2. D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69 59
neurons displayed reduced size and cellular RNA content, but con-
ventional global gene expression analysis revealed no change in
the majority of genes (Li et al., 2013). In contrast, addition of
spike-in standards proportional to cell numbers revealed global
transcriptional and translational repression as a consequence of
MECP2 deletion (Li et al., 2013), advocating application of this anal-
ysis technique to all comparisons involving differences in cellular
size.
The onset of the systems biology era in biotechnology has been
defined by a wide variety of ‘omics-based approaches to charac-
terize the biological basis of desired phenotypic parameters and
manipulate them to enhance heterologous protein expression in
Chinese Hamster Ovary (CHO) cell lines (Kildegaard et al., 2013).
So far, only conventional techniques that assume invariant total
RNA yield per cell have been used to evaluate gene expression
of CHO cells. For example, many studies have been performed to
elucidate the biological basis of specific productivity (qP) and pre-
dict qP from gene expression profiles (Clarke et al., 2011a,b, 2012;
Doolan et al., 2012; Kang et al., 2014; Nissom et al., 2006; Yee et al.,
2009), but none took into account potential differences in cellu-
lar size or total RNA content. Other studies examining effects of
small molecule treatments (e.g. sodium butyrate, Kantardjieff et al.,
2010) or bioprocess conditions (e.g. temperature, Kantardjieff et al.,
2010 and culture osmolarity, Shen et al., 2010) on gene expres-
sion in CHO cells also did not take into account changes in cellular
RNA content. For this study, we selected two small molecules, one
targeting cell cycle progression and the other targeting mTOR sig-
naling, which were hypothesized to have opposite effects on cell
size in our biological system. Cell cycle arrest, achieved by either
overexpression of an endogenous cell cycle inhibitor (p21CIP1) (Bi
et al., 2004) or addition of small molecule inhibitors that cause
G1 or G1/S arrest (Du et al., 2014; Fingar et al., 2002), has been
previously shown to increase mammalian cell size. mTOR sig-
naling through its downstream effectors, ribosomal protein S6
kinase (S6K1) and eukaryotic initiation factor 4E-binding protein
(4EBP1), has also been demonstrated to regulate mammalian cell
size (Fingar et al., 2002). In fact, mTOR overexpression in CHO-
K1 cells stimulated cell cycle progression by promoting G1-to-S
phase transition and increased cell size (Dreesen and Fussenegger,
2011). We showed that the assumption of constant cellular size and
total RNA yield was inaccurate for several small molecule treat-
ments, multi-cell line comparison studies and changes in culture
osmolarity. Therefore, we assessed the use of spike-in standards
for evaluation of differential gene expression in CHO cells, focusing
on case studies relevant to cell line development and production
processes.
2. Materials and methods
2.1. Cell culture and experimental treatments
Six CHO-derived cell lines (Rasmussen et al., 1998), each
expressing a different monoclonal antibody (cell lines A, B, C, D,
E and F), were cultured in a proprietary chemically-defined growth
media in vented shake-flasks at 36 ◦C, 5% CO2, 70% relative humid-
ity and shaken at 150–160 rpm in Kuhner incubators. Each cell
line was generated using a proprietary expression system. A sin-
gle mRNA encoded both the antibody light chain (LC) and the LC
selectable marker as they were linked by an Internal Ribosome
Entry Site (IRES) sequence. Similarly, single mRNA encoded both
the HC and the HC selectable marker as they were also linked by an
IRES sequence. Viable cell density (VCD), viability and cell diameter
were measured with a ViCell automated cell counter (Beckman-
Coulter, Inc., Brea, CA). Cellular volume was calculated using the
formula for the volume of a sphere: V = 4/3 r3.
For the CCI-mTORI treatment study, cells were seeded from
day 4 growth cultures at 10 × 106 viable cells/mL into proprietary
chemically-defined production medium in vented 24 deep-well
plates (3 mL volume per well) and shaken at 220 rpm in Kuhner
incubators. Cultures were treated on day 0 with either CCI (Du et al.,
2014) (Amgen proprietary, PCT/US2013/074366, 10 M final con-
centration), mTORI (Amgen proprietary, WO/2010/132598, 0.5 M
final concentration) or vehicle control (DMSO, 0.1% final concen-
tration) and daily medium exchanges were performed using fresh
media containing an appropriate small molecule (CCI, mTORI or
DMSO). Percent DMSO was kept constant among all experimental
conditions. Spent medium was used for daily titer measure-
ments and daily specific productivity (qP) calculations. Titer
measurements were performed by affinity High Performance Liq-
uid Chromatography (HPLC) using POROS A/20 Protein A column.
Daily qP (pg/cell/day) was calculated according to the simplified for-
mula: qP = daily titer/daily VCD. On day 3, 3 × 106 viable cells were
collected per condition for gene expression analysis (biological trip-
licates), snap-frozen and stored at −70 ◦C for further processing.
For the multi cell line study, ten-day production assays with
bolus feeds on days 3, 6 and 8 were performed in chemically-
defined production medium in vented shake-flasks as previously
described (Fomina-Yadlin et al., 2014). Titer samples were collected
on days 3, 6, 8 and 10. For each interval between days [m, n], qP was
calculated according to the formula: qP = titern/
n
m
VCDdt/(tn −
tm), where titern is the measured cumulative titer at tn and time (t)
is expressed in days. On day 6, 3 × 106 viable cells were collected
per condition for gene expression analysis (biological triplicates),
snap-frozen and stored at −70 ◦C.
For osmolarity level study, cells were seeded from day 3 growth
cultures by 1:5 split at ∼0.5 × 106 viable cells/mL into proprietary
chemically-defined growth media in 24 deep-well plates (3 mL vol-
ume per well). Osmolarity was adjusted at 0-h time-point with 5 M
NaCl solution in growth medium, as previously described (Shen
et al., 2010). Daily VCD, viability and cell diameter measurements
were performed. On day 2, 1 × 106 viable cells were collected per
condition for gene expression analysis (biological triplicates), snap-
frozen and stored at −70 ◦C.
2.2. ERCC spike-in and RNA extraction
Addition of External RNA Controls Consortium (ERCC) controls
was done as previously described (Loven et al., 2012). Specifically,
1 L of 1:10 diluted ERCC RNA Spike-In Mix 1 (Ambion®, Life Tech-
nologies, Grand Island, NY) was added per 1 × 106 cells. ERCC was
added to frozen cell pellets with RLT lysis buffer, and total RNA was
isolated with the RNeasy Mini kit (Qiagen, Valencia, CA) accord-
ing to the manufacturer’s protocol, including optional on-column
DNAse I digestion, and using 100 L elution volume. RNA concen-
tration was measured on the Nanodrop 2000 (Thermo Scientific,
Wilmington, DE), and RNA quality was assessed using the 2100
Bioanalyzer (Agilent, Santa Clara, CA) with the RNA 6000 Nano Kit
(Agilent, Santa Clara, CA) to ensure all samples used for RNA-Seq
analysis had RNA Integrity Number (RIN) >9.
2.3. RNA-Seq sample processing and analysis
RNA library preparations, sequencing reactions, and initial
bioinformatics analysis were conducted at GENEWIZ, Inc. (South
Plainfield, NJ). Illumina TruSeq RNA library preparation, clustering,
and sequencing reagents were used throughout the process follow-
ing the manufacturer’s recommendations (Illumina, San Diego, CA).
Briefly, 1 g of total RNA was used as starting material for library
preparation with the Illumina Truseq RNA preparation Kit V2. Poly-
T oligo-attached magnetic beads were used to purify mRNA, which
was then fragmented for 8 min. at 94 ◦C. First strand and second
3. 60 D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69
strand DNA were subsequently synthesized. Invitrogen Reverse
Transcriptase II was used with First Strand synthesis master mix.
Adapters were ligated after adenylation of the 3 ends followed by
enrichment and barcode addition for multiplexing by limited cycle
PCR. DNA libraries were validated using a DNA 1000 Chip on the
Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA), and quantified
by using the Qubit 2.0 Fluorometer and by real time PCR (Applied
Biosystems, Carlsbad, CA, USA). The samples were clustered on six
lanes of a flow cell, using the cBOT from Illumina. After clustering,
the samples were loaded on the Illumina HiSeq 2500 instru-
ment according to manufacturer’s instructions. The samples were
sequenced using a 1 × 50 Single Read (SR) configuration. Image
analysis and base calling were conducted by the HiSeq Control
Software (HCS) on the HiSeq 2500 instrument. Sequence data was
aligned to the Cricetulus griseus reference genome along with its
mitochondrion, downloaded from NCBI (cgr ref CriGri 1.0 chrUn,
and NC 007936.1). 92 ERCC spike-in sequences were added as part
of the reference (Ambion®, Life Technologies, Grand Island, NY).
CLC Genomics Workbench 6.5.1 was used to align the reads to
the reference genome, allowing maximum of 2 mismatches. The
gene hit count was performed also within the CLC Genomics Work-
bench 6.5.1. The expression value was measured in RPKM, defined
as Reads Per Kilobase of exon model per Million mapped reads
(Mortazavi et al., 2008). Spike-in normalization of RPKM values was
performed as previously described (Loven et al., 2012). Specifically,
RPKM values were normalized to ERCC-spike-in standards with the
function normalize.loess within the affy R package. Loess regression
was performed on the ERCC subset and used to re-normalize the
matrix of all RPKM values. Differential expression analysis was per-
formed with 2-way ANOVA in Array Studio (OmicSoft Corporation,
Cary, NC) on either the raw RPKM values or the ERCC-normalized
RPKM values.
2.4. Quantitative real-time PCR
For quantitative real-time RT-PCR (qPCR), 1 g of total RNA was
reverse-transcribed into cDNA using the SuperScript® VILOTM Mas-
ter Mix, according to the manufacturer’s protocol (InvitrogenTM,
Life Technologies, Grand Island, NY). qPCR was performed with the
Power SYBR® Green PCR Master Mix following the manufacturer’s
recommendations (Applied Biosystems®, Life Technologies, Grand
Island, NY) on the 7900HT Fast Real-Time PCR System with the
384-Well Block Module (Applied Biosystems®, Life Technologies,
Grand Island, NY). Primer pairs for ERCC1-4 were designed with
Primer3Plus and listed in Supplementary Table S1. For housekeep-
ing CHO genes (ActB, B2M and TBP), previously published primer
sequences were utilized (Fomina-Yadlin et al., 2014). Average cycle
number (Ct) for either ERCC1-4 or the housekeeping genes was used
to normalize mAb expression.
Supplementary material related to this article can be found,
in the online version, at http://dx.doi.org/10.1016/j.jbiotec.
2014.08.037.
3. Results
Low-producing cell line (cell line A) cultures and high-producing
cell line (cell line B) cultures were treated with either a cell cycle
inhibitor (CCI), an mTOR inhibitor (mTORI) or vehicle control on
day 0 and maintained for 3 days with complete daily medium
exchanges. Daily spent medium was used for daily titer measure-
ments and specific productivity (qP) calculations. CCI suppressed
growth of both cell lines without affecting viability (Fig. 1A and B).
mTORI was less effective than the CCI at suppressing growth of the
high-producing cell line B without affecting viability (Fig. 1A and
B). In addition to growth suppression, mTORI treatment decreased
viability of the low-producing cell line A (Fig. 1A and B). CCI
increased cell diameter, cell volume (calculated as a volume of a
sphere) and qP in both cell lines (Fig. 1C, D and E, respectively).
However, most of the qP increase correlated with the increase in
cell volume (Fig. 1F). mTORI was hypothesized to decrease cell
size, but no experimental decreases in cell size were observed over
the course of the 3-day treatment for the two cell lines examined
(Fig. 1C and D). Furthermore, mTORI treatment did not change qP
of either cell line (Fig. 1E).
An underlying assumption of all conventional gene expression
measurements is no change in total RNA yield per cell. Since 3-day
CCI treatment increased cellular volume by 73% and 74% for cell
lines A and B, respectively, (Fig. 1D) we sought to evaluate the “no
change in RNA yield” assumption for this study. Indeed, CCI treat-
ment increased the total RNA content per cell for both cell lines,
while the RNA content per unit volume did not change significantly,
indicating no change in “total RNA density” (Fig. 2A). Surprisingly,
3-day mTORI treatment of both cell lines, which did not affect cell
volume, decreased the total RNA content per cell (Fig. 2A). There-
fore mTORI decreased the “total RNA density,” suggesting global
transcriptional repression.
Since the total RNA yield per cell changed with each experi-
mental treatment, we used normalization to ERCC spike-in controls
to study global gene expression changes, as previously described
(Loven et al., 2012). Three million cells were collected per con-
dition on day 3, and ERCC controls were added to cell pellets
prior to total RNA extraction proportionally to the cell number col-
lected per sample. Next generation sequencing (RNA-Seq) analysis
was performed on all day 3 samples, with the same RNA amount
sequenced per sample. Statistical comparisons among experimen-
tal and control conditions performed using 2-way ANOVA on
un-normalized RPKM values revealed symmetrical volcano plots
with similar numbers of significantly up- and down-regulated
genes in compound-treated cell lines A and B (Fig. 2B and C
(top panels), respectively). However, performing 2-way ANOVA on
ERCC-normalized RPKM values resulted in skewed volcano plots,
with mostly up-regulated genes for the CCI and down-regulated
genes for the mTORI treatment of both cell lines A and B (Fig. 2B
and C (bottom panels), respectively).
Using un-normalized RPKM values for examination of differ-
ential expression yielded nearly symmetrical plots of average
transcript abundance versus log 2-fold change between treatment
and control conditions (Fig. 3). In contrast, normalization to spike-
in controls proportional to cell number revealed global up- and
down-regulation of gene expression corresponding to differences
in total RNA yield per cell following either the CCI or the mTORI
treatments (Fig. 3A and B, respectively). Under equal total RNA
assumption characteristic of conventional analysis strategies, 18.5%
and 23.7% of total detected transcripts were significantly up- and
down-regulated by the CCI treatment of low-producing cell line
A, respectively (Fig. 3A (top panel)). In contrast, under equal cell
number assumption facilitated by normalization to spike-in con-
trols, 63.0% and 0.6% of total detected transcripts were significantly
up- and down-regulated by the CCI treatment, respectively (Fig. 3A
(top panel)). These observations indicate that the CCI treatment
results in global transcriptional amplification that is correlated
to the cell volume increase. mTORI treatment had the opposite
effect on global gene expression. Under equal total RNA assump-
tion, 27.9% and 30.7% of total detected transcripts were significantly
up- and down-regulated by the mTORI treatment of cell line A,
respectively (Fig. 3B (bottom panel)). In contrast, under equal cell
number assumption facilitated by normalization to spike-in con-
trols, 2.6% and 55.2% of total detected transcripts were significantly
up- and down-regulated by the mTORI treatment, respectively
(Fig. 3B (bottom panel)), suggesting global transcriptional repres-
sion. Analogous effects of normalization strategy on determination
4. D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69 61
Fig. 1. Cellular responses of two antibody-producing CHO cell lines to treatment with either the cell cycle inhibitor (CCI) or the mTOR inhibitor (MTORI). (A) Viable cell
density (VCD), (B) culture viability, (C) cell diameter, (D) cell volume, (E) specific productivity (qP ) and (F) specific productivity adjusted by volume factor are shown for the
3-day time-course of compound treatment. Volume factor represents fold change with respect to the average cellular volume of day 0 controls for each cell line. Cell line A
is a low-producing cell line and cell line B is high-producing. Data represent mean ± SD of three biological replicates.
of differential expression between conditions were observed in
high-producing cell line B (Fig. S1), suggesting robustness of the
spike-in normalization approach.
Supplementary material related to this article can be found,
in the online version, at http://dx.doi.org/10.1016/j.jbiotec.
2014.08.037.
The ERCC normalization did not significantly alter the list of
genes identified as most highly regulated. In fact, the list of sig-
nificantly up-regulated genes by the CCI treatment obtained by
performing ANOVA on un-normalized RPKM values was a subset
of a larger list obtained by performing ERCC normalization prior to
differential expression analysis in both cell lines (Fig. S2). Similarly,
the list of significantly down-regulated genes by mTORI treatment
generated by ANOVA on un-normalized RPKM values was a subset
of a larger list obtained by performing ANOVA on ERCC-normalized
RPKM values in both cell lines (Fig. S2). However, ERCC normal-
ization shifted the fold change up for those samples treated with
the CCI and shifted it down for those treated with the mTORI. The
detailed analysis of the impact of the CCI and the mTORI on specific
genes will be the subject of a separate manuscript.
5. 62 D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69
Fig. 2. Global gene-expression comparison of control samples to samples treated with either the cell cycle inhibitor (CCI) or the mTOR inhibitor (MTORI). (A) Total RNA
extracted from 3 million cells per condition on day 3 of the treatment time-course with cell lines A and B. Three panels represent comparisons of total RNA, cell volume and
the ratio of total RNA to cell volume by cell line and treatment. Data represent mean ± SD of three biological replicates plotted as fold of control condition. **P < 0.01 and
***P < 0.001 represent statistically significant differences between untreated controls and either CCI or MTORI treated conditions. Volcano plots depicting results of 2-way
ANOVA analyses comparing treatment and control conditions in cell line A (B) and cell line B (C) using either un-normalized or ERCC-normalized RPKM values are shown.
Each panel is constructed by plotting the log 2-fold change against the – log 10 of the p-value. Red lines designate fold-change and significance cutoffs: |log 2-fold change| = 1
and −log 10(p-value) = 2.
Supplementary material related to this article can be found,
in the online version, at http://dx.doi.org/10.1016/j.jbiotec.
2014.08.037.
In addition to the application of the ERCC spike-in normaliza-
tion to measuring global gene expression, ERCC spike-in controls
could also be used for analysis of individual transcripts by qPCR.
Application of ERCC normalization to measurement of monoclonal
antibody (mAb) expression in cell lines A (Fig. 4A and B) by qPCR
demonstrated advantages over conventional normalization to
“housekeeping” genes. In order to assess mAb expression, sets
of primers were designed to measure the levels of the selectable
markers associated with the LC and the HC expression. In addition,
molecule-specific LC and HC primers were used to further evaluate
mAb expression in the low- and the high-producing cell lines.
mAb expression in each cell line was assessed utilizing either the
conventional qPCR normalization via comparison to the average
of the housekeeping gene expression (Fig. 4A and B, top panel) or
the normalization to the expression average of the spiked-in ERCC
controls (Fig. 4A and B, bottom panel). Sets of primers to detect the
top 4 most abundant ERCC spike-in standards were designed to
enable spike-in normalization strategy for qPCR. Normalization to
spike-in controls, but not to housekeeping genes, revealed statisti-
cally significant differences in mAb expression between untreated
controls and either the CCI or the mTORI treated conditions in both
cell line A and cell line B (Fig. 4A and B, respectively).
The CCI-mTORI study established the utility of spike-in nor-
malization for the experimental treatments that change cell size
and/or cause global transcriptional amplification or repression. We
sought to examine other types of experiments that would benefit
from ERCC normalization for gene expression comparisons. Multi-
cell line studies represent a common type of omic experiments
frequently performed in the biopharmaceutical industry setting
to examine the biological basis of desired phenotypic parameters
(Kang et al., 2014). Six CHO-derived mAb-expressing cell lines (A–F)
with different productivity levels were run in a 10-day fed-batch
production process with bolus feeds on days 3, 6 and 8 (Fig. 5), as
6. D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69 63
Fig. 3. Effects of ERCC normalization on detection of significant changes in gene expression. Significantly up-regulated (red) and down-regulated (blue) transcripts detected
by RNA-seq following 3-day treatment of cell line A with either (A) cell cycle inhibitor (CCI) or (B) mTOR inhibitor (MTORI) for either un-normalized (upper panel) on
ERCC-normalized (lower panel) RPKM data. For each panel, average expression value (intensity) is plotted against log 2-fold change, and statistical significance of differential
expression is determined by the p-value cutoff (p-value ≤ 0.01). (For interpretation of the references to color in this figure legend, the reader is referred to the web version
of this article.)
previously described (Fomina-Yadlin et al., 2014). The six cell lines
exhibited different growth profiles (Fig. 5A), but all maintained rea-
sonably high viability throughout the 10-day fed-batch (Fig. 5B).
The two largest cell lines, cell lines E and F, achieved lower cell den-
sities in the fed-batch production cultures, a characteristic we have
also observed for other large cell lines (unpublished observations).
In addition to growth differences, the cell lines covered a wide
range of qP values during the production process, and qP s increased
over the course of the fed-batch for all but one cell line (cell line F)
(Fig. 5C). These six cell lines were originally selected for this study
because of their diverse cell sizes during growth and propagation.
In production, cellular volume fluctuated slightly among different
daily measurements, but remained fairly consistent for 5 cell lines,
whereas the volume of cell line F increased dramatically over the
course of the fed-batch production process (Fig. 5D). Cell lines in
this study can be ranked by productivity (Fig. 5C), but the ranking
changes if cellular volume is factored into the calculation (Fig. 5E).
Specific productivity is defined independent of cell size. However,
certain cell lines that have an apparent sought-after productivity
profile become less desirable when you factor in their cellular vol-
ume (cell line F), while others remain top-ranked (cell lines B and
D). The advantages of the cell lines B and D can be further visualized
by plotting specific productivity against cell line volume (Fig. 5F).
Total RNA extracted from the same number of cells on day 6 of
the fed-batch production process varied significantly across the six
cell lines in this small multi-cell line study (Fig. 6A), with larger cell
lines having a greater total RNA yield (Fig. 6B). However, normaliza-
tion of total RNA to cell volume eliminated most of the significant
differences (Fig. 6C). Conventional normalization of mAb expres-
sion to housekeeping genes was able to distinguish the cell lines
from the opposite ends of the productivity spectrum (cell lines A
and B) (Fig. 6D). However, only ERCC normalization revealed that
larger cells (cell lines E and F) had more product expression, which
was masked by conventional normalization to housekeeping genes
(Fig. 6D and E). The ERCC-normalized mAb signal to cellular volume
identified cell line B as the most productive in terms of cell size
(Fig. 6E).
In order to further expand the application of the spike-in nor-
malization methodology, we sought to examine other culture
conditions relevant to industrial bioprocess that can affect cell
size. Specifically, a sudden increase in culture osmolarity has been
shown to induce rapid cellular shrinking followed by hyperosmotic
7. 64 D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69
Fig. 4. Gene expression measurements by qPCR for cell line A (A) and cell line B (B) following 3-day treatment with either the cell cycle inhibitor (CCI) or the mTOR inhibitor
(MTORI). Gene expression measurements are classified into three categories: ERCC spike-in controls (ERCC1, 2, 3, 4), mAb expression, and housekeeping genes (ActB, B2M,
TBP). Two panels correspond to two normalization strategies: conventional qPCR normalization to the average of housekeeping gene expression (top panel) and normalization
to the expression average of spiked-in ERCC controls (bottom panel). Data represent mean ± SD of three biological replicates. *P < 0.05, **P < 0.01 and ***P < 0.001 represent
statistically significant differences in mAb expression between untreated controls and either CCI or MTORI treated conditions.
regulatory volume increase (Schliess et al., 2007). In a third study,
two osmolarity levels were chosen to model normal (300 mOsm/L)
and high (450 mOsm/L) culture osmolarity. Three different cell lines
were chosen from the panel of cell lines used for the multi-cell line
comparison to examine osmolarity effects: cell lines B, D and F.
High osmolarity arrested cell growth, while maintaining culture
viability over the course of 48-h treatment in all 3 cell lines (Fig.
S3A). Furthermore, high culture osmolarity caused cellular volume
to increase in all cell lines examined (Fig. S3A). Consistent with
the volume increases, total RNA extracted from the same number
of cells at the 48-h time-point differed significantly between the
normal and the high osmolarity levels in each cell line (Fig. S3B).
However, observed differences in the total RNA yield resulted from
the cell size increases, as evident through normalization to cell vol-
ume (Fig. S3C). Normalization to spike-in controls corresponding
to cell numbers revealed significant differences between normal
and high osmolarity conditions for each cell line (Fig. S3D). Further
adjustment by volume established that observed volume increases
accounted for transcriptional amplification induced by high osmo-
larity in cell lines B and F (Fig. S3D). However, mAb expression in
cell line D was exceptionally transcriptionally responsive to the
osmolarity changes, because the cellular volume increase could
not account for the entire magnitude of the up-regulation of its
mAb-specific gene expression in high osmolarity condition (Fig.
S3D).
Supplementary material related to this article can be found,
in the online version, at http://dx.doi.org/10.1016/j.jbiotec.
2014.08.037.
4. Discussion
Experimental treatments can alter either transcriptome com-
position, RNA amount per cell, or both (Fig. 7A). When cellular
RNA amount does not change, conventional normalization is well
suited for differential gene expression comparisons among exper-
imental conditions. However, in all cases of changing cellular RNA
amount, conventional normalization strategies mask those changes
and can, therefore, only be used for comparisons of RNA compo-
sition. In contrast, spike-in normalization to cell number allows
comparisons of RNA amount. The three studies described in this
manuscript unequivocally establish the utility of spike-in normal-
ization for experimental comparisons that involve differences in
cell size and RNA yield per cell.
These three case studies challenge traditional normalization
strategies used for RNA-Seq and qPCR readouts and expand the
application of previously-described spike-in normalization (Loven
et al., 2012) to several representative experimental comparisons
frequently performed during gene expression studies by the
biopharmaceutical industry. Conventional gene expression mea-
surement techniques, which assume no change in total RNA yield
8. D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69 65
Fig. 5. Phenotypic comparisons of 6 antibody-expressing CHO cell lines over the course of a fed-batch production process. (A) Viable cell density (VCD), (B) culture viability,
(C) specific productivity (qP ), (D) cell volume and (E) specific productivity adjusted by volume factor are shown during 10-day fed-batch productions for 6 cell lines with
diverse expression levels (cell lines A, B, C, D, E and F). Volume factor represents fold change with respect to the average cellular volume of cell line C on day 0. Data represent
mean ± SD of three biological replicates. (F) Relationship between cell volume and specific productivity on day 6 of a fed-batch production process. Desirable cell lines are
circled.
from a set number of cells, inherently mask any differences in cel-
lular RNA amount among experimental conditions (Fig. 7A and
B). We demonstrate that cell size, which varies with certain small
molecule treatments, among cell lines and during production pro-
cesses, should be factored into normalization and determination of
differential expression. Synthetic spike-in standards allow normal-
ization to cell number and reveal differences in cellular RNA content
among experimental conditions with unequal cell size (Fig. 7C).
Experimental conditions that cause global transcriptional
amplification/repression also require alternative normalization
strategies because conventional strategies distort differential gene
expression comparisons. Only spike-in normalization to cell num-
ber was able to detect the drastic differences in the directionality
of global gene expression changes between CCI and mTORI treat-
ments in the two phenotypically different cell lines. Utilizing this
technique, we were able to demonstrate that inhibition of cell cycle
progression leads to global transcriptional amplification and inhi-
bition of mTOR signaling leads to global transcriptional repression.
A previous study with a genetic cell cycle inhibitor showed that
cell cycle progression stopped, yet the cell size, RNA content, pro-
tein content, qP, and mitochondrial content continued to increase
(Bi et al., 2004). To account for these effects, the authors pro-
posed that cell cycle inhibition decouples cell growth from cell
cycle progression, i.e. it blocks cell division, but cell growth con-
tinues, resulting in larger cells (Bi et al., 2004). Consistent with
that interpretation, RNA content per cell and qP increased during
treatment of our two cell lines with the CCI (Fig. 1A). There-
fore, global transcriptional amplification can account for much
of the qP increase observed for cells treated with the CCI. How-
ever, the volume adjusted titer was higher in CCI treated cells
indicating the CCI may have effects independent of the volume
increase (Fig. 1F). Furthermore, the mTORI result suggests that
9. 66 D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69
Fig. 6. Effects of normalization on gene expression measurements for the 6 antibody-expressing CHO cell lines. (A) Total RNA extracted from 3 million cells per cell line
on day 6 of the fed-batch production process, ranked by the RNA yield (left-to-right). (B) Scatter plot of the total RNA yield from 3 million cells and cell volume. (C) Total
RNA yield for each cell line adjusted by volume factor. Volume factor represents fold change with respect to the average total RNA yield of cell line C. (D) Gene expression
measurements of two spike-in controls (ERCC1 and ERCC2), selectable markers of the antibody light chain (LC) and heavy chain (HC) and two housekeeping genes (ActB and
B2M) by qPCR normalized to the housekeeping gene expression average and expressed as fold cell line C. (E) Gene expression measurements by qPCR normalized to the
ERCC expression average (top panel) and normalized to ERCC average and adjusted by volume (bottom panel). All data represent mean ± SD of three biological replicates.
*P < 0.05, **P < 0.01 and ***P < 0.001 represent statistically significant differences in gene expression between cell line C and 5 other cell lines.
transcriptional amplification/repression can be independent of
detectable cell size changes. Thus, spike-in normalization should
be required for all experimental comparisons with unequal total
RNA yield extracted from the same number of cells. Hypotheti-
cally, an alternate strategy could be to normalize to total RNA yield
from equal cell numbers rather than to synthetic spike-in controls.
However, this approach requires that the ratio of mRNA-to-rRNA
is consistent across cell lines and treatments, but previous studies
indicate that the ratio can vary significantly (Johnson et al., 1977;
Solanas et al., 2001), arguing against this strategy. In addition, nor-
malization to spike-in controls added before RNA extraction should
account for all the variability introduced by sample processing and
sequencing, further arguing that spike-in normalization should be
the method of choice.
One puzzling result was that cells treated with the mTOR
inhibitor had similar antibody qP despite lower levels of total RNA
as well as lower LC- and HC-associated RNAs. However, others have
shown that mTOR inhibitors can increase expression of heterolo-
gous proteins due to a delay in both apoptosis induction and cell
viability drop (Lee and Lee, 2012). These authors also found that
mTOR inhibitors had minimal impact on specific productivity (Lee
and Lee, 2012). In addition, previous studies have demonstrated
that mTOR inhibitors reduce translation of specific sets of mRNAs
more than others (Hsieh et al., 2012). We speculate that in our
10. D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69 67
Fig. 7. Graphical comparison of normalization strategies for analysis of global gene expression. (A) Graphical representation of the four possible impacts of experimental
treatment on gene expression. Experimental treatment outcomes are classified based on changes in RNA composition and RNA amount. Transcript abundance of genes A
(black), B (blue) and C (red) is color coded to visualize changes. (B) Conventional normalization strategy assuming equal cellular RNA content across experimental conditions
is compared to (C) the spike-in normalization strategy that allows normalization by cell number. Two outcomes for the volcano plots shown in (C) depend on the direction of
differential expression comparison utilizing the spike-in normalized data, i.e. whether the black cell is the control condition and the blue cell is the experimental condition
(left volcano plot), or vice versa (right volcano plot). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
11. 68 D. Fomina-Yadlin et al. / Journal of Biotechnology 189 (2014) 58–69
experimental system, the mTOR inhibitor decreases translation of
some cellular proteins, but has a limited impact on heterologous
protein translation, so the cells maintain similar levels of antibody
expression.
In addition to application of spike-in normalization for com-
parison of experimental treatments that alter cellular RNA content
within a cell line (e.g. small molecule treatments and culture osmo-
larity), we have demonstrated the utility of this normalization
strategy for multi-cell line studies. Specifically, we suggest that the
spike-in normalization to cell number should be performed before
correlating gene expression with desirable phenotypic parameters
of CHO cell lines. Furthermore, these data indicate that qP cal-
culations could give misleading information concerning cell line
suitability for certain types of manufacturing. Since large cells typ-
ically have more RNA (Figs. 2A and 6B), and presumably protein,
the qP calculation is inherently biased toward larger cells. In fact
several studies have demonstrated cell size to be the major pro-
ductivity determinant in CHO cell lines (Dinnis and James, 2005;
Kim et al., 2001; Lloyd et al., 2000). In our multi-cell line study,
plotting qP versus cell volume can help rank cell lines (Fig. 5F), and
clearly distinguishes cell lines B and D that have lower qP, compared
to cell line F, but higher qP adjusted for cellular volume (Fig. 5E).
Therefore, a less biased qP would be based on a “per unit volume”
rather than a “per cell” calculation. This type of calculation would
be more appropriate for cell line development and final clone selec-
tion, which aim to develop cell lines with small size and high qP.
It would be particularly valuable for perfusion cultures where cell
mass can account for >40% of the reactor volume (Schirmer et al.,
2010), and smaller cell lines can achieve the higher cell densities
than larger cells for the same amount of cell mass.
The three studies described in this manuscript demonstrate
that in addition to application of the ERCC normalization to global
gene expression measurements, this normalization strategy can be
applied to measurements of individual gene expression by qPCR.
The problem of “housekeeping” gene selection that plagues the
whole approach to qPCR stems from the variability in the baseline
“housekeeping” gene expression and distinct responses of “house-
keeping” genes to various experimental treatments. Spike-in ERCC
controls eliminate the need for housekeeping gene selection and
reveal cell size dependent differences in gene expression by allow-
ing normalization to cell number. Application of the spike-in
normalization for qPCR would not require addition of the entire
ERCC Master Mix containing 92 synthetic RNAs to the sample, but
could rely on spiking-in either individual or a few synthetic RNA
standards proportional to the number of collected cells.
Data normalization strategies affect interpretation of both
global transcriptional analysis and analysis of individual trans-
cripts. Our results argue that spike-in normalization to cell number
should become a widespread practice for evaluation of gene expres-
sion, and that addition of spike-in controls to reflect cell number
should at least be used in all gene expression experiments where
the “no change in RNA yield per cell” assumption is not valid across
conditions. Furthermore, similar spike-in normalization strategies
should be developed and applied to other ‘omic techniques, such
as proteomics and metabolomics, where the cellular content of
the analyte of interest (e.g. protein or metabolite) varies among
experimental conditions.
Acknowledgements
We thank the Small Molecule Team for CHO Cell Growth and
Metabolism for sharing data and experimental conditions. We also
thank Scott Freeman, Kelsey Anderson and Natalie Jones for per-
forming antibody titer analysis. We also thank Rajnita Charan,
Sumana Dey and Louiza Dudin for media preparation. We thank
GENEWIZ, Inc. for performing RNA library preparations, sequenc-
ing reactions, and initial bioinformatics analysis. Finally, we thank
Brian Follstad for useful discussions on data analysis and presenta-
tion.
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neurons displayed reduced size and cellular RNA content, but con-
ventional global gene expression analysis revealed no change in
the majority of genes (Li et al., 2013). In contrast, addition of
spike-in standards proportional to cell numbers revealed global
transcriptional and translational repression as a consequence of
MECP2 deletion (Li et al., 2013), advocating application of this anal-
ysis technique to all comparisons involving differences in cellular
size.
The onset of the systems biology era in biotechnology has been
defined by a wide variety of ‘omics-based approaches to charac-
terize the biological basis of desired phenotypic parameters and
manipulate them to enhance heterologous protein expression in
Chinese Hamster Ovary (CHO) cell lines (Kildegaard et al., 2013).
So far, only conventional techniques that assume invariant total
RNA yield per cell have been used to evaluate gene expression
of CHO cells. For example, many studies have been performed to
elucidate the biological basis of specific productivity (qP) and pre-
dict qP from gene expression profiles (Clarke et al., 2011a,b, 2012;
Doolan et al., 2012; Kang et al., 2014; Nissom et al., 2006; Yee et al.,
2009), but none took into account potential differences in cellu-
lar size or total RNA content. Other studies examining effects of
small molecule treatments (e.g. sodium butyrate, Kantardjieff et al.,
2010) or bioprocess conditions (e.g. temperature, Kantardjieff et al.,
2010 and culture osmolarity, Shen et al., 2010) on gene expres-
sion in CHO cells also did not take into account changes in cellular
RNA content. For this study, we selected two small molecules, one
targeting cell cycle progression and the other targeting mTOR sig-
naling, which were hypothesized to have opposite effects on cell
size in our biological system. Cell cycle arrest, achieved by either
overexpression of an endogenous cell cycle inhibitor (p21CIP1) (Bi
et al., 2004) or addition of small molecule inhibitors that cause
G1 or G1/S arrest (Du et al., 2014; Fingar et al., 2002), has been
previously shown to increase mammalian cell size. mTOR sig-
naling through its downstream effectors, ribosomal protein S6
kinase (S6K1) and eukaryotic initiation factor 4E-binding protein
(4EBP1), has also been demonstrated to regulate mammalian cell
size (Fingar et al., 2002). In fact, mTOR overexpression in CHO-
K1 cells stimulated cell cycle progression by promoting G1-to-S
phase transition and increased cell size (Dreesen and Fussenegger,
2011). We showed that the assumption of constant cellular size and
total RNA yield was inaccurate for several small molecule treat-
ments, multi-cell line comparison studies and changes in culture
osmolarity. Therefore, we assessed the use of spike-in standards
for evaluation of differential gene expression in CHO cells, focusing
on case studies relevant to cell line development and production
processes.
2. Materials and methods
2.1. Cell culture and experimental treatments
Six CHO-derived cell lines (Rasmussen et al., 1998), each
expressing a different monoclonal antibody (cell lines A, B, C, D,
E and F), were cultured in a proprietary chemically-defined growth
media in vented shake-flasks at 36 ◦C, 5% CO2, 70% relative humid-
ity and shaken at 150–160 rpm in Kuhner incubators. Each cell
line was generated using a proprietary expression system. A sin-
gle mRNA encoded both the antibody light chain (LC) and the LC
selectable marker as they were linked by an Internal Ribosome
Entry Site (IRES) sequence. Similarly, single mRNA encoded both
the HC and the HC selectable marker as they were also linked by an
IRES sequence. Viable cell density (VCD), viability and cell diameter
were measured with a ViCell automated cell counter (Beckman-
Coulter, Inc., Brea, CA). Cellular volume was calculated using the
formula for the volume of a sphere: V = 4/3 r3.
For the CCI-mTORI treatment study, cells were seeded from
day 4 growth cultures at 10 × 106 viable cells/mL into proprietary
chemically-defined production medium in vented 24 deep-well
plates (3 mL volume per well) and shaken at 220 rpm in Kuhner
incubators. Cultures were treated on day 0 with either CCI (Du et al.,
2014) (Amgen proprietary, PCT/US2013/074366, 10 M final con-
centration), mTORI (Amgen proprietary, WO/2010/132598, 0.5 M
final concentration) or vehicle control (DMSO, 0.1% final concen-
tration) and daily medium exchanges were performed using fresh
media containing an appropriate small molecule (CCI, mTORI or
DMSO). Percent DMSO was kept constant among all experimental
conditions. Spent medium was used for daily titer measure-
ments and daily specific productivity (qP) calculations. Titer
measurements were performed by affinity High Performance Liq-
uid Chromatography (HPLC) using POROS A/20 Protein A column.
Daily qP (pg/cell/day) was calculated according to the simplified for-
mula: qP = daily titer/daily VCD. On day 3, 3 × 106 viable cells were
collected per condition for gene expression analysis (biological trip-
licates), snap-frozen and stored at −70 ◦C for further processing.
For the multi cell line study, ten-day production assays with
bolus feeds on days 3, 6 and 8 were performed in chemically-
defined production medium in vented shake-flasks as previously
described (Fomina-Yadlin et al., 2014). Titer samples were collected
on days 3, 6, 8 and 10. For each interval between days [m, n], qP was
calculated according to the formula: qP = titern/
n
m
VCDdt/(tn −
tm), where titern is the measured cumulative titer at tn and time (t)
is expressed in days. On day 6, 3 × 106 viable cells were collected
per condition for gene expression analysis (biological triplicates),
snap-frozen and stored at −70 ◦C.
For osmolarity level study, cells were seeded from day 3 growth
cultures by 1:5 split at ∼0.5 × 106 viable cells/mL into proprietary
chemically-defined growth media in 24 deep-well plates (3 mL vol-
ume per well). Osmolarity was adjusted at 0-h time-point with 5 M
NaCl solution in growth medium, as previously described (Shen
et al., 2010). Daily VCD, viability and cell diameter measurements
were performed. On day 2, 1 × 106 viable cells were collected per
condition for gene expression analysis (biological triplicates), snap-
frozen and stored at −70 ◦C.
2.2. ERCC spike-in and RNA extraction
Addition of External RNA Controls Consortium (ERCC) controls
was done as previously described (Loven et al., 2012). Specifically,
1 L of 1:10 diluted ERCC RNA Spike-In Mix 1 (Ambion®, Life Tech-
nologies, Grand Island, NY) was added per 1 × 106 cells. ERCC was
added to frozen cell pellets with RLT lysis buffer, and total RNA was
isolated with the RNeasy Mini kit (Qiagen, Valencia, CA) accord-
ing to the manufacturer’s protocol, including optional on-column
DNAse I digestion, and using 100 L elution volume. RNA concen-
tration was measured on the Nanodrop 2000 (Thermo Scientific,
Wilmington, DE), and RNA quality was assessed using the 2100
Bioanalyzer (Agilent, Santa Clara, CA) with the RNA 6000 Nano Kit
(Agilent, Santa Clara, CA) to ensure all samples used for RNA-Seq
analysis had RNA Integrity Number (RIN) >9.
2.3. RNA-Seq sample processing and analysis
RNA library preparations, sequencing reactions, and initial
bioinformatics analysis were conducted at GENEWIZ, Inc. (South
Plainfield, NJ). Illumina TruSeq RNA library preparation, clustering,
and sequencing reagents were used throughout the process follow-
ing the manufacturer’s recommendations (Illumina, San Diego, CA).
Briefly, 1 g of total RNA was used as starting material for library
preparation with the Illumina Truseq RNA preparation Kit V2. Poly-
T oligo-attached magnetic beads were used to purify mRNA, which
was then fragmented for 8 min. at 94 ◦C. First strand and second](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)