Dataset Information

Technical Variations in Low-Input RNA-seq Methodologies

ABSTRACT: Transcriptomics data obtained from limiting amounts of mRNA is often noisy, providing primarily qualitative changes in transcript expressions. So far, technical variations arising out of the library preparation protocols have not been adequately characterized at reduced levels of mRNA. Here, we generated sequencing libraries from limiting amounts of mRNA using three amplification-based methods, viz. Smart-seq, DP-seq and CEL-seq, and demonstrated significant technical variations in these libraries. Reduction in mRNA levels led to inefficient amplification of the majority of low to moderately expressed transcripts. Furthermore, stochasticity in primer hybridization and/or enzyme incorporation was magnified during the amplification step resulting in significant distortions in fold changes of the transcripts. Consequently, the majority of the differentially expressed transcripts identified were either high-expressed and/or exhibited high fold changes. High technical variations, which were sequencing depth independent, ultimately masked subtle biological differences mandating the development of improved amplification-based strategies for quantitative transcriptomics from limiting amounts of mRNA.

ORGANISM(S): Mus musculus

PROVIDER: GSE50856 | GEO | 2014/01/13

SECONDARY ACCESSION(S): PRJNA219182

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background: DNA microarrays provide a powerful method for global analysis of gene expression. The application of this technology to specific cell types and tissues, however, is typically limited by small amounts of available mRNA, thereby necessitating amplification. Here we compare microarray results obtained with two different methods of RNA amplification to profile gene expression in the C. elegans larval nervous system. Results: We used the mRNA-tagging strategy to isolate transcripts specifically from C. elegans larval neurons. The WT-Ovation Pico System (WT-Pico) was used to amplify 2 ng of Pan-neural RNA to produce labeled cDNA for microarray analysis. These WT-Pico-derived data were compared to microarray results obtained with a labeled aRNA target generated by two rounds of In Vitro Transcription (IVT) of 25 ng of Pan-neural RNA. WT-Pico results in a higher fraction of Present calls than IVT, a finding consistent with the proposal that DNA-DNA hybridization results in lower mismatch signals than the RNA-DNA heteroduplexes produced by IVT amplification. Microarray data sets from these samples were compared to a Reference profile of all larval cells to identify transcripts with elevated expression in neurons. These results were validated by the high proportion of known neuron-expressed genes detected in these profiles and by promoter-GFP constructs for previously uncharacterized genes in these data sets. Together, the IVT and WT-Pico methods identified 2,173 unique neuron-enriched transcripts. Only about half of these transcripts (1,044), however, are detected as enriched by both IVT and WT-Pico amplification. Conclusion: We show that two different methods of RNA amplification, IVT and WT-Pico, produce valid microarray profiles of gene expression in the C. elegans larval nervous system with a low rate of false positives. However, our results also show that each method of RNA amplification detects a unique subset of bona fide neural-enriched transcripts and thus a wider array of authentic neural genes are identified by the combination of these data sets than by the microarray profiles obtained with either method of RNA amplification alone. With its relative ease of implementation and greater sensitivity, WT-Pico is the preferred method of amplification for cases in which sample RNA is limiting. Keywords: expression profile

Project description:Single-cell whole-transcriptome analysis is a powerful tool for quantifying gene expression heterogeneity in populations of cells. Many techniques have, thus, been recently developed to perform transcriptome sequencing (RNA-Seq) on individual cells. To probe subtle biological variation between samples with limiting amounts of RNA, more precise and sensitive methods are still required. We adapted a previously developed strategy for single-cell RNA-Seq that has shown promise for superior sensitivity and implemented the chemistry in a microfluidic platform for single-cell whole transcriptome analysis. In this approach, single cells are captured and lysed in a microfluidic device, where mRNAs with poly(A) tails are reverse-transcribed into cDNA. Double-stranded cDNA is then collected and sequenced using a next-generation sequencing platform. We prepared 94 libraries consisting of single mouse embryonic cells and technical replicates of extracted RNA and thoroughly characterized the performance of this technology. Microfluidic implementation increased mRNA detection sensitivity as well as improved measurement precision compared with tube-based protocols. With 0.2M reads per cell, we were able to reconstruct a majority of the bulk transcriptome with 10 single cells. We also quantified variation between and within different types of mouse embryonic cells and found that enhanced measurement precision, detection sensitivity, and experimental throughput aided the distinction between biological variability and technical noise. With this work, we validated the advantages of an early approach to single-cell RNA-Seq and showed that the benefits of combining microfluidic technology with high-throughput sequencing will be valuable for large-scale efforts in single-cell transcriptome analysis. We investigated gene expression in mouse embryonic cells using microfluidic-facilitated RNA-Seq to analyze 56 single mouse ES cell (mESC) transcriptomes and 6 single mouse embryonic fibroblast (MEF) transcriptomes. To quantitatively evaluate the sensitivity and precision of our technique, we also sequenced 23 libraries from extracted mESC RNA, representing three sets of technical replicates with varying starting amounts of material.

Dataset Information

Technical Variations in Low-Input RNA-seq Methodologies

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets