4 Discussion
The increasing use of RNA-Seq for ecological, physiological, and evolutionary studies on wild caught organisms has required appraisal of the influence of different sampling techniques, storage methods, processing time, and tissue types on RNA quality and data production (Camacho-Sanchez et al., 2013; Cheviron et al., 2011; Nakatsuji et al., 2019; Yu et al., 2013). Among the most important applications of RNA-Seq currently is testing for rapid adaptation to environmental change (e.g., Narum & Campbell, 2015; Connon et al., 2018) and for its inheritance (e.g., Christie et al. 2016, Charlesworth et al. 2017, Skvortsova et al. 2018, Navarro-Martin et al. 2020, Sävilammi et al. 2020), and for addressing questions in evo-devo (e.g., Roux et al., 2015; Liu et al., 2020).
In our work, we tested if different sampling techniques influenced gene expression across different tissues in westslope cutthroat trout. Overall, we obtained high RNA quality for all tissues (mean RIN> 9.0 for the different tissues) except liver (mean RIN = 8.0). Liver has a high rate of protein synthesis and degradation, and the higher RNA degradation observed for this tissue in comparison to blood, muscle, and gills is likely the result of higher enzymatic activity in the liver (Carter et al. 2001, Wiseman et al. 2007). In our experiment, liver was the third tissue sampled after euthanasia, after blood and muscle, and it took us between 2 and 3 minutes to sample. Because of its importance in detoxification mechanisms, physiological studies may require target sampling of this tissue. We therefore suggest sampling of liver first if more than one tissue is sampled to minimize RNA degradation.
We also found no difference in RNA quality among samples obtained through dip netting or electrofishing even when tissue was not harvested until 5 minutes after euthanasia. While opinions on a cutoff threshold RIN value to obtain reliable gene expression data differ, it has been shown that partially degraded RNA may still detect the same uniquely mapped genes as non-degraded RNA, although the coverage of mapped reads is lower for partially degraded RNA and gene specific (Romero et al. 2014, Wang et al. 2016). However, while RNA degradation may not strongly affect mapping, it may drastically affect estimates of differential gene expression (Chen et al. 2014, Romero et al. 2014). Furthermore, different RNA-Seq techniques may be differentially affected by RNA degradation (Adiconis et al. 2013), requiring selecting the most appropriate RNA-Seq library depending on RNA quality (Adiconis et al. 2013).
We found that gene expression among individuals belonging to the same group and tissue type were very similar for the majority of comparisons (correlation coefficients > 0.9), independent of the sampling method or harvesting time. However, we observed among-sample variation in gene expression, reflecting the importance of larger sample size in RNA-Seq studies to decrease the influence of stochastic effects on variation in gene expression that could otherwise be interpreted as biologically relevant (Ching et al. 2020). Furthermore, we also observed similarity of expression levels among samples obtained with the two sampling methods, dip netting or electrofishing, or subjected to different tissue harvest times (immediate or 5 minutes after euthanasia). Sampling individuals of the same age, in the same environment and on the same day, with many biological replicates per treatment and using only samples with highly similar RNA quality most likely reduced the effects of non-biological variation and of non-relevant biological variation in our experiments (Fang & Cui 2010, Wong et al 2012, Yu et al. 2014).
We detected a higher number of mapped and expressed genes (~30% higher) for samples processed with NEB than with QuantSeq, independent of gene transcript length. Others have proposed traditional whole mRNA to detect more genes than 3’ RNA-Seq libraries (e.g., Crow et al., 2022; Ma et al., 2019; Xiong et al., 2017). Furthermore, we observed that while NEB detected 30% more expressed genes than QuantSeq, QuantSeq also detected a smaller number of expressed genes that were not found with NEB. Finally, for the same samples processed with both library types, we found different gene expression between NEB and QuantSeq, with a higher proportion of genes (58%) with greater gene expression for NEB relative to QuantSeq. As we did not find differentially expressed genes between sampling techniques and processing time after euthanasia (see below), we could not estimate if NEB and QuantSeq would detect a different number of differentially expressed genes, as previously reported by others (e.g., Crow et al., 2022; Ma et al., 2019; Tandonnet and Torres, 2017). Different detection of genes and differentially expressed genes between the two library types has been proposed to depend on the length of the transcript and how accurate and complete the annotation of the genome of the organism is. In general, QuantSeq seems to perform better in detecting shorter transcripts and whole mRNA-Seq in detecting longer ones. Furthermore, since QuantSeq library data rely on mapping the reads to the 3’ UTR on the species’ genome to detect genes, and since UTR regions are generally more variable in the genome than protein coding regions, better annotated and complete genomes facilitate mapping and detection of transcripts/genes. In this study, mapping was carried out on a closely related salmonid species, since the genome of the westslope cutthroat trout is currently not available, and this can explain why many more expressed genes were detected with NEB than QuantSeq. The higher number of detected expressed genes suggests that researchers should use whole mRNA-Seq for work on species with limited genomic resources (Crow et al., 2022). Furthermore, as QuantSeq libraries only allow amplification of the 3’ end of the transcript, different transcripts resulting from alternative cleavage sites and splicing would only be detected if the 3’ UTR were different. Traditional whole mRNA-Seq should therefore be preferred if identifying distinct spliced transcripts may be of interest for the study question. Finally, others (Crow et al., 2022) have reported how the increased sequencing depth of traditional whole mRNA-Seq methods may produce redundancy of reads without increasing the power of detection of differentially expressed genes. As one of the major advantages that have been reported for QuantSeq (and 3’ RNA-Seq in general) is the reduced sequencing cost, a reduced sequencing depth for whole mRNA-Seq library can produce the desired data at a reduced cost.
One of the goals of this study was to test if different sampling methods and processing time would affect gene expression. Although stress levels associated with dip netting and electrofishing may differ, we found that sampling technique did not affect gene expression levels. This result was independent of the RNA-Seq library type (QuantSeq or NEB) and tissue used. Although whole mRNA-Seq has been reported to be more sensitive to differentially expressed genes than 3’ RNA-Seq methods (Ma et al. 2019), independent of the RNA-Seq library used, we found no difference in estimated gene expression between the two field collection methods. As field conditions often change among sampling locations, researchers could opt to use electrofishing, where more efficient, and compare with fish obtained by netting in other localities without introducing extraneous variation in gene expression.
We also found that harvesting the tissue immediately or 5 minutes after euthanasia did not produce variation in gene expression, suggesting that it is safe to euthanize fish in batches and then proceed to tissue harvesting. In our work, the maximum processing time of the last tissue harvested after euthanasia was approximately 10 min (for fish processed 5 minutes after euthanasia). Although sampling techniques and tissue processing time did not influence variation in gene expression, we observed a large proportion of differentially expressed genes among the different tissues.
We found fewer expressed genes in blood compared to gill and muscle, and a smaller proportion of genes with higher expression in blood than in the other two tissues. Blood and muscle were also the tissues with the fewest expressed genes in common. Gill tissue had the highest number of detected expressed genes. This may be due to active cellular processes occurring in gills further amplified in actively growing animals (Stolper et al. 2019). Depending on the study question, sampling different tissues may ensure that multiple genes and multiple biological processes are considered for studies on differential gene expression.
In summary, our study indicates that differential gene expression results are likely to be comparable for dip netting and electrofishing. Additionally, gill, blood, and muscle all produce good quality RNA with reliable results if sampled within 5-10 minutes from euthanasia. Only liver samples showed RNA degradation. Finally, although NEB library detected more expressed genes, this did not lead to different results in terms of distinct gene expression among the groups tested here. If detecting alternative splicing is not of interest for the study question and if working with an organism with good genomic resources (available genome or a good genome available for a closely related species), researchers can rely on using either of the library types tested here, QuantSeq or NEB and sequence them at similar depth, reducing the cost of NEB library sequencing. However, when it is crucial to detect as many genes as possible and when working with an organism lacking good genomic resources, whole RNA-Seq is recommended. These findings advance the use of RNA-Seq to investigate gene expression variation and its role in phenomena such as adaptation to environmental variation and climate change in natural populations