Sage Journals: Discover world-class research

Abstract

The nonadherent mammosphere assay has been commonly used to investigate cancer stem cell activities in breast cancers that have the ability to form tumorspheres and maintain tumor growth. The sphere formation step is critical, in that it enables the construction of the mammosphere models for downstream assays. The mammosphere assay has also been used to assess the effects of drug treatment on the tumorspheres formed from primary cancer cells or cell lines. Traditionally, the mammosphere formation has been evaluated by standard microscopy systems that required external software for additional analyses. However, this method can be time-consuming and low-throughput, thus impractical for high-throughput characterization of mammosphere models and screening for potential therapeutic cancer drugs. To overcome these challenges, we developed a plate-based high-throughput method to rapidly analyze mammospheres in whole wells using the Celigo Image Cytometer. The method is employed to characterize mammosphere formation and morphology for adherent and nonadherent propagation of four breast cancer cell lines (MCF7, MDA-MB-436, MDA-MB-231, and SKBR3). Next, the dose-dependent effects of four small molecule drugs (doxorubicin, paclitaxel, 8-quinolinol, and salinomycin) are characterized based on sphere formation and viability stained with calcein AM and propidium iodide. We observed growth and morphometric differences between adherent and nonadherent propagation of the four cell lines. Furthermore, drug treatments induced various effects on mammosphere formation, morphology, and viability. The proposed image cytometry method provides a useful tool suitable for high-throughput characterization and analysis of mammospheres, which can improve assay efficiency when investigating the formation capabilities and drug-induced cytotoxicity effects.

Keywords

mammosphere tumorsphere high-throughput drug screening image cytometry Celigo

Introduction

The nonadherent mammosphere assay was previously developed based on modification of the neurosphere assay to assess cancer stem cell activities in the mammary glands.^1
-3 Due to the physiological relevance to the in vivo model, the in vitro nonadherent breast cancer culture model allows scientists to investigate the physical formation of the mammospheres, as well as downstream assays to characterize their genomic and proteomic expressions.^4
-6 One of the critical downstream assays is utilizing mammospheres formed by primary tumor cells or cell lines as a three-dimensional (3D) model to study the effects of cancer drug treatments, and screening for potential drug candidates. Typically, the mammospheres are formed with single-cell-suspension breast cancer cells that are cultured as a monolayer (adherent propagation) and passaged with standard media; however, other culturing or passaging conditions (nonadherent propagation) may affect the mammosphere formation efficiency (MFE). Therefore, it would be critical for researchers building breast cancer models for drug screening to understand the effects of cell culture conditions on the mammosphere formation prior to finalizing the model.

Mammospheres are commonly assessed by manual or automated standard light and fluorescence microscopy systems, requiring external software for image analysis and segmentation.^7,8 However, this method can be time-consuming, tedious, and low-throughput, and thus impractical for high-throughput screening for potential therapeutic cancer drugs. To overcome the challenges, we developed a plate-based high-throughput image cytometry method to rapidly analyze mammospheres in whole wells. Previously, the Celigo Image Cytometer has demonstrated endpoint or kinetic measurement of 3D tumor spheroid formation, viability, and apoptosis using bright-field and fluorescence image analysis in ultra-low-attachment U-bottom plates, as well as Z-prime calculation for high-throughput assays.^9
-11 Image cytometry was used to analyze and characterize single tumor spheroids formed in the U-bottom plates, where a cancer drug compound scoring method was developed for a high-throughput drug screening assay.¹²

In this work, the Celigo Image Cytometer is employed to characterize mammosphere formation, morphological changes, and drug efficacy in ultra-low-attachment flat-bottom plates that allowed the formation of multiple mammospheres or clusters. We demonstrate the detection capability of the image cytometry method by analyzing the formation of mammospheres with adherent and nonadherent propagation of four breast cancer cell lines (MCF7, MDA-MB-436, MDA-MB-231, and SKBR3). The morphological changes are assessed with the image cytometer to characterize the differences between cell culture propagation methods, as well as different cell lines. Finally, the dose-dependent effects of four cancer drug compounds (doxorubicin, paclitaxel, 8-quinolinol, and salinomycin)^3,13,14 are assessed based on mammosphere formation, morphology, and viability analysis stained with calcein AM and propidium iodide (PI). These drug compounds are selected based on previous publications,^{13,15

-27} which showed tumor growth inhibition and cytotoxicity effects against the four breast cancer cell lines. The results showed visual and quantifiable differences in mammosphere growth and morphology between adherent and nonadherent propagation of the four breast cancer cell lines. The cancer drug treatments induced various dose-dependent effects on mammosphere formation, morphological changes, and viability. The proposed image cytometry method provides a useful tool suitable for high-throughput characterization of mammospheres in whole wells, which can improve assay efficiency and statistical analysis compared with the conventional low-throughput microscopy method. Furthermore, the proposed detection method can be adopted for high-throughput screening for potential cancer drug candidates utilizing the presented mammosphere models.

Materials and Methods

Adherent Propagation of Breast Cancer Cell Cultures

MCF7 is a breast adenocarcinoma cell line from the mammary gland (HTB-22, ATCC, Manassas, VA). The cell line is maintained in adherent T-25 flasks (690175, Greiner Bio-One, Monroe, NC) in Eagle’s Minimum Essential Medium (30-2003, ATCC) basal media, supplemented with 10% fetal bovine serum (FBS) and 10 µg/mL bovine insulin (I1882, Sigma-Aldrich, St. Louis, MO) at 37 °C and 5% CO₂. The cells are regularly passaged two times per week.

SKBR3 is a breast adenocarcinoma cell line from the mammary gland (HTB-30, ATCC). The cell line is maintained in adherent T-25 flasks in McCoy’s 5a Medium Modified (30-2007, ATCC) basal media supplemented with 10% FBS at 37 °C and 5% CO₂. The cells are regularly passaged two times per week.

MDA-MB-231 is a breast adenocarcinoma cell line from the mammary gland (HTB-26, ATCC). The cell line is maintained in adherent T-25 flasks in Leibovitz’s L-15 basal media (30-2008, ATCC) supplemented with 10% FBS at 37 °C and atmospheric air. It is important to note that the L-15 medium formulation is devised for use in a free gas exchange with atmospheric air.

MDA-MB-436 is a breast adenocarcinoma cell line from the mammary gland (HTB-130, ATCC). The cell line is maintained in adherent T-25 flasks in Leibovitz’s L-15 basal media, supplemented with 10% FBS, 10 µg/mL bovine insulin, and 16 µg/mL glutathione at 37 °C and atmospheric air. It is important to note that the atmospheric air environment was media dependent as indicated by ATCC, which may not be required by the two cell lines.

Preparation of Mammosphere Growth Medium

The mammosphere growth medium was prepared with a combination of Mammary Epithelial Cell Growth Medium BulletKit (MEGM, CC-3150, Lonza, Basel, Switzerland) supplemented with 2% B27 (17504, Life Technologies, Carlsbad, CA), 20 ng/mL EGF (315-09, PeproTech, Rocky Hill, NJ), and 20 ng/mL bFGF (100-18B, PeproTech).

Nonadherent Propagation of Mammosphere Cell Cultures

The four adherent breast cancer cell cultures in the T-25 flasks were first washed with phosphate-buffered saline (PBS), dissociated with 0.25% trypsin-EDTA (25200-056, Gibco, Gaithersburg, MD), and incubated for 5–10 min at 37 °C and 5% CO₂, or until cells detached from the bottom. Next, the respective cell culture media for the four cell lines were added to the flasks to break up large clumps of cells. The cell suspension was transferred to a 15 mL conical tube and centrifuged at 1200 rpm for 5 min. The supernatant was aspirated and cell pellet was resuspended in the mammosphere growth medium described previously. The cell suspensions were sieved sequentially through a 100 µm and a 40 µm cell strainer (BD Falcon, Billerica, MA) to ensure a single-cell suspension. A small aliquot of the cell suspensions were then stained with 0.2% trypan blue and counted using a hemacytometer to determine cell viability, and subsequently the viable cells were plated at 1000 cells/mL in low-attachment T-25 flasks (690195, Greiner Bio-One).¹

Preparation of Drug Compounds

Image cytometry was used to assess the cancer cytotoxicity effects of four drug compounds; doxorubicin (44583), paclitaxel (T1912), 8-quinolinol (H6778), and salinomycin (S6201) were all purchased from Sigma-Aldrich. Doxorubicin and 8-quinolinol (powder) were reconstituted in 100% DMSO to a stock concentration of 17.2 and 100 mM, respectively. The stock solution was further diluted in 100% DMSO to the initial working concentrations of 2, 1, and 0.2 mM. Finally, the initial working concentrations were diluted 100× in mammosphere growth medium to produce 20, 10, and 2 µM final working concentrations prior to adding to the cells. Paclitaxel (powder) was reconstituted in 100% DMSO to a stock concentration of 10 mM. The stock solution was further diluted in 100% DMSO to the initial working concentrations of 2, 1, and 0.5 µM. Finally, the initial working concentrations were diluted 100× in mammosphere growth medium to produce 20, 10, and 5 nM final working concentrations prior to adding to the cells. Salinomycin (liquid) has a stock concentration of 2.66 mM. The stock solution was further diluted in 100% DMSO to the initial working concentrations of 1, 0.2, and 0.02 mM. Finally, the initial working concentrations were diluted 100× in mammosphere growth medium to produce 10, 2, and 0.2 µM final working concentrations prior to adding to the cells.

Mammosphere Formation Assay

The adherent and nonadherent cell cultures were washed and trypsinized into single-cell suspensions in the mammosphere growth media and subsequently seeded in a flat-bottom, 96-well, ultra-low-attachment plate (3474, Corning Life Sciences, Corning, NY) at 1000 cells/well in triplicate. The cells were allowed to culture at 37 °C and 5% CO₂, and the plate was imaged in bright field on days 2, 5, 7, 9, 12, 14, and 17 using the image cytometer. For direct comparison, the culturing conditions for the formation assay were kept consistent for both adherent and nonadherent propagation. The bright-field images were analyzed to count the number of formed mammospheres and their respective morphometric parameters. In addition, the MFE percentages were calculated at day 17 to compare each cell line and propagation method.

Mammosphere Drug Treatment Characterization Assay

The effects of the drug compounds were characterized by treating the adherent and nonadherent cell culture-formed mammospheres with doxorubicin, paclitaxel, 8-quinolinol, or salinomycin at various concentrations. The plate map of the experiment is shown in Supplemental Figure S1. First, the adherent and nonadherent cell cultures were washed and trypsinized into single-cell suspensions in the mammosphere growth media, and subsequently seeded in a flat-bottom, 96-well, ultra-low-attachment plate at 1000 cells/well at 100 µL. The cells were allowed to culture at 37 °C and 5% CO₂. On day 2, the working concentrations of the four cancer drugs were added at 100 µL, producing final concentrations for doxorubicin (10, 5, and 1 µM), paclitaxel (10, 5, and 2.5 nM), 8-quinolinol (10, 5, and 1 µM), and salinomycin (5, 1, and 0.1 µM) at 200 µL/well. Controls were set up for adherent and nonadherent cell cultures in the absence of drug compounds. Each condition was set up in triplicate. The drugs were allowed to remain in the wells for up to 17 days, and the media were not replenished over time. The plate was imaged in bright field on days 2, 5, 7, 9, 12, 14, and 17 using the image cytometer. Bright-field images were analyzed to count the number of formed mammospheres and their respective morphometric parameters. It is important to note that the amount of volume in the wells did not induce observable evaporation and edge effects with the standard plate cover.

Fluorescent Staining for Mammosphere Viability Measurement

To measure viability, we utilized a combination of Hoechst 33342, calcein AM, and PI from Nexcelom Bioscience (CSK-V0006-1, Lawrence, MA) to stain the mammospheres. Following the manufacturer’s protocol, a 5 mL staining solution of Hoechst (8 µL), calcein AM (10 µL), PI (40 µL), and PBS (4942 µL) was produced, equivalent to concentrations of 0.5 µM, 2 µg/mL, and 4 µg/mL, respectively. The drug-treated 96-well plate was collected after the bright-field imaging on day 17 in culture, and 50 µL of the staining solution was added to each well. The plate was incubated for 30 min at 37 °C in the dark, and subsequently imaged and analyzed in the bright-field, blue, green, and red channels using the image cytometer.

Celigo Image Cytometer Instrumentation

The Celigo Image Cytometer has been previously employed to measure mammospheres.^28
-30 It employed one bright-field and four fluorescent imaging channels, blue (excitation [EX] 377/50 nm, emission [EM] 470/22 nm), green (EX 483/32 nm, EM 536/40 nm), red (EX 531/40 nm, EM 629/53 nm), and far red (EX 628/40 nm, EM 688/31 nm), to perform high-throughput cell-based assays. The Celigo operation and analysis software consisted of five major steps, START, SCAN, ANALYZE, GATE, and RESULTS, where the users can enter general information, set up scan/analysis parameters, perform scan/analysis, and then view/export images and results.

For the sphere formation assessment, the software application “Embroid Body” was utilized to autofocus and acquire bright-field images at 2 µm²/pixel with exposure times set to autoexposure. Next, the software was used to directly count the number of mammospheres and measure the morphometric parameters: area and long- and short-axis diameters (Suppl. Fig. S2) for all samples and at all time points. The ANALYZE parameters for the bright-field channel were set to “Precision = Medium,” “Border Dilation = 30,” “Separation = 200,” “Minimum Thickness = 80,” “Separation Detail = 100,” “Background Correction = Uncheck,” “Minimum EB Area = 7800,” and “Minimum EB Aspect Ratio = 0.210.”

For the viability assessment, the software application “Target 1 + 2 + 3 + Mask” was utilized to acquire green (Target 1 – calcein AM), red (Target 2 – PI), blue (Target 3 – Hoechst), and bright-field (Mask) images at exposure times of 0.4, 4, and 10 ms for fluorescence, respectively, and autoexposure for the bright field. The application “Colony 1 + 2 + 3 + Mask” was used for analysis to determine the fitness of the mammospheres at the endpoint of day 17. The ANALYZE parameters for the bright-field mask channel were set to “Algorithm = Texture,” “Intensity Threshold = 30,” “Saturated Intensity = 0,” “Precision = High,” “Diameter = 8,” “Background Correction = Uncheck,” “Separate Touching Colonies = Check,” “Minimum Thickness = 15,” and “Minimum Colony Size = 100.” After counting the mammospheres, the average integrated green and red fluorescence intensities (calcein and PI) were exported into Microsoft Excel to calculate mammosphere viability with the following equation: $V i a b i l i t y = \frac{a v e . i n t e g r a t e d c a l c e i n F L i n t .}{a v e . i n t e g r a t e d P I F L i n t .}$ , where higher values indicated higher viability. For both applications, an artificial well mask of approximately 95% was applied to each well in order to eliminate edge artifacts.

Results and Discussion

Mammosphere Formation and Morphometric Analysis

The breast cancer cell lines were allowed to form mammospheres for 17 days, which showed various morphology depending on adherent or nonadherent initial culturing. The time-dependent bright-field images are shown in Figure 1 . MCF7 mammospheres visually displayed similar sizes and shapes between different culturing processes. MDA-MB-436 mammospheres showed dark clusters for both, but adherent cultures grew larger mammospheres compared with nonadherent ones. MDA-MB-231 mammospheres showed drastically different formation between the two culturing processes, where the nonadherent culture presented smaller and more clusters, while the adherent one displayed much larger and lower in number clusters. Finally, SKBR3 showed darker and smaller clusters for the nonadherent culture, and vice versa for the adherent one.

Figure 1.

Time-dependent bright-field images of mammosphere formation from days 2 to 17 for each breast cancer cell line and their respective cell culturing method.

The mammospheres from the entire well were counted and analyzed by the image cytometry software, where the results are highly correlated to the captured bright-field images ( Fig. 2 ). The image analysis was set up to only count mammospheres and clusters greater than 100 µm in diameter, and disregard small debris and single cells in the well. This parameter set a highly stringent threshold in order to clearly observe the mammosphere formation differences between the cell lines and propagation methods. MCF7 mammospheres showed a higher number for the nonadherent culture, while the morphometric analysis remained similar for both. MDA-MB-436 mammospheres displayed a slightly lower number for the adherent culture, and larger morphology compared with the nonadherent culture. MDA-MB-231 mammospheres presented a much higher number for the nonadherent culture, but the size and morphology were significantly smaller than those for the adherent culture. Finally, SKBR3 mammospheres also presented a much higher number for the nonadherent culture; however, the size and morphology were only slightly smaller than those for the adherent culture. The calculated MFE percentages for the adherent and nonadherent propagations, respectively, are 11.0 ± 2.2% and 9.0 ± 1.0% for MCF7, 2.5 ± 0.3% and 5.3 ± 0.6% for MDA-MB-436, 0.5 ± 0.3% and 11.6 ± 0.8% for MDA-MB-231, and 5.9 ± 1.0% and 13.0 ± 0.8% for SKBR3, which are highly correlated to the number of mammospheres formed.

Figure 2.

Time-dependent plots showing mammosphere counts, average long- and short-axis diameters, and average area of the mammospheres comparing each breast cancer cell line and their respective cell culturing method.

Depending on the type of initial cell culturing method, the mammosphere formation can be drastically different. In general, we observed that nonadherent cultures showed a greater number of mammospheres formed, but the size and shape were typically smaller. The differences in growth pattern between the two culturing methods could be attributed to the type of media used, which consisted of various growth factors for the nonadherent cultures. The environment may have introduced changes to cell phenotype and fitness that could have enabled better growth and viability for the nonadherent cultures. It is important to note that the formation of these tumorspheroids has different categories based on a previous publication: condensed spheroids, spheroidal shape, aggregates, and loose aggregates.³¹ The categories of spheroids and observation of growth patterns enable researchers to select the passaging method for the desired mammosphere models for downstream assays.

Cancer Drug Effects on Mammosphere Formation and Morphology

To demonstrate the ability for high-throughput imaging and analysis, we treated the mammospheres with four different drug compounds: doxorubicin, paclitaxel, 8-quinolinol, and salinomycin at various titrations. Paclitaxel has been shown to promote microtubule assembly and stabilize microtubules against depolymerization.³² Doxorubicin has been shown to be antiangiogenic and inhibit vascularization.³³ Both paclitaxel and doxorubicin have shown tumor growth inhibition and cytotoxicity for all four tested breast cancer cell types.^21

-27 8-Quinolinol is an aromatic alkaloid that has shown antibacterial, antifungal, and cytotoxic properties.³⁴ Salinomycin has anticancer properties and has been shown to induce apoptosis.³⁵ Both 8-quinolinol and salinomycin have demonstrated anticancer properties against the tested breast cancer cell types, but most importantly, they have been shown to target cancer stem cells, which inhibit the formation of mammospheres.^13,15

-20 The endpoint (day 17) bright-field images for each breast cancer cell line, initial culturing method, and drug concentrations are shown in Supplemental Figures S3–S6, which showed reduction or disintegration of the mammospheres as the drug concentration increased. Generally, the drugs seemed to show no preference to the type of breast cancer culturing method. However, the images showed that doxorubicin and salinomycin were the most effective against all the breast cancer cell lines.

The endpoint mammosphere count and morphometric measurement results are shown in Figure 3 and Supplemental Figure S7 for each breast cancer cell line. For reduction of MCF7 mammosphere counts, doxorubicin was the most effective, paclitaxel and salinomycin were comparable, and 8-quinolinol was the least effective (doxorubicin > paclitaxel ≈ salinomycin > 8-quinolinol). The morphological effects were consistent between adherent and nonadherent cell cultures for each drug treatment. For reduction of MDA-MB-436 mammosphere counts, again doxorubicin was the most effective and paclitaxel was the least effective (doxorubicin > salinomycin > 8-quinolinol > paclitaxel). Similarly, morphological effects were consistent between adherent and nonadherent cell cultures. For reduction of MDA-MB-231 mammosphere counts, the drug effects were similar to MCF7 (doxorubicin > paclitaxel ≈ salinomycin > 8-quinolinol); however, they were only significant for the nonadherent passaging method because the adherent method formed much fewer mammospheres. Interestingly, the morphology of the adherent mammospheres was affected, which showed decreasing trends in size as drug concentration increased for paclitaxel, 8-quinolinol, and salinomycin. Finally, for reduction of SKBR3 mammosphere counts, doxorubicin and salinomycin showed comparable effects, and paclitaxel showed the least effect (doxorubicin ≈ salinomycin > 8-quinolinol > paclitaxel). Morphologically, the drug effects were consistent between the adherent and nonadherent cell culturing methods. Additional kinetic data were generated and plotted in Supplemental Figures S8–S11, where the time-dependent mammosphere counts and three morphometric parameters are shown. It is important to note that from the kinetic plots, we were able to observe immediate growth inhibition or cytotoxic effects as the drug induction commenced. In addition, although the drugs showed effects against the mammospheres, different dosages were only able to inhibit the formation, and not completely eradicate the tumor cells.

Figure 3.

Endpoint dose–response plots on day 17 for (a) mammosphere counts and (b) average area of the mammospheres comparing each breast cancer cell line and their respective cell culturing method.

The ability to characterize various mammosphere models via kinetic measurement of count and morphology allows researchers to determine the optimal model to move to downstream biological assays. One of the critical assays is to identify potential therapeutic drug candidates, thus making throughput an important parameter for any characterization method. In the drug treatment experiment, the image cytometer was able to perform high-throughput drug screening by scanning and analyzing 96 whole wells of mammospheres in bright field within approximately 5–6 min. It is important to note that because of the rapid scanning time, kinetic data can be acquired and utilized to interpret the mammosphere formation results.

Viability Analysis of Drug-Treated Mammospheres

In order to determine or validate the cytotoxic effects of the drug compounds, fluorescent staining using calcein AM, PI, and Hoechst was employed to measure viability. This method was developed in a previous publication^10,12 to determine the fitness of tumor spheroids. Essentially, the ratio between calcein and PI fluorescent intensities can indicate the fitness of the mammospheres. The fluorescent overlay images for calcein (green), PI (red/orange), and Hoechst (blue) are shown in Figure 4a for each breast cancer cell line without drug treatment. Both MCF7 and SKBR3 showed viable mammospheres with bright green fluorescence and a few red cells inside. The MDA-MB-231 mammospheres were viable, but green fluorescence was much dimmer and more single cells spread, which may indicate that the mammosphere formation method can not be optimized.

Figure 4.

(a) Fluorescent overlay images on day 17 of MCF7, MDA-MB-231, and SKBR3 mammospheres stained with calcein (green), PI (red/orange), and Hoechst (blue). (b) Endpoint plots on day 17 for the calculated dose-dependent viability ratios (calcein FL int./PI FL int.) of MCF7, MDA-MB-231, and SKBR3 mammospheres treated with doxorubicin, paclitaxel, 8-quinolinol, and salinomycin at different concentrations. The viability ratios are compared between nonadherent and adherent cell culturing methods.

The calculated calcein/PI ratios are shown in Figure 4b , which showed the various drug effects on the viability of mammospheres for each breast cancer cell line. The endpoint fluorescent overlay images of drug-treated mammospheres are shown in Supplemental Figures S12–S14. For doxorubicin, all of the tested concentrations were cytotoxic against all cell types and culturing methods. For paclitaxel, it seemed that although the drug inhibited the formation of mammospheres, it did not induce cytotoxicity to eliminate all tumor cells, where the ratios remained high and consistent for all concentrations. Paclitaxel did show higher killing against adherent passage SKBR3 mammospheres compared with the nonadherent method. For 8-quinolinol, it showed high cytotoxic effects at the higher concentrations. MCF7 and SKBR3 mammospheres showed similar viability ratio results for both culturing methods. In contrast, MDA-MB-231 nonadherent culture was more resistant to 8-quinolinol, showing a significantly reduced viability ratio at the highest drug concentration. For salinomycin, the viability ratios for each cell type and culturing method stayed higher, indicating that the remaining mammospheres were still viable. However, it seemed that the drug affected the adherent culturing method more with lower-viability ratios compared with the nonadherent method. It is important to note that the bright-field images were used to segment the mammospheres for fluorescence analysis because Hoechst only stained individual cells in the spheroids, and thus was unable to segment the entire spheroid accurately. Interestingly, the fluorescent background induced by doxorubicin in the red channel was eliminated by the software’s background subtraction algorithm.

Overall, fluorescent staining of mammospheres with calcein, PI, and Hoechst can provide a simple method to determine their viability. By calculating the ratios of calcein and PI fluorescence, one can perform high-throughput screening of the cytotoxic efficacy of potential drug candidates. Plate-based image cytometry is a helpful tool suitable for high-throughput characterization and analysis of various mammosphere models. The rapid whole-well scanning in bright field and fluorescence allow kinetic and multiplex assays to be performed, which can increase the amount of data output from each experiment, thus improving assay efficiency when investigating formation capabilities and drug treatment effects. Furthermore, fluorescent assays can characterize drug-treated mammospheres by investigating viability, apoptosis, necrosis, and hypoxia.^10,12,36

Supplemental Material

Supplemental Material, Kessel_and_Chan_Supplemental_Figures - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Kessel_and_Chan_Supplemental_Figures for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_1 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_1 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_10 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_10 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_11 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_11 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_12 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_12 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_13 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_13 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_14 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_14 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_2 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_2 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_3 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_3 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_4 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_4 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_5 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_5 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_6 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_6 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_7 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_7 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_8 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_8 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Supplemental Material

Supplemental Material, Supplementary_Figure_9 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material, Supplementary_Figure_9 for A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres by Sarah L. Kessel and Leo Li-Ying Chan in SLAS Discovery

Footnotes

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: S.L.K. and L.L.C. declare competing financial interests. The research in this manuscript is for reporting on a high-throughput mammosphere analysis method using an instrument of Nexcelom Bioscience, LLC.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Supplemental material is available online with this article.

References

Dontu

Abdallah

W. M.

Foley

J. M.

, et al. In Vitro Propagation and Transcriptional Profiling of Human Mammary Stem/Progenitor Cells. Genes Dev. 2003, 17, 1253–1270.

Iglesias

J. M.

Beloqui

Garcia-Garcia

, et al. Mammosphere Formation in Breast Carcinoma Cell Lines Depends upon Expression of E-Cadherin. PLoS One 2013, 8, e77281.

Rappa

Mercapide

Anzanello

, et al. Growth of Cancer Cell Lines under Stem Cell-Like Conditions Has the Potential to Unveil Therapeutic Targets. Exp. Cell Res. 2008, 314, 2110–2122.

Grimshaw

M. J.

Cooper

Papazisis

, et al. Mammosphere Culture of Metastatic Breast Cancer Cells Enriches for Tumorigenic Breast Cancer Cells. Breast Cancer Res. 2008, 10, R52.

Liao

M.-J.

Zhang

C. C.

Zhou

, et al. Enrichment of a Population of Mammary Gland Cells That Form Mammospheres and Have In Vivo Repopulating Activity. Cancer Res. 2007, 67, 8131–8138.

Rota

L. M.

Lazzarino

D. A.

Ziegler

A. N.

, et al. Determining Mammosphere-Forming Potential: Application of the Limiting Dilution Analysis. J. Mammary Gland Biol. Neoplasia 2012, 17, 119–123.

Wang

Jones

H. M.

, et al. Evaluation of the Antitumor Effects of c-Myc-Max Heterodimerization Inhibitor 100258-F4 in Ovarian Cancer Cells. J. Transl. Med. 2014, 12, 1–11.

Zhou

Wang

Sun

Y. A

Reliable Parameter to Standardize the Scoring of Stem Cell Spheres. PLoS One 2015, 10, e0127348.

Kessel

Cribbes

Déry

, et al. High-Throughput 3D Tumor Spheroid Screening Method for Cancer Drug Discovery Using Celigo Image Cytometry. J. Lab. Autom. 2017, 22, 454–465.

10.

Kessel

Cribbes

Bonasu

, et al. Real-Time Apoptosis and Viability High-Throughput Screening of 3D Multicellular Tumor Spheroids Using the Celigo Image Cytometer. SLAS Discov. 2018, 23, 202–210.

11.

Kessel

Cribbes

Bonasu

, et al. Real-Time Viability and Apoptosis Kinetic Detection Method of 3D Multicellular Tumor Spheroids Using the Celigo Image Cytometer. Cytometry A 2017, 91, 883–892.

12.

Cribbes

Kessel

McMenemy

, et al. A Novel Multiparametric Drug-Scoring Method for High-Throughput Screening of 3D Multicellular Tumor Spheroids Using the Celigo Image Cytometer. SLAS Discov. 2017, 22, 547–557.

13.

Y.-Z.

Yan

Y.-Y.

, et al. Salinomycin Induces Selective Cytotoxicity to MCF-7 Mammosphere Cells through Targeting the Hedgehog Signaling Pathway. Oncol. Rep. 2016, 35, 912–922.

14.

Zhou

Zhang

, et al. Cancer Stem/Progenitor Cell Active Compound 8-Quinolinol in Combination with Paclitaxel Achieves an Improved Cure of Breast Cancer in the Mouse Model. Breast Cancer Res. Treat. 2009, 115, 269–277.

15.

Hagen

. Characterization of potential cancer initiating cells in breast cancer cell lines [master's thesis]. Vienna University, Vienna: Chemical Biology. 2015.

16.

Jangamreddy

J. R.

Ghavami

Grabarek

, et al. Salinomycin Induces Activation of Autophagy, Mitophagy and Affects Mitochondrial Polarity: Differences between Primary and Cancer Cells. Biochim. Biophys. Acta 2013, 1833, 2057–2069.

17.

Kurebayashi

Kanomata

Moriya

, et al. Preferential Antitumor Effect of the Src Inhibitor Dasatinib Associated with a Decreased Proportion of Aldehyde Dehydrogenase 1-Positive Cells in Breast Cancer Cells of the Basal B Subtype. BMC Cancer 2010, 10, 1–10.

18.

Prud’homme

G. J

. Cancer Stem Cells and Novel Targets for Antitumor Strategies. Curr. Pharm. Des. 2012, 18, 2838–2849.

19.

Timerbaev

A. R.

Advances in Developing Tris (8-Quinolinolato) Gallium (III) as an Anticancer Drug: Critical Appraisal and Prospects. Metallomics 2009, 1, 193–198.

20.

Zhou

Zhang

Cancer Stem Cells: Models, Mechanisms and Implications for Improved Treatment. Cell Cycle 2008, 7, 1360–1370.

21.

Aghaee

Islamian

J. P.

Baradaran

, et al. Enhancing the Effects of Low Dose Doxorubicin Treatment by the Radiation in T47D and SKBR3 Breast Cancer Cells. J. Breast Cancer 2013, 16, 164–170.

22.

Alexandre

, et al. Novel Action of Paclitaxel against Cancer Cells: Bystander Effect Mediated by Reactive Oxygen Species. Cancer Res. 2007, 67, 3512–3517.

23.

Doroshow

J. H

. Prevention of Doxorubicin-Induced Killing of MCF-7 Human Breast Cancer Cells by Oxygen Radical Scavengers and Iron Chelating Agents. Biochem. Biophys. Res. Commun. 1986, 135, 330–335.

24.

Moudgil

Ross

H. J.

, et al. Apoptosis of Non-Small-Cell Lung Cancer Cell Lines after Paclitaxel Treatment Involves the BH3-Only Proapoptotic Protein Bim. Cell Death Differ. 2005, 12, 292–303.

25.

Long

Huang

, et al. Methylseleninic Acid to Enhance the Efficacy of Doxorubicin and Paclitaxel in Triple-Negative Breast Cancer Cells. J. Clin. Oncol. 2017, 34, e12563.

26.

Patel

N. M.

Nozaki

Shortle

N. H.

, et al. Paclitaxel Sensitivity of Breast Cancer Cells with Constitutively Active NF-kB Is Enhanced by IkBa Super-Repressor and Parthenolide. Oncogene 2000, 19, 4159–4169.

27.

Reeder

Attar

Nazario

, et al. Stress Hormones Reduce the Efficacy of Paclitaxel in Triple Negative Breast Cancer through Induction of DNA Damage. Br. J. Cancer 2015, 112, 1461–1470.

28.

Debeb

B. G.

Lacerda

, et al. Histone Deacetylase Inhibitors Stimulate Dedifferentiation of Human Breast Cancer Cells through WNT/β-Catenin Signaling. Stem Cells 2012, 30, 2399–2377.

29.

Wolfe

A. R.

Debeb

B. G.

Lacerda

, et al. Simvastatin Prevents Triple-Negative Breast Cancer Metastasis in Pre-Clinical Models through Regulation of FOXO3a. Breast Cancer Res. Treat. 2015, 154, 495–508.

30.

Yang

Perez

A. A.

Fujie

, et al. Wnt Modulates MCL1 to Control Cell Survival in Triple Negative Breast Cancer. BMC Cancer 2014, 14, 124.

31.

Selby

Delosh

Laudeman

, et al. 3D Models of the NCI60 Cell Lines for Screening Oncology Compounds. SLAS Discov. 2017, 22, 473–483.

32.

Yukimune

Tabata

Higashi

, et al. Methyl Jasmonate-Induced Overproduction of Paclitaxel and Baccatin III in Taxus Cell Suspension Cultures. Nat. Biotechnol. 1996, 14, 1129–1132.

33.

Hekmatara

Bernreuther

Khalansky

A. S.

, et al. Efficient Systemic Therapy of Rat Glioblastoma by Nanoparticle-Bound Doxorubicin Is Due to Antiangiogenic Effects. Clin. Neuropathol. 2009, 28, 153–164.

34.

Chobot

Drage

Hadacek

. Redox Properties of 8-Quinolinol and Implications for Its Mode of Action. Nat. Prod. Commun. 2011, 6.

35.

Steverding

Sexton

D. W

. Trypanocidal Activity of Salinomycin Is Due to Sodium Influx Followed by Cell Swelling. Parasit. Vectors 2013, 6, 1–5.

36.

Arai

Eguchi

Rahman

M. M.

, et al. A Novel High-Throughput 3D Screening System for EMT Inhibitors: A Pilot Screening Discovered the EMT Inhibitory Activity of CDK2 Inhibitor SU9516. PLoS One 2016, 11, e0162394.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

2.32 MB

0.17 MB

1.38 MB

1.33 MB

3.05 MB

11.20 MB

7.77 MB

1.04 MB

8.43 MB

8.37 MB

8.43 MB

8.25 MB

0.61 MB

1.32 MB

1.37 MB

A High-Throughput Image Cytometry Method for the Formation,Morphometric,and Viability Analysis of Drug-Treated Mammospheres

Abstract

Keywords

Introduction

Materials and Methods

Adherent Propagation of Breast Cancer Cell Cultures

Preparation of Mammosphere Growth Medium

Nonadherent Propagation of Mammosphere Cell Cultures

Preparation of Drug Compounds

Mammosphere Formation Assay

Mammosphere Drug Treatment Characterization Assay

Fluorescent Staining for Mammosphere Viability Measurement

Celigo Image Cytometer Instrumentation

Results and Discussion

Mammosphere Formation and Morphometric Analysis

Cancer Drug Effects on Mammosphere Formation and Morphology

Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Kessel_and_Chan_Supplemental_Figures - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_1 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_10 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_11 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_12 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_13 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_14 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_2 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_3 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_4 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_5 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_6 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_7 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_8 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Supplemental Material

Supplemental Material, Supplementary_Figure_9 - A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Footnotes

Declaration of Conflicting Interests

Funding

References

Supplementary Material