods outperform others across all scenarios. We present SAME-clustering, a mixture model-based approach that takes clustering solutions from multiple methods and selects a maximally diverse subset to produce an improved ensemble solution. We tested SAME-clustering across 15 scRNA-seq datasets generated by different platforms, with number of clusters varying from 3 to 15, and number of single cells from 49 to 32,695. Results show that our SAME-clustering ensemble method yields enhanced clustering, in terms of both cluster assignments and number of clusters. The mixture model ensemble clustering is not limited to clustering scRNA-seq data and may be useful to a wide range of clustering applications.

scholarly journals ENDOGENOUS RETROVIRUSES AS GENETIC MODULES THAT SHAPE THE GENOME REGULATORY NETWORKS DURING EVOLUTION

2018 ◽  
Keyword(s):  
Regulatory Networks ◽  
Gene Activation ◽  
Cell Types ◽  
Regulatory Elements ◽  
Endogenous Retroviruses ◽  
Protein Coding ◽  
Transcriptional Enhancers ◽  
Wide Range ◽  
Gene Regulatory Elements ◽  
Gene Regulatory

Endogenous retroviruses (ERV) are the descendants of exogenous retroviruses that integrated into the germ cells genome, fixed and became inheritable. ERVs have evolved transcriptional enhancers and promoters that allow their replication in a wide range of tissue. Because ERVs comprise the regulatory elements it could be assume that ERVs capable to shape and reshape genomic regulatory networks by inserting their promoters and enhancers in new genomic loci upon retrotransposition. Thus retroransposition events can build new regulatory regions and lead to a new pattern of gene activation in the cell. In this review we summarize evidence which revealed that ERVs provide a plethora of novel gene regulatory elements, including tissue specific promoters and enhancers for protein-coding genes or long noncoding RNAs in a wide range of cell types. The accumulated findings support the hypothesis that the ERVs have rewired the gene regulatory networks and act as a major source of genomic regulatory innovation during evolution.

scholarly journals SAME-clustering: Single-cell Aggregated Clustering via Mixture Model Ensemble

2019 ◽  
Vol 48 (1) ◽  
pp. 86-95 ◽  
Author(s):  
Ruth Huh ◽  
Yuchen Yang ◽  
Yuchao Jiang ◽  
Yin Shen ◽  
Yun Li
Keyword(s):  
Single Cell ◽  
Mixture Model ◽  
Single Cells ◽  
Cell Types ◽  
Rna Seq ◽  
Model Ensemble ◽  
Clustering Ensemble ◽  
Number Of Clusters ◽  
Wide Range ◽  
Level Cluster

Abstract Clustering is an essential step in the analysis of single cell RNA-seq (scRNA-seq) data to shed light on tissue complexity including the number of cell types and transcriptomic signatures of each cell type. Due to its importance, novel methods have been developed recently for this purpose. However, different approaches generate varying estimates regarding the number of clusters and the single-cell level cluster assignments. This type of unsupervised clustering is challenging and it is often times hard to gauge which method to use because none of the existing methods outperform others across all scenarios. We present SAME-clustering, a mixture model-based approach that takes clustering solutions from multiple methods and selects a maximally diverse subset to produce an improved ensemble solution. We tested SAME-clustering across 15 scRNA-seq datasets generated by different platforms, with number of clusters varying from 3 to 15, and number of single cells from 49 to 32 695. Results show that our SAME-clustering ensemble method yields enhanced clustering, in terms of both cluster assignments and number of clusters. The mixture model ensemble clustering is not limited to clustering scRNA-seq data and may be useful to a wide range of clustering applications.

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