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 used is testing for rapid adaptation to environmental change
(e.g., to captivity or climate warming), and to determine if
environmentally-induced gene expression shifts are transgenerationally
transmitted (e.g., Christie et al. 2016, Charlesworth et al. 2017,
Skvortsova et al. 2018, Navarro-Martin et al. 2020, Sävilammi et al.
2020). Our results will facilitate future research testing for
transgenerational transmission of potentially epigenetic
hatchery-adaptive traits in wild fish populations (e.g., Christie et al.
2016, Le Luyer et al. 2017, Wellband et al. 2020).
Evidence is accumulating regarding the effects that sampling techniques,
sample processing time, RNA degradation, and different RNA-Seq libraries
have on RNA-Seq data (e.g., Gayral et al. 2012, Romero et al. 2014, Ma
et al. 2019; see also Introduction). We tested these effects on samples
of westslope cutthroat trout sampled using dip-netting or
electrofishing. We also tested if distinct tissues may be differently
affected by these conditions. Samples were sourced from a wild
non-introgressed population raised in controlled environments in order
to minimize variation in gene expression.
Overall, we obtained high RNA quality for all tissues (mean RIN> 9.0 for the different tissues) except liver (mean
RIN = 8.0). Liver is a tissue with 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 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 death. While opinions on a cutoff threshold RIN
value to obtain reliable gene expression data differ, it has been shown
that degraded RNA still recovers the same uniquely mapped genes as
non-degraded RNA, although the coverage of mapped reads is lower for
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 were generally 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 death).
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 recovered a higher number of reads per sample with the whole mRNA-Seq
library technique used here (NEB) than with 3’ Tag-Seq (around 10 times
higher in NEB than in 3’ Tag-Seq), as expected (Ma et al. 2019). Similar
to results reported by Ma et al. (2019), our recovered number of mapped
genes was also higher (at least 2X higher) for samples processed with
NEB than with 3’ Tag-Seq, independent of the number of reads per gene
and gene transcript length. This higher number suggests researchers
should use whole mRNA-Seq when their research question requires
genome-wide coverage of genes and study of large numbers of genes.
Selection of 11M reads and 40 M reads for 3’ RNA-Tag and whole mRNA
(NEB) libraries, respectively, resulted in a very similar number of
unique mapped reads on the O. mykiss reference genome for the two
library techniques (75% NEB versus 77% 3’ Tag-Seq). Therefore, while
RNA-Seq samples prepared using NEB libraries allow recovering more raw
reads than when using the 3’ Tag-Seq library, this number did not
increase the proportions of uniquely mapped reads on the reference
genome. Previous studies (Liu et al. 2014, Ma et al. 2019) also found
similar estimates of gene expression for sequencing depth equal or above
10M reads. However, independently of the sequencing depth (in this study
NEB: 40M reads and 3’ Tag-Seq: 11M reads), we found different gene
expression between NEB and 3’ Tag-Seq, with higher estimated gene
expression being gene-specific and not library-dependent. Whole mRNA-Seq
has been found to detect more differentially expressed genes, even at
lower than 10M reads sequencing depth, potentially as a consequence of
the increased number of mapped reads for longer transcripts for whole
mRNA-Seq vs 3’ RNA-Seq (Ma et al. 2019). We did find a very slight trend
toward a higher proportion of genes with greater gene expression for NEB
relative to 3’ Tag-Seq with increasing transcript length.
Although stress levels associated with dip netting and electrofishing
may differ, sampling techniques did not affect gene expression levels.
This result was independent from the RNA-Seq library type (3’ Tag-Seq 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), the fact that independently of the method used we
found no differences in estimated gene expression between the two
sampling methods further supports that researchers can confidently use
either one or both of these sampling methods to obtain fish tissues for
studies using RNA-Seq. 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 worrying about introducing extraneous variation in gene
expression.
We also found that harvesting the tissue immediately or 5 minutes after
death 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 death was approximately 10 min (for fish processed 5
minutes after death). 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
least number of expressed genes in common. Gill was the tissue in which
the higher number of total expressed genes was recovered. This may be
due to the active cellular processes occurring in gills (Stolper et al.
2019) - especially in animals that are experiencing growth as were the
ones sampled by us - as supported by our finding on the type of genes
found to be highly expressed in this tissue (e.g., gene related to
metabolic and growth-related processes). 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 10 minutes from death. Only liver
samples showed reduced quality results. Finally, although whole mRNA-Seq
detects more differentially expressed genes, this did not produce
different results in terms of distinct gene expression among the groups
tested here. 3’ Tag-Seq can therefore be more cost effective, ensuring a
sufficient depth coverage and allowing processing larger samples sizes
at a lower cost, thus potentially increasing statistical power of
detection of differential gene expression. Consequently, depending on
the study question, sequencing a large number of individuals using 3’
Tag-Seq (and a subset of samples with whole mRNA-Seq) will often be the
best strategy to test for differences in gene expression among tested
groups. Our study provides data crucially-needed to advance 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