Similar Datasets
Project description:RNA high-throughput sequencing for Lupinus micranthus Guss
| PRJNA811775 | ENA
Project description:RNAseq of CLDN6 sorted Epiblast Stem Cells (EpiSCs), WT vs Pbx1-KO lines in mouse embryonic stem cells (ES (2i/LIF and serum/LIF) and EpiSCs, as well as across differentiation (EpiSCs differentiated towards Posterior Primitive Streak (PPS) and Extraembryonic Mesoderm (ExM). Also EpiSCs treated with PD03 (MEK inhibitor) and 6h of WNT stimulation (CHIR).
2024-11-29 | E-MTAB-13618 | biostudies-arrayexpress
Project description:RNAseq was prepared in parallel with ribosome profiling, using the same protocol for cDNA library preparation.
2015-03-01 | E-MTAB-2940 | biostudies-arrayexpress
Project description:Obtaining and performing single-cell RNAseq by aspirating only1-5% of sub-single cell RNA content from individual cells within fresh frozen tissue samples.
2022-09-30 | GSE210115 | GEO
Project description:Diarrhea remains a major cause of death in children. Current diagnostic methods largely rely on stool culture and suffer from low sensitivity and inadequate specificity, often leading to inappropriate treatment. The objective of the present study was to use RNA sequencing (RNAseq) analysis to determine blood transcriptional profiles specific for several common pathogenic bacteria and viruses that cause diarrhea in children. We collected whole blood samples from children in Mexico having diarrhea associated with a single pathogen and without systemic complications. Our RNAseq data suggested that the blood signatures can differentiate children with diarrhea from healthy children either with or without bacterial colonization. Moreover, we detected different expression profiles from bacterial and viral infection, demonstrating for the first time the use of RNAseq to identify the etiology of infectious diarrhea. Whole blood from 207 children including children with diarrhea caused by rotavirus (n=55), E.coli (n=55), Salmonella (n=36), Shigella (n=37) and control children (n=24).
2015-06-04 | E-GEOD-69529 | biostudies-arrayexpress
Project description:Diarrhea remains a major cause of death in children. Current diagnostic methods largely rely on stool culture and suffer from low sensitivity and inadequate specificity, often leading to inappropriate treatment. The objective of the present study was to use RNA sequencing (RNAseq) analysis to determine blood transcriptional profiles specific for several common pathogenic bacteria and viruses that cause diarrhea in children. We collected whole blood samples from children in Mexico having diarrhea associated with a single pathogen and without systemic complications. Our RNAseq data suggested that the blood signatures can differentiate children with diarrhea from healthy children either with or without bacterial colonization. Moreover, we detected different expression profiles from bacterial and viral infection, demonstrating for the first time the use of RNAseq to identify the etiology of infectious diarrhea.
2015-06-04 | GSE69529 | GEO
Project description:Sequencing of 5' and 3'ends and RNA-seq of PROMPT and mRNA molecules from control and exosome-depleted cells. CAGE, 3'TAG and RNAseq library construction from RNA extracted from control and exosome-depleted cells.
2013-07-03 | E-GEOD-48286 | biostudies-arrayexpress
Project description:The presence and relative stability of extracellular RNAs (exRNAs) in biofluids has led to an emerging recognition of their promise as 'liquid biopsies' for diseases. Most prior studies on discovery of exRNAs as disease-specific biomarkers have focused on microRNAs (miRNAs) using technologies such as qRT-PCR and microarrays. The recent application of next-generation sequencing to discovery of exRNA biomarkers has revealed the presence of potential novel miRNAs as well as other RNA species such as tRNAs, snoRNAs, piRNAs and lncRNAs in biofluids. At the same time, the use of RNA sequencing for biofluids poses unique challenges, including low amounts of input RNAs, the presence of exRNAs in different compartments with varying degrees of vulnerability to isolation techniques, and the high abundance of specific RNA species (thereby limiting the sensitivity of detection of less abundant species). Moreover, discovery in human diseases often relies on archival biospecimens of varying age and limiting amounts of samples. In this study, we have tested RNA isolation methods to optimize profiling exRNAs by RNA sequencing in individuals without any known diseases. Our findings are consistent with other recent studies that detect microRNAs and ribosomal RNAs as the major exRNA species in plasma. Similar to other recent studies, we found that the landscape of biofluid microRNA transcriptome is dominated by several abundant microRNAs that appear to comprise conserved extracellular miRNAs. There is reasonable correlation of sets of conserved miRNAs across biological replicates, and even across other data sets obtained at different investigative sites. Conversely, the detection of less abundant miRNAs is far more dependent on the exact methodology of RNA isolation and profiling. This study highlights the challenges in detecting and quantifying less abundant plasma miRNAs in health and disease using RNA sequencing platforms.
| S-EPMC5218574 | biostudies-literature
Project description:Single-cell transcriptomic studies that require intracellular protein staining, rare cell sorting, or inactivation of infectious pathogens are severely limited. This is because current high-throughput single-cell RNA sequencing methods are either incompatible with or necessitate laborious sample preprocessing for paraformaldehyde treatment, a common tissue and cell fixation and preservation technique. Here we present FD-seq (Fixed Droplet RNA sequencing), a high-throughput method for droplet-based RNA sequencing of paraformaldehyde-fixed, permeabilized and sorted single cells. We show that FD-seq preserves the RNA integrity and relative gene expression levels after fixation and permeabilization. Furthermore, FD-seq can detect a higher number of genes and transcripts than methanol fixation. We first apply FD-seq to analyze a rare subpopulation of cells supporting lytic reactivation of the human tumor virus KSHV, and identify TMEM119 as a potential host factor that mediates viral reactivation. Second, we find that infection with the human betacoronavirus OC43 leads to upregulation of pro-inflammatory pathways in cells that are exposed to the virus but fail to express high levels of viral genes. FD-seq thus enables integrating phenotypic with transcriptomic information in rare cell subpopulations, and preserving and inactivating pathogenic samples.
| S-EPMC8463713 | biostudies-literature
Project description:BackgroundSkeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested.MethodsBy using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22).ResultsThe correlation between the sequencing-based method and the other two were rATPas = 0.44 [0.13-0.67], [95% CI], and rmyosin = 0.83 [0.61-0.93], with p = 5.70 × 10-3 and 2.00 × 10-6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads.ConclusionsThis new method ( https://github.com/OlaHanssonLab/PredictFiberType ) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.
| S-EPMC9250227 | biostudies-literature