A single workflow for multi-species blood transcriptomics.

BACKGROUND: Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons. RESULTS: Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated. CONCLUSIONS: Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.

Human Respiratory Syncytial Virus-Induced Immune Signature of Infection Revealed by Transcriptome Analysis of Clinical Pediatric Nasopharyngeal Swab Samples.

Human respiratory syncytial virus (HRSV) constitutes one the main causes of respiratory infection in neonates and infants worldwide. Transcriptome analysis of clinical samples using high-throughput technologies remains an important tool to better understand virus-host complex interactions in the real-life setting but also to identify new diagnosis/prognosis markers or therapeutics targets. A major challenge when exploiting clinical samples such as nasal swabs, washes, or bronchoalveolar lavages is the poor quantity and integrity of nucleic acids. In this study, we applied a tailored transcriptomics workflow to exploit nasal wash samples from children who tested positive for HRSV. Our analysis revealed a characteristic immune signature as a direct reflection of HRSV pathogenesis and highlighted putative biomarkers of interest such as IP-10, TMEM190, MCEMP1, and TIMM23.