Whole blood RNA extraction efficiency contributes to variability in RNA sequencing data sets.

Numerous methodologies are used for blood RNA extraction, and large quantitative differences in recovered RNA content are reported. We evaluated three archived data sets to determine how extraction methodologies might influence mRNA and lncRNA sequencing results. The total quantity of RNA recovered /ml of blood affects RNA sequencing by impacting the recovery of weakly expressed mRNA, and lncRNA transcripts. Transcript expression (TPM counts) plotted in relation to transcript size (base pairs, bp) revealed a 30% loss of short to midsized transcripts in some data sets. Quantitative recovery of RNA is of considerable importance, and it should be viewed more judiciously. Transcripts common to the three data sets were subsequently normalized and transcript mean TPM counts and TPM count coefficient of variation (CV) were plotted in relation to increasing transcript size. Regression analysis of mean TPM counts versus transcript size revealed negative slopes in two of the three data sets suggesting a reduction of TPM transcript counts with increasing transcript size. In the third data set, the regression slope line of mRNA transcript TPM counts approximates zero and TPM counts increased in proportion to transcript size over a range of 200 to 30,000 bp. Similarly, transcript TPM count CV values also were uniformly distributed over the range of transcript sizes. In the other data sets, the regression CV slopes increased in relation to transcript size. The recovery of weakly expressed and /or short to midsized mRNA and lncRNA transcripts varies with different RNA extraction methodologies thereby altering the fundamental sequencing relationship between transcript size and TPM counts. Our analysis identifies differences in RNA sequencing results that are dependent upon the quantity of total RNA recovery from whole blood. We propose that incomplete RNA extraction directly impacts the recovery of mRNA and lncRNA transcripts from human blood and speculate these differences contribute to the "batch" effects commonly identified between sequencing results from different archived data sets.

Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data.

BACKGROUND: RNA sequencing analysis focus on the detection of differential gene expression changes that meet a two-fold minimum change between groups. The variability present in RNA sequencing data may obscure the detection of valuable information when specific genes within certain samples display large expression variability. This paper develops methods that apply variance and dispersion estimates to intra-group data to identify genes with expression values that diverge from the group envelope. STRING database analysis of the identified genes characterize gene affiliations involved in physiological regulatory networks that contribute to biological variability. Individuals with divergent gene groupings within network pathways can thereby be identified and judiciously evaluated prior to standard differential analysis. RESULTS: A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to detect potentially divergent "trendlines" for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify the "trendline" profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65-70% of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as a linear trendline. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11-20% of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤1.92 E-15. CONCLUSIONS: This analysis provides a rationale for identifying and characterizing notable gene expression variability within a study group. The identification of highly variable genes and their network associations within specific individuals empowers more judicious inspection of the sample group prior to differential gene expression analysis.

Rapid processing of SARS-CoV-2 containing specimens for direct RT-PCR.

Widespread diagnostic testing is needed to reduce transmission of COVID-19 and manage the pandemic. Effective mass screening requires robust and sensitive tests that reliably detect the SARS-CoV-2 virus, including asymptomatic and pre-symptomatic infections with a low viral count. Currently, the most accurate tests are based on detection of viral RNA by RT-PCR. We developed a method to process COVID-19 specimens that simplifies and increases the sensitivity of viral RNA detection by direct RT-qPCR, performed without RNA purification. In the method, termed Alkaline-Glycol Processing (AG Processing), a SARS-CoV-2-containing biological specimen, such as saliva or a swab-collected suspension, is processed at pH 12.2 to 12.8 for 5 min at room temperature. An aliquot of the AG-processed specimen is used for detection of SARS-CoV-2 RNA by direct RT-qPCR. AG processing effectively lyses viruses and reduces the effect of inhibitors of RT-PCR that are present in biological specimens. The sensitivity of detecting viral RNA using AG processing is on par with methods that include a viral RNA purification step. One copy of SARS-CoV-2 virus per reaction, equivalent to 300 copies per ml of saliva, is detectable in the AG-processed saliva. The LOD is 300 viral copies per ml of initial saliva specimen. AG processing works with saliva specimens or swab specimens collected into Universal Transport Medium, is compatible with heat treatment of saliva, and was confirmed to work with a range of CDC-approved RT-qPCR products and kits. Detection of SARS-CoV-2 RNA using AG processing with direct RT-qPCR provides a reliable and scalable diagnostic test for COVID-19 that can be integrated into a range of workflows, including automated settings.

Coordinated analysis of exon and intron data reveals novel differential gene expression changes.

RNA-Seq expression analysis currently relies primarily upon exon expression data. The recognized role of introns during translation, and the presence of substantial RNA-Seq counts attributable to introns, provide the rationale for the simultaneous consideration of both exon and intron data. We describe here a method for the coordinated analysis of exon and intron data by investigating their relationship within individual genes and across samples, while taking into account changes in both variability and expression level. This coordinated analysis of exon and intron data offers strong evidence for significant differences that distinguish the profiles of the exon-only expression data from the combined exon and intron data. One advantage of our proposed method, called matched change characterization for exons and introns (MEI), is its straightforward applicability to existing archived data using small modifications to standard RNA-Seq pipelines. Using MEI, we demonstrate that when data are examined for changes in variability across control and case conditions, novel differential changes can be detected. Notably, when MEI criteria were employed in the analysis of an archived data set involving polyarthritic subjects, the number of differentially expressed genes was expanded by sevenfold. More importantly, the observed changes in exon and intron variability with statistically significant false discovery rates could be traced to specific immune pathway gene networks. The application of MEI analysis provides a strategy for incorporating the significance of exon and intron variability and further developing the role of using both exons and intron sequencing counts in studies of gene regulatory processes.

Inter-Individual Differences in RNA Levels in Human Peripheral Blood.

Relatively little is known about the range of RNA levels in human blood. This report provides assessment of peripheral blood RNA level and its inter-individual differences in a group of 35 healthy humans consisting of 25 females and 10 males ranging in age from 50 to 89 years. In this group, the average total RNA level was 14.59 µg/ml of blood, with no statistically significant difference between females and males. The individual RNA level ranged from 6.7 to 22.7 µg/ml of blood. In healthy subjects, the repeated sampling of an individual's blood showed that RNA level, whether high or low, was stable. The inter-individual differences in RNA level in blood can be attributed to both, differences in cell number and the amount of RNA per cell. The 3.4-fold range of inter-individual differences in total RNA levels, documented herein, should be taken into account when evaluating the results of quantitative RT-PCR and/or RNA sequencing studies of human blood. Based on the presented results, a comprehensive assessment of gene expression in blood should involve determination of both the amount of mRNA per unit of total RNA (U / ng RNA) and the amount of mRNA per unit of blood (U / ml blood) to assure a thorough interpretation of physiological or pathological relevance of study results.