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\begin{document}

\title{Single-cell transcriptome analyses reveal heterogeneity in suspension
cultures and clonal markers of CHO-K1 cells}



\author[1]{Norichika Ogata}%
\author[2]{Takeshi Omasa}%
\affil[1]{Nihon BioData Corporation}%
\affil[2]{Osaka University}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
Cell-to-cell variability in cell populations arises from a combination
of intrinsic factors and extrinsic factors related to the milieu.
However, the heterogeneity of high cell density suspension cultures for
therapeutic protein production remains unknown. Here, we illustrate the
increasing heterogeneity in the cellular transcriptome of serum-free
adapted CHO K1 cells during high cell density suspension culture over
time without concomitant changes in the genomic sequence. Cell
cycle--dependent subpopulations and cell clusters, which typically
appear in other single-cell transcriptome analyses of adherent CHO K1
cultures, were not found in these suspension cultures. Our results
indicate that cell division changes the intracellular microenvironment
and leads to cell cycle--dependent heterogeneity. Whole mitochondrial
single-cell genome sequencing showed cell-to-cell mitochondrial genome
variation and heteroplasmy within cells. Indeed, the mitochondrial
genome sequencing method developed here enables the validation of cell
clonality. The culture time-dependent increase in cellular heterogeneity
observed in this study did not show any attenuation in this increasing
heterogeneity. Future advances in bioengineering such as culture
upscaling, prolonged culturing, and complex culture systems will be
confronted with the need to assess and control cellular heterogeneity,
and the method described here may prove useful for this purpose.%
\end{abstract}%



\sloppy


\emph{Keywords}

Chinese hamster ovary (CHO) cells, single-cell transcriptome sequencing,
heterogeneity, heteroplasmy, monoclonality

\emph{1. Introduction}

Chinese hamster ovary (CHO) cells (Kao \& Puck, 1968) play a key role as
heterologous hosts for the commercial-scale production of protein
pharmaceuticals (Greber \& Fussenegger, 2007; Hacker, De Jesus, \& Wurm,
2009; Omasa, Onitsuka, \& Kim, 2010). The adaptivity of CHO cells to
high cell density processes has ensured their successful application for
this purpose. In the biopharmaceutical industry, a large number of
products have been introduced into the market using high cell density
processes such as batch and fed-batch cultures and different cell
culture strategies for reducing the production cost and improving
product quality (Pollock, Ho, \& Farid, 2013; Tapia, Vazquez-Ramirez,
Genzel, \& Reichl, 2016). Cell culture times are generally long (12 days
for fed-batch cultures) and advances are further prolonging culture
times; perfusion continuous bioprocessing (60 days) is widely recognized
as the next-generation biomanufacturing platform for achieving superior
product quality and reducing manufacturing costs (Konstantinov \&
Cooney, 2015; Zydney, 2015).

A potential issue when using long-term cultures for therapeutic protein
production is the increasing cellular heterogeneity over time
(Altschuler \& Wu, 2010). Cellular proliferation and gene expression are
critical cell processes but genomic mutations arise during DNA
replication (Furusawa, 2014) and RNA transcription (Bachl, Carlson,
Gray-Schopfer, Dessing, \& Olsson, 2001) inherent to cell division and
gene expression, respectively. The limited knowledge regarding cellular
heterogeneity in high cell density processes poses a barrier to solving
this issue.

It has been proposed that insisting on ``true'' clonality is
scientifically unjustifiable (O'Callaghan et al., 2015; Wurm, 2013),
with cell line heterogeneity being included in the FDA ``Challenges to
Potency Assay Development for CGT products'' industry guidelines
(Guidance for Industry: Potency Tests for Cellular and Gene Therapy
Products) (FDA, 2011). We can only hope that the trend toward a modified
gene pool can be minimized by maintaining cell populations in stable
environments (Wurm, 2013). Deeper characterization such as single-cell
sequencing (Tang et al., 2009) of the heterogeneity within cell line
cultures would enable the study of cooperative and competitive
interactions between cell populations and the mechanisms underlying
related cell functions (Ben-David et al., 2018).

In this study, we performed single-cell transcriptome sequencing of
serum-free suspension-adapted CHO K1 cells in shake flasks. The degree
of cellular heterogeneity and the existence of subpopulations were
determined based on gene expression analysis and whole mitochondrial
genome sequencing. To facilitate comparisons of our data to those from
other single-cell transcriptome studies, we also performed single-cell
transcriptome sequencing of adherent CHO K1 cells in culture dishes.
Additionlly, we developed clonal markers to validate the clonality of
the cells based on their mitochondrial genome sequences.

\emph{2. Materials and Methods}

\emph{2.1. Cell Lines and Culture Conditions}

CHO K1 cells (Kao \& Puck, 1968) (ATCC, Manassas, VA, USA) and CHO
K1-derived cells adapted to serum-free suspension culture were used in
this study. The CHO K1 cells were maintained in Dulbecco's Modified
Eagle's Medium: Nutrient Mixture F-12 (DMEM/F-12; Gibco, Paisley, UK)
supplemented with 10\% dialyzed fetal bovine serum (SAFC Biosciences,
Lenexa, KS, USA). The CHO K1-derived, serum-free suspension-adapted
cells were cultured in suspension using T-25 flasks (Greiner, N\selectlanguage{ngerman}ürtingen,
Germany) on a rotary shaker at 120 rpm (Taitec, Saitama, Japan). A total
of 0.1×10\textsuperscript{6}/mL CHO cells were cultivated in 7 mL
BalanCD CHO Growth A media (Irvine Scientific, Santa Ana, CA, USA). The
suspension culture conditions were assessed using computational fluid
and particle dynamics analyses with OpenFOAM version 2.3.0. (H. G.
Weller, 1998). We modified the stirringInterPTFoam application to
compute the culture conditions in box-shaped T-25 flasks on a rotary
shaker
(https://github.com/akionux/OpenFOAM-2.3.x/tree/master/applications/solvers/multiphase/stirringInterFoam/stirringInterPTFoam).
All cells were cultivated at 37°C in a humidified atmosphere containing
5\% CO\textsubscript{2}.

\emph{2.2. Library Preparation and Sequencing (Serum-free Suspension
Cultures)}

After days 4 (D4), 8 (D8), and 11 (D11), the CHO cells in three
replicate serum-free suspension culture flasks were harvested for
single-cell sequencing, and the remainder was discarded. Samples of the
CHO cells in suspension culture were directly applied to the SH800 Cell
Sorter (Sony, Tokyo, Japan), and individual cells were collected
directly into PCR tubes as previously reported (Hayashi et al., 2010). A
total of 0.5×10\textsuperscript{6} cells were stained with 1 mL
phosphate-buffered saline (PBS) containing 10 \selectlanguage{greek}µ\selectlanguage{english}g/mL Hoechst 33342 at
37\selectlanguage{ngerman}°C for 15 min. The stained cells were sorted based on the Hoechst
33342-stained cell area of the fluorescence-activated cell sorting
(FACS) distribution (Hayashi et al., 2010). Library preparation for
single-cell Quartz-Seq was performed as previously reported (Sasagawa et
al., 2013). We prepared a library for conventional RNA-seq using a
commercial kit (TruSeq RNA Library Prep Kit; Illumina, San Diego, CA,
USA) in accordance with a previous study (Yamano-Adachi et al., 2019).
Conventional RNA-seq and single-cell Quartz-Seq were performed with a
commercial sequencer (NextSeq 500; Illumina) in accordance with the
manufacturer's protocols. We performed single-cell Quartz-Seq as
previously reported (Sasagawa et al., 2013) on 85 D4, 70 D8, and 78 D11
CHO cells. The resulting short-read data have been deposited in the
Short Read Archive of the DNA Data Bank of Japan (DDBJ) under project ID
DRA004159.

\emph{2.3. Library Preparation and Sequencing (Adherent Cultures)}

Adherent CHO cell cultures in the log phase of growth were harvested for
single-cell sequencing. A total of 2.5×10\textsuperscript{6} cells were
stained with 500 \selectlanguage{greek}µ\selectlanguage{english}L DMEM/F-12 containing L-glutamine (Gibco; Thermo
Fisher Scientific, Waltham, MA, USA) and 10 \selectlanguage{greek}µ\selectlanguage{english}g/mL Hoechst 33342
(Funakoshi, Tokyo, Japan) by stirring at 600 rpm for 40 min at 37\selectlanguage{ngerman}°C.
Cells were centrifuged for 5 min at 300×\emph{g} and the supernatant was
removed. Next, 500 \selectlanguage{greek}µ\selectlanguage{english}L of 1\selectlanguage{ngerman}× PBS (no calcium, no magnesium, pH 7.2,
Gibco; Thermo Fisher Scientific) was added to the cell pellet, which was
pipetted up and down five times for thorough resuspension. Cells were
centrifuged for 5 min at 300×\emph{g} , the supernatant was removed, and
the cells were thoroughly resuspended in 500 \selectlanguage{greek}µ\selectlanguage{english}L of 30\% (w/v) Albumin
D-PBS(-) Solution (fatty acid-free from bovine serum albumin; FujiFilm
Wako Pure Chemical Corporation, Osaka, Japan) by pipetting the cell
pellet up and down five times. Cells in the albumin-containing PBS were
filtered with a 35-\selectlanguage{greek}μ\selectlanguage{english}m nylon mesh filter (Falcon, Corning, NY, USA).
Cells were applied to the SH800S Cell Sorter and individual cells were
sorted based on the Hoechst 33342-stained cell area of the FACS
distribution. RNA isolation and complementary DNA (cDNA) synthesis were
performed with the SMART-Seq v4 Ultra Low Input RNA Kit (Clontech, Palo
Alto, CA, USA) for sequencing according to the manufacturer's protocol
(Ziegenhain et al., 2017). Library preparation was performed with the
Nextera XT DNA Library Prep Kit (Illumina) according to the
manufacturer's protocol. The finished cDNA libraries were quantified
with the Agilent 2200 TapeStation system (Agilent Technologies, Santa
Clara, CA, USA) and the QuantiFluor dsDNA System (Promega, Fitchburg,
WI, USA) and then sequenced on an Illumina NextSeq 500 platform using
75-bp single-end reads. We performed single-cell RNA-seq analysis with
24 G1 cells and 8 G2 cells.

\emph{2.4. Gene Expression Heterogeneity Analyses}

All short reads were mapped to the CHO K1 RefSeq assembly (22,516
sequences, RefSeq Assembly ID: GCF\_000223135.1) and CHO K1
mitochondrial DNA (1 sequence, RefSeq Assembly ID: GCF\_000055695.1)
using Bowtie2 v. 2.3.0 (Langmead, Wilks, Antonescu, \& Charles, 2018).
Estimating gene and isoform expression levels from the RNA-Seq data was
performed with RSEM v. 1.2.31 (B. Li \& Dewey, 2011). Principal
component analyses (PCA) of the single-cell RNA-seq data were performed
with R v. 3.0.1 and the R Stats Package v. 3.2.2 (RCoreTeam, 2013). The
Bartlett test for the homogeneity of variances in principal components 1
between different culture times (Days) was performed with the R Stats
Package v. 3.2.2.

\emph{2.5. Genome Mutation Heterogeneity Analyses}

All short reads were mapped to the \emph{Cricetulus
griseus}mitochondrion complete genome
(gi\textbar{}186694636\textbar{}gb\textbar{}EU660217.1\textbar{}) using
HISAT2 v. 2.0.5 (Kim, Langmead, \& Salzberg, 2015). PCR duplicate
removal, and SNP (single nucleotide polymorphism) and short indel
(insertion or deletion) calling were performed with the SAMtools v.
1.8.0 utilities, rmdup and mpileup, respectively (H. Li et al., 2009).
The called variant loci were annotated as ``heteroplasmy,'' ``homo
alternative,'' ``homo reference,'' or ``low coverage.'' A correlation
test between the culture time (Days) and the hetero/homo ratio was
performed with the R Stats Package v. 3.2.2.

\emph{3. Results and Discussion}

\emph{3.1. Time Course Transcriptome Heterogeneity in Suspension
Cultures}

We performed single-cell Quartz-Seq as previously reported (Sasagawa et
al., 2013) of 85 D4, 70 D8, and 78 D11 CHO cells. To compare the
transcriptomes of the cells cultured for different lengths of time
(days), we performed PCA of all the expression data (Figure 1). The
proportion of variance in principal component 1 (PC1) was 0.3102, while
that of PC2 was 0.077. Using this approach, we could quantitatively
evaluate the cellular heterogeneity, which previously was not possible.
The standard deviations in PC1 were 1634 for D4, 3024 for D8, and 5029
for D11. We rejected the null hypothesis that heterogeneity did not
increase during culture, based on the Bartlett test for the homogeneity
of variances in the PC1 between different culture times (p \textless{}
2.2e-16). Thus, the cellular heterogeneity increased during cultivation
and this increase showed no sign of attenuation. Hence, the maximum
attainable heterogeneity of the CHO cells in a long-term high cell
density culture was not predictable from these data. To assess cellular
heterogeneity in future bioengineering processes, very long-term
cultures such as perfusion continuous cultures need to be studied. We
can control it if it can be measured.

\emph{3.3. Genomic Mutation Analyses of Single Cells}

To compare the mutation rate in cells cultured for different lengths of
time, we performed mitochondrial genomic sequence analyses. Recently,
many single-cell genomics technologies have been developed. However, it
is difficult to obtain genomic/epigenomic information from limited
numbers of cells because of sample loss during chromatin preparation and
inefficient immunoprecipitation (Harada et al., 2018). Each cell has
only a single nuclear genome but several mitochondrial genomes. Using
our single-cell transcriptome sequence data, we obtained sufficient
coverage of the mitochondrial genome to call SNPs and short indels, with
498 genomic loci identified with variation at single-cell resolution. We
annotated variations as ``heteroplasmy,'' ``homo alternative,'' ``homo
reference,'' or ``low coverage'' (Figure 2). Homo alternative and homo
reference variations were shared between most cells. However,
heteroplasmic SNPs were unique to individual cells. Stochastically,
recently acquired mutations do not show a high frequency in the gene
pool. The mutations that arose during our experiment should have
appeared as new hetero SNPs; indeed, we could not reject the null
hypothesis that the number of novel mutations did not increase during
culture, based on a correlation test between the culture time and the
hetero SNP/homo SNP ratio in each cell (p = 0.14; Figure 3). This
indicated that there was a low genomic mutation rate during cultivation
and that the increasing heterogeneity observed in genome-wide gene
expression was independent of genomic mutations.

We would like to emphasize the usefulness of the identified
heteroplasmic mitochondrial mutations in the context of single-cell
cloning. Regulatory authorities require that cell lines used in the
commercial production of recombinant proteins must be derived from a
single cell progenitor or clone (Evans et al., 2015). To provide
evidence of monoclonality, multiple rounds of the limiting dilution
method or cell sorting coupled with high-resolution cell imaging are
necessary. However, our study indicates that the unique heteroplasmic
mitochondrial mutations of individual cultured cells could potentially
provide evidence of monoclonality. In other words, evidence of
monoclonality could be obtained by single-cell sequencing of the cell
strain in question.

\emph{3.4. Transcriptome Heterogeneity Analyses of Adherent Cultures}

In our single-cell transcriptome analysis of suspension cultures, no
subpopulations were apparent. This finding differs from that of previous
studies, which indicated a strong correlation between genome-wide gene
expression and the cell cycle stage of adherent cells (Buettner et al.,
2015; Santos, Wernersson, \& Jensen, 2015; Sasagawa et al., 2013;
Scialdone et al., 2015). Cellular heterogeneity is known to be
influenced by the cellular microenvironment in adherent, two-dimensional
cultures (Snijder et al., 2009). It is possible that cell
cycle--dependent genome-wide gene expression is an adherent
culture-specific phenomenon. In this context, the origin of cell
cycle--dependent cellular heterogeneity in adherent cultures would not
be the product of intrinsic factors but rather the product of the
extrinsic milieu, with cellular division altering genome-wide gene
expression in cells by changing the microenvironment in adherent
cultures. A previous study supports this interpretation; suspended T
cells did not show any cell cycle--dependent cellular transcriptome
heterogeneity (Mahata et al., 2014).

To further investigate cell cycle--dependent cellular transcriptome
heterogeneity, we performed single-cell SMART-Seq as previously reported
(Ziegenhain et al., 2017) on 24 G1 cells and 8 G2 cells from adherent
cultures. To compare the transcriptomes between cells in different
phases of the cell cycle, we performed PCA of all the expression data
(Figure 4). The proportion of variance of PC1 was 0.86, while that of
PC2 was 0.033. The G2 cells formed a distinct cluster. We, therefore,
rejected the null hypothesis that cell cycle--dependent subpopulations
do not appear during adherent culture, based on the Bartlett test for
the homogeneity of variances in PC1 between G1 and G2 cells (p =
0.000245). Thus, cell cycle--dependent heterogeneity in genome-wide gene
expression was found to be a phenomenon specific to adherent cultures of
CHO cells.

\emph{3.5. Subpopulation Analyses of Suspension Cultures}

In suspension cultures, there were no clear subpopulations of cells
based on PCA. However, there remained the possibility of hidden
subpopulations of cells. We generated histograms of gene expression and
found a gene, enolase, that had a multipeak distribution. We divided the
individual D4 cells into groups with high enolase expression
(giI625221705IrefIXM\_003512016.2I; 2000--4200 RPM) and low enolase
expression (0--2000 RPM; Figure 5a) and used PCA to analyze these
individual D4 cells. The PC1 of the individual D4 cells were plotted
against those for PC2. Individual D4 cells with high and low enolase
expression formed distinct clusters (Figure 5b), which represents the
hidden subpopulations of CHO cells in suspension cultures. This
phenomenon is contrary to expectations. Hidden cellular subpopulations
such as those found in this study hint at the future discovery of
intrinsic factors giving rise to cell-to-cell variability in cell
populations.

\emph{4. Conclusions}

In summary, we sequenced the single-cell transcriptomes of high cell
density suspension mammalian cell cultures. Our findings indicated that
cellular transcriptome heterogeneity increased with increasing culture
time, while genomic mutations did not increase during suspension
culture. We also established a method for the quantitative evaluation of
cellular heterogeneity, which was previously not possible. This method
entails examining the evidence of monoclonality by single-cell
sequencing of the mitochondria of cell strains. Further, we uncovered
several differences between adherent cells and suspension cells based on
single-cell genomics. Fundamental biological observation and assessment
are of incremental importance in successful bioengineering. Instead,
further single-cell resolution observations (Norichika Ogata, 2018) and
improved measurement technologies are needed. The intercellular
variations in the mitochondrial genome sequences discovered in this
study are also useful for generating clonal markers for future
applications.

Acknowledgments

This work was funded partly by the Ministry of Economy, Trade, and
Industry (METI) of Japan, and the Japan Agency for Medical Research and
Development (AMED) for ``Developing key technology for discovering and
manufacturing pharmaceuticals used for next-generation treatments and
diagnoses'' (JP17ae0101003 and JP18ae0101054). We thank Dr. Itoshi
Nikaido and Dr. Hiroki Danno for performing Quartz-Seq library
preparation. We thank Natasha Beeton-Kempen, Ph.D., from Edanz Group
(https://en-author-services.edanzgroup.com/) for editing a draft of this
manuscript.

Conflict of Interest

The authors declare no conflict of interest.

Data accessibility

The resulting short-read data have been deposited in the Short Read
Archive of the DNA Data Bank of Japan (DDBJ) under project ID DRA004159.

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\textbf{Figure legends}

\textbf{Figure 1.} Increased single-cell transcriptome heterogeneity of
suspension-cultured CHO cells over time. Single-cell transcriptome
sequencing was performed on 85, 70, and 78 cells from cultures
maintained for 4, 8, and 11 days, respectively. Principal components 1
(proportion of variance, 0.31) and 2 (proportion of variance, 0.077) are
plotted. Blue, black, and red plots represent individual day 4, day 8,
and day 11 cells, respectively. Variances in principal component 1
between the different culture times had a p-value \textless{} 2.2e-16.

\textbf{Figure 2.} Single-cell resolution mitochondrial genomic
heterogeneity in suspension-cultured CHO cells. Genotypes of 498
variable loci in each cell were visualized.

\textbf{Figure 3.} Correlation between mutation rate and culture time.
Single-cell resolution genomic mutation analysis was performed on the
mitochondrial genomes of CHO cells cultured for different periods. No
statistical correlation was observed between culture time (Days) and the
heteroplasmic variant rate (rate of new mutations at called variable
loci; p = 0.14).

\textbf{Figure 4.} Cell cycle--dependent subpopulations in traditional
adherent CHO cultures. Single-cell transcriptome sequencing was
performed on 24 G1 cells (circles) and 8 G2 cells (crosses).

\textbf{Figure 5.} Hidden subpopulations in suspension cultures of CHO
K1 cells. Individual D4 cells clustered into groups with high enolase
expression (2000--4200 RPM, red) and low enolase expression (0--2000
RPM, black). (a) Histogram of enolase expression degree (RPM) in
suspension CHO K1 cells after 4 days. (b) A colored plot of principal
components 1 and 2 in Day 4 suspension cultures of CHO K1 cells.\selectlanguage{english}
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