INSTITUTE OF PHYSICS PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY

Phys. Med. Biol. 48 (2003) 1551–1563 PII: S0031-9155(03)61551-4

An objective method to measure cell survival bycomputer-assisted image processing of numeric imagesof Petri dishes

Marie-Claude Biston1, Stephanie Corde1, Emmanuel Camus2,Ramon Marti-Battle2, Francois Esteve1 and Jacques Balosso1

1 Equipe d’Accueil no 2941 ‘Rayonnement Synchrotron et Recherche Medicale’, Unite IRM,CHU, BP 217, F-38043 Grenoble Cedex 09, France2 Samba Technologies, ZIRST 53, chemin du Vieux Chene 38240 Meylan, France

E-mail: JBalosso@chu-grenoble.fr

Received 1 April 2003Published 20 May 2003Online at stacks.iop.org/PMB/48/1551

AbstractThis work establishes an objective method to measure cell clonogenic survivalby computer-assisted image processing using images of cell cultures fixed andstained in Petri dishes. The procedure, developed by Samba Technologies,consists of acquiring Petri dish pictures with a desktop scanner and analysingthem by computer, using algorithms based on the ‘top hat’ filter. The resultsfrom the automated count for the cell line SQ20B are compared with thosefound by two observers, before and after normalization of the counting.

After normalization, the shape of the survival curves of the ‘manual’counting of the Petri dishes shows a good correlation between both observers.The software enables the small visible differences in count between observers tobe eliminated. The comparison between the absolute number of colonies showsan increased difference between the two manual scorings that can be as greatas 67 colonies, whereas the difference between the two automated counts isnever greater than 8 colonies.

These results demonstrate that the ‘manual’ count is inter- and intra-observer variable, whereas the automatic count performs reproducible cellcolony counts, thereby minimizing user-generated bias. The large amount ofdata produced also gives information about cell and colony characteristics.Thus, this computer-assisted method has considerably improved the reliabilityof our statistical results.

1. Introduction

Clonogenic survival curves for in vitro cultured cells have to be established very carefully.Indeed, the survival curve parameters can be falsified because of the difficulty for the observer

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to discern survivor clones from non-survivors. The complexity of the process relies on the factthat some radiation-sterilized cells still undergo several mitoses before stopping proliferation(Puck and Marcus 1956) and that surviving cells proliferate with diverse doubling times,producing colonies with a large range of sizes.

To facilitate the viable clones ‘manual’ count, Puck and Marcus (1956) proposed thatcells which gave rise to colonies of 50 or more daughter cells (five to six generations) beconsidered as viable colonies. This 50-cell threshold is arbitrary as a wide range of cell linesand colony-growth periods is used for in vitro experiments. Therefore, it would be usefulto study the influence of the threshold on the survival curve of each cell line. Nevertheless,this method is convenient since it enables a rapid counting of survival clones, as a 50-cellcolony is easily discernable by the naked eye, especially if measured effects are sufficientlylarge (Spadinger et al 1994). Although three survivor-cell categories are encountered:(i) normal growth cells, (ii) slow growth cells and (iii) very slow growth cells (Joshi et al1982), discerning slow and very slow growth cells from several abortive colonies becomes atedious task, especially when small effects are observed. This ‘choice’ becomes more andmore delicate as the size of survivor colonies significantly decreases with increasing radiationdoses (Michalowski 1984). In addition, we note that the size of the cells present in the coloniescan become abnormally large because of the continuation of their growth without mitosis. Soin this case, the precise definition of a survivor clone may significantly influence experimentalresults. As a consequence, manual counting of the colonies is a laborious and subjectivemethod to measure survival (Lumley et al 1997). Furthermore, we miss information aboutthe effect of the treatment on cells, as one small colony (slow growth) is considered andcounted exactly as a large one (fast growth). Yohem et al (1987) have consistently observedthat a modification of the cell number threshold for colony counting could alter the results ofradiosensitivity measure.

The use of an automated colony counter based on image analysis would enable an objectivecolony count to be made and would offer the possibility to improve the analysis by havingaccess to parameters describing cell and clone characteristics.

The concept of computer-aided colony counting is not new and has been implementedby other groups (Barber et al 2001, Dobson et al 1999, Hoekstra et al 1998, Wilson 1995,Parry et al 1991, Mukherjee et al 1995, Marotz et al 2001, Bernard et al 2001). Parry et al(1991) described a controlled method to count the number of colonies in wells of 24-wellplates using a meticulous preparation of the samples to obtain a white background to allow thescanning by a regular Apple Scanner. There was no statistical data on colony characteristics;however, this work pioneered the method with time saving as the principal goal. Mukherjeeet al (1995) developed an algorithm for coliform colony counting in drinkable water, based on aPetri dish image obtained by a CCD camera. Their contribution was to use a statistical approachwith surface analysis to solve the problem of merging colonies. Wilson (1995) described ascanning system used to count spiral colonies by image analysis and automatic loading of upto 60 plates of about 100 mm diameter. This system was designed only to save time for thistask. Hoekstra et al (1998) described a similar microscopic system used for bone marrowcolony counting. Dobson et al (1999) needed to obtain a count of the number of cells presentin fibroblastic colony-forming units because this number does not follow a normal distribution.The analysis is made on one image of a 4 cm Petri dish obtained by a CCD camera with aninverted microscope. More recently, Barber et al (2001) described a process using an invertedmicroscope associated with a monochrome CCD camera with a wide-angle lens to capturethe image of a cell culture flask. This optical system introduces a deformation that precludesany direct statistical analysis of surface and density of the colonies. Marotz et al (2001) useda computer system to detect the microbial colonies in agar in Petri dishes. The recognition

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system enables to detect the local maxima and a measure of goodness is performed to selectpixels as potential objects centres. The good objects recognition is then performed usingfinite, predefined set of factors (shape, structure, measure of goodness) divided into subsets(dreadful, bad, medium, good . . . ) corresponding to qualitative categories (fuzzy formalism).The method of Bernard et al is based on an hypothesis generation-selection principle. Dataon the shape of the colonies are incorporated into a parametric model. It approximates theshape of the colonies by the Gaussian model, taking into account the position, the size and theamplitude of the colony.

This paper describes an automated colony counter developed by Samba Technologies(Meylan, France), whose image-processing algorithms are based on the ‘top hat’ operator(Russ 1995). In addition, with an appropriate colouration, some characteristics such as colonysize and number of cells in each colony can be calculated using the colony optical density.The automated counter also allows colonies that merge into one another to be separated andcounted. We compared the results between the automated counter and two ‘manual’ countsby skilled observers. We utilized the cell characteristics to study the influence of differentparameters on the mean colony surface and its mean cell number. We finally studied theinfluence of the threshold value on the shape of the survival curves.

2. Material and methods

2.1. Cell line

The SQ20B cell line was derived from a human head and neck squamous cell carcinoma(Weichselbaum et al 1986) and was obtained from William K Dahlberg (Dr John B LittleLaboratory, Harvard School of Public Health). These cells are p53 mutated and do notproduce apoptosis.

2.2. Cell culture technique

Cells were seeded and grown as a monolayer in 25 cm2 plastic tissue culture disposable flasks(Falcon) with 0.4 ml cm−2 Dubbelcco’s modified Eagle’s minimum medium (Gibco-BRL),added with 10% foetal calf serum (Gibco-BRL), penicillin and streptomycin (Gibco-BRL).Cells were grown at 37 ◦C in a humidified atmosphere of 5% CO2 in air. Experiments werecarried out with cells in plateau phase. After irradiation, the medium was changed for 10 mlconditioned medium and the flasks were then returned to the incubator for 12 h before low-density subculture for colony-forming assay.

2.3. Irradiation

Irradiations were carried out at ESRF’s Medical Beamline (ID17) (Elleaume et al 1999). Cellswere irradiated at room temperature with monochromatic x-rays of 50 keV energy. The doserange was between 0.5 and 8 Gy.

2.4. Staining of colonies

Colonies were fixed and stained with Violet Crystal oxalate (Merck Eurolab) (75% Alcool/25%Crystal Violet) after 15 days cell-growing. The growth medium was first decanted and replacedwith 2 ml of stain. After exactly 1 min, the stain was removed by immersing the Petri dishesin cold water. We strictly respected the quantity of product and the time exposure in orderto have a reproducible colouration. Indeed, the exactness of the cell and colony parametersdepends strictly on the colouration, since it is based on optic density measures.

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2.5. Image acquisition

The image acquisition was done with a desktop scanner (Agfa Snapscan 1236) equipped witha plate for glassless transparency scanning. The chosen scanner resolution was 200 dpi. Theimage selected was 748 × 748 pixels and saved as a bitmap image file format (BMP).

2.6. Calibration

The calibration of the software is necessary to calculate the number of cells in each colony.To make this calibration, the user has to first choose some colonies on a reference Petri dish,and, with a microscope, to count the cells contained in each colony. The reference Petri dishis then scanned and the resulting picture analysed by the software. The reference colonies areselected by the user and their integrated optical density (DO) measured. The sum of the DOof all the points (x, y) belonging to each colony is then associated with a cell quantity. Thus,for each object the surface and the integrated density are measured and associated with a cellnumber. It is important to take into account either very small or very clear colonies to ensurethat the software will give the most accurate number of cells in poorly-populated colonies.This is important for the validity of the threshold. It is also important to take into accounteither very large or very dark colonies to have a sufficiently wide calibration scale. Therefore,the range of calibration introduced by the user is crucial for the exactitude of the count.

An experimental curve of the integrated optic density as a function of the number of cellsis finally obtained and will be used for the entire analysis by automated interpolation.

2.7. Image processing

The image-processing principle is to segment picture into objects that are separated from thebackground. Indeed, an image in levels of grey is rarely usable in its original form to makemeasurements. If an optimal precision is required, the image has to be made binary, that isto say, on two levels. The black takes value 0 and, depending on the software, the white isequal to 1 or 255. To make picture segmentation, the software uses a morphological operationcalled ‘top hat processing’ (THP) to equalize the background of the picture and to eliminateundesirable objects.

The THP operator is composed of a transformation called ‘opening’ followed by abrightness thresholding, which is the ‘top hat filter’ itself (Russ 1995). The ‘openingtransformation’ consists of making a series of ‘erosions’ followed by ‘dilatations’ (see figure 1).The result of this operation is to extract the wide background variations. Then a subtractionbetween the initial image and the ‘opened’ image enables the extraction of the other imagevariations, which contain the information (see figure 2). The top hat filter is then appliedto the resulting picture to select the peaks of interest of the image. A top hat filter consistsof a flat disk and a central crown of a smaller diameter as shown in figure 2. The pixelbrightness values are treated as elevation of the ‘hat’. The filter is centred on each pixel in theimage with the brim resting on the surface. The top hat keeps only the objects entering the hat(colonies smaller than the maximal height fixed for the biggest colonies) and exceeding its node(colonies brighter than the adjusted threshold). Before analysis, the user brings the followingparameters in a file: the minimal and maximal surface to analyse, the scanner resolution, theradius proportion of the analysed surface to subtract (edge subtraction). At the beginning ofthe analysis, the software gives a threshold of brightness value of 25 by default. Each colonyhaving a grey level value superior to this threshold will be kept for analysis. The user hasthe possibility to change the threshold value. Nevertheless, it has to be kept constant for aseries of Petri dishes belonging to a same study to obtain a reproducible count. An interactive

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Figure 1. Representation of erosion and dilatation. The erosion removes the small particles,reduces the size of the other particles and removes some peaks to keep only the structuring elementthat is totally included in the object. The erosion is represented by the internal line on the drawing.Dilatation is represented by the external line of the object. Dilatation increases the size of theparticles, can fill concavities and connect particles. During erosion and dilatation some informationis lost but the essential characteristics of the image are emphasized.

thresholding is then applied that enables a binary image for counting to be obtained. The nextstep consists of labelling all the spots that remain after the thresholding. The inconvenienceof this step is that some dirtiness or dust may be also labelled and considered as colonies bythe software. This is why, combined with this labelling, the software uses a ‘thresholding insurface’ by taking into account the parameter ‘minimal surface to analyse’. Any object havinga smaller area is excluded. At this stage of the analysis, we only select the minimal surfacefor a colony to be analysed and we do not set a cell number threshold for the viability of thecolony. The remaining colonies are then enumerated.

The different steps of the analysis as they are displayed on the computer screen aredescribed below (see figure 3). The software first proposes to the user to select a circulararea that will be analysed (see figures 3(a) and (b)). Then an iteration number is displayedon the screen for the top hat process. This number corresponds to the diameter of the largestcolony detectable in the picture. It is directly correlated to the parameter ‘maximum size ofthe colony detectable’ that has been entered in the annex file by the user. This iteration numberoffers the possibility to modify the parameter ‘maximum size of the colony detectable’ withoutexiting the analysis. The link between ‘maximum size’ and ‘iteration number’ is the processof opening itself, the choice of a relevant number of iteration according to the effective sizeof the objects to count optimizes the time of the counting process. The second step is theimage thresholding (see figure 3(b)). The threshold value is 25 by default. Both parameterscan be adjusted but must keep the same value for an identical series of Petri dishes in orderto have valid statistical data. A binary picture is then obtained, where the background of thedish appears in black (value = 0) and the colonies that have been identified appear in white(value = 255). The next step will be to proceed to an image in which the colonies thathave been retained for counting appear in orange. In this step, the software has automaticallyseparated some merged colonies (see figure 3(c)) by an additional method based on the analysisof the maximum and minimum diameters of connecting objects, then cutting at the minimum

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Figure 2. If we take a brightness profile (b) of a Petri dish (a) and we apply the ‘openingtransformation’, the result is shown in (c). A subtraction between the initial image and the‘opened’ image enables the extraction of the other image variations, which contain the information(e). Then we apply the top hat filter (d) on the resulting image. The adjustable parameters are thediameter of the brim and crown, and the height of the crown (d). Any pixel entering the hat andexceeding its node is selected. Several positions of the filter are shown on this profile (e). Only theobjects entering the hat and exceeding its node are kept (dotted profiles). The top hat operation istherefore an iterative morphological operation that consists of sorting the elements of the pictureaccording to their size and their intensity.

diameter. Then, the software proposes to the user to select, separate or remove coloniesthat may have been mishandled; tools such as a pen and an eraser can be used for this (seefigure 3(d)). The next step consists of labelling all the spots that remain after thresholding.The colonies are then enumerated.

In this example, the edge of the dish is not comprised into the analysed area. If the userwants to make the entire dish analysis, the algorithm is going to eliminate some colonies insame time as the edge of the dish. The user has then the possibility at the last stage of theanalysis (d) to recuperate the lost colonies with the different tools. Another possibility is tomake a subtraction with an image of a blank dish to eliminate the edges, before the analysis.But this is never perfect because of imperfection in positioning both flasks on the scanner.

2.8. Statistical analysis

Each Petri dish analysis produces a file where each line describes one colony. These filescontain physical characteristics and optical parameters (surface, perimeter, maximum andminimum diameter, mean and integrated optical density) and also a calculated number ofcells resulting from the calibration curve. A graphic application makes it possible to drawhistograms and curves representing one of the parameters as a function of another, allowingstatistical analysis. Statistical functions enable one parameter of the Petri dish to be chosen

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Figure 3. The different steps of the image processing. Starting from image (a), the user choosesa surface to be analysed. The user has selected a surface smaller than the Petri dish (b). A binaryimage is obtained containing only the colonies that will be counted. In the following step, all thecolonies are visible and those that will be counted appear in orange. The automated analyser hasalso made some automatic separation of merged colonies; the user has the possibility to separatesome merged colonies (d).

(This figure is in colour only in the electronic version)

and statistical results about this parameter to be given (maximum, minimum, area, average,standard deviation . . . ).

2.9. Cell survival measurement

Cell survival measurement was first carried out by taking into account Puck and Marcuscriterion of 50 cells per colony. The same Petri dishes were counted by two observers and bythe computer program. Then we applied variable viability thresholds, using the automated fileinterpretation and we tested the influence of the threshold choice on the shape of the curves.

3. Results

Two observers first made the ‘manual’ counting of the Petri dishes. The series was made upof 24 Petri dishes that corresponded to three Petri dishes for each dose point. The results arepresented in figure 4. Each point represents the mean value of the number of living coloniescounted in the three Petri dishes treated at each radiation dose, and the error bars represent the

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0 2 4 6 8

0.01

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1

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viva

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o

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Observer 1 Observer 2

Figure 4. Comparison between two ‘manual’ counts of the same series of Petri dishes. Three Petridishes were counted for each radiation dose. The error bars represent the standard deviation.Normalization of data is done with the non-irradiated sample. The measurements are notsignificantly different.

0 2 4 6 8

0.01

0.1

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observer1 observer2

Figure 5. Comparison between the computer-assisted counting of the same series of Petri dishesby the same two observers. Whereas some differences between observers were visible for the‘manual’ count, they totally disappeared when using the software.

standard deviation. Normalization of data is done with the non-irradiated sample. Figure 4shows a good correlation between observers 1 and 2 who are experienced in ‘manual’ clonecounting.

Both observers also counted the same series using the computer-assisted image processing.The results are presented in figure 5. Whereas some small differences were visible in figure 4,the gaps have totally disappeared in figure 5, which enables us to conclude that the softwaregives reproducible and observer-independent results. The difference between manual andautomatic count is not obvious at low doses but becomes increasingly significant with theincrease in the toxicity of the treatment. At a dose of 8 Gy, the software counts more surviving

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0

50

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Dose (Gy)

Nu

mb

er o

f co

lon

ies

manual observer 1

manual observer 2

automatic observer 1

automatic observer 2

0 0.5 1 1.5 2 4 5 8

Figure 6. Comparison of the absolute number of colonies found by the two observers using thetwo methods of counting. We note that the differences between the scores are accented. Whereasthis difference is negligible if we compare the two computer-assisted countings, it is obvious thatone observer manually counts less colonies than the other.

Table 1. p values following a t-test to make comparisons between manual counting of the observer1 (MC1), manual counting of the observer 2 (MC2), automatic counting of the observer 1 (AC1) andautomatic counting of the observer 2 (AC2), for each radiation dose. Differences are consideredto be significant for p-value <0.05.

Dose (Gy) 0 0.5 1 1.5 2 4 5 8

MC1/MC2 0.270 0.300 0.167 0.013 0.002 0.068 0.004 0.187AC1/AC2 0.481 0.695 0.608 0.423 0.716 0.295 0.147 0.940MC1/AC1 0.169 0.752 0.944 0.370 0.100 0.648 0.051 0.031MC1/AC2 0.084 0.552 0.608 0.835 0.149 0.393 0.302 0.048MC2/AC1 0.0401 0.207 0.132 0.030 0.017 0.112 0.009 0.142MC2/AC2 0.0232 0.163 0.608 0.027 0.010 0.044 0.005 0.218

clones than the ‘manual’ counters. However, this type of chart hides deeper differences, sincethe normalization method (dividing the mean number of colonies at each radiation dose bythe mean number of colonies of the control series, 0 Gy) eliminates the absolute number ofcolonies.

Therefore, comparison between the absolute numbers of colonies found by the twomethods has been made to verify whether there is a good correlation between the two ‘manual’scores and in particular the two automatic ones. Figure 6 shows the results of the counting. Ifwe compare the two ‘manual’ countings, we note that one observer systematically counts manymore clones than the other. The difference between both observers reaches 67 colonies at adose of 2 Gy, which is quite large. On the other hand, this difference is never greater than eightcolonies when the software is used. At high radiation exposure the cell line exhibits significantchanges in the colonies morphology. This can cause variations between observers, in manualcolony counting. As a result, significant differences between manual counts were observedat 1.5, 2 and 5 Gy when p-values were calculated (table 1). In addition, the observer 1 and

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0 2 4 6 8

0 ,01

0 ,1

1

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l S/S

o

D o se (G y)

th resho ld 100 th resho ld 200 th resho ld 500 th resho ld 1000 th resho ld 50

Figure 7. One of the possible interpretations of the software: influence of the viability thresholdvalue on the shape of the survival curve. There is no obvious difference at low doses. For highdoses, the threshold value has a big influence on the value of the survival.

0 2 4 6 8

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n su

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e of

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(m

m )2

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Figure 8. This graph shows an example of extensive research on the effects of a treatment thatcan be made with the software. We can see on this graph the evolution of the mean surface of thecolony in relation to the dose. In this case, we observe a phenomenon of fluctuation of the meancolony surface at a low radiation dose.

the program counts are well correlated, except for 8 Gy. However, no significant differencein the two automatic counts was observed. Consequently, the automated counter appears tobe a robust and useful tool to measure cell survival, as it makes an objective and reproduciblecount of the survivors, whatever the toxicity of the treatment.

Using the files containing the different characteristics of the enumerated colonies, thesurvival was analysed by using five different viability threshold values (50, 100, 200, 500and 1000 cells per viable colony). The results for our cell line are shown in figure 7. Itis demonstrated on the chart that the threshold value does not have much influence at lowradiation doses. Indeed, between 0 and 2 Gy, we note that the survival value is roughlyidentical for all the thresholds. This is easily understandable as the majority of the cells giverise to large and well-developed colonies because of the low toxicity of the treatment. On

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the other hand, beyond 4 Gy, the threshold value becomes increasingly significant and has amaximal influence on the survival value at 8 Gy. Indeed, enhancing the toxicity causes thegrowth of the colonies to slow down, and at higher toxicity, most of the colonies have lessthan 500 cells.

Figure 8 shows another possible interpretation of the files that can be made with theautomatic analyser. The evolution of the mean surface of the colonies has been representedas a function of the radiation dose. This graph shows us the evolution of the morphology ofthe colonies according to the toxicity of the treatment. We note that our cell line presents apeak characteristic of a fluctuation of the size of the colonies between 0.5 and 1.5 Gy. Thisfluctuation is non-specific to this cell line since it was also highlighted with others for identicalamounts of irradiations (Calkins et al 1989, Korystov et al 1996). Many other characteristicsof the clones and cells are easily available (data not shown).

4. Discussion

The results show that ‘manual’ count is inter-observer dependent. It is also intra-observerdependent since the same counter is unlikely to count twice the same number of colonies inthe same Petri dish, especially when the colony number is high or when colony sizes are veryheterogeneous (Lumley et al 1997). To have valid results, it is important to minimize thedifferences between observers and to ensure the consistency in the overall counting procedure.The consistency is indeed more important than the difference in counts. As the results of thispaper show in figure 4, the difference in counts can be quite large, yet the resulting survivalcurves are very similar. The difference in computer calibration can be different from a userto another that can give differences in count for a same series of Petri dishes. Nevertheless,after normalization, the survival curves will be identical. Therefore, we established that theutilization of the automated counter is a robust method that gives objective and consistentresults.

This high-performance tool enables extensive research on the effects of the treatment onthe morphology of the clones and the cells to be made. This complementary information isavailable from the picture acquisition equipment that is in fact a simple desktop scanner. Theprincipal advantage of this instrument compared to the camera is that there is no deformationof the picture and thus we have the possibility to acquire directly parameters on the physicalcharacteristics of the colonies. A camera induces optical distortion due to the imperfectionof the lens, which must be determined and taken into account if physical characteristicsparameters have to be acquired. The software uses the densitometry to have a quantitativeapproach and to estimate the number of cells per colony. If we want to be quantitative with acamera, the use of a reference image of a blank dish is necessary. Therefore, the scanner isthe ideal tool as it provides direct images of densitometry.

Another advantage of a scanner compared to a camera is that it is cheaper. To improve theanalysis, we recommend trying the scanner and verifying that it gives images of good qualityconsidering the colouration of the colonies.

It should be noted that the required time to analyse a series of Petri dishes with theautomatic analyser is globally the same as the ‘manual’ counting. Nevertheless, the efficiencyof the software depends on the quality of the computer used and especially on the speed ofthe process. This can be optimized by using the latest generation of processors (we used a PCwith a Pentium II, 200 MHz processor).

As it is based on optic density measurements, the correctness of the cell and colonyparameters also depends on the colouration. A stoechiometric DNA colouration protocolknown as FEULGEN has the theoretical advantage of giving an absolute measurement of

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DNA content (Yamada et al 1990). However, we chose to use the crystal violet solutionbecause the FEULGEN protocol was not adapted to plastic Petri dishes as it contained HCl.We took many precautions in quantity of colourant and time exposure for each Petri dish tohave a reproducible colouration.

We used the SQ20B cell lines, which are cells that do not produce apoptosis. It isessentially a model that tends to have a wide scale of size in the surviving colonies andtherefore was a good model to test the efficiency of the software.

We used the viability threshold proposed by Puck and Marcus that is based on theprinciple that a non-viable colony rarely undergoes more than five to six divisions before itstops growing. It is arbitrary, as it does not take into account the cell line, nor all its physicaland biological characteristics. The utilization of the automated counter enables the viabilitythreshold to be altered and consequently the shape of the survival curves as a function of itsvalue to be compared.

Therefore, the choice of the threshold is a critical point in order to have a good estimationof the survival for high toxicity treatments. Nevertheless, if there is no scientific reason tothink that the size of the colonies is a key criterion to appreciate the effect or mechanism of theaction of the treatment, we do not have to modify the threshold and must stay in agreement withother works by using the 50 cells viability threshold. On the other hand, if one of the effectsof the experimental treatment is to increase the range of size of the colonies, for example thephenomenon of the microcolonies (Sakka et al 1976, Chang and Little 1992), the displayedmodification of the threshold allows the objective quantification of such a phenomenon.

5. Conclusion

To conclude, we have demonstrated the robustness and the reproducibility of the automatedcounting of the colonies by the software produced by Samba Technologies. The originalcontribution of this counting procedure is to enable a detailed study of the morphological andstatistical data for each colony in the form of descriptive files. This information opens thepossibility to evaluate more accurately the treatment effects on the morphology of the cells andcolonies. Moreover, these files and the images of the Petri dishes can be saved as permanentrecords of the experiment.

Therefore, this method is observer-independent and gives reproducible results providedthat the experimental conditions are optimized. The effort must be related to the colourationtechnique and the calibration. Concerning the calibration, we intend to adapt a camera on ourmicroscope to take images of whole colonies and to count the nucleus of the cells by usingthe software. We also intend to buy a more powerful PC to reduce the time of analysis. Thus,this computer-assisted method is very helpful and has considerably improved the reliability ofour statistical results.

Acknowledgments

This work was supported by a research grant from the Association pour la Recherche contre leCancer. Stephanie Corde was supported by a fellowship from Region Rhone-Alpes and fromthe Fondation pour la recherche medicale. We are gratefully indebted to Michel Decorps,Chantal Remy and Regine Farion for technical support in INSERM U438 and to Michel Peoc’hfor technical support in microscopic calibration. All experiments were conducted thanks tothe ESRF beam shift allocations on ID17.

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