A single workflow for multi-species blood transcriptomics

Abstract

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.

Keywords: Blood samples; Clinical models; Data analysis; Library preparation; Preclinical models; Quality controls; RNA extraction; Report; Standardization; Total RNA sequencing; Transcriptomics; Workflow.

Fig. 1

Blood transcriptomics workflow from sample collection to data report. (1) Blood samples from all species (human and model animals) were collected on PAXgene tubes. The PAXgene buffer stabilized samples before extraction. After the first step-by-step freezing, samples were frozen at -80 °C until extraction. Samples came from 6 donors for human, rabbit, and mouse, and 4 donors for NHP in triplicate. (2) Total RNA was manually extracted with Maxwell HT Simply RNA kit custom (#AX2420, Promega). RNA was then processed using the RNA Clean and Concentrator kit, including an additional DNase-I treatment (#R1013, ver.2.2.1, Zymo Research). (3) Total RNA libraries were prepared using the Zymo-SeqRiboFree Total RNA Library Kit (#R3003, Ver.1.04, Zymo Research) which integrates the depletion of globin mRNA and rRNA. The conditions of the library preparation were adjusted according to the associated species and the extraction yields. Libraries were sequenced on the NextSeq500 system (Illumina). (4) Quality control of the data was performed using an in-house pipeline that includes four main stages: (i) quality assessment, (ii) read mapping, (iii) transcript quantification and (iv) filtering and normalization. (5) The pipeline generated a final report assembling all the necessary plots to evaluate the quality of the data

Fig. 2

The quantity of extracted RNA from blood samples (µg). Each colour represents an individual. The average quantity between samples per species is shown as a black triangle. The original blood volume is shown in parentheses

Fig. 3

Total RNA profiles after extraction. One example of extracted RNA profiles is shown per species (Samples: Human1_3, NHP2_1, Mouse4_2 and Rabbit3_2). The profiles were generated using the Bioanalyzer system for the mouse samples and using the Fragment Analyzer for the three remaining species

Fig. 4

Quantity of the libraries (nM). The library molarity was calculated from the concentration (ng/µl) and the average size. Each colour represents an individual. The average quantity between samples per species is shown as a grey triangle. The original blood volume is shown in parentheses

Fig. 5

Performance of rRNA and globin depletions. Percentage of reads aligning to the rRNA reference database (blue) and to the globin index reference (red) for each of the four species

Fig. 6

Read distribution by genomic features. Bar plots show the percentage of reads mapping over the different genome features for each species: in blue, the exons, in orange, the introns, in red, the TSS/TES (Transcription start and end sites), and in green, other intergenic regions which regroup reads mapping outside the genes on the genome

Fig. 7

Number of detected genes. The number of genes detected was computed as expressed with at least 10 reads. Each dot represents an individual and each colour samples from the same triplicate

Fig. 8

Example of RNASEQ-QC analysis for transcriptomics QC

Fig. 9

Four QCs to detect residual genomic DNA in rabbit samples. The samples were analysed after a single DNase I treatment or double DNase I treatment. a Boxplots show the effect of DNase I treatment on read directionality (percentage of sense reads and the percentage of intergenic reads). b Dot plots describe the number of expressed genes detected. c Rarefaction curves describe the relationship between the sequencing depth and the number of detected genes, with each curve corresponding to one sample