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Big data from small tissues: extraction of high-quality RNA for RNA-sequencing from different oilseed Brassica seed tissues during seed development.

ABSTRACT: Background:Obtaining high-quality RNA for gene expression analyses from different seed tissues is challenging due to the presence of various contaminants, such as polyphenols, polysaccharides and lipids which interfere with RNA extraction methods. At present, the available protocols for extracting RNA from seeds require high amounts of tissue and are mainly focused on extracting RNA from whole seeds. However, extracting RNA at the tissue level enables more detailed studies regarding tissue specific transcriptomes during seed development. Results:Seeds from heart stage embryo to mature developmental stages of Brassica napus and B. oleracea were sampled for isolation of the embryo, endosperm and seed coat tissues. Ovules and ovary wall tissue were also collected from pre-fertilized buds. Subsequent to testing several RNA extraction methods, modifications applied to E.Z.N.A. Plant RNA and Picopure RNA Isolation kit extraction methods resulted in RNA with high yield and quality. Furthermore, the use of polyvinylpolypyrrolidone for seed coats and endosperm at green stages resulted in high-quality RNA. As a result of the introduced modifications to established RNA extraction methods, the RNA from all the above-mentioned tissues presented clear 28S and 18S bands and high RIN values, ranging from 7.0 to 10.0. The protocols reported in this study are not only suitable for different and challenging seed tissue types, but also enable the extraction of high-quality RNA using only 2 to 3 mg of starting tissue. Conclusions:Here, we present efficient, reproducible and reliable high-quality RNA extraction methods for diverse oilseed Brassica spp reproductive tissue types including pre-fertilization and developing seed tissues for diploid and polyploid species. The high-quality RNA obtained is suitable for RNA-Sequencing and subsequent gene expression analysis.

SUBMITTER: Siles L

PROVIDER: S-EPMC7275424 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Big data from small tissues: extraction of high-quality RNA for RNA-sequencing from different oilseed <i>Brassica</i> seed tissues during seed development.

Siles Laura L Eastmond Peter P Kurup Smita S

Plant methods 20200605

<h4>Background</h4>Obtaining high-quality RNA for gene expression analyses from different seed tissues is challenging due to the presence of various contaminants, such as polyphenols, polysaccharides and lipids which interfere with RNA extraction methods. At present, the available protocols for extracting RNA from seeds require high amounts of tissue and are mainly focused on extracting RNA from whole seeds. However, extracting RNA at the tissue level enables more detailed studies regarding tiss ...[more]

PMID: 32518582

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Project description:BACKGROUND:Utilization of RNA sequencing methods to measure gene expression from archival formalin-fixed paraffin-embedded (FFPE) tumor samples in translational research and clinical trials requires reliable interpretation of the impact of pre-analytical variables on the data obtained, particularly the methods used to preserve samples and to purify RNA. METHODS:Matched tissue samples from 12 breast cancers were fresh frozen (FF) and preserved in RNAlater or fixed in formalin and processed as FFPE tissue. Total RNA was extracted and purified from FF samples using the Qiagen RNeasy kit, and in duplicate from FFPE tissue sections using three different kits (Norgen, Qiagen and Roche). All RNA samples underwent whole transcriptome RNA sequencing (wtRNAseq) and targeted RNA sequencing for 31 transcripts included in a signature of sensitivity to endocrine therapy. We assessed the effect of RNA extraction kit on the reliability of gene expression levels using linear mixed-effects model analysis, concordance correlation coefficient (CCC) and differential analysis. All protein-coding genes in the wtRNAseq and three gene expression signatures for breast cancer were assessed for concordance. RESULTS:Despite variable quality of the RNA extracted from FFPE samples by different kits, all had similar concordance of overall gene expression from wtRNAseq between matched FF and FFPE samples (median CCC 0.63-0.66) and between technical replicates (median expression difference 0.13-0.22). More than half of genes were differentially expressed between FF and FFPE, but with low fold change (median |LFC| 0.31-0.34). Two out of three breast cancer signatures studied were highly robust in all samples using any kit, whereas the third signature was similarly discordant irrespective of the kit used. The targeted RNAseq assay was concordant between FFPE and FF samples using any of the kits (CCC 0.91-0.96). CONCLUSIONS:The selection of kit to purify RNA from FFPE did not influence the overall quality of results from wtRNAseq, thus variable reproducibility of gene signatures probably relates to the reliability of individual gene selected and possibly to the algorithm. Targeted RNAseq showed promising performance for clinical deployment of quantitative assays in breast cancer from FFPE samples, although numerical scores were not identical to those from wtRNAseq and would require calibration.

Dataset Information

Big data from small tissues: extraction of high-quality RNA for RNA-sequencing from different oilseed Brassica seed tissues during seed development.

Publications

Big data from small tissues: extraction of high-quality RNA for RNA-sequencing from different oilseed <i>Brassica</i> seed tissues during seed development.

Similar Datasets

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