Systems vaccinology and big data in the vaccine development chain

Raeven, René H. M.; van Riet, Elly; Meiring, Hugo D.; Metz, Bernard; Kersten, Gideon F. A.

For decades, RNA analysis has been applied to study vaccine-induced responses 6 and has evolved from (quantitative) polymerase chain reaction to high-throughput methods like microarrays and next-generation sequencing (RNAseq). The applications range from assessing transcriptional profiles in whole tissues and blood to purified cell populations 7, 8 and, more recently, even single cells. 9, 10 In contrast to conventional transcriptome analysis that results in loss of tissue or cells, transcriptome in vivo analysis allows us to monitor mRNA expression in situ in live cells or animals. 11 Transcriptomics of the complete genome is unbiased, relatively straightforward and is offered as a service by many contract laboratories. Gene expression levels and kinetics provide information on involvement of genes following immunization. [12] [13] [14] [15] However, mRNA is often not an effector molecule in itself, but needs translation into protein and mRNA levels often do not correlate with protein content. One study reported that in mice, under those conditions, only 27% of the mRNA transcript correlates with protein expression. 16 To that end, transcriptomic profiling in vaccine research serves as an excellent tool to find potential markers of vaccineinduced responses that subsequently need to be confirmed on protein or cellular level. Novel methods such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) try to overcome this translational challenge by combining in parallel the targeting of single cells with protein markers with unbiased transcriptome profiling to obtain more specific information on gene expression in cellular phenotypes than RNA-seq in the complete tissue. 17 In addition to mRNA profiles, investigating the role of noncoding microRNAs becomes relevant given their role in immunology. 18