TempO-Seq sequencing of Dimethylamine
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1008258 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25732407.fastq.gz | Fastqsanger.gz | |||
| SRR25732408.fastq.gz | Fastqsanger.gz | |||
| SRR25732409.fastq.gz | Fastqsanger.gz | |||
| SRR25732410.fastq.gz | Fastqsanger.gz | |||
| SRR25732411.fastq.gz | Fastqsanger.gz |
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Project description:Comparison of luminal and basal breast cancer cells under acute normoxia and hypoxia. Cells were plated in 96-well plates and incubated 24 hrs under normoxia or hypoxia after which the wells were washed once with cold PBS and lysed using TempO-Seq lysis buffer for 15 min at room temperature. Samples were stored at −80 °C before shipping to BioClavis for whole genome TempO-Seq analysis. For normoxia (NX) 21% oxygen was used and for hypoxia (HX) 1%oxygen was used. Each condition has 3 biological replicates.
2023-06-01 | E-MTAB-12234 | biostudies-arrayexpress
Project description:Comparison of luminal and basal breast cancer cells under chronic normoxia and hypoxia. Cells were plated in 96-well plates and incubated 5 days under normoxia or hypoxia after which the wells were washed once with cold PBS and lysed using TempO-Seq lysis buffer for 15 min at room temperature. Samples were stored at −80 °C before shipping to BioClavis for whole genome TempO-Seq analysis. For normoxia (NX) 21% oxygen was used and for hypoxia (HX) 1%oxygen was used. Each condition has 3 biological replicates.
2023-06-01 | E-MTAB-12233 | biostudies-arrayexpress
Project description:The use of gene expression signatures to classify compounds, identify efficacy or toxicity, and differentiate close analogs relies on the sensitivity of the method to identify modulated genes. We used a novel ligation-based targeted whole transcriptome expression profiling assay, TempO-Seq®, to determine whether previously unreported compound-responsive genes could be identified and incorporated into a broad but specific compound signature. TempO-Seq exhibits 99.6% specificity, single cell sensitivity, and excellent correlation with fold differences measured by RNA-Seq (R2 = 0.9) for 20,629 targets. Unlike many expression assays, TempO-Seq does not require RNA purification, cDNA synthesis, or capture of targeted RNA, and lacks a 3′ end bias. To investigate the sensitivity of the TempO-Seq assay to identify significantly modulated compound-responsive genes, we derived whole transcriptome profiles from MCF-7 cells treated with the histone deacetylase inhibitor Trichostatin A (TSA) and identified more than 9,000 differentially expressed genes. The TSA profile for MCF-7 cells overlapped those for HL-60 and PC-3 cells in the Connectivity Map (cMAP) database, suggesting a common TSA-specific expression profile independent of baseline gene expression. A 43-gene cell-independent TSA signature was extracted from cMAP and confirmed in TempO-Seq MCF-7 data. Additional genes that were not previously reported to be TSA responsive in the cMAP database were also identified. TSA treatment of 5 cell types revealed 1,136 differentially expressed genes in common, including 785 genes not previously reported to be TSA responsive. We conclude that TSA induces a specific expression signature that is consistent across widely different cell types, that this signature contains genes not previously associated with TSA responses, and that TempO-Seq provides the sensitive differential expression detection needed to define such compound-specific, cell-independent, changes in expression.
2017-05-25 | GSE91395 | GEO
Project description:Using the TempO-Seq rat S1500+ platform we performed gene expression analysis of using 63 purified RNA samples from the livers of rats exposed to controls or chemicals that fall into one of five modes of action (MOAs): constitutive androstane receptor/pregnane X receptor (CAR/PXR) activation, aryl hydrocarbon receptor (AhR) activation, peroxisome proliferator-activated receptor-alpha (PPARA) activation, cytotoxicity or DNA damage. The TempO-Seq data generated was used to compare to gene expression data acquired from the same samples run on Affymetrix microarays (GEO: GSE47875) and Illumina RNA-Seq (GEO: GSE55347, SRA:SRP039021).
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Project description:Free radical-initiated peptide sequencing (FRIPS) is a tandem mass spectrometry (MS/MS) technique that generates sequence informative ions via collisionally-initiated radical chemistry. Collision activation (CA) homolytically cleaves an installed radical precursor, initiates radical formation, extensive hydrogen atom transfer, and peptide backbone dissociation. While FRIPS technique shows great promise, when applied to multiply charged derivatized peptide ions, a series of high abundance mass losses are observed which syphon ion abundance from radically generated sequence ions. This loss of ion abundance reduces the sequence coverage generated by FRIPS fragmentation. In this work, we hypothesized that these mass losses were instigated by the ortho-orientation of the radical precursor undergoing facile conversion into five- or six-membered intermediates and that the para-precursor would not undergo this chemistry. To test this assertion, we synthesized para-TEMPO-Bz, conjugated it to these peptides, and collisionally activated each. And indeed, we see the complete elimination of these undesired collisional processes and the significant increase in radical precursor ion abundance. The increase in ion abundance leads to a significant increase in the sequence coverage generated. These results demonstrate that p-TEMPO-Bz significantly improves the performance of positive-ion mode FRIPS and may be a suitable alternative to the currently utilized ortho-TEMPO-Bz-based FRIPS.
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Project description:The experiment investigates the effects of five well-known chemicals on the transcriptome of the HepaRG cell line, a metabolically competent hepatic cell line. The cells were treated individually with an increasing concentrations of aflatoxin B1, benzo[a]pyrene, cyclosporine A, rotenone or trichostatin A at five exposure time points followed by targeted RNA-seq using TempO-Seq technology (the panel of human whole transcriptome). The aim of the study was to explore how and to what extent the point-of-departure (POD) obtained from an in vitro transcriptomics study varied as a function of exposure time.
2023-08-01 | E-MTAB-13168 | biostudies-arrayexpress
Project description:These datasets are associated with the application of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. The approach is fundamentally different than almost all other single-cell transcriptome profiling methods in that it does not rely on reverse transcription of mRNA. Instead, TempO-LINC is a novel combination of sensitive oligonucleotide ligation-based chemistry and highly scalable combinatorial barcoding. In this study, we applied TempO-LINC to profile the transcriptomes of multiple sample types. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies with high data quality.
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Project description:High-throughput transcriptomics has advanced through the introduction of TempO-seq, a targeted alternative to traditional RNA-seq. TempO-seq platforms use 50 nucleotide probes, each specifically designed to target a known transcript, thus allowing for reduced sequencing depth per sample compared with RNA-seq without compromising the accuracy of results. Thus far, studies using the TempO-seq method have relied on existing tools for processing the resulting short read data. However, these tools were originally designed for other data types. While they have been used for processing of early TempO-seq data, they have not been systematically assessed for accuracy or compared to determine an optimal framework for processing and analyzing TempO-seq data. In this work, we re-analyze several publicly available TempO-seq data sets covering a range of experimental designs and use corresponding RNA-seq data sets as a gold standard to rigorously assess accuracy at multiple levels. We compare 6 aligners and 5 normalization methods across various accuracy and performance metrics. Our results demonstrate the overall robust accuracy of the TempO-seq platform, independent of data processing methods. Complex aligners and advanced normalization methods do not appear to have any general advantage over simpler methods when it comes to analyzing TempO-seq data. The reduced complexity of the sequencing space, and the fact that TempO-seq probes are all equal length, appears to reduce the need for elaborate bioinformatic or statistical methods used to address these factors in RNA-seq data.
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Project description:Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on large resources for consumables and bioinformatics. TempO-Seq is a templated, multiplexed RNA-Seq platform that interrogates a panel of sentinel genes representative of genome-wide transcription. Nuances of the technology require proper preprocessing of the data. Various methods have been proposed and compared for normalizing bulk RNA-Seq data, but there has been little to no investigation of how the methods perform on TempO-Seq data. We simulated count data into two groups (treated vs. untreated) at seven-fold change (FC) levels (including no change) using control samples from human HepaRG cells run on TempO-Seq and normalized the data using seven normalization methods. Upper Quartile (UQ) performed the best with regard to maintaining FC levels as detected by a limma contrast between treated vs. untreated groups. For all FC levels, specificity of the UQ normalization was greater than 0.84 and sensitivity greater than 0.90 except for the no change and +1.5 levels. Furthermore, K-means clustering of the simulated genes normalized by UQ agreed the most with the FC assignments [adjusted Rand index (ARI) = 0.67]. Despite having an assumption of the majority of genes being unchanged, the DESeq2 scaling factors normalization method performed reasonably well as did simple normalization procedures counts per million (CPM) and total counts (TCs). These results suggest that for two class comparisons of TempO-Seq data, UQ, CPM, TC, or DESeq2 normalization should provide reasonably reliable results at absolute FC levels ≥2.0. These findings will help guide researchers to normalize TempO-Seq gene expression data for more reliable results.
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