GE Healthcare
Application note 28-9673-96 AA
IN Cell Analyzer 2000
Use of online cell counting for micronucleus and neurite
outgrowth assays on IN Cell Analyzer 2000
Key words: IN Cell Analyzer • online cell counting •
micronuclei analysis • neurite outgrowth
A critical factor in establishing robust high-content cell assays
is the assurance that enough cells are imaged per treatment
condition. This can be achieved on IN Cell Analyzer 2000 by
automatically counting cells “online” as each image is acquired.
In this optional acquisition mode, successive fields of view are
acquired until a pre-set cell count threshold is achieved. Online
cell counting has the additional advantage of reducing plate
read times and the data storage burden, since no excess images
are acquired once the desired number of cells has been imaged.
Here we provide data demonstrating the ability of the IN Cell
Analyzer 2000 online cell counting feature to improve assays
containing variable cell counts. The feature is applied to increase
speed and ensure robust performance of micronucleus and
neurite outgrowth assays. The online cell counting feature
requires that a fluorescent marker is present for cell identification.
For the assays in this application, we used Hoechst™ 33342
nucleic acid stain to identify cell nuclei. However, other fluorescent
cell markers may be substituted for this purpose.
Fig 1. Online cell counting. The Image Preview window, displaying object
segmentation results (yellow outlines) generated during set up of online cell counting.
Segmentation outlines are a useful visual aid to help with parameter optimization.
Micronucleus assay
Micronucleus induction is a key characteristic of genotoxic
compounds. The analysis of micronuclei formation resulting
from DNA strand breakage (clastogens) or interference with
chromosome segregation (aneugens) is an important component
of toxicology screening of new drug candidates. Guidelines for
genotoxicity testing using in vitro micronucleus assays typically
recommend scoring at least 2000 cells per treatment condition
for single samples, or at least 1000 cells per condition if the assay
employs replicates. Online cell counting ensures that the minimum
cell count is reliably achieved even when test compound toxicity
results in a cell count decrease. For the examples presented
here, mononucleated micronucleus assays (1,4) are configured
for automated high-content analysis in 96-well format with
multiple replicates per treatment condition.
Neurite outgrowth assay
Neurite outgrowth plays a fundamental role in embryonic
development, neuronal differentiation, and nervous system
function. The process is also critical in some neuropathological
disorders as well as neuronal injury and regeneration. Highcontent analysis allows direct screening of the morphological
effects of various treatments and can be multiplexed with
additional structural and biochemical probes to increase
information content. For accurate neurite measurements,
cell counts of 300 or more are typically required. A confounding
factor is that the differentiated neuronal phenotype is
characterized not only by neurite outgrowth, but also by lack
of cell proliferation. In addition, various differentiation conditions
result in cell detachment and death, particularly at higher
concentrations. Online cell counting minimizes the number
of fields per well required to reach the desired cell count and
ensures that, even at toxic doses, cells are sufficiently sampled
to ensure accurate and robust data are obtained.
Materials
Products used
IN Cell Analyzer 2000, standard chip CCD camera
IN Cell Analyzer 2000, large chip CCD camera
IN Cell Investigator, single seat license
96 well Matriplate™
(0.72 mm glass bottom thickness)
G1S Cell Cycle Phase Marker Assay, Screening
Mouse IgG Cy5™-Linked (from goat), 1 mg
28-9534-63
28-9535-10
28-4085-71
28-9324-00
25-9003-97
PA45002
Other materials used
µClear™ plates,
96-well tissue culture treated, black Greiner Bio-One, 655090
CHO-K1 cells
(Hamster Chinese Ovary epithelial)
ECACC, 85051005
Neuroblastoma neuro-2a cells
ATCC, CCL-131
McCoys 5A media
Sigma, M8403
Nutrient Mixture F12 HAM
Sigma, N4888
Eagle’s Minimum Essential Medium (EMEM)
ATCC, 30-2003
Fetal Bovine Serum Gold
PAA, A15-151
Penicillin-streptomycin, 100×
Sigma, P4333
L-glutamine, 200 mM solution
Sigma, G7513
Mitomycin C*, 2 mg
Sigma, M4287
Etoposide*, 25 mg
Sigma, E1383
FITC, 10 mg
Invitrogen, F1906
Hoechst 33342 (16.2 mM stock)
Invitrogen, H3570
Ethanol 100%
Hayman chemicals
PBS
Sigma, D8537
DMSO
Sigma, D2650
Sterile water
Fresenius Kabi, 22-96-985
Retinoic acid†
Sigma, R2625
Geneticin
Sigma G8168
™
†
Triton X-100 Sigma, T8787
Monoclonal anti-α-tubulin antibody,
clone DM1A
Sigma, T9026
* Mitomycin C and etoposide are classified as toxic. Handle in accordance with MSDS
and local laboratory safety guidelines.
† Retinoic acid and Triton X-100 are classified as harmful. Handle in accordance with MSDS
and local laboratory safety guidelines.
Methods
Unless indicated otherwise, media formulations for the cell
lines were as recommended by the supplier.
Variable cell count assay
Two cell lines were used to assess performance of the online
cell counting function in cases of variable cell count: CHO-K1,
a Chinese hamster ovary-derived cell line lacking the gene for
proline synthesis; and G1S Cell Cycle Phase Marker, derived from
2
12/2009 28-9673-96 AA
the U2-OS human osteosarcoma cell line and engineered to
express an EGFP-tagged cell cycle phase reporter. Cells were
seeded onto 96-well µClear plates at 20, 15, 10, 5, 2.5, and
1.25 × 103 cells/well (n = 16 wells of each dilution). Plates were
incubated overnight under standard tissue culture conditions
and then fixed using 2% formaldehyde. Cells were then stained
with Hoechst 33342 and imaged on IN Cell Analyzer 2000
configured with the standard chip CCD camera and a 20×/0.45
NA objective.
Micronucleus assay
CHO-K1 cells seeded onto 96 well Matriplate microplates were
incubated with mitomycin C (clastogen) or etoposide (aneugen)
to induce micronuclei formation for 48 h, fixed at room
temperature for 30 min with ethanol, and stained with FITC and
Hoechst 33342 (1). With the online cell count threshold set to
1000 cells/well, plates were imaged on IN Cell Analyzer 2000
with the 20×/0.45 NA objective.
Neurite outgrowth assay
Neuro-2a murine neuroblastoma-derived cells were seeded
onto a 96-well µClear plate in media containing 2% FBS, and
incubated for 6 h. Retinoic acid at concentrations ranging from 5
to 50 µM were then added to the cells and the plate incubated
for 18 h at 37ºC/5% CO2 in a humidified incubator. The cells were
fixed with 4% formaldehyde for 2 h and then permeabilized with
Triton X-100 detergent in PBS. The cells were then incubated
with monoclonal anti-α-tubulin antibody to identify cell bodies
and neurites, followed by a Cy5-conjugated mouse anti-IgG
secondary antibody. Nuclei were then stained with Hoechst
33342 (2). With the online cell count threshold set to 300 cells/
well, plates were imaged on an IN Cell Analyzer 2000 configured
with the large chip CCD camera, which has a field of view 4×
that of the standard chip CCD camera.
Analysis and data processing
To assess performance of the online cell counting feature in
variable cell count assays, results were benchmarked against
those obtained using an offline analysis protocol developed
using the IN Cell Investigator Multi-Target Analysis module as
described in the product user manual. Cell count data and the
number of fields per well obtained with IN Cell Analyzer 2000
acquisition were retrieved from the IN Cell Analyzer session
log and processed using Microsoft™ Excel™.
For micronuclei formation assay analysis, the IN Cell Investigator
Micronuclei Analysis Module was used as described in the
product user manual. Proliferation index was calculated (Fig 4).
To obtain cell count for calculation of the proliferation index in
assays employing the online cell count feature, total cell count
per well was divided by the number of fields acquired per well.
For neurite outgrowth assays, IN Cell Developer Toolbox was used
to create an analysis protocol to analyze cells and associated
neurites. The analysis routine was designed to identify neurites by
subtracting a binary image of cell bodies (derived by eroding a
whole-cell binary image to exclude neurites) from a binary image
of the entire cell (including neurites). In conjunction, information
from the nuclear channel was used to derive individual cell data.
Details of the analysis protocol can be found in a separate
application note (3). For assays employing online cell counting,
mean cell count data was derived by dividing the total cell
count per well by the number of fields acquired in the well.
(a)
(b)
Note: The online cell counting feature is easily implemented
during set up of an imaging run. Within Protocol Designer, select
the cell counting function and then specify the wavelength to be
used for counting, the minimum nuclear area parameter used
to optimize object identification, and the cell count threshold.
During set up, the object identification (segmentation) results
and cell count are displayed to help with assessment and
optimization of the settings (Fig 1).
Results
Assays with variable cell count
Various cell culture conditions and treatments can result in
decreases in cell number within sample wells. In many cases,
the total cell number following treatment is unpredictable, which
can present a challenge to ensuring data are captured from a
statistically significant population of cells in each case. Typically,
this problem is addressed by acquiring an excess number of
fields of view from all sample wells in order to accommodate the
worst-case conditions. By contrast, the IN Cell Analyzer 2000
online cell counting feature automatically acquires the minimum
number of fields required to achieve a user-set count threshold.
To investigate the utility of the online cell counting feature in
rendering variable cell counts, two cell lines were seeded at a
range of densities across separate sample plates. Online cell
counting was then applied, with the cell count threshold set to
500 for a hamster (CHO-derived) cell line and to 300 for a slowergrowing human (U2-OS-derived) cell line. As shown in Figure 2,
the number of cells acquired was consistently above the set
threshold for all seeding densities and the number of fields
required to achieve the threshold decreased with increasing
cell density. The online cell counting feature thus saved time
and storage space for the imaging run by acquiring only the
minimum number of images required to capture data from
a statistically acceptable number of cells for each well.
Fig 2. Online cell counting to achieve sufficient cell count with a minimum
number of fields. Cell count and number of fields acquired for (a) CHO-derived
and (b) U2-OS-derived cell lines seeded at increasing densities across the plate.
Cells were imaged using the standard chip CCD camera with the 20×/0.45 NA
objective. Points and error bars represent the mean +/- 1 standard deviation
for 16 replicate wells.
Micronucleus assays
For micronucleus assays configured with multiple replicates per
treatment condition, a minimum cell count of 1000 cells/well
is recommended. Without online cell counting, 34 fields of view
needed to be acquired from each well to ensure a sufficient
cell count was achieved (based on the maximum field count
obtained when online cell counting was applied). This resulted
in a plate acquisition time of 80 min with the standard chip
camera, compared to an acquisition time of 37 min when
online cell counting was applied under comparable conditions.
As summarized in Table 1, the use of online cell counting
significantly decreased plate acquisition times for the assay.
To maximize acquisition rate for micronuclei formation assays,
the large chip CCD camera is preferable to the standard chip
size. With a 2048 × 2048 pixel array, the large chip camera
acquires a field of view approximately four times that of the
standard chip camera (Fig 3). Consequently, fewer fields of view
are required to obtain the desired cell count. In addition, capture
rate with the large chip camera configuration is significantly
more rapid than with the standard chip camera in place. Both
of these factors contribute to increased speed of acquisition.
For dose-response assays with etoposide and mitomycin C
as test compounds, the online cell count threshold was set to
1000. The number of fields imaged per well to achieve the
threshold ranged from 1 to 34 using the standard chip CCD
camera and 1 to 16 using the large chip CCD camera. Use of
the large chip camera reduced plate acquisition times by ~50%
compared to those achieved with the standard chip camera
under comparable conditions (Table 1).
12/2009 28-9673-96 AA
3
Table 1. Plate acquisition times achieved for 96-well micronuclei formation
dose-response assays
Run
Camera chip
Test compound
Online cell count threshold
Objective
Exposure(s)
Number of fields required
Plate acquisition time* (min)
1
Standard
Etoposide
Feature not applied
20×
0.03
34
80
2
Large
Mitomycin C
Feature not applied
20×
0.05
16
54
3
Standard
Etoposide
1000
20×
0.03
Variable
37
4
Large
Etoposide
1000
20×
0.045
Variable
18
5
Standard
Mitomycin C
1000
20×
0.03
Variable
69
6
Large
Mitomycin C
1000
20×
0.05
Variable
30
* Acquisition rates required to ensure 1000 cells/well without (Runs 1 and 2) or with
(Runs 3 to 6) online cell counting.
For dose-response assays, percentage maximum cell count
and percentage of cells with micronuclei were plotted against
drug concentration (Fig 4). As indicated in the plots, micronuclei
count increases with drug treatment until toxic doses are
reached and cells begin to detach from the plate. Percentage
maximum cell count relative to untreated controls can be used
as a crude index of cell proliferation (PI), which typically decreases
with increasing exposure to genotoxins. Drug concentration at
half maximal proliferation (PI50) was determined to be 127 nM
for mitomycin C and 176 nM for etoposide (Table 2). These values
are within the 95% confidence intervals of values obtained with
the standard chip camera, and correspond well with those
reported in the literature (198 and 216 nM respectively) during
validation of an automated micronucleus screening assay using
IN Cell Analyzer 1000 (4). The percentage of cells presenting
with micronuclei at PI50 was also determined for each assay.
Table 2 shows that values obtained with the standard and
large chip cameras were comparable to each other as well
as to the values reported by Ovechkina et al (4).
(c)
(a)
(a)
(b)
Fig 3. Micronuclei formation assay. Following treatment of CHO-K1 cells with
250 µM etoposide, and staining with Hoechst 33342, cells were imaged with
(a) the large chip CCD camera or (b) the standard chip CCD camera. Relative size
of images from the two cameras is as shown. (c) Enlargement of the region of
interest outlined in white in (a). Arrow: micronucleus formed at the periphery
of a nucleus. Cell count for the standard chip camera image is 70 for field 1,
compared with a cell count of 386 for field 1 for the large chip camera.
(b)
Fig 4. Dose-response micronuclei formation assays. CHO K1 cells were treated
with increasing doses of (a) etoposide, or (b) mitomycin C, and imaged on IN Cell
Analyzer 2000 using the large chip camera. Data points represent mean +/- 1
SEM, n = 8 wells per concentration. Percentage maximum cell count was
calculated as [100 × (mean field cell count of treated sample)/(mean field cell
count of untreated control sample)].
4
12/2009 28-9673-96 AA
Table 2. Dose-response micronuclei formation assays imaged with the large
and standard chip cameras
Mitomycin C
Etoposide
PI50* (nM)
PI50 95% CI†
% cells with micronuclei at PI50
PI50 (nM)
PI50 95% CI
% cells with micronuclei at PI50
9.5
Standard chip camera
70
48 to 103
2.6
175
126 to 243
Large chip camera
127
89 to 180
7.5
176
100 to 313
8.1
Reported Value‡
198
–
5.6
216
–
8.5
* PI50 = drug concentration at 50% maximum cell proliferation.
† CI = Confidence interval.
‡ PI50 is taken to be analogous to published GI50 values, where the growth index (GI)
is calculated as a ratio of the change in cell count for treated and untreated sample
populations during the treatment time period (4).
Neurite outgrowth assays
Retinoic acid treatment induces Neuro-2a cell differentiation
to a neuronal phenotype (Fig 5), which is characterized by
formation of neurites and lack of cell proliferation. At higher
drug concentrations, cell detachment and death can also occur.
Consequently, to ensure robust assay results, the use of online
cell counting to achieve counts exceeding 300 per well is
recommended. Since neurites can extend up to tens or even
hundreds of microns in length, use of the large chip camera
configuration ensures the most accurate neurite segmentation
results. As shown in Figure 5, many more neurites can be
captured in their entirety with the large chip camera, compared
to the standard chip camera. Alternatively, if the assay is run with
the standard chip camera, image stitching can be performed
post-acquisition using IN Cell Investigator software.
(a)
(b)
Fig 5. Neurite outgrowth in populations of differentiated neuroblastoma cells.
Serum-starved neuro-2a cells were treated for 18 h with retinoic acid, stained,
and imaged with (a) the large chip camera, or (b) the standard chip camera.
Images shown were acquired in the green channel (tubulin staining) with the
10×/0.45 NA objective. Cells were co-stained with Hoechst 33342 to identify
nuclei (blue channel image not shown).
12/2009 28-9673-96 AA
5
Following treatment of serum-starved cells with increasing
doses of retinoic acid for 18 h, samples were imaged with the
online cell count threshold set to 300. Cell count per field and
number of fields acquired per well were plotted against retinoic
acid concentration. As shown in Figure 6, cell count decreases
with increasing retinoic acid concentration. This is consistent
with decreased cell proliferation associated with differentiation
and indicative of toxic effects occuring at higher doses. The data
also indicate an inverse relationship between field count and
drug concentration, demonstrating that the online cell counting
feature minimizes the number of required fields needed to obtain
the desired cell count.
Fig 6. Effect of retinoic acid treatment on cell count. Cell count per field and
number of fields acquired per well are plotted against retinoic acid concentration.
Plates were imaged with the large chip camera. The minimum cell count threshold
was set to 300 cells/well with a maximum field limit set to 9. Data points represent
the mean +/- 1 SD, n = 4 wells per treatment condition.
The data in Table 3 demonstrate the significant time savings
achieved by applying online cell counting for the 10-point
(40‑well) dose-response assay. The read-time required to
ensure capture of at least 300 cells per well was reduced from
17 min without online cell counting to 7 min with online cell
counting applied.
The effect of online cell counting on dose-response assay
performance was examined by comparing the mean per field
cell counts obtained with online cell counting to the cell counts
obtained from a single field of view per well. In each case, four
replicate wells were imaged per treatment condition. As shown
in Figure 7, a clear retinoic acid-dependent decrease in cell
number is observed when online cell counting is applied. By
contrast, the dose-response relationship is much less apparent
when data are acquired from a single field of view per replicate
well. The standard errors of the means are also significantly
larger in the absence of online cell counting, demonstrating
that cell proliferation data are more robust with online cell
counting applied.
Fig 7. Comparison of cell count data with and without online cell counting.
Cell count was obtained by dividing the total cell count per well by the number
of fields acquired in the well. Data points represent the mean +/- 1 SEM, n = 4
wells per concentration of retinoic acid.
Table 3. Reduction in assay read-time using online cell counting
Camera chip
Objective Hoechst channel exposure(s) Cy5 channel exposure(s) Online cell count threshold Number of fields required Acquisition time for 40 wells (min)
Large
10×
0.025
2.0
Feature not applied
7
17
Large
10×
0.025
2.0
300
Variable
7
6
12/2009 28-9673-96 AA
Similarly, Figure 8 demonstrates the improvement in robustness
of neurite length measurements for the assay. The doseresponse relationship between neurite length and retinoic acid
concentration is more pronounced when online cell counting is
applied, reflecting the fact that data are derived from a sufficiently
large cell sample at each dose.
Fig 8. Comparison of neurite length data with and without online cell counting.
Data points represent the mean +/- 1 SEM, n = 4 wells per concentration of
retinoic acid.
Conclusions
Acquisition of data from a sufficiently large cell population can
be critical to the performance of high-content assays, yet this
can result in prohibitively long plate read times in the absence
of an online cell counting functionality. The data presented here
clearly demonstrate that application of the IN Cell Analyzer 2000
online cell counting feature provides significant time savings
during image acquisition, at the same time reducing file size
by minimizing the number of required images. This can be a
major advantage in screening applications, such as analysis
of micronucleus formation, where assays must be both robust
and rapid. Use of the large chip camera option enables even
faster results, with plate read times reduced by as much as
50%. Measurement accuracy is essential to interpreting data
from dose-response and other drug characterization assays.
Comparative data obtained from the neurite outgrowth assay
demonstrate a clear improvement in the quality of pharma­
codynamic data, together with substantial improvements in
imaging speed, when online cell counting is applied.
References
1.
Application Note: Online cell counting in a micronuclei formation assay using the
IN Cell Analyzer 1000. GE Healthcare, 28-9495-88, Edition AA (2009).
2.
Application Note: A high-content assay for neurite outgrowth using the IN Cell
Analyzer 2000. GE Healthcare, 28-9538-32, Edition AA (2009).
3.
Application note: Neurite outgrowth cell-by-cell analysis using the IN Cell Developer
Toolbox, GE Healthcare, 14-0005-35, Edition AA (2005).
4.
Ovechkina Y et al.; Development and validation of automated in vitro mammalian
micronucleus screening assay using IN Cell Analyzer 1000, poster presented at
SBS 12th Annual Conference and Exhibition (2006).
12/2009 28-9673-96 AA
7
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First published December 2009
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28-9673-96 AA 12/2009