Discussion
The production of viral vectors for gene therapy, such as recombinant
AAV, is complex and usually demands coordinated expression of multiple
genes within a cell to produce a packaged and functional virus
(Srivastava et al., 2021). Typically, multiple plasmids and/or
adenovirus infection (mammalian systems), or various baculoviruses
(insect cell system) are needed, resulting in significant levels of
defective product (e.g., empty or non-infectious particles) and other
process-related contaminants (e.g., helper virus) (reviewed in Merten,
2016; Penaud-Budloo et al., 2018). Insect cells with the dual
baculovirus expression vector system, herein used, have shown high
titers for different AAV serotypes (Cecchini et al., 2011; Pais et al.,
2019; Smith et al., 2009), although implications for final product
quality remain a concern (e.g. genome packaging, ratio of capsid
proteins and their correct folding). The host cell transcriptional
response to virus infection, as well as the heterogeneity reported in
clonal CHO cells (Tzani et al., 2021) and virus populations (Sun et al.,
2020), could suggest that such production processes are highly
heterogeneous and can potentially impact product titers and/or quality.
Thus, to assess the characteristics of the dual baculovirus system, as
well as how Sf9 cells responds to baculovirus infection by transcriptome
alterations, single-cell RNA-sequencing was employed.
In this study, we observed heterogeneity in non-infected Sf9 insect
cells, similar to what has been reported in clonally derived cell lines
(Tzani et al., 2021). While this highlights the individuality of cells
within the population, the most dominant influence observed herein was
associated to cell cycle, with cells identified in G2/M phase showing
distinct clustering compared to others, as reported elsewhere (Tirosh et
al., 2016; Tzani et al., 2021). Moreover, baculovirus infection has been
hypothesized to arrest cells in a “pseudo” S phase (Rohrmann, 2019),
which we could confirm in our study, i.e. we observed an increase in S
phase association to infected cells. The high association to G1 phase in
late infection samples was however unexpected. Upon further evaluation
of the cell cycle scoring, it was observed that cell cycle phase
association was biased, as high baculovirus gene expression at later
infection stages masks cell cycle genes. In this scenario, cells cannot
be associated to either S or G2M phases and thus are attributed to G1 by
default. Despite being useful to decipher heterogeneity in non-infected
samples, cell cycle scoring was not considered as a correct approach to
describe heterogeneity in samples in which the host cell transcriptome
is overwhelmed by foreign virus replication. An additional source of
variation observed in a small sub-population of non-infected cells was
correlated to the activation of stress response mechanism. While more
stringent quality control parameters might exclude this cluster, this
would not be possible in this system as the impact of baculovirus on the
transcriptome limits the regression of some quality control parameters
(i.e., number of detected genes).
Baculovirus infection has been shown to follow a random Poisson
distribution (Palomares et al., 2002). However, in a dual baculovirus
system this tends to be more complex, as interference and synergistic
effects of both viruses can be observed (Mena et al., 2007), as well as
differences in virus replication and/or infection kinetics could occur
(Galibert et al., 2021). Indeed, along infection cells had highergfp expression when compared to rep and/or cap ,
highlighting possible differences in infection kinetics between both
rBACs. While this could arise from the promoters regulating the
expression of each transgene (cmv is an earlier promoter than the
later viral promoters polh/p10 ), other possible sources of
asynchronous infection and replication of both baculoviruses include
titer determination and random variations in infection kinetics,
emphasizing a potential need for customized infection strategies.
The heterogeneity of infected cells was linked to the overexpression of
baculovirus genes, infection progression and host cell transcriptome
responses. However, it was also probably augmented because of the low
MOI infection strategy employed here. While high MOI processes are less
desirable due to challenges associated with generation of master virus
stocks (e.g., larger production scale are needed), these could prove
more useful if a synchronous infection between two baculoviruses is
desired. Another limitation of low MOI, dual-baculovirus based processes
is the fact that in case one of these baculoviruses infects and
replicates faster than the other, cells infected with the more
replicative virus might be unable to receive the other, in a process
called super-infection exclusion (i.e., previously infected cells cannot
be re-infected) (Beperet et al., 2014; Folimonova, 2012). The
possibility of baculovirus reinfection has however been reported (Gotoh
et al., 2008; Mena et al., 2007), nevertheless it is still unknown how
long infected cells are susceptible to new viruses entry and how
efficient is the expression of the newly arrived transgene (Sokolenko et
al., 2012). A recent report showed the limitations of re-infection, as a
maximum of 40% of cells were found infected with both rBACs in a dual
baculovirus system using similar conditions (Galibert et al., 2021),
highlighting the need to assess possibilities to increase this number to
improve product quality and titer.
The production of fully packed AAV particles is dependent on the
presence of both recombinant baculoviruses carrying their respective
transgenes in the same cell. In our study, at 24 hpi, although all cells
are infected, only 29.4% of cells were shown to have all the necessary
transgenes expressed to produce packaged AAV particles. While this does
not necessarily correlate to subsequent protein expression levels, as
AAV proteins have been shown to undergo post-transcriptional and
translational regulation (Virag et al., 2009), this data raises the
possibility of a potential production bottleneck. Nevertheless, the
number of cells showing transcriptional capacity for producing packed
AAVs could have been underestimated here, since both rep andcap transgenes are expressed using late viral promoters and thus
expression of these genes in cells that have been infected in the second
infection cycle (occurring between 18 and 24 hpi) might not yet be
detectable at 24 hpi.
Baculovirus infection has been shown to significantly impact the host
cell machinery, activating stress response, cell cycle arrest and
reorganization of the cytoskeleton and cell nucleus, while shutting down
cell growth and protein folding capacities among others (as reviewed in
Monteiro et al., 2012 and Rohrmann, 2013). Here, similar biological
processes to those previously reported were shown to vary along
infection, such as stress response mechanisms (e.g., heat shock
protein 68 ) (Chen et al., 2014; Koczka et al., 2018). This might arise
due to the response to unfolded proteins, as folding capacity has been
found impacted (Koczka et al., 2018) and could result in reduced product
quality along infection. Additionally, energy metabolism alterations
along infection have been reported (Bernal et al., 2009; Bernal et al.,
2010; Monteiro et al., 2017) and also be associated with mitochondrial
function (Chen et al., 2014; Xue et al., 2012), which were also found
altered along infection here. Alteration of the glutamate
dehydrogenase and glutamate synthase genes predicted to encode
proteins involved in the ammonia recycling system (Bernal et al., 2009;
Doverskog et al., 2000), identified herein, has been shown in bulk
analysis (Virgolini et al., 2022) and further confirms the impact of
infection on host cell machinery.
Overall, 75% of enriched gene ontology terms identified herein were
also found in our previous bulk transcriptomics analysis (Virgolini et
al., 2022). Nonetheless, the majority of the GO biological processes
identified in previous bulk RNA-seq data were only found at later stages
of infection, suggesting that transcriptomic alterations of a
sub-population of cells might be masked in this analysis. This further
underlines the added benefit of single-cell analysis in heterogeneous
production systems, as it is able to dissect early transcriptional
alterations due to stress and/or infection in a sub-population of cells,
thus having the potential to predict subsequent population response.