Dataset Information

Quality assessment and control of tissue specific RNA-seq libraries of Drosophila transgenic RNAi models

ABSTRACT: Next-generation RNA-sequencing (RNA-seq) is rapidly emerging as the technology of choice for whole-transcriptome studies. However, RNA-seq is not a bias free technique. It requires large amounts of RNA and library preparation can introduce multiple artifacts, compounded by problems from later stages in the process. Nevertheless, RNA-seq is increasingly used in multiple studies, including the characterization of tissue-specific transcriptomes from invertebrate models of human disease. The generation of samples in this context is complex, involving the establishment of mutant strains and the delicate contamination prone process of dissecting the target tissue. Moreover, in order achieve the required amount of RNA, multiple samples need to be pooled. Such datasets pose extra challenges due to the large variability that may occur between similar pools, mostly due to the presence of cells from surrounding tissues. Therefore, in addition to standard quality control of RNA-seq data, analytical procedures for control of 'biological quality’ are critical for successful comparison of gene expression profiles. In this study, the transcriptome of the central nervous system of a Drosophila transgenic strain with neuronal-specific RNAi of a ubiquitous gene was profiled using RNA-seq. While conducting our investigation, we observed the existence of an unusual variance in the libraries and showed that the expression profile of a small panel of genes was sufficient to identify libraries with low levels of contamination from neighboring tissues, enabling the selection of a robust dataset for differential expression analysis. We additionally analyzed the potential of profiling a complex tissue to identify cell-type specific changes in response to target gene down-regulation. Finally, we demonstrated that trimming 5’ ends of reads decreases nucleotide frequency biases, increasing the coverage of protein coding genes with a potential positive impact in the incurrence of sampling errors.

ORGANISM(S): Drosophila melanogaster

PROVIDER: GSE54724 | GEO | 2014/04/01

SECONDARY ACCESSION(S): PRJNA243075

REPOSITORIES: GEO

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Project description:Next-generation RNA-sequencing (RNA-seq) is rapidly emerging as the technology of choice for whole-transcriptome studies. However, RNA-seq is not a bias free technique. It requires large amounts of RNA and library preparation can introduce multiple artifacts, compounded by problems from later stages in the process. Nevertheless, RNA-seq is increasingly used in multiple studies, including the characterization of tissue-specific transcriptomes from invertebrate models of human disease. The generation of samples in this context is complex, involving the establishment of mutant strains and the delicate contamination prone process of dissecting the target tissue. Moreover, in order achieve the required amount of RNA, multiple samples need to be pooled. Such datasets pose extra challenges due to the large variability that may occur between similar pools, mostly due to the presence of cells from surrounding tissues. Therefore, in addition to standard quality control of RNA-seq data, analytical procedures for control of 'biological quality’ are critical for successful comparison of gene expression profiles. In this study, the transcriptome of the central nervous system of a Drosophila transgenic strain with neuronal-specific RNAi of a ubiquitous gene was profiled using RNA-seq. While conducting our investigation, we observed the existence of an unusual variance in the libraries and showed that the expression profile of a small panel of genes was sufficient to identify libraries with low levels of contamination from neighboring tissues, enabling the selection of a robust dataset for differential expression analysis. We additionally analyzed the potential of profiling a complex tissue to identify cell-type specific changes in response to target gene down-regulation. Finally, we demonstrated that trimming 5’ ends of reads decreases nucleotide frequency biases, increasing the coverage of protein coding genes with a potential positive impact in the incurrence of sampling errors. Central Nervous System profiles of third instar larvae WT (N=6), C24 (N=7), and X7/C24 D. melanogaster were generated by sequencing using the Illumina Hiseq2000.

Project description:Purpose: Characterization of murine innate lymphoid cells (ILC) by next generation expression analysis Methods: Lymph node cells from Rorc(gt)-GFP reporter mice were independently sorted three times by flow cytometry to isolate ILC1, ILC2, ILC3 and lymphoid tissue inducer (LTi) cells. Subsequently RNA was prepared (Rneasy Micro Kit, Qiagen) and subjected to cDNA synthesis by using the SMART Seq v4 Ultra Low Input RNA Kit (Takara Bio) according to the manufacturer's protocol. Library preparation was performed with Nextera XT DNA Library Prep Kit (Illumina) and next generation sequencing was carried out on an Illumina HiSeq2500 using 50 bp single reads. Adapter contamination were removed by trim_galore and cutadapt from the sequence reads that passed quality filters (Phred score > 30). Resulting libraries were aligned to the mouse reference genome (GRCm38) using the RNA-seq aligner STAR with default parametrization. Reads aligned to annotated genes were quantified with the HTseq-count program using gene annotations from Ensembl release 81. The determined read counts served as input to DESeq2 for pairwise detection and quantification of differential gene expression. For DESeq2 parametrization we used a beta prior and disabled the Cook distance cutoff filtering. Results: The sequencing depth of our libraries varies between 25.5 and 39.2 million reads. The mapping rates for the libraries were found in the range between 93.9 and 98.0 %. Unsupervised hierarchical clustering of the top 50 differentially expressed genes show a close relationship of the samples within a group and higher similarity between ILC3 and LTi cells whereas ILC1 and ILC2 appear as separated populations. Among the differentially expressed genes we identified already described transcripts like Gzma (ILC1), Klrg1 (ILC2) and Il22 (ILC3, LTi) as well as uncharacterized genes that may play a role for the ILC lineage and function in lymphoid organs. Conclusions: We report a detailed next generation transcriptome analysis of ILC populations from murine lymph nodes. Our analysis helps to define the developmental status of ILC in secondary lymphoid organs, which might represent a precursor state that can be further modulated in tissues.

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Quality assessment and control of tissue specific RNA-seq libraries of Drosophila transgenic RNAi models

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