Comparison of whole transcript and 3’ RNA Sequencing methods
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE116949 | GEO | 2018/10/24
REPOSITORIES: GEO
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Project description:Here we present a performance evaluation of three different plate-based scRNA-seq protocols. Two commercially available kits; NEBNext Single Cell/ Low Input RNA Library Prep Kit (NEB), SMART-seq HT kit (Takara), and non-commercially available protocol Genome & transcriptome sequencing (G&T). We evaluated each kit based upon sensitivity, reproducibility, ease of use and price point per cell. This evaluation is specifically relevant for implementation of scRNA-seq in e.g. diagnostics, requiring high sensitivity in regards of gene detection, high reproducibility between samples, minimum hands on time and smooth sample preparation for technicians, as well as keeping a reasonable price point per patient. G&T protocol delivered the highest detection of genes per single cell, at the absolute lowest price point. Takara's kit delivered likewise high gene detection per single cell, and high reproducibility between sample, however at the absolute highest price points. Here we present pros and cons of each protocol, sequencing breast cancer cell line T47D.
2020-08-01 | GSE155506 | GEO
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Project description:Comparison of whole transcript and 3’ RNA Sequencing methods
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Project description:Background: ATAC-seq is widely used to measure the chromatin accessibility and identify the open chromatin regions (OCRs). OCRs usually indicate the active regulatory elements in the genome and are directly associated with gene regulatory networks. Identification of differential accessibility regions (DARs) between different biological conditions is critical to measure the differential activity of regulatory elements. Differential analysis of ATAC-seq shares many similarities to differential expression analysis of RNA-seq data. However, the distribution of ATAC-seq signal is different from RNA-seq data, and higher sensitivity is desired for DARs identification. Many different tools can be used to perform differential analysis of ATAC-seq data, but a comprehensive comparison and benchmarking of these methods is still missing. Methods: Here, we used simulated datasets to systematically measure the sensitivity and specificity of 6 different methods. We further discussed the statistical and signal density cutoff in the differential analysis of ATAC-seq by applying to real data. Batch-effect is very common in high-throughput sequencing experiments. Results: We illustrated that batch-effect correction can dramatically improve the sensitivity in differential analysis of ATAC-seq data. Finally, we developed an easily usable package, BeCorrect, to perform batch-effort correction for visualizing corrected ATAC-seq signals on a genome browser. Conclusions: It is important to use PCA to check the samples distribution, and the Remove Unwanted Variation strategy can be used to correct the data to improve the sensitivity when strong batch effects are found in the data. Finally, BeCorrect can be used to correct the batch-effect of ATAC-seq data signal based on DARs analysis, and generate a proper visualization on a genome browser.
2020-06-29 | GSE131144 | GEO
Project description:Peripheral blood is an accessible and informative source of transcriptomal information for many human disease and pharmacogenomic studies. While there can be significant advantages to analyzing RNA isolated from whole blood, particularly in clinical studies, the preparation of samples for microarray analysis is complicated by the need to minimize artifacts associated with highly abundant globin RNA transcripts. The impact of globin RNA transcripts on expression profiling data can potentially be reduced by using RNA preparation and labeling methods that remove or block globin RNA during the microarray assay. We compared four different methods for preparing microarray hybridization targets from human whole blood collected in PAXGene tubes. Three of the methods utilized the Affymetrix one-cycle cDNA synthesis/in vitro transcription protocol but varied treatment of input RNA as follows: i. no treatment; ii. treatment with GLOBINclear; or iii. treatment with globin PNA oligos. In the fourth method cDNA targets were prepared with the Ovation amplification and labeling system. Results: We find that microarray targets generated with labeling methods that reduce globin mRNA levels or minimize the impact of globin transcripts during hybridization detect more transcripts in the microarray assay compared with the standard Affymetrix method. Comparison of microarray results with quantitative PCR analysis of a panel of genes from the NF-kappa B pathway shows good correlation of transcript measurements produced with all four target preparation methods, although method-specific differences in overall correlation were observed. The impact of freezing blood collected in PAXGene tubes on data reproducibility was also examined. Expression profiles show little or no difference when RNA is extracted from either fresh or frozen blood samples. Conclusion: RNA preparation and labeling methods designed to reduce the impact of globin mRNA transcripts can significantly improve the sensitivity of the DNA microarray expression profiling assay for whole blood samples. While blockage of globin transcripts during first strand cDNA synthesis with globin PNAs resulted in the best overall performance in this study, we conclude that selection of a protocol for expression profiling studies in blood should depend on several factors, including implementation requirements of the method and study design. RNA isolated from either freshly collected or frozen blood samples stored in PAXGene tubes can be used without altering gene expression profiles. Keywords: Whole Blood, Protocol Variation, Expression Profiling, Microarray, globin, PAXGene, PNA
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Project description:Background & aims: MicroRNAs (miRNAs) encapsulated in EVs are potential diagnostic and prognostic biomarkers. However, discrepancies on miRNA patterns and their validation are still frequent due to differences in sample origin, EVs isolation, miRNA extraction and sequencing methods. Selecting appropriate EVs isolation methods is therefore a critical step for miRNA-based biomarker discovery. The aim of the present study is to find the most suitable EVs isolation method for miRNAs sequencing adequate for clinical application. Material & Methods EVs were isolated by Size Exclusion Chromatography (SEC), iodixanol gradients (GRAD) and the combination of both (SEC+GRAD), using the same plasma sample, in triplicate isolation assays. Isolated EVs were characterized and RNA was extracted. Three different protocols for miRNA library preparation were compared (NEBNext, NEXTFlex and SMARTer smRNA-seq) and miRNAs encapsulated on EVs were sequenced using NextSeq 500 system (Illumina). Finally, the yield, abundance and diversity of miRNAs using the three different EVs isolation protocols were analyzed and compared between them. Results The majority of lipoproteins, total cholesterol and plasma proteins were removed from the EVs-containing fractions by using SEC, GRAD, and SEC+GRAD. SEC method recovered a larger amount of EVs followed by GRAD and SEC+GRAD, while GRAD and SEC+GRAD yielded the purest vesicles. NEBNext was the library preparation kit that showed the highest reproducibility among replicas, higher number of reads corresponding to miRNAs and more different miRNAs, followed by NEXTFlex and SMARTer smRNA-seq. GRAD method showed the highest reproducibility among replicas, a higher number of reads corresponding to miRNAs and more different miRNAs, followed by SEC and SEC+GRAD methods. Conclusions These results render the GRAD method to isolate EVs as one of the most appropriate to detect miRNAs from Evs.
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