Automated cell cycle and cell size measurements for single-cell gene expression studies
Guillemin et al. BMC Res Notes
Automated cell cycle and cell size measurements for single-cell gene expression studies
Anissa Guillemin anissa.guillemin@ens‑lyon.fr 0 2
Angélique Richard 0 2
Sandrine Gonin‑Giraud 0 2
Olivier Gandrillon 0 1 2
0 Laboratoire de biologie et modélisation de la cellule. LBMC‐Ecole Normale Supérieure‐Lyon, Université Claude Bernard Lyon 1, Institut National de la Santé et de la Recherche Médicale: U1210‐ Ecole Normale Supérieure de Lyon, Centre National de la Recherche Scientifique: UMR5239 , 46 Allée d'Italie, 69007 Lyon , France
1 Inria Dracula , 69100 Villeurbanne , France
2 Laboratoire de biologie et modélisation de la cellule. LBMC‐Ecole Normale Supérieure‐Lyon, Université Claude Bernard Lyon 1, Institut National de la Santé et de la Recherche Médicale: U1210‐Ecole Normale Supérieure de Lyon, Centre National de la Recherche Scientifique: UMR5239 , 46 Allée d'Italie, 69007 Lyon , France
Objectives: Recent rise of single‑ cell studies revealed the importance of understanding the role of cell‑ to‑ cell variability, especially at the transcriptomic level. One of the numerous sources of cell‑ to‑ cell variation in gene expression is the heterogeneity in cell proliferation state. In order to identify how cell cycle and cell size influences gene expression variability at the single‑ cell level, we provide an universal and automatic toxic‑ free label method, compatible with single‑ cell high‑ throughput RT‑ qPCR. The method consists of isolating cells after a double‑ stained, analyzing their morphological parameters and performing a transcriptomic analysis on the same identified cells. Results: This led to an unbiased gene expression analysis and could be also used for improving single‑ cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell‑ to‑ cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.
Cell size; Cell cycle; Gene expression; Single‑ cell transcriptomic
It has been known for decades that isogenic cells can
differ from each other in their molecular composition
]. The refinement of molecular techniques together
with computational approaches has recently allowed to
get a quantitative view on this cell-to-cell variability. This
strongly highlighted the importance of understanding
the causes in such variations, leading to a recent turning
point in single-cell studies [
A leading source of cell-to-cell variability can be
attributed to stochastic gene expression [
factors contribute to cell-to-cell variability such as reactions
involving a low-copy number of molecules especially
during transcription processes [
5, 7, 10, 11
differences in the internal states of a cell population such as
cellular age or cell cycle stage. In litterature, we can find
contradictory results regarding the influence of cell cycle
and cell size on gene expression. Some studies argued
that both of these morphological parameters affect gene
expression variation [
] whereas, others support
that this impact is negligible [
Stochastic gene expression takes various biological
]. In a cell fate context, stochastic gene
expression could drive cells into the differentiation
]. It has been shown that during the erythroid
differentiation process, we can observe an increase in
cell-to-cell variability among genes expression that may
participate to the decision making process within
Together, these information highlight the importance
to precisely identify the sources of gene expression
variability involved in these phenomena in order to
understand their role, and to discard potential confounding
Cell cycle variability can be identified and suppressed
by fluorescent-labeling of cell cycle-specific genes,
however this method requires genetical alteration of the
investigated cells [
]. Other studies, based on
computational approach, deconvolute the cell cycle variables in
order to normalize their single-cell gene expression data.
Most of them use cell cycle marker genes to train
algorithms that can predict cell cycle stage of individual cells
14, 27, 28
]. However, these genes have different function
or timing according to cell type, even in a same organism
In this article, we propose a more direct approach that
consists in measuring morphological parameters in a
high-throughput single-cell RT-qPCR study. Using a
noncytotoxic double-staining technique we measured
automatically cell cycle phase and cell size of every single-cell
isolated from T2EC, a primary chicken erythroid
progenitor cells [
]. We demonstrated that the labelling had no
detectable effects at the single-cell transcriptomic level in
those primary progenitors, suggesting that this technique
could be an useful tool for molecular single-cell based
We finally showed that in our cellular system neither
cell size nor cell cycle state could be deemed responsible
for the cell-to-cell variation we observed, ruling out their
potential confounding effects.
T2EC were extracted from bone marrow of 19 days-old
SPAFAS white leghorn chickens embryos (INRA, Tours,
France). The composition of the culture medium has
been previously described [
Cells were incubated in their initial medium for 30 min
with CFSE (5-(and 6)-carboxyfluorescein diacetate
succinimidyl ester, Life Tech.) at 5 μM and Hoechst 33342
(Life Tech.) at 5 μg/mL at 37 °C in a tube protected from
light. After 2 washings in phosphate-buffered saline (PBS,
Life Tech.), cells were loaded in the C1 system (Fluidigm).
RT‑qPCR at population level
Cell culture were washed with PBS 4 h after the
doublestaining. Total RNA was extracted using RNeasy Mini Kit
Reverse transcription assays were performed using the
Superscript III First-Strand Synthesis System (Invitrogen)
for 500 ng of total RNA.
Real-time PCR was performed with SYBR Green PCR
Kit (ClonTech) in the CFX96 real-time PCR system
(Biorad). Specific primers were used to quantify the
expression of genes [
RT‑qPCR at single‑cell level
• From cell isolation to pre-amplification Cells were
diluted with C1 cell suspension reagent (Fluidigm) at
a concentration of 4 × 105 cells/mL. This step was
followed by a cell filtration in a cellular sieve (50 μm).
Cells were loaded in the C1 IFC (5–10 μm trap size,
Fluidigm). The C1 system performed the cell isolation
and pictures were taken with 2 different lasers (UV
laser providing excitation at ∼ 350 nm and another
at ∼ 488 nm) using a PALM-STORM NIKON
Microscope (CIQLE). Then, the microplate was back in the
C1 system where lysis, reverse-transcription and
preamplification was performed. Primers have been
previously described [
]. cDNA were loaded in a classic
96 well plate and conserved at − 20 °C until the
• Biomark real-time PCR quantification of cDNA were
performed using EvaGreen following the Fluidigm
user guide available on their website. Each
condition was loaded in parallel in the same
microfluidicbased chip to avoid chip-to-chip technical variability.
An IFC Controller HX performed the load of cDNA
samples and primers from the inlets into the chip.
The Biomark HD analyzed the chip according to
the GE 96 × 96 PCR + Melt v2.pcl program. RNA
spikes were used as positive control to validate the
RT-qPCR experiment. From this outlet, the analysis
software generated cycle of quantification values (Cq)
for each reaction.
Each image corresponding at each lasers used were
analyzed following a previously described procedure [
We visually confirmed the capture for each well and
extracted automatically morphological information using
ImageJ. After checking that all cells were detected by the
software, we run the measurement of cell area (CFSE),
nucleus area and intensity (Hoechst). The cell-volume (2)
was then calculated from area measurements (1) using
these following formulae:
with r the radius of cell, S the area and V the cell volume
Analysis of gene expression
For population RT-qPCR analysis, ratios of gene
expression variation between conditions were calculated
following this following formulae [
]. Because of its low
variability between all conditions, HnRNP was used as
referential gene in these analyses.
V = 3
× π × r3
For single-cell RT-qPCR, raw Cq data was then
computed using R [
] via a specific script that was previously
]. Some genes were excluded from analyses
due to the quality control during the RTqPCR. The
output file comprising absolute values of mRNA was used
as a template for all following analysis. Statistical
nonparametric tests were performed: correlations between
gene expression and cell morphological parameters were
performed using spearman tests. Wilcoxon tests were
used to compare gene expression between stained and
unstained conditions. Each time, Bonferroni correction
was applied to p-values for the use of multiple tests.
PCAs were performed using ade4 package [
]. PCA was
centered (mean substraction) and normalized (dividing
by the standard deviation). PCA was displayed according
to PC1 and PC2, which are the first and second axis of
the PCA respectively.
Cellular morphological automatic measuring
We choose the two low toxic fluorescent dyes, CFSE
and Hoechst 33342 that stably incorporates into cells. In
this study, CFSE was used as a cell area marker in
tandem with Hoechst 33342 [
] as a nuclear marker. The
use of two different lasers allowed revealing each
staining (Fig. 1a, b) merged in Fig. 1c. It allowed us to
automatically measure morphological cell parameters and
We can observe that the cell volume is very poorly
correlated with the nucleus volume (Fig. 2a). Therefore cell
size by itself does not seem to be a good proxy for
determining cell cycle position probably because it integrated
other unknown parameters. Both cell and nucleus
volume density distributions confirm that cell size spans a
much larger range than the nucleus size which displays
the classical 2n/4n distribution (Fig. 2b). Nuclear-volume
was clearly more correlated with Hoechst fluorescence
intensity than cell-volume (Fig. 2a, c). The nucleus
volume can therefore be considered as a good indicator for
the position of the cell in the cell cycle. Furthermore it
should be noted that volume is a purely geometrical
object that is not influenced by the laser bleaching, as
Hoechst fluorescence intensity parameter.
We therefore described a double-staining procedure
compatible with microscopy associated at the C1 system
to measure, for each cell, their size and cell cycle state
First, we assessed the influence of the double-staining
procedure on gene expression at the population level by
performing RT-qPCR on 5 selected genes known to be
involved in erythroid differentiation or metabolism. The
relative value of these gene expressions did not change
significantly compared to unstained cells (Fig. 3a). These
results suggested that cell and nucleus staining had no
major influence on T2EC mean gene expression.
We then needed to discard possible modifications
visible only at the individual-cell level. Therefore we
performed high-throughput RT-qPCR on single cells using
77 genes that cover various functions as metabolism,
differentiation process and proliferation [
]. We compared
30 single stained cells and 47 single unstained cells in the
same microchip. Data was analyzed using a PCA-based
dimensionality reduction algorithm (Fig. 3b) as well as
Wilcoxon signed-rank tests (see Additional file 1: Table
S1). The PCA does not show any separation between
both conditions (PC1 and PC2 explained less than 12%
of the variability), and the statistical analysis shows
that no gene was significantly varying between the two
c 1000 pρ-v=al0u.e92= 3.217 e-16
Nucleus volume (μm3)
conditions. These results therefore show that the staining
did not affect the expression of these 77 genes in T2EC
even when examined at the single-cell level.
Finally as an application example for our
doublestaining approach, we investigated the influence of cell
cycle and cell size on cell-to-cell variability among our
gene expressions using the coupling of labeling and gene
expression measurements at the single-cell level.
Cell morphological impact on T2EC gene expression
For each single cell, we measured the size, the
position in the cell cycle and the mRNA amount. Among
69 genes analyzed (retained in this study for technical
quality control), none presented a significant spearman
correlation between its expression level among single
cell volumes or cell cycle: all p-values were above the
5% threshold. These results confirmed that neither cell
size nor the position in cell cycle were relevant
parameters in explaining the cell-to-cell variations observed
for 69 genes examined. This information is important
for stochastic single-cell-based gene expression
analysis, for which these morphological parameters can be
excluded of the potential sources of variability between
We performed a non-cytotoxic CFSE/Hoechst
doublestaining compatible with the C1 system. This approach
allowed automatic identification and measure of
morphological parameters. It can be used to measure the
influence of cell cycle and cell size on single-cell gene
expression analysis without any potential
misleading cell state effects induced by cell cycle
synchronization methods. It could be also represent an alternative
method to avoid artificial cell sorting according to their
size or their cell cycle phase, which could be interesting
for low amount of cells. This is equivalent to the recently
described technique using flow cytometry [
applicable in the C1 system. As an alternative, it has recently
been described that predefined gene combination could
be used a posteriori [
]. Unfortunately, the best
combinations seems to be cell type dependent, making it
potentially limited [
We then used the Biomark system to perform gene
expression quantification. We showed that the
double staining did not impact gene expression in our cells.
Moreover, by measuring the influence of cell cycle and
cell size on the expression level of 69 genes, our results
support our previous claim that cell cycle and cell size
have a negligible influence on gene expression
variability in certain settings [
]. This is in line with the recent
demonstration that the cell cycle explains only a very
small amount (5–17%) of gene expression variability [
In this study, the main limitation was the
optimization of cell capture in the C1 microchip. We obtained a
maximum of 65% of capture whereas with other cells,
this percent raise up to 95%. Numerous parameters were
involved and have to be optimized in order to obtain
more individual cells per microchip.
Fig. 3 Analysis of the influence of the staining procedure on gene expression. a Real‑time PCR gene expression analysis of stained and unstained
cells. Total RNA was extracted from T2EC cells stained or not. Reverse transcription and real‑time PCR analyses, with specific primers [
], were per ‑
formed to quantify the amount of GLOBIN (β‑ GLOBIN), SLC (SLC25A37), HSP (HSP90AA1), CRIP2 and LDHA mRNA (Cq for cycle of quantification). The
fold variations represented here correspond to the ratio of mRNA of staining cells compared to unstained cells. The black line corresponds to the
null variation between the two conditions. The vertical bars represent the standard error of the mean value (n = 3). b Principal Component Analysis
of single cell expression data acquired on stained or unstained cells. Projection of 77 T2EC single‑ cell stained or not onto PC1 and PC2 results in a
cloud of points without any clear separation. Percentages shown are the percentage of variance explained by each component
Additional file 1: Table S1. Statistical analysis of gene expression
according to the stained or the unstained condition. Statistical tests (Wil‑
coxon) were performed for each gene between their expression in stained
and unstained condition. A Bonferroni correction was applied in p‑ values
for multiple tests.
(RT‑ q)PCR: reverse transcription quantitative polymerase chain reaction; T2EC:
TGF‑α/TGF‑β‑induced erythrocytic cells; (c)DNA: complementary deoxyri‑
bonucleic acid; (m)RNA: messenger ribonucleic acid; CFSE: carboxyfluores‑
cein diacetate succinimidyl ester; PBS: phosphate‑buffered saline; HnRNP:
heterogeneous nuclear ribonucleoprotein; GE: gene expression; PCA: Principal
Component Analysis; PC1: first principal component; PC2: second principal
AG designed and performed experiments and drafted the manuscript. ARd‑
esigned and performed experiments. OG and SGG supervised the study.All
the authors reviewed the manuscript. All authors read and approved thefinal
We thank ProfileXpert and “Plateforme d’imagerie CIQLE” (Genomique &
Microgenomique Unit, Universite Lyon 1, SFR Sante LYON‑EST, UCBL ‑INSERM
US 7‑ CNRS UMS 3453) for the use of C1 system and imagery.
The authors declare that they have no competing interests.
Availability of data and materials
The data set regarding the simultaneous measurement of gene expression,cell
volume and nucleus volume is available at: https://osf.io/8x5n7/.
Consent for publication
Ethics approval and consent to participate
This work was supported by funding from the French agency ANR (ICEBERG;
ANR‑IABI‑3096) and La Ligue Contre le Cancer (Comite de Haute Savoie).
Springer Nature remains neutral with regard to jurisdictional claims in pub‑
lished maps and institutional affiliations.
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