Abstract
BackgroundGene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood. These limitations can be overcome by the use of QuantSeq 3’mRNA sequencing (QuantSeq) combined with a method to deplete or block the processing of globin mRNA prior to or during library construction. Here, we validated the effectiveness of QuantSeq using a novel specific globin blocker (GB) that is included in the library preparation step of QuantSeq.ResultsIn data set 1, four concentrations of the GB were applied to RNA samples from two pigs. The GB significantly reduced the proportion of globin reads compared to non-GB (NGB) samples (P = 0.005) and increased the number of detectable non-globin genes. The highest evaluated concentration (C1) of the GB resulted in the largest reduction of globin reads compared to the NGB (from 56.4 to 10.1%). The second highest concentration C2, which showed very similar globin depletion rates (12%) as C1 but a better correlation of the expression of non-globin genes between NGB and GB (r = 0.98), allowed the expression of an additional 1295 non-globin genes to be detected, although 40 genes that were detected in the NGB sample (at a low level) were not present in the GB library. Concentration C2 was applied in the rest of the study. In data set 2, the distribution of the percentage of globin reads for NGB (n = 184) and GB (n = 189) samples clearly showed the effects of the GB on reducing globin reads, in particular for HBB, similar to results from data set 1. Data set 3 (n = 84) revealed that the proportion of globin reads that remained in GB samples was significantly and positively correlated with the reticulocyte count in the original blood sample (P < 0.001).ConclusionsThe effect of the GB on reducing the proportion of globin reads in porcine blood QuantSeq was demonstrated in three data sets. In addition to increasing the efficiency of sequencing non-globin mRNA, the GB for QuantSeq has an advantage that it does not require an additional step prior to or during library creation. Therefore, the GB is a useful tool in the quantification of whole gene expression profiles in porcine blood.
Highlights
Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood
In order to evaluate the effectiveness and the effects of the globin blocker (GB), QuantSeq data from three independent data sets were used, as illustrated in Fig. 1: 1) technical replicates of a blood sample from each of two pigs to evaluate different concentrations of the GB, 2) biological replicates consisting of 373 blood samples from 56 pigs to evaluate variation in globin depletion, and 3) biological replicates consisting of blood samples from 86 pigs to identify factors that affect the efficiency of globin depletion by the GB
Effect of GB and its concentration on QuantSeq globin reads To investigate whether the GB is effective in reducing globin reads in QuantSeq results, a total of 7 aliquoted RNA samples from two weaned pigs were used in QuantSeq library construction
Summary
Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood. These limitations can be overcome by the use of QuantSeq 3’mRNA sequencing (QuantSeq) combined with a method to deplete or block the processing of globin mRNA prior to or during library construction. In the QuantSeq approach, only one read per transcript, targeting the 3′ end, is generated, so that gene expression can be quantified with a much smaller number of sequencing reads per sample compared to standard RNA-seq. The application of QuantSeq is limited to quantifying gene expression, annotating the 3’end of transcripts, and detecting alternative polyadenylation, because QuantSeq only provides reads for the 3’end of genes
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