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