We would like to thank Dr Hamish Campbell and Prof Antony Braithwaite for their help in developing the transduced MCF-7-LeGO cell lines. We would like to acknowledge our funding support by the Bloomfield Group Foundation through the Hunter Medical Research Institute. B.C.M is supported by an APA scholarship through the University of Newcastle and the MM Sawyer Scholarship through the Hunter Medical Research Institute.

1. Preparing Cells for Proliferation Assays
Note: Prepare the two cell lines in the same manner and seed in the same format for each method to be analyzed.
<ol>
	<li>Grow MCF-7-LeGO and MCF-7-&#916;40p53 7 cells to 75-80% confluence in T 75 cm2 tissue culture flasks using phenol-red free Dulbecco&#39;s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 200 mM L-glutamine, 2 &#181;g/ml insulin and 1 &#181;g/ml Puromycin at 37 &#176;C with 5% CO2. Handle cells in a sterile biosafety cabinet class II. 
	Note: The amount of time it takes to grow the cells to confluence varies depending on the seeding dilution. In preparation for plating the cells for the proliferation assays, seed the cells at a 1:3 dilution in supplemented DMEM and maintain in normal growth conditions for 3 days until 75-80% confluency.</li>
	<li>To remove the cells from the flask, pour off the media into a waste container. Immediately wash the cell layer with 2 ml of pre-warmed 2x trypsin. Aspirate trypsin. Add a further 2 ml of pre-warmed trypsin to the cell layer and place in an incubator for 5 min at 37 &#176;C with 5% CO2.
	<ol>
		<li>Once the cells detach, wash the cells with 10 ml warmed fresh supplemented DMEM. Transfer the cell suspension to a sterile 15 ml tube and centrifuge at 491 x g for 5 min at RT. Carefully aspirate and dispose of the supernatant.</li>
	</ol>
	</li>
	<li>Resuspend the cell pellet in 5 ml of fresh supplemented DMEM. Perform a cell count to determine the correct density to seed the cells in 96-well plates.
	<ol>
		<li>Dilute 100 &#181;l of the cell suspension in 900 &#181;l 1x Dulbecco&#39;s Phosphate Buffered Saline (DPBS). Place a new 60 &#181;m sensor onto the automated cell counter. Hold down the plunger and submerge the sensor in the diluted cell suspension. Slowly release the plunger on the cell counter. Remove the sensor from the cell counter when completed. 
		Note: The cell count will be displayed on the cell counter in cells/ml.</li>
	</ol>
	</li>
	<li>Seed cells at a final volume of 100 &#181;l (in DMEM) in a 96-well plate at ~20% confluency to allow room for cell growth and measurement of proliferation over 3-5 days (see Table 1). Seed all cell lines in triplicate. 
	Note: For this experiment, three different cell densities were assessed to determine the optimal cell density. The required cell numbers are summarized in Table 1. Seed cells at a lower density if measurement of more long-term growth is required.
	<ol>
		<li>For the hemocytometer and luminescence-based assays, prepare one plate per time point (i.e. 4 plates per assay). For the cell imager assay, prepare only one plate for the duration of the experiment.</li>
	</ol>
	</li>
	<li>Maintain cells at 37 &#176;C with 5% CO2 for 24 hr.</li>
</ol>
2. Determining Cell Count Using a Hemocytometer
<ol>
	<li>Pre-warm both medium and trypsin to 37 &#176;C in a cell culture incubator, oven or water bath. Aspirate media from cells into a waste container, wash once with 30 &#181;l of 2x trypsin, and aspirate the trypsin.</li>
	<li>Wash cells again with 30 &#181;l of 2x trypsin, and incubate for 5 minutes at 37 &#176;C. Gently tap edge of plate to dislodge cells from the plate. Add 50 &#181;l of supplemented DMEM and mix cells by pipetting until the cells form a single cell suspension.</li>
	<li>Prepare hemocytometer by cleaning surface and glass cover with 70% ethanol.</li>
	<li>Place the glass cover-slip over counting chambers and affix until &#39;Newton&#39;s refraction rings&#39; can be seen between the cover-slip edges and the hemocytometer. 
	Note: These indicate that the cover-slip has correctly adhered to the hemocytometer.</li>
	<li>Gently pipette 20 &#181;l of cell suspension under the cover-slip, filling the counting chamber by capillary motion. Place hemocytometer under 10x magnification of a microscope and visualize the counting chambers in the grid layout.</li>
	<li>Count cells in the 4 outer squares of the grid layout. To improve accuracy, repeat counts of additional outer squares if required, or until the count has reached 70 - 100 cells14,15.
	<ol>
		<li>Calculate cell concentration per ml as follows:Average cell count per square x dilution factor (if used) x 104</li>
	</ol>
	</li>
	<li>Repeat steps 2.1-2.8 every 24 hr for 96 hr post-seeding.</li>
</ol>
3. Determining Cell Proliferation Using a Luminescence-based Assay
Note: This is an endpoint measurement. Once the reagent is added to the cells, the plate can only be quantified once.
<ol>
	<li>Thaw luminescence reagent in a 22 &#176;C water bath for 30 min.</li>
	<li>Gently mix reagent by inverting the bottle to obtain a homogenous mix.</li>
	<li>Equilibrate one plate of seeded cells (from step 1.5) at RT for 30 min.</li>
	<li>Add 100 &#181;l of luminescence reagent to each well. Mix contents on an orbital shaker for 2 min. Allow plate to incubate for 10 min at RT.</li>
	<li>Setting up a luminescence experiment using the multi-mode plate reader software.
	<ol>
		<li>Turn on multi-mode plate reader and open the software. In the &#39;task manager&#39;, select &#39;experiments&#39; and &#39;create new&#39;.Select &#39;file&#39; from side toolbar, and under &#39;protocol&#39; tab, select &#39;procedure&#39;.</li>
		<li>Select &#39;Grenier 96 flat bottom&#39; plate type, and check the &#39;use lid&#39; box. Select &#39;read&#39; action under &#39;read method&#39; box. Select &#39;luminescence&#39; detection method. Click &#39;OK&#39;.</li>
		<li>In the read step box, select the wells to be scanned under the &#39;full plate&#39; tab, and select wells to act as blank wells.</li>
		<li>Set the filter set to empty filter (e.g., plug, Em: hole, mirror: none). Set the gain to 135, with an integration time of 0.5 sec per well and a read height of 6.5 mm. Click &#39;OK&#39; to save settings for the luminescence read.</li>
	</ol>
	</li>
	<li>Performing a Luminescent Read
	<ol>
		<li>Eject plate holder by selecting &#39;instrument control&#39; tab and click on &#39;plate out&#39;.Place 96-well plate onto the plate holder, ensuring the lid is on. Close the plate holder by clicking on &#39;plate in&#39; under &#39;instrument control&#39; tab. Click on &#39;read now&#39; from toolbar to perform luminescent read.</li>
		<li>Export the relative luminescent unit (RLU) measurements for each well to a spreadsheet for further analysis.</li>
	</ol>
	</li>
	<li>Repeat steps 3.1-3.6 to record proliferation every 24 hr up to 96 hr after seeding cells.</li>
</ol>
4. Determining Cell Count Using a Cell Imager
<ol>
	<li>Setting up a cell imaging experiment using the multi-mode cell imager software
	<ol>
		<li>Turn on multi-mode cell imager and open the software.</li>
		<li>In the &#39;task manager&#39;, select &#39;experiments&#39; tab and &#39;create new&#39;.On the &#39;file&#39; toolbar, click on &#39;protocol&#39; tab and open &#39;procedure&#39; tab.Select &#39;set temperature&#39; action. Set incubator to &#39;on&#39; and set temperature to 37 &#176;C and check &#39;preheat&#39; before continuing with next step&#39; box. Click &#39;OK&#39; to save settings.</li>
		<li>Select &#39;read&#39; action. Click on &#39;image&#39; detection method, select &#39;endpoint/kinetic&#39; read type and &#39;filters&#39; optics type. Click &#39;OK&#39;.Click on &#39;full plate&#39; tab and select wells on the plate to be imaged. Click &#39;OK&#39; to save the settings.</li>
		<li>Select the 2.5X objective from the drop down &#39;objectives&#39; options. Under &#39;channels&#39; tab, select two channels: GFP 469, 525 and Bright Field. Check &#39;auto&#39; exposure for both channels and select the auto-exposure well.</li>
		<li>To determine auto focus settings, select &#39;auto-focus&#39; and click on &#39;options&#39;. For the auto-focus options, select the &#39;scan and then auto focus&#39; method. Click &#39;OK&#39; to save settings.</li>
		<li>Set the horizontal and vertical offset from center of well (&#181;m) to 0.Select to scan multiple images per well in a 3 x 2 montage, with horizontal spacing (&#181;m) = 2,881 and vertical spacing (&#181;m) = 2,127. Click &#39;OK&#39; to save procedure settings. 
		Note: See Table 2 for read parameters.</li>
	</ol>
	</li>
	<li>Performing Read Experiment on Selected Plate
	<ol>
		<li>Click on the &#39;instrument control&#39; tab and select the &#39;plate out&#39; function.Place 96-well plate in plate holder, ensuring the lid is on. Under the &#39;instrument control&#39; tab, select the &#39;plate in&#39; function. Select &#39;read now&#39; from plate tab. Repeat read every 24 hr up to 96 hr post-seeding.</li>
	</ol>
	</li>
	<li>Analysis of Cell Count Using the Cell Imager Software.
	<ol>
		<li>To analyze an image, click on the &#39;data&#39; tab on the imaged plate, select &#39;picture [GFP 469, 525 + Bright field]&#39;. Double click on an imaged well.</li>
		<li>Click on a loaded image. Select &#39;analyze&#39; and select a single image of the montage to be analyzed. Click &#39;OK&#39;.Check the &#39;GFP&#39; channel only, and set parameters as outlined in Table 3. Click &#39;START&#39; to apply parameters to the imaged cells.</li>
		<li>Observe the cell counting mask placed over the imaged cells, which is used to determine the cell count per image. Click &#39;apply changes&#39; and maintain these settings for each plate imaged throughout the experiment.</li>
		<li>Once back at the imaged plate, click on the &#39;data&#39; drop down menu and select &#39;cell count&#39;. This will generate the total cell count per well.</li>
		<li>Export all data to a spreadsheet by clicking on the &#39;export&#39; tab for further analysis of the cell counts per well.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Lane, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer.+p53,+guardian+of+the+genome.">Cancer. p53, guardian of the genome.</a> Nature. 358 (6381), 15-16 (1992).</li><li>Olivier, M., Hollstein, M., Hainaut, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TP53+mutations+in+human+cancers:+origins,+consequences,+and+clinical+use.">TP53 mutations in human cancers: origins, consequences, and clinical use.</a> Cold Spring Harb Perspect Biol. 2 (1), a001008 (2010).</li><li>Olivier, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+clinical+value+of+somatic+TP53+gene+mutations+in+1,794+patients+with+breast+cancer.">The clinical value of somatic TP53 gene mutations in 1,794 patients with breast cancer.</a> Clin Cancer Res. 12 (4), 1157-1167 (2006).</li><li>Bourdon, J. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=p53+isoforms+can+regulate+p53+transcriptional+activity.">p53 isoforms can regulate p53 transcriptional activity.</a> Genes Dev. 19 (18), 2122-2137 (2005).</li><li>Avery-Kiejda, K. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Small+molecular+weight+variants+of+p53+are+expressed+in+human+melanoma+cells+and+are+induced+by+the+DNA-damaging+agent+cisplatin.">Small molecular weight variants of p53 are expressed in human melanoma cells and are induced by the DNA-damaging agent cisplatin.</a> Clin Cancer Res. 14 (6), 1659-1668 (2008).</li><li>Avery-Kiejda, K. A., Morten, B., Wong-Brown, M. W., Mathe, A., Scott, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+relative+mRNA+expression+of+p53+isoforms+in+breast+cancer+is+associated+with+clinical+features+and+outcome.">The relative mRNA expression of p53 isoforms in breast cancer is associated with clinical features and outcome.</a> Carcinogenesis. 35 (3), 586-596 (2014).</li><li>Weber, K., Bartsch, U., Stocking, C., Fehse, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+multicolor+panel+of+novel+lentiviral+&amp;#34;gene+ontology&amp;#34;+(LeGO)+vectors+for+functional+gene+analysis.">A multicolor panel of novel lentiviral &amp;#34;gene ontology&amp;#34; (LeGO) vectors for functional gene analysis.</a> Mol Ther. 16 (4), 698-706 (2008).</li><li>Riss, T. L., Moravec, R. A., Celis, J. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cell Biology. , 25-31 (2006).</li><li>Ng, K. W., Leong, D. T., Hutmacher, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+challenge+to+measure+cell+proliferation+in+two+and+three+dimensions.">The challenge to measure cell proliferation in two and three dimensions.</a> Tissue Eng. 11 (1-2), 182-191 (2005).</li><li>Riss, T. L., Moravec, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+multiple+assay+endpoints+to+investigate+the+effects+of+incubation+time,+dose+of+toxin,+and+plating+density+in+cell-based+cytotoxicity+assays.">Use of multiple assay endpoints to investigate the effects of incubation time, dose of toxin, and plating density in cell-based cytotoxicity assays.</a> Assay Drug Dev Technol. 2 (1), 51-62 (2004).</li><li>Butzler, M., Worzella, T., Hungriano, M., Osorio, F., Hanegraaff, I., Cowan, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Automated cytotoxicity profiling using the CyBi-FeliX instrument, cell health assays and thaw-and-use cells. , (2014).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> CellTiter-Glo 2.0 Assay Technical Manual #403. , (2015).</li><li>Banks, P., Brescia, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Application Guide: Automated Digital Microscopy. , (2015).</li><li>Bastidas, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cell Counting with Neubauer Chamber: Basic Hemocytometer Usage. , (2016).</li><li>Bastidas, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> , (2012).</li><li>Riss, T. L., Moravec, R., Niles, A. L., Sittampalam, G. S., Coussens, N. P., Nelson, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell+Viability+Assays.">Cell Viability Assays.</a> Assay Guidance Manual[Internet]. , (2013).</li><li>Quent, V. M., Loessner, D., Friis, T., Reichert, J. C., Hutmacher, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Discrepancies+between+metabolic+activity+and+DNA+content+as+tool+to+assess+cell+proliferation+in+cancer+research.">Discrepancies between metabolic activity and DNA content as tool to assess cell proliferation in cancer research.</a> J Cell Mol Med. 14 (4), 1003-1013 (2010).</li><li>Crane, J., Mittar, D., Soni, D., McIntyre, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cell Cycle Analysis Using the BD BrdU FITC Assay on the BD FACSVerse System. , 1-12 (2011).</li></ol>

The authors declare that they have no competing financial interests.

In this protocol three different methods of measuring cell proliferation in cultured cells were examined. Each method was capable of reproducible and accurate measurements of cell proliferation over 96 hr, and the results were comparable between each of the methods tested (Figure 2 and 3). Both the luminescence-based assay and cell imaging method produced the most robust results, showing linear increases in cell proliferation after 96 hr (Figure 2b, c). Additionally, cel...

The tumor suppressor gene, p53, is an essential regulator of a number of cellular processes, including cell cycle arrest, apoptosis and senescence1. It is responsible for maintaining genomic stability, and is therefore crucial for maintaining the balance of cell death and cell growth. Mutations in p53 are common in cancer and are the major cause of p53 inactivation leading to uncontrolled cancer cell proliferation2. Interestingly, mutations in p53 only account for approximately 25% of breast cancers3, suggesting that other mechanisms are responsible for the loss of p53 function. The recently discovered p53 isoforms have been shown to be overexpressed in a number of human cancers, and can modulate p53 function4,5. We have previously shown that the p53 isoform, &#916;40p53, is the most highly expressed isoform in breast cancer, and is significantly upregulated in breast cancer cells, when compared to normal adjacent tissue6. Following this, we stably transduced the human breast cancer cell line MCF-7 to overexpress &#916;40p53 using the LeGO-iG2-puro+ vector (GFP+)7. These cells were used to investigate if high &#916;40p53 expression increases cell proliferation rates in breast cancer cells.
There are many direct and indirect methods of measuring cell proliferation of cultured cells in vitro8,9. These can be performed either as continuous measurements over time, or as endpoint assays10. Conventional methods are still useful, such as cell counting using a hemocytometer. This assay is a low cost and direct measure of the cell number, but it does rely on large cell counts and highly skilled training to minimize error and large standard deviations from the counts. The need to perform measurements compatible with high-throughput formats has led to the development of multiwell-plate assays. These luminescence-based assays measure cell numbers based on a luminescent signal that is proportional to the metabolic activity of the cell11,12. More recently, the introduction of high content imaging platforms has allowed for new tools which monitor cell proliferation while providing quantitative and qualitative phenotypic data collection, and includes a variety of systems13. All of these methods provide avenues to measure cell growth, either by continuous measurement or endpoint assays, and each possess a range of advantages and disadvantages with regards to sensitivity, throughput of sample numbers, and cell information, all of which can be weighed accordingly depending on the research question.
This protocol describes three different methods for measuring cell proliferation in vitro, with each method utilizing different ranges of sensitivity, reproducibility and multi-well plate formats. This protocol aimed to compare the use of a hemocytometer counting chamber, a luminescence-based cell viability assay, and cell imager, in the measurement of cell proliferation over a 96 hour time course. To do this, the growth of vector-transduced cells (MCF-7-LeGO) was compared to cells transduced to overexpress &#916;40p53 (MCF-7-&#916;40p53), using three different cell densities. Cell proliferation was measured every 24 hr for up to 96 hr. Each method was found to have its own advantages and disadvantages, and depending on the aim of the experiment, each still is a valuable method for providing information on the rate of proliferation.

<table><tbody><tr><td>Dulbecco&#039;s Modified Eagle Medium, no phenol-red</td><td>ThermoFisher Scientific</td><td>21063-045</td><td>Supplemented with 10% FBS, 200 mM L-glutamine, 2 &amp;micro;g/ml insulin and 1 &amp;micro;g/ml puromycin</td></tr><tr><td>L-glutamine solution (100x)</td><td>ThermoFisher Scientific</td><td>25030-081</td><td></td></tr><tr><td>Insulin solution human</td><td>Sigma-Aldrich</td><td>I9278-5ML</td><td></td></tr><tr><td>Fetal bovine serum (FBS)</td><td>Bovogen Biologicals</td><td>SFBS-F-500ml</td><td></td></tr><tr><td>Puromycin dihydrochloride</td><td>Sigma-Aldrich</td><td>P9620-10ML</td><td></td></tr><tr><td>0.5% trypsin-EDTA solution (10x)</td><td>ThermoFisher Scientific</td><td>15400-054</td><td>Dilute to 2x in DPBS</td></tr><tr><td>Dulbecco&#039;s Phosphate Buffered Saline (DPBS) (1x)</td><td>ThermoFisher Scientific</td><td>30028-02</td><td></td></tr><tr><td>Tissue culture flask, 75 cm&lt;sup&gt;2 &lt;/sup&gt;growth area</td><td>Greiner Bio-One</td><td>658175</td><td></td></tr><tr><td>Scepter 2.0 Cell Counter</td><td>Merck Millipore</td><td></td><td>Automated cell counter</td></tr><tr><td>96 well multiwell plate, flat bottom</td><td>Nunc</td><td>167008</td><td></td></tr><tr><td>Improved Neubauer Hemocytometer</td><td>BOECO Germany</td><td>BOE 01</td><td></td></tr><tr><td>Olympus IX51 inverted microscope</td><td>Olympus</td><td>IX51</td><td></td></tr><tr><td>CellTiter-Glo 2.0 Assay</td><td>Promega</td><td>G9242</td><td>Luminescence-based assay</td></tr><tr><td>Cytation 3 Cell Imaging Multi-Mode Reader</td><td>BioTek</td><td></td><td>Plate reader for luminescence, fluorescence and brightfield cell imaging</td></tr><tr><td>Gen5 Data Analysis Software</td><td>BioTek</td><td>GEN5</td><td></td></tr></tbody></table>

comparison of three different methods for determining cell proliferation in breast cancer cell lines

Measuring cell proliferation can be performed by a number of different methods, each with varying levels of sensitivity, reproducibility and compatibility with high-throughput formatting. This protocol describes the use of three different methods for measuring cell proliferation in vitro including conventional hemocytometer counting chamber, a luminescence-based assay that utilizes the change in the metabolic activity of viable cells as a measure of the relative number of cells, and a multi-mode cell imager that measures cell number using a counting algorithm. Each method presents its own advantages and disadvantages for the measurement of cell proliferation, including time, cost and high-throughput compatibility. This protocol demonstrates that each method could accurately measure cell proliferation over time, and was sensitive to detect growth at differing cellular densities. Additionally, measurement of cell proliferation using a cell imager was able to provide further information such as morphology, confluence and allowed for a continual monitoring of cell proliferation over time. In conclusion, each method is capable of measuring cell proliferation, but the chosen method is user-dependent.

To study different methods of measuring the proliferation of cultured cells, the cell proliferation of MCF-7-&#916;40p53 transduced cells was compared to the non-transduced MCF-7-LeGO breast cancer cell line. The three methods that were compared&#160;&#8211; the conventional hemocytometer method, cell viability luminescence assay, and cell imaging analysis- are outlined in the schematic diagram (Figure 1). Each method has advantages and disadvantages to accurately measure...

Watch this Scientific Journal Video about Comparison of Three Different Methods for Determining Cell Proliferation in Breast Cancer Cell Lines at JoVE.com

Comparison of Three Different Methods for Determining Cell Proliferation in Breast Cancer Cell Lines

This protocol describes the use of three different methods for analyzing cell proliferation in breast cancer cell lines. This includes the use of conventional cell counting, luminescence-based cell viability, and cell counting through the use of a cell imager. Each offers advantages for the reproducible measurement of cell proliferation.

Measuring cell proliferation can be performed by a number of different methods, each with varying levels of sensitivity, reproducibility and compatibility ...

comparison-three-different-methods-for-determining-cell-proliferation

Universidad de Cartagena UDEC

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JoVE Journal

Biology

19.4K Views.  Hunter Medical Research Institute. This protocol describes the use of three different methods for analyzing cell proliferation in breast cancer cell lines. This includes the use of conventional cell counting, luminescence-based cell viability, and cell counting through the use of a cell imager. Each offers advantages for the reproducible measurement of cell proliferation.

Video: Comparison of Three Different Methods for Determining Cell Proliferation in Breast Cancer Cell Lines

Assessing Cell Viability and Death in 3D Spheroid Cultures of Cancer Cells

Three-dimensional spheroids of cancer cells are important tools for both cancer drug screens and for gaining mechanistic insight into cancer cell biology. The power of this preparation lies in its ability to mimic many aspects of the in vivo conditions of tumors while being fast, cheap, and versatile enough to allow relatively high-throughput screening. The spheroid culture conditions can recapitulate the physico-chemical gradients in a tumor, including the increasing extracellular acidity, increased lactate, and decreasing glucose and oxygen availability, from the spheroid periphery to its core. Also, the mechanical properties and cell-cell interactions of in vivo tumors are in part mimicked by this model. The specific properties and consequently the optimal growth conditions, of 3D spheroids, differ widely between different types of cancer cells. Furthermore, the assessment of cell viability and death in 3D spheroids requires methods that differ in part from those employed for 2D cultures. Here we describe several protocols for preparing 3D spheroids of cancer cells, and for using such cultures to assess cell viability and death in the context of evaluating the efficacy of anticancer drugs.

Here, we present several simple methods for evaluating viability and death in 3D cancer cell spheroids, which mimic the physico-chemical gradients of in vivo tumors much better than the 2D culture. The spheroid model, therefore, allows evaluation of the cancer drug efficacy with improved translation to in vivo conditions.

Three-dimensional spheroids of cancer cells are important tools for both cancer drug screens and for gaining mechanistic insight into cancer cell biology. ...

Cancer Research

In Vitro Evaluation of Oncogenic Transformation in Human Mammary Epithelial Cells

Tumorigenesis is a multi-step process in which cells acquire capabilities&#160;that allow their growth, survival, and dissemination under hostile conditions. Different tests seek to identify and quantify these hallmarks of cancerous cells; however, they often focus on a single aspect of cellular transformation and, in fact, multiple tests are required for their proper characterization. The purpose of this work is to provide researchers with a set of tools to assess cellular transformation in vitro from a broad perspective, thereby making it possible to draw sound conclusions.
A sustained proliferative signaling activation is the major feature of tumoral tissues and can be easily monitored under in vitro conditions by calculating the number of population doublings achieved over time. Besides, the growth of cells in 3D cultures allows their interaction with surrounding cells, resembling what occurs in vivo. This enables the evaluation of cellular aggregation and, together with immunofluorescent labeling of distinctive cellular markers, to obtain information on another relevant feature of tumoral transformation: the loss of proper organization. Another remarkable characteristic of transformed cells is their capacity to grow without attachment to other cells and to the extracellular matrix, which can be evaluated with the anchorage assay.
Detailed experimental procedures to evaluate cell growth rate, to perform immunofluorescent labeling of cell lineage markers in 3D cultures, and to test anchorage-independent cell growth in soft agar are provided. These methodologies are optimized for Breast Primary Epithelial Cells (BPEC) due to its relevance in breast cancer; however, procedures can be applied to other cell types after some adjustments.

This protocol provides experimental in vitro tools to evaluate the transformation of human mammary cells. Detailed steps to follow-up cell proliferation rate, anchorage-independent growth capacity, and distribution of cell lineages in 3D cultures with basement membrane matrix are described.

Tumorigenesis is a multi-step process in which cells acquire capabilities&#160;that allow their growth, survival, and dissemination under hostile ...

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment

Multicellular tumor spheroid (MCTSs) models have demonstrated increasing utility for in vitro study of cancer progression and drug discovery. These relatively simple avascular constructs mimic key aspects of in vivo tumors, such as 3D structure and pathophysiological gradients. MCTSs models can provide insights into cancer cell behavior during spheroid development and in response to drugs; however, their requisite size drastically limits the tools used for non-destructive assessment. Optical Coherence Tomography structural imaging and Imaris 3D analysis software are explored for rapid, non-destructive, and label-free measurement of regional cell density within MCTSs. This approach is utilized to assess MCTSs over a 4-day maturation period and throughout an extended 5-day treatment with Trastuzumab, a clinically relevant anti-HER2 drug. Briefly, AU565 HER2+ breast cancer MCTSs were created via liquid overlay with or without the addition of Matrigel (a basement membrane matrix) to explore aggregates of different morphologies (thicker, disk-like 2.5D aggregates or flat 2D aggregates, respectively). Cell density within the outer region, transitional region, and inner core was characterized in matured MCTSs, revealing a cell-density gradient with higher cell densities in core regions compared to outer layers. The matrix addition redistributed cell density and enhanced this gradient, decreasing outer zone density and increasing cell compaction in the cores. Cell density was quantified following drug treatment (0 h, 24 h, 5 days) within progressively deeper 100 &#181;m zones to assess potential regional differences in drug response. By the final timepoint, nearly all cell death appeared to be constrained to the outer 200 &#181;m of each aggregate, while cells deeper in the aggregate appeared largely unaffected, illustrating regional differences in the drug response, possibly due to limitations in drug penetration. The current protocol provides a unique technique to non-destructively quantify regional cell density within dense cellular tissues and measure it longitudinally.

The present protocol develops an image-based technique for rapid, non-destructive, and label-free regional cell density and viability measurement within 3D tumor aggregates. Findings revealed a cell-density gradient, with higher cell densities in core regions than outer layers in developing aggregates and predominantly peripheral cell death in HER2+ aggregates treated with Trastuzumab.

Multicellular tumor spheroid (MCTSs) models have demonstrated increasing utility for in vitro study of cancer progression and drug discovery. These ...

Preparation and Analysis of In Vitro Three Dimensional Breast Carcinoma Surrogates

Three dimensional (3D) culture is a more physiologically relevant method to model cell behavior in vitro than two dimensional culture. Carcinomas, including breast carcinomas, are complex 3D tissues composed of cancer epithelial cells and stromal components, including fibroblasts and extracellular matrix (ECM). Yet most in vitro models of breast carcinoma consist only of cancer epithelial cells, omitting the stroma and, therefore, the 3D architecture of a tumor in vivo. Appropriate 3D modeling of carcinoma is important for accurate understanding of tumor biology, behavior, and response to therapy. However, the duration of culture and volume of 3D models is limited by the availability of oxygen and nutrients within the culture. Herein, we demonstrate a method in which breast carcinoma epithelial cells and stromal fibroblasts are incorporated into ECM to generate a 3D breast cancer surrogate that includes stroma and can be cultured as a solid 3D structure or by using a perfusion bioreactor system to deliver oxygen and nutrients. Following setup and an initial growth period, surrogates can be used for preclinical drug testing. Alternatively, the cellular and matrix components of the surrogate can be modified to address a variety of biological questions. After culture, surrogates are fixed and processed to paraffin, in a manner similar to the handling of clinical breast carcinoma specimens, for evaluation of parameters of interest. The evaluation of one such parameter, the density of cells present, is explained, where ImageJ and CellProfiler image analysis software systems are applied to photomicrographs of histologic sections of surrogates to quantify the number of nucleated cells per area. This can be used as an indicator of the change in cell number over time or the change in cell number resulting from varying growth conditions and treatments.

Preparation and Analysis of In Vitro Three Dimensional Breast Carcinoma Surrogates

We demonstrate a method to generate 3D breast cancer surrogates, which can be cultured using a perfusion bioreactor system to deliver oxygen and nutrients. Following growth, surrogates are fixed and processed to paraffin for evaluation of parameters of interest. The evaluation of one such parameter, cell density, is explained.

Three dimensional (3D) culture is a more physiologically relevant method to model cell behavior in vitro than two dimensional culture. Carcinomas, ...

Bioengineering

Pharmacological Targeting of Ion Channels to Treat Solid Tumors

Screening Ion Channels in Cancer Cells

Ion channels are critical for cell development and maintaining cell homeostasis. The perturbation of ion channel function contributes to the development of a broad range of disorders or channelopathies. Cancer cells utilize ion channels to drive their own development, as well as to improve as a tumor and to assimilate in a microenvironment that includes various non-cancerous cells. Furthermore, increases in levels of growth factors and hormones within the tumor microenvironment can result in enhanced ion channel expression, which contributes to cancer cell proliferation and survival. Thus, the pharmacological targeting of ion channels is potentially a promising approach to treating solid malignancies, including primary and metastatic brain cancers. Herein, protocols to characterize the function of ion channels in cancerous cells and approaches to analyze modulators of ion channels to determine their impact on cancer viability are described. These include staining a cell(s) for an ion channel(s), testing the polarized state of mitochondria, establishing ion channel function using electrophysiology, and performing viability assays to assess drug potency.

Author Spotlight: Exploring the Role of Ion Channels in Cancer: Characterization and Potential Treatment Approaches 

The pharmacological targeting of ion channels is a promising approach to treating solid tumors. Detailed protocols are provided for characterizing ion channel function in cancer cells and assaying the effects of ion channel modulators on cancer viability.

Ion channels are critical for cell development and maintaining cell homeostasis. The perturbation of ion channel function contributes to the development ...

Rapid Quantification of Mitogen-induced Blastogenesis in T Lymphocytes for Identifying Immunomodulatory Drugs

Lymphocyte proliferation in response to antigenic or mitogenic stimulation is a readily quantifiable phenomenon useful for testing immunomodulatory (i.e., immunosuppressive or immunostimulatory) chemical compounds and biologics. One of the earliest steps during mitogenesis is cell enlargement or blastogenic transformation, whereupon the cell volume increases before division. It is usually detectable in the first several hours of T-lymphocyte stimulation. Here, we describe a rapid method to quantify blastogenesis in T lymphocytes isolated from mouse spleens and human peripheral blood mononuclear cells (PBMCs) using an automated cell counter. Various commonly used proliferation assays for the most part are laborious and only reflect the overall population effect rather than individual cellular effects within a population. In contrast, the presented automated cell counter assay provides rapid, direct, and precise measurements of cell diameters that can be used for assessing the effectiveness of various mitogens and immunomodulatory drugs in vitro.

T-lymphocyte mitogenesis is accompanied by blastogenic transformation, whereupon the cell volume enlarges before cell division. Here, we describe a method to quantify blastogenesis in T lymphocytes using an automated cell counter with the capability of measuring cell diameters.

Lymphocyte proliferation in response to antigenic or mitogenic stimulation is a readily quantifiable phenomenon useful for testing immunomodulatory (i.e., ...

Immunology and Infection

Analysis of Cell Cycle Position in Mammalian Cells

The regulation of cell proliferation is central to tissue morphogenesis during the development of multicellular organisms. Furthermore, loss of control of cell proliferation underlies the pathology of diseases like cancer. As such there is great need to be able to investigate cell proliferation and quantitate the proportion of cells in each phase of the cell cycle. It is also of vital importance to indistinguishably identify cells that are replicating their DNA within a larger population. Since a cell&prime;s decision to proliferate is made in the G1 phase immediately before initiating DNA synthesis and progressing through the rest of the cell cycle, detection of DNA synthesis at this stage allows for an unambiguous determination of the status of growth regulation in cell culture experiments.
DNA content in cells can be readily quantitated by flow cytometry of cells stained with propidium iodide, a fluorescent DNA intercalating dye. Similarly, active DNA synthesis can be quantitated by culturing cells in the presence of radioactive thymidine, harvesting the cells, and measuring the incorporation of radioactivity into an acid insoluble fraction. We have considerable expertise with cell cycle analysis and recommend a different approach. We Investigate cell proliferation using bromodeoxyuridine/fluorodeoxyuridine (abbreviated simply as BrdU) staining that detects the incorporation of these thymine analogs into recently synthesized DNA. Labeling and staining cells with BrdU, combined with total DNA staining by propidium iodide and analysis by flow cytometry1 offers the most accurate measure of cells in the various stages of the cell cycle. It is our preferred method because it combines the detection of active DNA synthesis, through antibody based staining of BrdU, with total DNA content from propidium iodide. This allows for the clear separation of cells in G1 from early S phase, or late S phase from G2/M. Furthermore, this approach can be utilized to investigate the effects of many different cell stimuli and pharmacologic agents on the regulation of progression through these different cell cycle phases.
In this report we describe methods for labeling and staining cultured cells, as well as their analysis by flow cytometry. We also include experimental examples of how this method can be used to measure the effects of growth inhibiting signals from cytokines such as TGF-&beta;1, and proliferative inhibitors such as the cyclin dependent kinase inhibitor, p27KIP1. We also include an alternate protocol that allows for the analysis of cell cycle position in a sub-population of cells within a larger culture5. In this case, we demonstrate how to detect a cell cycle arrest in cells transfected with the retinoblastoma gene even when greatly outnumbered by untransfected cells in the same culture. These examples illustrate the many ways that DNA staining and flow cytometry can be utilized and adapted to investigate fundamental questions of mammalian cell cycle control.

Determining the cell cycle position of a population of cells, or understanding how signals affect proliferation, can be readily measured by flow cytometry using this protocol. We report a simple experimental approach to staining cells and quantifying their position in the cell cycle.

The regulation of cell proliferation is central to tissue morphogenesis during the development of multicellular organisms. Furthermore, loss of control of ...

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of different cell types in vitro and in vivo.1-5 Although there are literally thousands of publications using such dyes, some of the most commonly encountered cell tracking applications include monitoring of:
<ol>
	<li>stem and progenitor cell quiescence, proliferation and/or differentiation6-8</li>
	<li>antigen-driven membrane transfer9 and/or precursor cell proliferation3,4,10-18 and</li>
	<li>immune regulatory and effector cell function1,18-21.</li>
</ol>
Commercially available cell tracking dyes vary widely in their chemistries and fluorescence properties but the great majority fall into one of two classes based on their mechanism of cell labeling. "Membrane dyes", typified by PKH26, are highly lipophilic dyes that partition stably but non-covalently into cell membranes1,2,11. "Protein dyes", typified by CFSE, are amino-reactive dyes that form stable covalent bonds with cell proteins4,16,18. Each class has its own advantages and limitations. The key to their successful use, particularly in multicolor studies where multiple dyes are used to track different cell types, is therefore to understand the critical issues enabling optimal use of each class2-4,16,18,24.
The protocols included here highlight three common causes of poor or variable results when using cell-tracking dyes. These are:
<ol>
	<li>Failure to achieve bright, uniform, reproducible labeling. This is a necessary starting point for any cell tracking study but requires attention to different variables when using membrane dyes than when using protein dyes or equilibrium binding reagents such as antibodies.</li>
	<li>Suboptimal fluorochrome combinations and/or failure to include critical compensation controls. Tracking dye fluorescence is typically 102 - 103 times brighter than antibody fluorescence. It is therefore essential to verify that the presence of tracking dye does not compromise the ability to detect other probes being used.</li>
	<li>Failure to obtain a good fit with peak modeling software. Such software allows quantitative comparison of proliferative responses across different populations or stimuli based on precursor frequency or other metrics. Obtaining a good fit, however, requires exclusion of dead/dying cells that can distort dye dilution profiles and matching of the assumptions underlying the model with characteristics of the observed dye dilution profile.</li>
</ol>
Examples given here illustrate how these variables can affect results when using membrane and/or protein dyes to monitor cell proliferation.

Successful use of cell tracking dyes to monitor immune cell function and proliferation involves several critical steps. We describe methods for: 1) obtaining bright, uniform, reproducible label-ing with membrane dyes; 2) selecting fluorochromes and data acquisition conditions; and 3) choosing a model to quantify cell proliferation based on dye dilution.

Fluorescent cell tracking dyes, in combination with flow and image cytometry, are powerful tools with which to study the interactions and fates of ...

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

This protocol describes a method to permit the tracking of cells through the cell cycle without requiring the cells to be synchronized. Achieving cell synchronization can be difficult for many cell systems. Standard practice is to block cell cycle progression at a specific stage and then release the accumulated cells producing a wave of cells progressing through the cycle in unison. However, some cell types find this block toxic resulting in abnormal cell cycling, or even mass death. Bromodeoxyuridine (BrdU) uptake can be used to track the cell cycle stage of individual cells. Cells incorporate this synthetic thymidine analog, while synthesizing new DNA during S phase. By providing BrdU for a brief period it is possible to mark a pool of cells that were in S phase while the BrdU was present. These cells can then be tracked through the remainder of the cell cycle and into the next round of replication, permitting the duration of the cell cycle phases to be determined without the need to induce a potentially toxic cell cycle block. It is also possible to determine and correlate the expression of both internal and external proteins during subsequent stages of the cell cycle. These can be used to further refine the assignment of cell cycle stage or assess effects on other cellular functions such as checkpoint activation or cell death.

This protocol describes the use of bromodeoxyuridine (BrdU) uptake to permit the temporal tracking of cells that were in S phase at a specific point in time. Addition of DNA dyes and antibody labeling facilitates detailed analysis of the fate of the S phase cells at later times.

This protocol describes a method to permit the tracking of cells through the cell cycle without requiring the cells to be synchronized. Achieving cell ...

Measuring Proliferation of Vascular Smooth Muscle Cells Using Click Chemistry

The ability of a cell to proliferate is integral to the normal function of most cells, and dysregulation of proliferation is at the heart of many disease processes. For these reasons, measuring proliferation is a common tool used to assess cell function. Cell proliferation can be measured simply by counting; however, this is an indirect means of measuring proliferation. One common means of directly detecting cells preparing to divide is by incorporation of labeled nucleoside analogs. These include the radioactive nucleoside analog 3H-thymidine plus non-radioactive nucleoside analogs such as 5-bromo-2&#8217; deoxyuridine (BrdU) and 5-ethynyl-2&#8242;-deoxyuridine (EdU). Incorporation of EdU is detected by click chemistry, which has several advantages when compared to BrdU. In this report, we provide a protocol for measuring proliferation by the incorporation of EdU. This protocol includes options for various readouts, along with the advantages and disadvantages of each. We also discuss places where the protocol can be optimized or altered to meet the specific needs of the experiment planned. Finally, we touch on the ways that this basic protocol can be modified for measuring other cell metabolites.

Proliferation is a critical part of cellular function, and a common readout used to assess potential toxicity of new drugs. Measuring proliferation is, therefore, a frequently used assay in cell biology. Here we present a simple, versatile method of measuring proliferation that can be used in adherent and non-adherent cells.

The ability of a cell to proliferate is integral to the normal function of most cells, and dysregulation of proliferation is at the heart of many disease ...

Comparison of Three Different Methods for Determining Cell Proliferation in Breast Cancer Cell Lines

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Assessing Cell Viability and Death in 3D Spheroid Cultures of Cancer Cells

In Vitro Evaluation of Oncogenic Transformation in Human Mammary Epithelial Cells

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment

Preparation and Analysis of In Vitro Three Dimensional Breast Carcinoma Surrogates

Screening Ion Channels in Cancer Cells

Rapid Quantification of Mitogen-induced Blastogenesis in T Lymphocytes for Identifying Immunomodulatory Drugs

Analysis of Cell Cycle Position in Mammalian Cells

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Measuring Proliferation of Vascular Smooth Muscle Cells Using Click Chemistry