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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">IJO</journal-id>
<journal-title-group>
<journal-title>International Journal of Oncology</journal-title></journal-title-group>
<issn pub-type="ppub">1019-6439</issn>
<issn pub-type="epub">1791-2423</issn>
<publisher>
<publisher-name>D.A. Spandidos</publisher-name></publisher></journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3892/ijo.2015.3230</article-id>
<article-id pub-id-type="publisher-id">ijo-48-01-0313</article-id>
<article-categories>
<subj-group>
<subject>Articles</subject></subj-group></article-categories>
<title-group>
<article-title>3D matrix-based cell cultures: Automated analysis of tumor cell survival and proliferation</article-title></title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>EKE</surname><given-names>IRIS</given-names></name><xref rid="af1-ijo-48-01-0313" ref-type="aff">1</xref><xref rid="af2-ijo-48-01-0313" ref-type="aff">2</xref></contrib>
<contrib contrib-type="author">
<name><surname>HEHLGANS</surname><given-names>STEPHANIE</given-names></name><xref rid="af1-ijo-48-01-0313" ref-type="aff">1</xref><xref rid="af3-ijo-48-01-0313" ref-type="aff">3</xref></contrib>
<contrib contrib-type="author">
<name><surname>SANDFORT</surname><given-names>VEIT</given-names></name><xref rid="af4-ijo-48-01-0313" ref-type="aff">4</xref></contrib>
<contrib contrib-type="author">
<name><surname>CORDES</surname><given-names>NILS</given-names></name><xref rid="af1-ijo-48-01-0313" ref-type="aff">1</xref><xref rid="af5-ijo-48-01-0313" ref-type="aff">5</xref><xref rid="af6-ijo-48-01-0313" ref-type="aff">6</xref><xref rid="af7-ijo-48-01-0313" ref-type="aff">7</xref><xref rid="af8-ijo-48-01-0313" ref-type="aff">8</xref><xref ref-type="corresp" rid="c1-ijo-48-01-0313"/></contrib></contrib-group>
<aff id="af1-ijo-48-01-0313">
<label>1</label>OncoRay-National Center for Radiation Research in Oncology, Medical Faculty Carl Gustav Carus, Technische Universit&#x000E4;t Dresden, D-01307 Dresden, Germany</aff>
<aff id="af2-ijo-48-01-0313">
<label>2</label>Radiation Oncology Branch, Center for Cancer Research, National Institutes of Health/National Cancer Institute, Bethesda, MD 20892, USA</aff>
<aff id="af3-ijo-48-01-0313">
<label>3</label>Department of Radiotherapy and Oncology, University of Frankfurt, D-60590 Frankfurt am Main, Germany</aff>
<aff id="af4-ijo-48-01-0313">
<label>4</label>Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA</aff>
<aff id="af5-ijo-48-01-0313">
<label>5</label>Department of Radiation Oncology, University Hospital and Medical Faculty Carl Gustav Carus, Technische Universit&#x000E4;t Dresden, D-01307 Dresden, Germany</aff>
<aff id="af6-ijo-48-01-0313">
<label>6</label>Helmholtz Center Dresden-Rossendorf, Institute of Radiooncology, D-01328 Dresden, Germany</aff>
<aff id="af7-ijo-48-01-0313">
<label>7</label>German Cancer Consortium (DKTK), 01307 Dresden, Germany</aff>
<aff id="af8-ijo-48-01-0313">
<label>8</label>German Cancer Research Center (DKFZ), D-69120 Heidelberg, Germany</aff>
<author-notes>
<corresp id="c1-ijo-48-01-0313">Correspondence to: Dr Nils Cordes, OncoRay-National Center for Radiation Research in Oncology, Medical Faculty Carl Gustav Carus, Dresden University of Technology, Fetscherstrasse 74/PF 41, D-01307 Dresden, Germany, E-mail: <email>nils.cordes@oncoray.de</email></corresp></author-notes>
<pub-date pub-type="collection">
<month>1</month>
<year>2016</year></pub-date>
<pub-date pub-type="epub">
<day>04</day>
<month>11</month>
<year>2015</year></pub-date>
<volume>48</volume>
<issue>1</issue>
<fpage>313</fpage>
<lpage>321</lpage>
<history>
<date date-type="received">
<day>04</day>
<month>08</month>
<year>2015</year></date>
<date date-type="accepted">
<day>23</day>
<month>09</month>
<year>2015</year></date></history>
<permissions>
<copyright-statement>Copyright: &#x000A9; Eke et al.</copyright-statement>
<copyright-year>2016</copyright-year>
<license license-type="open-access">
<license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivs License</ext-link>, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.</license-p></license></permissions>
<abstract>
<p>Three-dimensional <italic>ex vivo</italic> cell cultures mimic physiological <italic>in vivo</italic> growth conditions thereby significantly contributing to our understanding of tumor cell growth and survival, therapy resistance and identification of novel potent cancer targets. In the present study, we describe advanced three-dimensional cell culture methodology for investigating cellular survival and proliferation in human carcinoma cells after cancer therapy including molecular therapeutics. Single cells are embedded into laminin-rich extracellular matrix and can be treated with cytotoxic drugs, ionizing or UV radiation or any other substance of interest when consolidated and approximating <italic>in vivo</italic> morphology. Subsequently, cells are allowed to grow for automated determination of clonogenic survival (colony number) or proliferation (colony size). The entire protocol of 3D cell plating takes ~1 h working time and pursues for ~7 days before evaluation. This newly developed method broadens the spectrum of exploration of malignant tumors and other diseases and enables the obtainment of more reliable data on cancer treatment efficacy.</p></abstract>
<kwd-group>
<kwd>molecular therapeutics</kwd>
<kwd>3D cell culture</kwd>
<kwd>extracellular matrix</kwd>
<kwd>clonogenicity</kwd>
<kwd>proliferation</kwd>
<kwd>therapy testing</kwd></kwd-group></article-meta></front>
<body>
<sec sec-type="intro">
<title>Introduction</title>
<p>The role of targeted therapies in medical oncology has tremendously increased over the last ten years. A high number of novel molecular substances have already been approved for clinical use and several compounds are in ongoing trials at present (<xref rid="b1-ijo-48-01-0313" ref-type="bibr">1</xref>,<xref rid="b2-ijo-48-01-0313" ref-type="bibr">2</xref>). Despite some very successful therapeutics like the tyrosine kinase inhibitor imatinib, which greatly ameliorated the outcome of patients suffering from chronic myelogenous leukemia (<xref rid="b3-ijo-48-01-0313" ref-type="bibr">3</xref>), several of the molecular drugs have not met the expectations from preclinical data when applied clinically. We reason that one cause for this discrepancy could be that many drugs are tested under non-physiological two-dimensional (2D) cell culture conditions not sufficiently reflecting the microenvironment <italic>in vivo</italic>.</p>
<p>Three-dimensional (3D) cell culture models are in use for several decades now. Amongst scientists from various fields of biology and medicine, the culturing of cells in three dimensions opened new avenues of experimentation and thinking. Aside from its potential for tissues engineering, our understanding of cell biology has reached a new dimension ranging from gene expression to protein-protein interactions and signal transduction. The groundbreaking work of Bissell and co-workers and many others strikingly exhibited the essence of 3D growth conditions for single cells and higher order multicellular organisms (<xref rid="b4-ijo-48-01-0313" ref-type="bibr">4</xref>&#x02013;<xref rid="b7-ijo-48-01-0313" ref-type="bibr">7</xref>).</p>
<p>Today, a large body of literature evidently demonstrates that the response of 3D grown cells to external stress and stimuli such as drug treatment or exposure to ionizing radiation more reliably reflects the cell response <italic>in vivo</italic> than the results obtained under 2D cell monolayer growth conditions (<xref rid="b4-ijo-48-01-0313" ref-type="bibr">4</xref>,<xref rid="b8-ijo-48-01-0313" ref-type="bibr">8</xref>&#x02013;<xref rid="b16-ijo-48-01-0313" ref-type="bibr">16</xref>). This effect could be due to both, the change in morphology and the activation of integrins and other cell adhesion receptors by binding to the ECM components, which strongly impact on cell behavior, functionality, gene and protein expression, protein-protein interactions, signal transduction and cellular sensitivity to cytotoxic stress (<xref rid="b7-ijo-48-01-0313" ref-type="bibr">7</xref>,<xref rid="b15-ijo-48-01-0313" ref-type="bibr">15</xref>,<xref rid="b17-ijo-48-01-0313" ref-type="bibr">17</xref>&#x02013;<xref rid="b28-ijo-48-01-0313" ref-type="bibr">28</xref>). For <italic>in vitro</italic> investigation, cell phenotype and molecular processes can be conserved in 3D ECM-based scaffolds. This understanding gains particular relevance in the field of translational research. An example of even higher clinical relevance is a whole genome gene expression analysis of 3D grown human breast cancer cell lines, which was elegantly used to demonstrate predictive power for the probability of relapse and overall survival of breast cancer patients (<xref rid="b12-ijo-48-01-0313" ref-type="bibr">12</xref>,<xref rid="b22-ijo-48-01-0313" ref-type="bibr">22</xref>).</p>
<p>By keeping in mind the heterogeneous distribution and expression patterns of ECM proteins in the different types of human malignancies, cell phenotypes of normal epithelial cells and cancer cells can be reproducibly maintained or restored by culturing them in laminin-rich basement membrane extracellular matrix (lrECM; Matrigel) (<xref rid="b7-ijo-48-01-0313" ref-type="bibr">7</xref>,<xref rid="b29-ijo-48-01-0313" ref-type="bibr">29</xref>). Either embedded or &#x02018;on top&#x02019; with subsequent lrECM overlay, the lrECM isolated from the Engelbreth-Holm-Swarm mouse sarcoma provides a broad spectrum of applications for 3D cell investigations including measurement of apoptosis, cell proliferation, malignant transformation and differentiation. A variety of published protocols explains how cells can be isolated from lrECM gels for protein expression and functional exploration or examined <italic>in situ</italic> using microscopy on living cells or histology (immunohistochemistry, immunofluorescence) on fixed cells, organotypic cell cultures or tissues (<xref rid="b23-ijo-48-01-0313" ref-type="bibr">23</xref>,<xref rid="b29-ijo-48-01-0313" ref-type="bibr">29</xref>).</p>
<p>Cell survival <italic>in vitro</italic> is often measured in terms of apoptosis, dye exclusion or proliferation. Although more time consuming, the colony forming assay has been shown to reliably determine tumor cell kill and reflect tumor control, whereas proliferation assays are used to explore tumor growth delay (<xref rid="b30-ijo-48-01-0313" ref-type="bibr">30</xref>,<xref rid="b31-ijo-48-01-0313" ref-type="bibr">31</xref>). Consequently, the colony forming assay is the gold standard for all disciplines for evaluating dose-effect relationships between e.g. drug concentration or radiation dose and cell survival (<xref rid="b32-ijo-48-01-0313" ref-type="bibr">32</xref>).</p>
<p>However, to date, there is no existing assay to determine clonogenic cell survival as well as tumor proliferation under 3D cell culture conditions in a large scale for drug efficacy testing. On this basis, in the present study, we describe a high-throughput 3D lrECM based cell culture technique that greatly broadens the spectrum of already existing 3D cell culture protocols and enables a robust, reliable and reproducible analysis of the cancer cell response to cytotoxic drugs, targeted therapeutics or different kinds of radiation.</p></sec>
<sec sec-type="materials|methods">
<title>Materials and methods</title>
<sec>
<title>Cell culture</title>
<p>FaDu, A549 and DLD1 carcinoma cells were obtained from the American Type Culture Collection (ATCC; Manassas, VA, USA). The origin and stability of the cells were routinely monitored by short tandem repeat analysis (microsatellites). Cells were cultured in Dulbecco's modified Eagle's medium (DMEM; PAA Laboratories GmbH, Coelbe, Germany) supplemented with 10&#x00025; fetal bovine serum (FBS; PAA Laboratories) and 1&#x00025; non-essential amino acids (PAA Laboratories) at 37&#x000B0;C in a humidified atmosphere containing 7&#x00025; CO<sub>2</sub>. For all experiments asynchronously growing cell cultures were used.</p></sec>
<sec>
<title>Radiation exposure</title>
<p>Irradiation (X-rays, 200 kV, 20 mA) was performed at room temperature using a Yxlon Y.TU 320 (Yxlon International CT Development GmbH, Hattingen, Germany) containing a 0.5-mm copper filter. For measurement of the absorbed dose a Duplex dosimeter (PTW Freiburg GmbH, Freiburg, Germany) was used. The dose-rate was ~1.3 Gy/min and applied doses ranged from 0 to 4 Gy.</p></sec>
<sec>
<title>2D colony formation assay</title>
<p>Asynchronously growing cells were trypsinized, counted using a Neubauer counting chamber (Paul Marienfeld GmbH &amp; Co. KG, Lauda-K&#x000F6;nigshofen, Germany) and plated as single cells in 6-well cell culture plates. After 24 h, cells were irradiated with 4 Gy or treated with cisplatin (25 &#x003BC;M) or cetuximab (5 &#x003BC;g/ml; Merck, Darmstadt, Germany) or left untreated. After 1 h cells were washed with 1X PBS to remove cisplatin from the cell culture medium. For determination of long-term survival cells were cultured for 8 days (A549, DLD1) or 11 days (FaDu) enabling colony growth. After fixation with 80&#x00025; ethanol cells were stained with Coomassie blue (Merck). Counting of cell colonies with &gt;50 cells was performed using a Stemi 2000 microscope (Carl Zeiss, Jena, Germany). Surviving fractions were calculated as follows: numbers of colonies formed/&#x0005B;numbers of cells plated (irradiated) &#x000D7; plating efficiency (unirradiated)&#x0005D;. Each point on survival curves represents the mean surviving fraction from at least three independent experiments.</p></sec>
<sec>
<title>3D colony formation assay</title>
<p>Asynchronously growing cells were trypsinized, counted and mixed with cell culture medium containing 0.5 mg/ml lrECM (cat. no. 354248; BD Biosciences, Heidelberg, Germany). Then, 100 &#x003BC;l of this mixture was placed in 96-well plates precoated with 50 &#x003BC;l of 1&#x00025; agarose. After 2 h, the cell-lrECM layer was covered with 100 &#x003BC;l of cell culture medium. To prevent evaporation of medium, circumjacent wells were filled with 1X PBS (<xref rid="f1-ijo-48-01-0313" ref-type="fig">Fig. 1</xref>). After 24 h cells were irradiated with 4 Gy or treated with cisplatin (25 &#x003BC;M) or cetuximab (5 &#x003BC;g/ml) or left untreated similar to 2D cell culture conditions. To withdraw cisplatin from the cell culture, medium was carefully removed without touching the cell-lrECM layer and new cell culture medium was added. This step was repeated five times. Cells were cultured for 8 days (A549, DLD1) or 11 days (FaDu). Cell clusters (with the minimum size of a cell cluster containing 50 cells) were either counted microscopically without staining using a Axiovert 25 with a 2.5&#x000D7; objective (Carl Zeiss) or evaluated automatically as described below.</p></sec>
<sec>
<title>Automated evaluation of colony number and size</title>
<p>For automated analysis of survival and proliferation, each well was imaged in at least 7 different Z-levels using an Axio Observer microscope with a 2.5&#x000D7; objective ( Carl Zeiss). ImageJ/Fiji (<xref rid="b33-ijo-48-01-0313" ref-type="bibr">33</xref>) was used for image processing and an example ImageJ macro is shown in <xref rid="tI-ijo-48-01-0313" ref-type="table">Table I</xref>. Briefly, focus stacking was applied to the Z-level images to yield a single clear image of all 3D colonies. Further processing included background subtraction, median filtering and thresholding steps. A watershed algorithm was used to separate overlapping colonies, and automatic colony counting followed. Tables of object sizes and numbers are written to disk. Overlays of the microscopic images and the segmentation are also saved for quality review. The resulting tables can be further summarized and analyzed using R (<xref rid="b34-ijo-48-01-0313" ref-type="bibr">34</xref>). The example scripts (ImageJ: <xref rid="tI-ijo-48-01-0313" ref-type="table">Table I</xref>; R: <xref rid="tII-ijo-48-01-0313" ref-type="table">Table II</xref>) automatically process images from multiple wells. The R script generates a histogram of colony size for each well and a summary result table of all wells imaged containing the number of colonies, the average colony area and the total colony area as a measurement of proliferation.</p></sec></sec>
<sec sec-type="other">
<title>Results and Discussion</title>
<p>In the present study, we describe a novel method to measure automatically clonogenic survival and proliferation of cells in a 3D matrix consisting of lrECM which has been reported to mimic physiologic <italic>in vivo</italic> growth conditions in a better way than conventional 2D cell culture plastic (<xref rid="b4-ijo-48-01-0313" ref-type="bibr">4</xref>&#x02013;<xref rid="b11-ijo-48-01-0313" ref-type="bibr">11</xref>,<xref rid="b14-ijo-48-01-0313" ref-type="bibr">14</xref>,<xref rid="b16-ijo-48-01-0313" ref-type="bibr">16</xref>). Importantly, this approach can also be used in a high-throughput setting. According to previous data, we found that the response of all three tested human carcinoma cell lines exposed to the chemotherapeutic drug cisplatin (CDDP) or to X-ray irradiation was affected by the growth conditions with cells being significantly more resistant when cultured in 3D (<xref rid="f1-ijo-48-01-0313" ref-type="fig">Fig. 1</xref>) (<xref rid="b9-ijo-48-01-0313" ref-type="bibr">9</xref>,<xref rid="b11-ijo-48-01-0313" ref-type="bibr">11</xref>,<xref rid="b20-ijo-48-01-0313" ref-type="bibr">20</xref>,<xref rid="b24-ijo-48-01-0313" ref-type="bibr">24</xref>,<xref rid="b35-ijo-48-01-0313" ref-type="bibr">35</xref>). This cell adhesion-mediated radioresistance and therapy resistance might result from a multitude of cellular processes including differences in transcriptional, translational, post-translational processes and signal transduction (<xref rid="b8-ijo-48-01-0313" ref-type="bibr">8</xref>&#x02013;<xref rid="b11-ijo-48-01-0313" ref-type="bibr">11</xref>,<xref rid="b22-ijo-48-01-0313" ref-type="bibr">22</xref>,<xref rid="b36-ijo-48-01-0313" ref-type="bibr">36</xref>). Not surprising and particularly alerting with regard to molecular drug efficacy is that molecular compounds like the EGFR inhibitor cetuximab are also less effective under 3D growth conditions and that this cellular drug response correlates more closely with <italic>in vivo</italic> results (<xref rid="f1-ijo-48-01-0313" ref-type="fig">Fig. 1</xref>) (<xref rid="b9-ijo-48-01-0313" ref-type="bibr">9</xref>&#x02013;<xref rid="b11-ijo-48-01-0313" ref-type="bibr">11</xref>,<xref rid="b37-ijo-48-01-0313" ref-type="bibr">37</xref>). These data confirm the necessity to test targeted substances and more conventional therapeutics in a 3D matrix-based <italic>in vitro</italic> assay prior to animal studies to minimize costs, time and effort.</p>
<p>The workflow of plating and treatment of cells for the 3D clonogenic assay is depicted in <xref rid="f2-ijo-48-01-0313" ref-type="fig">Figs. 2</xref> and <xref rid="f3-ijo-48-01-0313" ref-type="fig">3</xref>. Agarose, cell/lrECM mixture and medium can be applied with a multi-channel pipette allowing time-efficient plating for large-scale analysis. Another advantage over most of the existing matrix-based 3D methods is that the lrECM solution with the concentration of 0.5 mg/ml can be produced with pre-heated medium (37&#x000B0;C) and processed at room temperature for at least 30 min without becoming solid. Therefore, cells do not have to be cooled down which likely provoke a cold stress response and perturb molecular processes (<xref rid="b38-ijo-48-01-0313" ref-type="bibr">38</xref>). To assess the cell number per colony and proliferation of cells embedded into lrECM, we evaluated the number of grown A549 cells over a period of 8 days microscopically (<xref rid="f4-ijo-48-01-0313" ref-type="fig">Fig. 4</xref>). Phase contrast microscopy and DAPI/f-Actin staining revealed similar proliferation rates of this cell line in a 3D matrix in comparison to 2D monolayer cell cultures (~22 h according to ATCC) with doubling times of about 24 h after a lag phase of 1 day. Importantly, at the time of treatment (1 day after plating), 3D cell cultures are still in the single cell status, a key requirement to measure clonogenic survival (<xref rid="f4-ijo-48-01-0313" ref-type="fig">Fig. 4</xref>) (<xref rid="b30-ijo-48-01-0313" ref-type="bibr">30</xref>&#x02013;<xref rid="b32-ijo-48-01-0313" ref-type="bibr">32</xref>).</p>
<p>Manually counting of colonies is a time-consuming and error-prone process. Therefore, automated evaluation can reduce the working time and improve the inter-observer reliability and validity of data. As the colonies are in a 3D matrix, we took images of the wells in different Z-levels and performed focus stacking to create a clear image of all colonies (<xref rid="f5-ijo-48-01-0313" ref-type="fig">Fig. 5A</xref>). After image segmentation, colony number and size were determined (<xref rid="f5-ijo-48-01-0313" ref-type="fig">Fig. 5B</xref>) enabling the evaluation of the specific treatment effects on tumor cell proliferation as well as on clonogenic survival (<xref rid="f5-ijo-48-01-0313" ref-type="fig">Fig. 5C</xref>). While irradiation or cisplatin treatment reduced both, the size of the colonies and the colony number, cetuximab mainly affected the tumor growth and had no significant impact on cell survival (<xref rid="f5-ijo-48-01-0313" ref-type="fig">Fig. 5C</xref>). A comparison with manually counted results showed an excellent correlation (R=0.81) indicating a high reliability of the obtained data (<xref rid="f5-ijo-48-01-0313" ref-type="fig">Fig. 5D</xref>).</p>
<p>Considering the heterogeneity in human tumors and the role of cancer stem cells for therapy resistance (<xref rid="b39-ijo-48-01-0313" ref-type="bibr">39</xref>), analysis on a single cell base can be crucial to evaluate the potential of targeted therapeutics. With the described technique the size and distribution of every single colony (which grows out of one single cell) could easily be determined and plotted in a histogram (<xref rid="f6-ijo-48-01-0313" ref-type="fig">Fig. 6</xref>). As shown in <xref rid="f6-ijo-48-01-0313" ref-type="fig">Fig. 6</xref>, control cell cultures had a wide spectrum of colony sizes with several small and medium-sized but also few larger colonies. In contrast, after exposure to cisplatin, ionizing radiation or cetuximab the distribution shifted to the left resulting in an overall decrease of colony size. These data could give valuable information about the different treatment effects in a tumor cell population.</p>
<p>In summary, the described protocol is a cost and time-efficient method to analyze the tumor response to cancer therapy in a more physiologic cell culture model. Taking into account that 3D lrECM based assays have been shown to reflect the <italic>in vivo</italic> conditions more reliably than 2D monolayer cells, it would be beneficial to employ this technique in a large-scale evaluation of molecular compounds prior to <italic>in vivo</italic> studies.</p></sec></body>
<back>
<ack>
<title>Acknowledgements</title>
<p>The authors were in part supported by a grant from the Bundesministerium f&#x000FC;r Bildung und Forschung (BMBF Contracts 03ZIK041 and BMBF-02NUK006B to N.C.), the Deutsche Forschungsgemeinschaft (CO668/4-1 to N.C.), the Deutsche Krebshilfe (108976 to N.C.), the EFRE Europ&#x000E4;ische Fonds f&#x000FC;r regionale Entwicklung, Europa f&#x000F6;rdert Sachsen (100066308) and by the NIH Intramural Research Program, National Cancer Institute, Center for Cancer Research (to I.E.).</p></ack>
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<floats-group>
<fig id="f1-ijo-48-01-0313" position="float">
<label>Figure 1</label>
<caption>
<p>Cell culture conditions affect the tumor cell resistance to irradiation, chemotherapy and molecular therapeutics. Clonogenic cell survival data of a variety of human carcinoma cell lines treated with different clinically applied therapeutics as examples for anticipated results. Two (2D)- or three (3D)-dimensionally grown FaDu squamous cell carcinoma, A549 lung carcinoma and DLD1 colorectal carcinoma cells were treated either with cisplatin (CDDP), X-rays (4 Gy single dose) or the anti-EGFR antibody cetuximab. Images illustrate characteristic growth of cell colonies in 2D and 3D. Data show mean &#x000B1; SD (n=3; t-test; <sup>*</sup>P&lt;0.05, <sup>**</sup>P&lt;0.01).</p></caption>
<graphic xlink:href="IJO-48-01-0313-g00.gif"/></fig>
<fig id="f2-ijo-48-01-0313" position="float">
<label>Figure 2</label>
<caption>
<p>Summary of steps for 3D cell plating and analysis. Detailed workflow of experimental setup including incubation times. See also <xref rid="f3-ijo-48-01-0313" ref-type="fig">Figs. 3</xref> and <xref rid="f5-ijo-48-01-0313" ref-type="fig">5</xref> for more information.</p></caption>
<graphic xlink:href="IJO-48-01-0313-g01.gif"/></fig>
<fig id="f3-ijo-48-01-0313" position="float">
<label>Figure 3</label>
<caption>
<p>Procedure of 96-well preparation and cell plating into 3D lrECM. To prevent cell adhesion to bottom, wells are coated with agarose. Single cell suspension is prepared from permanent cell culture, diluted with lrECM and pipetted on top of polymerized agarose. Medium is added after polymerization of cell-lrECM mixture. Upon treatment, cell colonies grown from single cells can be counted and/or analyzed microscopically.</p></caption>
<graphic xlink:href="IJO-48-01-0313-g02.gif"/></fig>
<fig id="f4-ijo-48-01-0313" position="float">
<label>Figure 4</label>
<caption>
<p>Colonies in 3D lrECM continuously grow out of one single cell. Representative cell colony growth over 8 days illustrated by microscopic pictures and cartoons. Securing a more than 50 cell stage of a single cell colony was achieved by DAPI staining (optional f-actin staining with phalloidin).</p></caption>
<graphic xlink:href="IJO-48-01-0313-g03.gif"/></fig>
<fig id="f5-ijo-48-01-0313" position="float">
<label>Figure 5</label>
<caption>
<p>Automated analysis of 3D colony formation assay can differentiate between effects on survival and proliferation. (A) To account for limited field depth when imaging 3D cell cultures, multiple Z-levels are recorded and merged using an algorithm that preserves the sharpness of each plane (focus stacking). (B) The resulting image is post-processed and segmented to delineate the colonies. (C) Colonies are automatically counted followed by colony size evaluation as surrogate for cell proliferation. Data show mean &#x000B1; SD (n=3; t-test; <sup>*</sup>P&lt;0.05, <sup>**</sup>P&lt;0.01). (D) Comparative analysis of automatically measured colonies and manually measured colonies including correlation calculation (R=0.81; P&lt;0.0001).</p></caption>
<graphic xlink:href="IJO-48-01-0313-g04.gif"/></fig>
<fig id="f6-ijo-48-01-0313" position="float">
<label>Figure 6</label>
<caption>
<p>Detailed data analysis can reveal effects of cancer treatment on distribution of measured colony sizes on a single-cell-base. With the described method, the size of every single colony under each treatment condition is recorded and the distribution can be analyzed and illustrated with R. Histograms are plotted for each well separately showing the number of colonies (colony count) with a specific colony size (colony area).</p></caption>
<graphic xlink:href="IJO-48-01-0313-g05.gif"/></fig>
<table-wrap id="tI-ijo-48-01-0313" position="float">
<label>Table I</label>
<caption>
<p>Example ImageJ macro to be used on a directory with multiple subdirectories containing images.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th colspan="2" valign="bottom" align="left">Steps</th></tr></thead>
<tbody>
<tr>
<td valign="top" align="left">Step 1</td>
<td valign="top" align="left">//ATTENTION: This macro will close all other open images in ImageJ<break/>Dialog.create(&#x0201C;ATTENTION&#x0201D;);<break/>Dialog.addMessage(&#x0201C;This macro will close all other open images in ImageJ/Fiji!!<break/>Please press cancel if there is any unsaved data&#x0201D;);<break/>Dialog.show();</td></tr>
<tr>
<td valign="top" align="left">Step 2</td>
<td valign="top" align="left">//Chose directory containing the image subdirectories<break/>dir = getDirectory(&#x0201C;Choose a Directory &#x0201C;);<break/>count = 1;<break/>list = getFileList (dir);</td></tr>
<tr>
<td valign="top" align="left">Step 3</td>
<td valign="top" align="left">//Chose minimum colony size<break/>Dialog.create(&#x0201C;Minimum colony size&#x0201D;);<break/>Dialog.addMessage(&#x0201C;Please specify the minimum colony size (area) for counting (in pixels, smaller structures will be ignored)&#x0201D;);<break/>Dialog.addNumber(&#x0201C;Minimum colony size&#x0201D;, 600, 0, 5, &#x0201C;area pixels&#x0201D;)<break/>Dialog.show();<break/>MinColonySize = Dialog.getNumber();</td></tr>
<tr>
<td valign="top" align="left">Step 4</td>
<td valign="top" align="left">//Loop through directories<break/>for (i=0; i&lt;list.length; i++) {<break/>if (endsWith(list&#x0005B;i&#x0005D;, &#x0201C;/&#x0201D;)) {<break/>//Get files in directory<break/>files = getFileList(&#x0201C;&#x0201C;+dir+list&#x0005B;i&#x0005D;);</td></tr>
<tr>
<td valign="top" align="left">Step 5</td>
<td valign="top" align="left">//load images and perform focus stacking<break/>run(&#x0201C;Image Sequence...&#x0201D;, &#x0201C;open=&#x0201C; + dir + list&#x0005B;i&#x0005D; + files&#x0005B;0&#x0005D; + &#x0201C; sort&#x0201D;);<break/>run(&#x0201C;Extended Depth of Field (Easy mode)...&#x0201D;, &#x0201C;quality=&#x02018;0&#x02019; topology=&#x02018;0&#x02019;<break/>show-topology=&#x02018;off&#x02019; show-view=&#x02018;off&#x02019;&#x0201D;);</td></tr>
<tr>
<td valign="top" align="left">Step 6</td>
<td valign="top" align="left">//Wait for output to open<break/>while(!isOpen(&#x0201C;Output&#x0201D;)) {<break/>wait(50);}<break/>wait(1000); //Just to make sure not too early</td></tr>
<tr>
<td valign="top" align="left">Step 7</td>
<td valign="top" align="left">//Select stacked output<break/>selectImage(&#x0201C;Output&#x0201D;);<break/>rename(&#x0201C;OStack&#x0201D;);<break/>run(&#x0201C;Duplicate...&#x0201D;, &#x0201C; &#x0201C;);</td></tr>
<tr>
<td valign="top" align="left">Step 8</td>
<td valign="top" align="left">//Substract background, filter and do segmentation<break/>run(&#x0201C;8-bit&#x0201D;);<break/>run(&#x0201C;Subtract Background...&#x0201D;, &#x0201C;rolling=50 light&#x0201D;);<break/>run(&#x0201C;Median...&#x0201D;, &#x0201C;radius=3&#x0201D;);<break/>run(&#x0201C;Auto Threshold&#x0201D;, &#x0201C;method=Default white&#x0201D;);<break/>run(&#x0201C;Convert to Mask&#x0201D;);<break/>run(&#x0201C;Watershed&#x0201D;);<break/>rename(&#x0201C;Segmented&#x0201D;);</td></tr>
<tr>
<td valign="top" align="left">Step 9</td>
<td valign="top" align="left">//Count Colonies<break/>run(&#x0201C;Analyze Particles...&#x0201D;, &#x0201C;size=MinColonySize -Infinity circularity=0.00&#x02013;1.00<break/>show=&#x0005B;Overlay Outlines&#x0005D;<break/>display clear&#x0201D;);</td></tr>
<tr>
<td valign="top" align="left">Step 10</td>
<td valign="top" align="left">//Save overlay image to disk<break/>selectWindow(&#x0201C;OStack&#x0201D;);<break/>run(&#x0201C;Select All&#x0201D;);<break/>run(&#x0201C;Copy&#x0201D;);selectWindow(&#x0201C;Segmented&#x0201D;);<break/>run(&#x0201C;Paste&#x0201D;);<break/>run(&#x0201C;Invert&#x0201D;);<break/>setFont(&#x0201C;SansSerif&#x0201D;, 32);<break/>setColor(120,120,120);<break/>setJustification(&#x0201C;left&#x0201D;);<break/>drawString(&#x0201C;Min. Colony size: &#x0201C;+ MinColonySize, 10, 50);<break/>saveAs(&#x0201C;PNG&#x0201D;, dir + &#x0201C;segmented&#x0201D; + files&#x0005B;0&#x0005D;);</td></tr>
<tr>
<td valign="top" align="left">Step 11</td>
<td valign="top" align="left">//Save results to disk<break/>selectWindow(&#x0201C;Results&#x0201D;);<break/>saveAs(&#x0201C;Results&#x0201D;, dir+ substring(list&#x0005B;i&#x0005D;,0,lengthOf(list&#x0005B;i&#x0005D;)-1) + &#x0201C;.csv&#x0201D;);</td></tr>
<tr>
<td valign="top" align="left">Step 12</td>
<td valign="top" align="left">//Close all windows<break/>close(&#x0201C;*&#x0201D;);}}</td></tr></tbody></table>
<table-wrap-foot><fn id="tfn1-ijo-48-01-0313">
<p>Each subdirectory should contain multiple images of one well at various levels. Images will be focus stacked and cell colonies will be counted. Output will be a text file for each directory with information on all colonies identified and an image showing the counted colonies for quality check.</p></fn></table-wrap-foot></table-wrap>
<table-wrap id="tII-ijo-48-01-0313" position="float">
<label>Table II</label>
<caption>
<p>Example R code to be used to analyze the data.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th colspan="2" valign="bottom" align="left">Steps</th></tr></thead>
<tbody>
<tr>
<td valign="top" align="left">Step 1</td>
<td valign="top" align="left">#Set to directory containing the ImageJ output<break/>setwd(&#x0201C;C:/Users/xx/Images&#x0201D;)<break/>#Adjust this to the directory containing the image subfolders</td></tr>
<tr>
<td valign="top" align="left">Step 2</td>
<td valign="top" align="left">library(&#x0201C;ggplot2&#x0201D;)<break/>require(plyr)</td></tr>
<tr>
<td valign="top" align="left">Step 3</td>
<td valign="top" align="left">#load data<break/>#get names of csv files, read and add each filename to the dataframe<break/>files &lt;- dir(pattern = &#x0201C;*.csv&#x0201D;)<break/>data &lt;- read.csv(files&#x0005B;1&#x0005D;,header = TRUE)<break/>data$file &lt;- files&#x0005B;1&#x0005D;<break/>for (i in 2:length(files)){<break/>a &lt;- read.csv(files&#x0005B;i&#x0005D;,header = TRUE)<break/>a$file &lt;- files&#x0005B;i&#x0005D;<break/>data &lt;- rbind(a,data)}</td></tr>
<tr>
<td valign="top" align="left">Step 4</td>
<td valign="top" align="left">#create and output area histograms<break/>p &lt;-ggplot(data, aes(x=Area)) + geom_histogram() +scale_x_log10() + facet_wrap(~file)<break/>pdf(&#x0201C;histograms.pdf&#x0201D;, , width=8, height=10)<break/>print(p)<break/>dev.off()</td></tr>
<tr>
<td valign="top" align="left">Step 5</td>
<td valign="top" align="left">#summarize data for each filename (corresponding to each well) and write csv file<break/>resultdata &lt;- ddply(data, .(file), summarize, ColonyNumber=length(Area) ,<break/>MeanColonyArea=mean(Area), TotalColonyArea = sum(Area))<break/>write.csv(resultdata, file = &#x0201C;SummaryCounting.txt&#x0201D;)</td></tr></tbody></table>
<table-wrap-foot><fn id="tfn2-ijo-48-01-0313">
<p>The data are loaded to R and area histograms are created.</p></fn></table-wrap-foot></table-wrap></floats-group></article>
