Comparison of Transcriptomic Platforms for Analysis of Whole Blood from Ebola-Infected Cynomolgus Macaques

Abstract

Ebola virus disease (EVD) is a serious illness with mortality rates of 20–90% in various outbreaks. EVD is characterized by robust virus replication and strong host inflammatory response. Analyzing host immune responses has increasingly involved multimodal approaches including transcriptomics to profile gene expression. We studied cynomolgus macaques exposed to Ebola virus Makona via different routes with the intent of comparing RNA-Seq to a NanoString nCounter codeset targeting 769 non-human primate (NHP) genes. RNA-Seq analysis of serial blood samples showed different routes led to the same overall transcriptional response seen in previously reported EBOV-exposed NHP studies. Both platforms displayed a strong correlation in gene expression patterns, including a strong induction of innate immune response genes at early times post-exposure, and neutrophil-associated genes at later time points. A 41-gene classifier was tested in both platforms for ability to cluster samples by infection status. Both NanoString and RNA-Seq could be used to predict relative abundances of circulating immune cell populations that matched traditional hematology. This demonstrates the complementarity of RNA-Seq and NanoString. Moreover, the development of an NHP-specific NanoString codeset should augment studies of filoviruses and other high containment infectious diseases without the infrastructure requirements of RNA-Seq technology.

A conserved transcriptional response to intranasal Ebola virus exposure in nonhuman primates prior to onset of fever

“…This suggests that the cytokine response is similar in the symptomatic animals. To confirm the host response profiles that we observed with next-generation sequencing RNA-seq, we also used a NanoString CodeSet to profile the expression of 769 NHP genes that has been previously shown to be beneficial as a secondary means for RNA quantification in NHPs infected with EBOV (15). This analysis confirmed the up-regulation of the cytokine genes identified by RNAseq ( fig.…”

“…RNA-seq enables a hypothesis-neutral investigation of RNA abundance changes. By contrast, we also used a newly developed NanoString CodeSet that recognizes 769 NHP transcripts, which we have previously demonstrated as effective with studying EBOV/Mak-infected NHPs (15). Although NanoString does not provide the transcriptomic depth of RNA-seq, it is much more rapid compared to RNA-seq and does not have as high of a requirement for RNA quality.…”

“…The virus stock used has been previously described in (15). Briefly, a master stock was made from an isolate from the serum of a 2014 Sierra Leone fatal human case (SL 3864.1; GenBank accession #KR013754.1).…”

Emerging viral infections

“…On the other hand, transcriptomic approaches identifies the interactions between the pathogen and the host's genome by evaluating transcript levels, typically mRNAs. The most commonly used transcriptomic methods are RNA-seq [69,70] and microarrays [65,66]. RNA-seq is generally suitable for unbiased transcriptomic profiling and provides better understanding of global transcriptomic changes.…”

Transcriptomics reveals complex kinetics of dorsal–ventral patterning gene expression in the mandibular arch

“…In contrast, the NanoString technique uses direct labeling of transcripts with barcoded probes to measure gene expression levels (Geiss et al, 2008). This approach maintains a high degree of linearity, and has been shown to be at least as good and frequently better at linearity and reproducibility than amplification-based approaches, while being more robust to degradation of RNA (Geiss et al, 2008;Malkov et al, 2009;Neubert et al, 2016;Omolo et al, 2016;Raman et al, 2018;Reis et al, 2011;Richard et al, 2014;Speranza et al, 2017;Veldman-Jones et al, 2015). These advantages of NanoString measurements have also made them particularly useful for computational analysis and modeling of expression patterns (Dubrulle et al, 2015).…”