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A whole-tissue RNA-seq toolkit for organism-wide studies of gene expression with PME-seq.

ABSTRACT: The immune system operates at the scale of the whole organism in mammals. We currently lack experimental approaches to systematically track and study organism-wide molecular processes in mice. Here we describe an integrated toolkit for measuring gene expression in whole tissues, 3-prime mRNA extension sequencing, that is applicable to most mouse organs and any mouse model of interest. Further, the methods of RNA-seq described in this protocol are broadly applicable to other sample types beyond whole organs, such as tissue samples or isolated cell populations. We report procedures to collect, store and lyse a dozen organ types using conditions compatible with the extraction of high-quality RNA. In addition, we detail protocols to perform high-throughput and low-cost RNA extraction and sequencing, as well as downstream data analysis. The protocol takes 5 d to process 384 mouse organs from collecting tissues to obtaining raw sequencing data, with additional time required for data analysis and mining. The protocol is accessible to individuals with basic skills in (i) mouse perfusion and dissection for sample collection and (ii) computation using Unix and R for data analysis. Overall, the methods presented here fill a gap in our toolbox for studying organism-wide processes in immunology and physiology.

SUBMITTER: Pandey S

PROVIDER: S-EPMC7872736 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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A whole-tissue RNA-seq toolkit for organism-wide studies of gene expression with PME-seq.

Pandey Surya S Takahama Michihiro M Gruenbaum Adam A Zewde Makda M Cheronis Katerina K Chevrier Nicolas N

Nature protocols 20200219 4

The immune system operates at the scale of the whole organism in mammals. We currently lack experimental approaches to systematically track and study organism-wide molecular processes in mice. Here we describe an integrated toolkit for measuring gene expression in whole tissues, 3-prime mRNA extension sequencing, that is applicable to most mouse organs and any mouse model of interest. Further, the methods of RNA-seq described in this protocol are broadly applicable to other sample types beyond w ...[more]

PMID: 32076350

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Project description:Measuring allelic RNA expression ratios is a powerful approach for detecting cis-acting regulatory variants, RNA editing, loss of heterozygosity in cancer, copy number variation, and allele-specific epigenetic gene silencing. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying allelic expression imbalance (AEI), but numerous factors bias allelic RNA ratio measurements. Here, we compare RNA-Seq allelic ratios measured in nine different human brain regions with a highly sensitive and accurate SNaPshot measure of allelic RNA ratios, identifying factors affecting reliable allelic ratio measurement. Accounting for these factors, we subsequently surveyed the variability of RNA editing across brain regions and across individuals.We find that RNA-Seq allelic ratios from standard alignment methods correlate poorly with SNaPshot, but applying alternative alignment strategies and correcting for observed biases significantly improves correlations. Deploying these methods on a transcriptome-wide basis in nine brain regions from a single individual, we identified genes with AEI across all regions (SLC1A3, NHP2L1) and many others with region-specific AEI. In dorsolateral prefrontal cortex (DLPFC) tissues from 14 individuals, we found evidence for frequent regulatory variants affecting RNA expression in tens to hundreds of genes, depending on stringency for assigning AEI. Further, we find that the extent and variability of RNA editing is similar across brain regions and across individuals.These results identify critical factors affecting allelic ratios measured by RNA-Seq and provide a foundation for using this technology to screen allelic RNA expression on a transcriptome-wide basis. Using this technology as a screening tool reveals tens to hundreds of genes harboring frequent functional variants affecting RNA expression in the human brain. With respect to RNA editing, the similarities within and between individuals leads us to conclude that this post-transcriptional process is under heavy regulatory influence to maintain an optimal degree of editing for normal biological function.

Project description:BackgroundEarly application of second-generation sequencing technologies to transcript quantitation (RNA-seq) has hinted at a vast mammalian transcriptome, including transcripts from nearly all known genes, which might be fully measured only by ultradeep sequencing. Subsequent studies suggested that low-abundance transcripts might be the result of technical or biological noise rather than active transcripts; moreover, most RNA-seq experiments did not provide enough read depth to generate high-confidence estimates of gene expression for low-abundance transcripts. As a result, the community adopted several heuristics for RNA-seq analysis, most notably an arbitrary expression threshold of 0.3 - 1 FPKM for downstream analysis. However, advances in RNA-seq library preparation, sequencing technology, and informatic analysis have addressed many of the systemic sources of uncertainty and undermined the assumptions that drove the adoption of these heuristics. We provide an updated view of the accuracy and efficiency of RNA-seq experiments, using genomic data from large-scale studies like the ENCODE project to provide orthogonal information against which to validate our conclusions.ResultsWe show that a human cell's transcriptome can be divided into active genes carrying out the work of the cell and other genes that are likely the by-products of biological or experimental noise. We use ENCODE data on chromatin state to show that ultralow-expression genes are predominantly associated with repressed chromatin; we provide a novel normalization metric, zFPKM, that identifies the threshold between active and background gene expression; and we show that this threshold is robust to experimental and analytical variations.ConclusionsThe zFPKM normalization method accurately separates the biologically relevant genes in a cell, which are associated with active promoters, from the ultralow-expression noisy genes that have repressed promoters. A read depth of twenty to thirty million mapped reads allows high-confidence quantitation of genes expressed at this threshold, providing important guidance for the design of RNA-seq studies of gene expression. Moreover, we offer an example for using extensive ENCODE chromatin state information to validate RNA-seq analysis pipelines.

Dataset Information

A whole-tissue RNA-seq toolkit for organism-wide studies of gene expression with PME-seq.

Publications

A whole-tissue RNA-seq toolkit for organism-wide studies of gene expression with PME-seq.

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