Material and Methods
Cell culture
Spodoptera frugiperda Sf9 cells (Invitrogen, Cat#: 11496-015)
were routinely sub-cultured every 2-3 days at
0.6-1 × 106 cell.mL-1 in serum-free
Sf900-IITM SFM medium (Thermo Fisher Scientific) when
the cell concentration reached 2-4 × 106cell.mL-1. Cells were cultured in shake flasks
(Corning) using 10% (w/v) working volume and maintained at 27 °C in an
Inova 44R shaking incubator (orbital motion diameter of 2.54 cm;
Eppendorf) at 100 rpm.
Baculovirus amplification and
storage
Two recombinant baculoviruses (rBAC) were used for AAV production, one
incorporating a GFP transgene flanked by inverted terminal repeats of
AAV serotype 2 (AAV2) and under control of the cytomegalovirus promoter
(hereby named rBAC-GFP, kindly provided by Généthon) and a second rBAC
carrying AAV2 rep and cap genes (hereby named
rBAC-REP/CAP), produced in-house using Addgene plasmid #65214 (Pais et
al., 2019; Smith et al., 2009). Amplification of baculovirus stocks and
storage was performed as described previously (Virgolini et al., 2022).
Baculovirus titers were determined using the MTT assay as described
elsewhere (Mena et al., 2003; Roldão et al., 2009).
AAV production
AAV production was carried out in a 0.5 L stirred tank bioreactor
(Sartorius BIOSTAT Qplus) by infecting Sf9 cells at
2 × 106 cell.mL-1 with rBAC-REP/CAP
and rBAC-GFP, both at a multiplicity of infection (MOI) of 0.05
pfu/cell. Each vessel was equipped with one Rushton impeller; gas flow
of 0.01 VVM was supplied through a ring sparger. pO2(partial pressure of oxygen) setpoint of 30% of air saturation was set,
which was achieved through varying the agitation rate (70 - 300 rpm) and
percentage of O2 in the gas flow (0 - 100 % of
O2).
Cultures were maintained for up to 96 hours post-infection (hpi).
Samples for assessment of cell concentration, viability, intracellular
AAV titer and metabolite concentration were taken every 24 hours. For
scRNA-seq analysis, samples were taken at 0, 10 and 24 hpi.
Analytics
Cell concentration and
viability
Cell concentration and viability were determined with the trypan blue
dye exclusion method (Tennant, 1964) using the Cedex HiRes Analyzer
(Roche).
Intracellular AAV viral genomes
quantification
Cell culture samples were collected and intracellular AAV titer
quantified as described elsewhere (Pais et al., 2019). Intracellular AAV
viral genomes (VG) were quantified by real-time quantitative PCR
(RT-qPCR), as established previously (Virgolini et al., 2022).
Single-cell RNA
sequencing
Single-cell isolation and sample processing for scRNA-seq was performed,
using the BD RhapsodyTM Express Single-Cell Analysis
System (BD Biosciences) according to manufacturer’s instructions. In
short, cultured cells were centrifuged (300 × g, 4 °C, 5 min), washed
and strained (30 µm mesh size – CellTrics®) prior
dilution to the recommended cell concentration to target 6,000 single
cells. Cells were then captured in nanowell-containing cartridges,
lysed, and the released mRNA isolated using poly(dT)-coated magnetic
beads.
Sequencing libraries were prepared using the mRNA Whole Transcriptome
Analysis (WTA) Library Preparation Protocol (BD Biosciences), as
recommended by the manufacturer, and using unique index primers for each
library. The quality of each library was assessed using a
high-sensitivity D5000 kit (Agilent) and quantification was done by
Qubit analysis (Thermo Fisher Scientific). Finally, libraries were
pooled to achieve 40,000 reads per cell and sent for sequencing
(Illumina NovaSeq6000) elsewhere, spiked with 20% PhiX.
Single-cell RNA sequencing data
analysis
UMI count matrix
generation
Raw FASTQ files were processed using the BD Rhapsody™ WTA Analysis
Pipeline (Seven Bridges Genomics), using default parameters. A
customised reference genome, which included reference sequences ofS. frugiperda (RefSeq assembly accession: GCF_011064685.1)( Xiao
et al., 2020), Autographa californica multiple
nucleopolyhedrovirus (ViralProj14023)(Ayres et al., 1994) and AAV
transgene sequences (rep , cap and gfp ), was
supplied for mapping using STAR version 2.5.2b (Dobin et al., 2013).
Finally, recursive substitution error correction (RSEC)-adjusted
molecule count matrices were generated and used for downstream analysis.
Sample
pre-processing
Downstream analysis was performed in R (v4.2.1) using the Seurat package
(v4.1.1) (Hao et al., 2021); default parameters were used to create the
Seurat objects. Low-quality cells (≥ 5% mitochondrial UMI counts per
cell) were removed.
Cell cycle
scoring
To determine the cell cycle phase of each cell, a score indicating the
likelihood of cells being in either S or G2/M phase was assigned, based
on the supplied reference gene list for respective phases from Seurat
(according to mouse reference genes published from Tirosh et al., 2016).
The list of mouse cell cycle genes was associated to the S.
frugiperda genome using a protein BLAST search (e-value cut-off 0.01).
Then, identified sequences were blasted back to the S. frugiperdagenome. Genes validated after both steps were considered for cell cycle
association.
Seurat
analysis
To evaluate cell heterogeneity along infection, data sets (from 0, 10
and 24 hpi samples) were merged prior to log-normalization. Next, 2,000
variable genes of each sample were identified using the “vst” method.
Data was scaled, regressing effects caused by the total number of UMIs
and dimensionality reduced using principal component analysis (PCA)
using all variable genes. A specified number of principal components was
chosen for subsequent UMAP analysis. Relative gene expression,
differential expression analysis and gene correlation analysis were
performed using the data slot of the Seurat object. Gene markers for
clusters were identified using the FindMarkers function and the MAST
test (Finak et al., 2015). Genes with an absolute expression change of
at least 1.5-fold and a BH-adjusted p -value ≤ 0.05 were deemed
significant.
Trajectory analysis and enrichment
analysis
To understand the changes in cellular response along infection, we
utilized the 10 hpi timepoint to conduct trajectory analysis within the
Monocle3 package v.1.2.9 (Trapnell et al., 2014). First, the processed
10 hpi Seurat object was transformed to a Monocle object using the
appropriate function within the SeuratWrappers package v.0.3.0. Next,
clusters were identified using the Leiden community detection and a
resolution of 0.002. A trajectory is learned, and cells ordered
according to pseudotime. Finally, genes correlated with the progression
of cells along the trajectory were identified. Genes were deemed
significant if the q- value was < 0.01 and the average
expression of the respective gene in the Seurat object was
> 0.5.
Overrepresentation of gene ontology (GO) terms within the gene list
found to have a significant difference along pseudotime were identified
using ClusterProfiler v4.0.5 (Yu et al., 2012) and a GO term reference
list established previously (Virgolini et al., 2022). Terms with an
adjusted p -value < 0.5 were deemed significant.