A New Parameter of Growth Inhibition for Cell Proliferation Assays

Abstract

Cell proliferation assays are performed by four decades to test the anti-proliferative activity of natural products and synthetic compounds in cell cultures. In cancer research, they are widely employed to evaluate drug efficacy in in vitro tumor models, such as established cell lines, primary cultures and recently developed three-dimensional tumor organoids. In this manuscript, we demonstrated that current employed parameters used by researchers to quantify in vitro growth inhibition, IC50 and GI50, lead to a misinterpretation of results based on the exponential, and not linear, proliferation of the cells in culture. Therefore, we introduce a new parameter for the analysis of growth inhibition in cell proliferation assays, termed relative population doubling capacity, that can be employed to properly quantify the anti-proliferative activity of tested compounds and to compare drug efficacy between distinct cell models. This article is protected by copyright. All rights reserved

Full text

Fiorentino, Bagella and Marchesi
A New Parameter of Growth Inhibition for Cell Proliferation Assays
Francesco Paolo Fiorentino1,2,*, Luigi Bagella2,3 and Irene Marchesi1,*
1: Kitos Biotech Srls, Tramariglio, 07041 Alghero (SS), Italy. 2: Department of Biomedical Sciences, University of Sassari, Sassari 07100, Italy. 3: Sbarro Institute for
Cancer Research and Molecular Medicine, Center for Biotechnology, Department of Biology, College of Science and Technology, Temple University, BioLife Science
Bldg. Suite 333, 1900 N 12th Street, Philadelphia, Pennsylvania 19122, USA.
*Both F.P.F.and I.M. are corresponding authors. FPF email: fpfiorentino@gmail.com; IM email: iremarchesi@gmail.com.
Cell proliferation assays are performed by four decades to test the anti-proliferative activity of natural products
and synthetic compounds in cell cultures. In cancer research, they are widely employed to evaluate drug efficacy in
in vitro tumor models, such as established cell lines, primary cultures and recently developed three-dimensional
tumor organoids. In this manuscript, we demonstrated that current employed parameters used by researchers to
quantify in vitro growth inhibition, IC50 and GI50, lead to a misinterpretation of results based on the exponential,
and not linear, proliferation of the cells in culture. Therefore, we introduce a new parameter for the analysis of
growth inhibition in cell proliferation assays, termed relative population doubling capacity, that can be employed
to properly quantify the anti-proliferative activity of tested compounds and to compare drug efficacy between
distinct cell models.
Cell proliferation assays are an extensively employed tool to
evaluate the efficacy of tested compounds on a biological ex
vivo model of interest. In anticancer drug development, they
are used to evaluate the anti-proliferative activity of tested
compounds on established tumor cell lines, primary tumor cells
and 3D tumor organoids1-6. In a typical assay, cells are plated
on a culture vessel and, after a sufficient amount of time
necessary to recover growing phase, tested compounds are
added to culture medium. At arbitrary chosen time-points, the
number of cells is estimated by cell count or by an indirect
method, such as measuring DNA synthesis, lactate-pyruvate
conv e r s i o n or ATP c o n c e n t r a t i o n7-12. A p arameter,
representative of drug efficacy, is subsequently calculated for
data representation. In this manuscript, we first describe the
two most employed parameters to analyze raw data and
represent in vitro drug efficacy: the relative cell number (R),
used to calculate the half maximal inhibitory concentration
(IC50), and the percentage of growth (PG), with an highlight
on their limitations13,14. We emphasize that, using these
parameters to compare drug efficacy between distinct cell
populations (such as cell lines), cells that grow “faster” in
culture will be inferred more sensitive than “slower” ones, and
therefore these parameters lead to a misinterpretation of the
results because of their dependency to the unique growth
properties of each cell population. Despite PG partially
overcomes this limitation, we provide in the first section of the
results a detailed description of the dependency of R, and
consequently the IC50, to cell proliferation rate because of its
frequent usage in curre nt antic ancer drug discovery
research15-32. Subsequently, in the second section of the results,
we show that PG is also dependent on the growth properties of
the cells, because of their exponential and not linear
proliferation in culture. Therefore, we introduce a new
parameter to determine growth inhibition, the relative doubling
capacity (RD), that can be used to properly quantify and
compare the anti-proliferative activity of tested compounds on
exponential growing cell models.
The Relative Cell Number is Function of Cell Proliferation
R is a widely used parameter to determine drug efficacy in
cell proliferation assays, and it is also used to calculate the
IC50 and compare drug efficacy between distinct cell models.
R can be described as:
( 1 )
where T is number of cells at the measuring point in the
compound-treated sample and U is number of cells in the
untreated control sample. R has a value from “0”, which means
maximum drug efficacy, to “100”, which means absence of any
effect by the treatment on cell proliferation. However, cell
populations that grow “faster” in culture, referring to cells that
duplicate more times in the same amount of time than “slower”
ones, tend to show lower R. This logical deduction can be
assumed if we imagine to treat two distinct cell lines (A and B)
with a drug that induces total arrest of cell growth, termed
cytostasis, in both cell lines (Figure 1A). Despite the same
phenotype is induced in both cell lines, RA of the “faster” cell
line A will be lower than RB of the “slower” cell line B, and
therefore A will be inferred more sensitive than B when
comparing drug efficacy between the two cell lines. For the
same reason, R is function of the period of treatment before
estimation of cell number: if we treat a cell line with cytostatic
doses of a drug and estimate the number of cells at distinct time
points, R logically tends to show lower values at longer period
of treatment because of the increased cell number in untreated
sample, independently by additive drug effects due to longer
exposition (Figure 1B). To experimentally evaluate these
assumptions, we treated a representative cell line, A549, with
serial dilutions of a well-characterized repr esentative
chemotherapy agent, etoposide, and live cell number was
monitored every 24 hours for three days (Figure 2).
This is the pre-peer reviewed version of the following article: A New Parameter of Growth Inhibition for Cell Proliferation Assays . Fiorentino FP, Bagella L and
Marchesi I. Journal of Cellular Physiology 2017 Oct 12. doi: 10.1002/jcp.26208. Copyright © 2017, which will be published in final form at this link
R=T
U⋅100

Fiorentino, Bagella and Marchesi
A preliminary normalization of cell number among wells at
each time point was carried out taking advantage of the
multiple readings with live-cell imaging:
where Ni is the normalized number of cells in well x at time
point i, ni is the number of cells counted in well x at time point
i and n0 is the number of cells in well x counted one hour after
treatment. Subsequently, R and IC50 were calculated using
normalized data at each time point (Supplemental Data S1 and
Table 1, respectively). A much higher coefficient of variation
was observed in the IC50 at 24 hour compared to 48 and 72
hours of treatment (Table 1). Since a concomitant higher
coefficient of variation in the proliferation of untreated cells
was not observed (Table 1), we concluded that tiny differences
in drug concentration and timing of data collection among the
biological replicates would strongly affect drug efficacy at a
short time of treatment, as 24 hour. Therefore, we compared R
and IC50 values between 48 and 72 hours of treatment. As
shown in Figure 1C, R values after 72 hours of treatment were
lower than those obtained at 48 hours. Consistently, IC50 after
72 hours of treatment was significantly lower than IC50 at 48
hours (Table 1, p<0.01, two-tail heteroscedastic t-test). Similar
results were obtained using a different set of data originated by
treatment of PC3 cells with serial dilutions of etoposide (Table
2, Figure 1D and Supplemental Data S2). Despite it could be
argued that lower R at 72 hours of treatment could be partially
or totally due to the longer exposure of cells to the drug, we
showed that R is function of cell proliferation rate, suggesting
that R would not be a proper parameter to determine drug
efficacy in cell proliferation assays, if the aim is to compare
drug sensitivity between cell lines with distinct proliferation
rates.
The Percentage of Growth Inhibition is Function of Cell
Proliferation
Researchers at the NCI’s Drug Discovery Program developed,
more than 20 years ago, a parameter to compare efficacy of
small molecules with potential anticancer activity in a panel of
60 tumor cell lines, which is still used in their program 34.
Cell number is estimated at the time of compound addition and
after 48 or 72 hours of treatment, and efficacy is determined as
percentage of growth (PG). PG is differently calculated based
on the type of effect: percentage of cell death (PGT) if a
cytotoxic effect occurs, or percentage of growth inhibition
(PGS) if a decrement of cell proliferation or cytostasis occurs.
PGT and PGS are described as:
This is the pre-peer reviewed version of the following article: A New Parameter of Growth Inhibition for Cell Proliferation Assays . Fiorentino FP, Bagella L and
Marchesi I. Journal of Cellular Physiology 2017 Oct 12. doi: 10.1002/jcp.26208. Copyright © 2017, which will be published in final form at this link
Figure 1. R is function of cell proliferation rate. (A) Cell line A
and cell line B are treated with a drug at a concentration that
induces total growth inhibition in both cell lines. Cell number is
measured at time point t. Relative cell number of A, RA, is 25 and
relative cell number of B, RB, is 50. (B) A cell line is treated at t0
with a drug at a concentration that induces cytostasis. Two
representative time points after compound addition were chosen to
estimate cell number, t1 and t2, where t1 - t0 = t2 - t1. Relative cell
number at t1, R1, is 50 and relative cell number at t2, R2, is 25. (C,
D) Relative cell number of etoposide-treated A549 (C) or PC3 (D)
cells after 48 and 72 hours of treatment. Each point refers to R
values calculated from each technical replicate of the three
biological replicates.
Figure 2. Representative time course of treatment of A549 with serial dilutions of etoposide. Multiple live-cell imaging was carried out 30
minutes, 24, 48 and 72 hours after drug addition to cells. Nuclei of cells were stained in far-red fluorescence by SiR-Hoechst and nuclei of dead
cells were highlighted in green fluorescence by Promega CellTox staining. Cell number at each time point was estimated by counting red nuclei
without positive green fluorescence signal.
i
N=i
n
0
n

Fiorentino, Bagella and Marchesi
(2)
(3)
where I is the number of cells measured at the time of drug
addition to cells. PG has a value from +100 to -100 and it is
used to determine three response parameters: 50% growth
inhibition (GI50, PG=50), total growth inhibition (TGI, PG=0)
and lethal concentration 50% (LC50, PG=-50)14. First, PG is a
more informative parameter than R to evaluate in vitro drug
efficacy since it distinguishes among growth inhibition and
cytotoxic effects. PGT describes the percentage of cells that die
as a consequence of the treatment, and therefore it does not
take into account the proliferation rate in untreated cells. In the
same manner, if cytostasis occurs, PGS is “0” regardless of cell
proliferation properties. In contrast, if a partial growth
inhibition occurs, PGS is calculated as the percentage of linear
increase in the number of treated cells to the linear increase in
the number of untreated cells. As a consequence, PGS will be
function of cell proliferation if cell proliferation shows a non-
linear progression (Figure 3A). To experimentally evaluate PG
dependency to cell proliferation rate, we calculated PG, GI50
and TGI of previously used data of etoposide-treated A549 and
PC3 cells. PG values in A549 cells after 72 hours of treatment
were lower than PG values obtained after 48 hours for drug
doses that induced growth inhibition (Figure 3B, right top
quadrant). Consistently, GI50 after 72 hours of etoposide
treatment in A549 cells was lower than GI50 after 48 hours,
despite with lesser extent than IC50 values (Table 1, p<0.05,
two-tail heteroscedastic t-test). We did not observe any
difference in PG values for drug doses that induced a cytostatic
(PG ∼0) or a cytotoxic (PG < 0) effect (Figure 3B, in proximity
of 0 values and left bottom quadrant, respectively).
Consistently, the TGI concentration did not significantly differ
between 48 and 72 hours of treatment in A549 cells (Table 1).
This latter fact supports our hypothesis that both R and PGS
decrements at 72 hours of treatment are consequence of the
increased cell number in untreated sample, and not of the
longer drug exposure, otherwise a PG decrement would be
observed also for drug doses that induced a cytostatic or
cytotoxic effect (Figure 3B). For what concerns PC3 cells,
GI50 and PGs at 72 hours of treatment showed a slighter, not
significant change between 48 and 72 hours of treatment (Table
2, Figure 3C and Supplemental Data S2).
Untreated Cell Populations Cultivated Under Standard
Conditions Grow in an Exponential Manner
We hypothesized that the occurrence of an exponential, and
not linear, growth of untreated A549 cells would contribute to
the gradual PGS decrement over the time of treatment in this
cell line. This hypothesis was based on the logical assumption
that each mother cell duplicates into two daughter cells, which
will duplicate into two new cells and so on. To evaluate which
mode of growth, linear or exponential, best fits cells
maintained under standard culture conditions, we performed
both linear and exponential regression analyses to the
previously used data of untreated A549 and PC3 cells and
calculated their coefficients of determination (R2) (Figure 4A
and Supplemental Data S3). The exponential regression of the
growth curves of untreated A549 samples showed a R2>0.99 in
all the three biological replicates performed, whereas the linear
regression showed a R2<0.95. In PC3 cells, both the
exponential and the linear regression analyses showed a
R2>0.95, with a slight higher R2 in the exponential one. It was
likely that the slighter, not significant difference of GI50 in
PC3 cells between the two time-points was due to the growth
properties of this cell line, whose fit both a linear and an
exponential regression analysis in standard culture conditions.
In contrast, our results confirmed that A549 cells grew in an
exponential manner, and the PGS decrement observed between
48 and 72 hours of etoposide treatment could be explained by
the non-linear proliferation of untreated samples in this cell
line. We next asked if a model of exponential growth would
better fit also growth curves of etoposide-treated samples
(Supplemental Data 4). We arbitrary partitioned data into
milder and stronger growth inhibition effects, using PGS 35 as
cut-off value, to evaluate more in detail the effects of growth
inhibition on the proliferation property of the cells. Samples
treated with doses of drug that induced a milder growth
inhibition (PG>35) showed similar R2 values of untreated cells
in both cell lines, and therefore we concluded that a mild
growth inhibition does not affect the growth properties of the
cells (Figures 4B and 4C). In contrast, both regression analyses
showed low R2 values in samples that induced a stronger
growth inhibition (0<PG<35), and no difference between linear
and exponential R2 values were observed (Figure 4B and 4C).
This is the pre-peer reviewed version of the following article: A New Parameter of Growth Inhibition for Cell Proliferation Assays . Fiorentino FP, Bagella L and
Marchesi I. Journal of Cellular Physiology 2017 Oct 12. doi: 10.1002/jcp.26208. Copyright © 2017, which will be published in final form at this link
T
PG =T−I
I
⋅100
S
PG =T−I
U−I
⋅100
Figure 3. PG is function of cell proliferation rate. (A) A cell line is treated at t0 with a drug at a concentration that induces a 50% cell growth
reduction. Two representative time points after compound addition were chosen to estimate cell number, t1 and t2, where t1 - t0 = t2 - t1. PGS1 at t1, is
50 and PGS2 at t2 is equal to PGS1 exclusively if cell growth progresses in a linear manner. (B, C) Percentage of growth inhibition of A549 (B) or
PC3 (C) cells after 48 and 72 hours of etoposide treatment. Each point refers to R values calculated from each technical replicate of the three
biological replicates.

Fiorentino, Bagella and Marchesi
Therefore, we concluded that a strong growth inhibition
negatively affect the exponential growth of the cells. Overall,
we confirmed that proliferation in untreated samples, in
particular A549 cells, better fit a model of exponential growth
and that this growth property is negatively affected by drug
treatment, in particular for drug doses that induce a strong
growth inhibition. We therefore speculated that PGS decrement
between 48 and 72 hours of etoposide treatment, shown in
Figure 3B, was consequence of the exponential growth
property of the cells.
The Relative Doubling Parameter Determines Growth
Inhibition of Exponential Growing Cell Populations
Based on our previous observations and conclusions, we
defined a parameter of drug efficacy representative of the
residual exponential growth in treated cell populations. Taking
into account that each cell duplicates into two daughter cells,
number of cells in an untreated population at any time point
can be described as:
(4)
where P is the number of population doublings that an
asynchronous cell population accomplished in a defined period
of time. Therefore, population doublings can be described as:
(5)
Based on the hypothesis that growth inhibition is consequence
of the impaired cell population doubling performance induced
by the treatment, and therefore capacity of cell duplication,
number of cells in a treated population (T) can be described as:
(6)
where D, the doubling capacity, is between +1 and 0 and can
consequently be described as:
(7)
To calculate the efficacy of treatment to impair the doubling
capacity of the cell population, the number of population
doublings accomplished by untreated cells was applied to
treated sample. RD can be described as:
(8)
Whereas PGS is the relative linear growth of treated sample to
the linear growth of untreated sample, RD can be described as
the doubling capacity of treated sample to the doubling
capacity of untreated sample. As for R and PGS, RD has a
value from 0 to 100 and can be used as well to determine two
response parameters: 50% doubling capacity inhibition (RD50,
RD=50) and total doubling capacity inhibition, which
corresponds to cytostasis as for TGI (RD0, RD=0). We
calculated RD, RD50 and RD0 of etoposide-treated A549 and
PC3 cells using previously used data of etoposide treatment. In
Figures 5A and 5C we show RD at 48 and 72 hours of
treatment, in addition to PGS values as previously shown in
Figures 3B and 3C, and in Figures 5C and 5D we provide a
more detailed analysis by reporting the decrements of RD and
PGs between 48 and 72 hours of treatment. In both cell lines,
the decrement at 72 hours compared to 48 hours of treatment
observed in PGS values was strikingly reduced in RD values.
Consistently, the decrement of RD50 at 72 hours, compared to
48 hours, was significantly less pronounced than GI50
decrement in both cell lines (Table 1 and Table 2, p<0.01 and
p<0.05 in A549 and PC3 cells, respectively. One-tail paired t-
test). As expected, we did not observe significant differences
between TGI and RD0 in A549 cells, since the value is “0” in
both parameters regardless of cell proliferation (Table 1).
These results confirmed that a parameter that measures the
ability to negatively affect the doubling capacity of cell
populations is more accurate than PGS to represent in vitro
growth inhibition, since it is not affected by the exponential
growth property of cells in culture. We invite researchers to
employ RD in their studies to confirm our conclusion with their
own data and to properly compare drug efficacy between
distinct cell models.
This is the pre-peer reviewed version of the following article: A New Parameter of Growth Inhibition for Cell Proliferation Assays . Fiorentino FP, Bagella L and
Marchesi I. Journal of Cellular Physiology 2017 Oct 12. doi: 10.1002/jcp.26208. Copyright © 2017, which will be published in final form at this link
U=I⋅2P
P=log2
U
I
T=I⋅(D+1)P
D=T
I
P−1
RD =T
I
log2
U
I−1
( )
⋅100
Figure 4. Drug treatment negatively affects the exponential growth of cells. (A) R2 calculated from linear or exponential regression analyses of
growth curves of untreated A549 or PC3 cells. Each point is a biological replicate of the cell proliferation assay. (B, C) R2 calculated from
regression analyses of growth curves of etoposide-treated A549 (B) or PC3 (C) cells that resulted in growth inhibition.

Fiorentino, Bagella and Marchesi
Conclusions
Cell proliferation assays are routinely performed to evaluate
in vitro efficacy of tested compounds in anticancer drug
discovery research. Relative cell number (R) and percentage of
growth (PG) are parameters widely employed by researchers to
show the efficacy of tested compounds on the proliferation of
cells in culture because of their simplicity and intuitiveness13,14.
However, here we show here that both parameters are function
of cell proliferation in tested cell model (Figure 1 and Figure
3). In order to experimentally evaluate our hypothesis, we
treated two representative cell lines with a well-known
chemotherapy agent, etoposide, and employed live-cell
imaging technique to monitor the number of cells at several
times of treatment (Figure 2). We also show here that PGS
dependency to cell proliferation is due to the exponential, and
not linear, growth of cell in culture (Figure 4). As consequence,
a drug that shows a high R or PGS value after 48 hours of
treatment could show lower values after longer period of
treatment because of the increased growth of untreated cells
and not the putative additive effects of the drug. As another
example, a cell line with a fast proliferation rate in culture
would show lower R or PGS values than a “slower” cell line
and would be inferred more sensitive to a drug when
comparing drug efficacy between the two cell lines. This is of
particular relevance in studies that compare drug efficacy in
distinct cell models, as cell lines, with distinct growth
properties. For instance, non-tumor cells, which usually tend to
duplicate slower than the tumor counterpart, shows higher IC50
and GI50 values than tumor cells and could be misinterpreted
as less sensitivity to tested compounds. Same misinterpretation
could occur in studies of personalized medicine that associate
drug sensitivity to genetic mutations or expression profiles,
since higher sensitivity will be attributed to faster growing cell
lines independently by their genome or transcriptome profiles.
Therefore, the development of a more precise and refined
method of analysis of proliferation assays would let researchers
to better pick up drug candidates for further analysis, and
ultimately provide more predictable results to in vivo tests. In
our representative experiments, the exponential growth of cells
was negatively affected by drug treatment (Figure 4). We
therefore developed a new parameter of drug efficacy, termed
relative doubling capacity (RD), which quantifies the
impairment of doubling capacity consequence of drug
treatment. RD is a more reliable parameter than PGS to
properly compare drug efficacy because it is less, or none,
affected by the unique growth properties of cells in culture
(Figure 5, Table 1 and Table 2).
Despite it could be argued that the desired endpoint of
antitumor preclinical drug discovery studies is to induce
massive cell death and therefore the LC50 parameter, which
quantifies drug toxicity, is the most relevant parameter for in
vitro tests, we believe that the proper determination of growth
inhibition is relevant as well. Indeed, the quantification of
growth inhibition is relevant to define side effects on non-
tumor cells or on tumor cells that do not carry the genetic
alteration targeted by the tested compounds in studies of
personalized medicine, or to evaluate effects on proliferation by
compounds that are designed to target other cancer-related
phenotypes, such as dedifferentiation or metastatization. We
therefore invite other researchers to employ RD in their studies,
together with PGT, to properly determine in vitro drug efficacy
of tested compounds.
Materials and Methods
Cell cultures
Lung adenocarcinoma A549 and prostate adenocarcinoma
PC3 cell lines were obtained from cell bank of the IRCCS
University Hospital San Martino – IST National Institute for
Cancer Research (Genova, Italy). Cells were cultured in
DMEM supplemented with L-Glutamine and 10% FBS
(Lifetech) at 37°C, 5% CO2 humidified air.
Kinetics of Cell Proliferation
500 A549 cells suspended in 20 µL of complete culture
medium without phenol red were plated in 384 well flat, clear
bottom black microplate (Corning #3764). After 18-24 hours,
10 µL of fresh supplemented culture medium, containing
Et opos ide ( Sigm a-Al dric h #E 260 0 000) , SiR-H oech st
(Spirochrome #SC007) 33 and CellTox™ Green Dye (Promega
#G873A), were added to each well of the cell plate, to a final
concentration of SiR-Hoechst 0.5µM in vehicle (DMSO) 0.8%.
A549 cells were treated with eleven 1:2 serial dilutions of
Etoposide in technical triplicate, ranging from 40 µM to 40
nM. Plating of cells, preparation of Etoposide serial dilutions
and addition of compound solutions to the cells were
performed using an automated liquid handling platform (Gilson
Pipetmax®). Multiple live-cell imaging in far-red fluorescence
(led cube 625 nm, filter cube excitation 650 ± 30 nm, emission
800 ± 90 nm), green fluorescence (led cube 465 nm, filter cube
excitation 469 ± 25 nm, emission 525 ± 25 nm) and phase
contrast was performed at 37°C, 5% CO2 using a gas controller
associated to the microscope, after 1, 24, 48 and 72 hours of
treatment with a 4x objective, using an automated digital
widefield microscope (BioTek Cytation 5). For each sample,
four images were taken to cover the entire area of the well.
Image merging, processing and cell count were performed
using BioTek Gen5 software. Briefly, the number of cells was
calculated as count of far-red fluorescence stained nuclei and
the number of dead cells was calculated as count of green
fluorescence stained cells. Thres ho ld of fluorescence
This is the pre-peer reviewed version of the following article: A New Parameter of Growth Inhibition for Cell Proliferation Assays . Fiorentino FP, Bagella L and
Marchesi I. Journal of Cellular Physiology 2017 Oct 12. doi: 10.1002/jcp.26208. Copyright © 2017, which will be published in final form at this link
Figure 5. RD shows reduced variability than PG between 48 and 72
hours of treatment. (A, B) Relative doubling capacity and percentage
of growth inhibition in A549 (A) or PC3 (B) cells after 48 and 72 hours
of etoposide treatment. (C, D) Dispersion plot of RD and PG
decrements (72 hours minus 48 hours of treatment) for doses of drug
that induced growth inhibition in A549 (C) or PC3 (D) cells.

Fiorentino, Bagella and Marchesi
background and range of nuclei size settings were manually
adjusted in each experiment by overlapping images of far-red
and green fluorescence with images in phase contrast as
reference. Count of live cells was calculated by subtracting
count of dead cells to the count of total cells. For what
concerns cytotoxicity assays in PC3 cells, 2000 cells suspended
in 80 µL of complete culture medium without phenol red were
plated in 96 well flat, clear bottom black microplate (Corning
#3603). After 18-24 hours, 20 µL of fresh supplemented culture
medium, prepared as previously described for A549 cells, was
added to each well of the cell plate. PC3 cells were treated with
nine 1:2 serial dilutions ranging from 10 µM to 40 nM in
technical duplicate. Live-cell imaging and analysis were
performed as previously described.
Statistical analysis
Regression analysis and T-tests were performed with microsoft
excel.
ACKNOWLEDGMENTS: F.P.F. acknowledges the support from
Fondazione Umberto Veronesi (Postdoctoral Grant 2017).
This is the pre-peer reviewed version of the following article: A New Parameter of Growth Inhibition for Cell Proliferation Assays . Fiorentino FP, Bagella L and
Marchesi I. Journal of Cellular Physiology 2017 Oct 12. doi: 10.1002/jcp.26208. Copyright © 2017, which will be published in final form at this link
µ: mean value of the three biological replicates
σ: standard deviation
CV: Coefficient of variation (σ/µ)
*out of scale
**(72hr/48 hr)-1

Fiorentino, Bagella and Marchesi
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Marchesi I. Journal of Cellular Physiology 2017 Oct 12. doi: 10.1002/jcp.26208. Copyright © 2017, which will be published in final form at this link
µ: mean value of the three biological replicates
σ: standard deviation
CV: Coefficient of variation (σ/µ)
*out of scale
**(72hr/48 hr)-1

Fiorentino, Bagella and Marchesi
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Marchesi I. Journal of Cellular Physiology 2017 Oct 12. doi: 10.1002/jcp.26208. Copyright © 2017, which will be published in final form at this link