Skip to main content

Comparative analysis of pediatric SHH medulloblastoma DAOY spheres and adherent monolayers: implications for medulloblastoma research

Abstract

Medulloblastoma, the most prevalent brain tumor among children, requires a comprehensive understanding of its cellular characteristics for effective research and treatment. In this study, we focused on DAOY, a permanent cell line of medulloblastoma, and investigated the unique properties of DAOY cells when cultured as floating multicellular aggregates called spheres, as opposed to adherent monolayers. Through our comprehensive analysis, we identified distinct characteristics associated with DAOY spheres. Our findings demonstrate that DAOY spheres express markers for both neural stem cells, such as CD133 (PROM1), and differentiated neurons, exemplified by MAP2. Additionally, our investigation revealed that spheres-derived cells exhibit heightened resistance to ionizing radiation compared to adherent cells. Consequently, our results indicate that caution is advised when interpreting experimental results obtained from adherent cell cultures and extrapolating them to in vivo situations.

Introduction

Medulloblastoma (MB) is the most common malignant brain tumor in children, whereas there are only sporadic cases in the adult population. MB is an embryonal tumor of the cerebellum that is thought to arise from different neural stem or progenitor cell populations. MB is characterized by high malignancy, rapid growth, easy metastasis, and easy recurrence, and the 5-year survival rate is 50–75% [1]. The current WHO classification divides MB into several molecularly defined subgroups: Wingless (WNT), Sonic hedgehog (SHH), Group C (Group 3), and Group D (Group 4). These subgroups differ in their molecular features and clinical course, and are associated with different prognoses [2]. Further studies have shown large intertumoral heterogeneity at the molecular level within subgroups, suggesting that MB should be further subdivided into distinct subtypes that share important deregulated signaling pathways [3].

Comprehensive treatment with surgery, radiotherapy, and chemotherapy is effective for most patients with MB. However, these combined therapeutic approaches often have severe long-term cognitive and endocrine side effects, especially in young patients [4, 5].

The SHH subtype accounts for approximately 30% of cases and is more common in infants and young adults. In most cases, the SHH subgroup involves somatic mutations in one or more genes of the SHH pathway (such as PTCH1, SUFU, SMO, and GLI2), which result in abnormal pathway activation [6, 7]. Patients in the SHH subgroup who in addition have TP53 mutation have a higher risk of recurrence and a lower median survival than patients with TP53 wild-type tumors [8].

DAOY is a permanent cell line derived from MB of a 4-year-old boy. Based on their molecular characteristics, DAOY cells belong to the SHH subgroup [9] with mutated TP53 gene [10]. The primary tumor showed evidence of both glial and neuronal differentiation, but minimal neuronal and no glial differentiation is found in DAOY cells cultured in vitro as adherent monolayers in serum-containing medium [11, 12]. DAOY cells grown under these conditions are much more radiosensitive than glioma cell lines [13], but they are more resistant to ionizing radiation than TP53 wild-type MB lines [14].

DAOY cells can grow in standard serum-containing medium as monolayers, but in serum-free medium supplemented with growth factors EGF and FGF-2, and under conditions of low adherence, these cells form floating multicellular aggregates – spheres (also called spheroids, neurospheres or medullospheres). Analysis of DAOY cells grown in adherence and as spheres has shown that expression of some genes, e.g., PROM1 (also known as CD133) and NES (nestin) is increased when the cells are grown as spheres [15]. CD133 is a 120 kDa five-transmembrane cell surface protein that is a marker of neural stem cells and is also considered as marker of cancer stem cells in human brain tumors [16]. Nestin is a class VI intermediate filament protein that has been detected in neural stem cells during brain development and is also discussed as a marker of cancer stem cells [17, 18].

The high toxicity of standard chemotherapy and the impossibility of radiotherapy in patients younger than three years [19] have led to the need to develop alternative therapies for MB. To better understand the biology of MB, it is important to find and use an appropriate experimental system. In recent years, in vitro technologies that allow researchers to grow tumor cells in three-dimensional cultures have improved significantly. This culturing aims to mimic the characteristics observed in patients’ tumors, creating a more realistic model of the tumor. While many tumor cell lines have been optimized for growth in spheres, there are relatively few studies using MB cell lines in this model [20,21,22,23,24]. MB cell line DAOY is commonly cultured in adherent conditions and transfer into spheres-propagating condition is used for testing tumorigenicity in limiting dilution analysis or colony formation assay [25, 26]. Changes in the expression of genes associated with neural cell stemness within spheres have already been documented, but the effects of culture conditions on the expression of genes related to cell differentiation are still unexplored. Therefore, this study aims to evaluate variations in the expression of genes related to neural differentiation in DAOY spheres. Furthermore, recognizing the importance of radiotherapy in the treatment of MB, we aimed to describe the effects of ionizing radiation on DAOY cells cultured as spheres compared to those cultured as monolayers.

Overall, in this study, we show that DAOY cells grown in spheres differ in a number of parameters from adherently grown cells, so some caution is needed when interpreting the results obtained with this cell line cultured adherently and when extrapolating results to the in vivo situation.

Materials and methods

Cell cultivation and sphere generation

Human MB cell lines DAOY, D341 MED and CHLA-01-MED were purchased from the American Type Culture Collection (ATCC); D425 MED from Merck. Adherent DAOY cells grown as monolayers were cultured in TPP tissue culture flasks or dishes in IMEM (Gibco) containing 10% FBS and 100 U penicillin/ml and 100 µg streptomycin/ml (Gibco). Cells were passaged every other day using 0.05% trypsin/EDTA and reseeded at a density of 0.6 × 104 cells/cm2. To prepare spheres, DAOY cells were grown in adherent conditions, trypsinized, and plated at a concentration of 0.2 × 105 cells/ml in suspension cell culture flasks in serum-free Neurobasal medium (Gibco) containing B27 supplement (Gibco), 2 mM L-glutamine (Gibco) and 100 U penicillin/ml and 100 µg streptomycin/ml (Gibco). The final medium was supplemented with FGF-2 (20 ng/ml, R&D Systems) and EGF (20 ng/ml, R&D Systems) growth factors. The spheres of the first passage were harvested four days after seeding, trypsinized, and seeded at the same concentration into new culture flasks to obtain subsequent passages. The third passage of spheres was used for all presented experiments. The experimental parameters were uploaded to https://www.mispheroid.org and received the MISpheroID string 1125 + bhkXUS + 6681. Adherent cells and spheres prepared from D341 MED and D425 MED cell lines were cultured in the same way as DAOY cells. CHLA-01-MED cells were grown in suspension cell culture flasks in DMEM: F12 medium (Gibco), containing B27 supplement (Gibco) and 100 U penicillin/ml and 100 µg streptomycin/ml (Gibco). The final medium was supplemented with FGF-2 (20 ng/ml, R&D Systems) and EGF (20 ng/ml, R&D Systems) growth factors. All cell lines were tested to avoid mycoplasma contamination using MycoStrip (InvivoGen) kit.

Growth curve and measurement of cell size

To obtain growth curves, adherent DAOY cells were trypsinized, and cells were plated at a density of 0.25 × 104 cells/cm2 in 6-well TPP tissue culture plates. DAOY spheres (the second passage) were harvested, trypsinized, and seeded as the third passage at a concentration of 0.2 × 105 cells/ml into 6-well suspension culture plates (CELLSTAR, Greiner bio-one). Each day after seeding, until day 7, one well of adherent cells and one well of spheres were trypsinized, resuspended to a single-cell suspension, and the number of cells was determined using the CASY Cell Counter and Analyzer System (Roche). The size of DAOY cells was measured with the CASY Cell Counter and Analyzer System on the third day after seeding in the single-cell suspension after trypsinization of adherent cells or spheres. Representative images were taken using an Olympus IX70 microscope, dry objective 10x/0.30, and an Olympus DP72 camera.

RNA extraction and real-time qRT-PCR

Total RNA was isolated from DAOY cells using a PureLink RNA Mini Kit (Ambion) according to the manufacturer’s protocol. Two hundred nanograms of total RNA were reverse transcribed using random hexamer primers (Invitrogen) and M-MLV reverse transcriptase (Promega). cDNAs were amplified by the LightCycler® 480 system (Roche) using the SYBR Green I master mix (Roche). All reactions were performed in triplicates and all mRNA levels were normalized to GAPDH mRNA. The following primers were used: GAPDH 5′-AGCCACATCGCTCAGACAC-3′ and 5′-GCCCAATACGACCAAATCC-3′, MAP2 5′-GGTGCTTTTTGGTGACCCAG-3′ and 5′-TGAGTGGTGTGGGTTTGCTC-3′, PROM1 5′-GTCCTGGGGCTGCTGTTTAT-3′ and 5′-TCTGTCGCTGGTGCATTTCT-3′, SOX2 5′-AGGATAAGTACACGCTGCCC-3′ and 5′-TAACTGTCCATGCGCTGGTT-3′, CSPG4 5′-CATCCCACTAGAGGCGCAAA-3′ and 5′-CCCAGGAGAGTGGGGAAGTA-3′, DLX2 5′-CCTACCAGTACCAAGCCAGC-3′ and 5′-AGGGAGCGTAGGAGGTGTAG-3′, TUBB3 5′-GCTCAGGGGCCTTTGGACATCTCTT-3′ and 5′-TTTTCACACTCCTTCCGCACCACATC-3′.

Flow cytometry

Adherent DAOY cells and DAOY spheres were trypsinized three days after seeding, resuspended into a single-cell suspension, and stained with human antibody CD133/1 (AC133)-PE (1:50, 130-113-670, MACS, Miltenyi Biotec) or antibody CD133 (clone 7)-PE/Cyanine7 (1:1000, 372810, BioLegend). For intracellular staining, cells were fixed with 4% paraformaldehyde, permeabilized with 0.1% Triton X-100 in 1% BSA, and blocked in a mixture of 1% BSA, 5% FBS and 5%NGS. Subsequently, cells were stained with antibody CD133-PE/Cyanine7 or MAP2 monoclonal antibody (1:6000, MA5-12826, Thermo Fisher Scientific), followed by staining with goat mouse IgG Alexa Fluor 488 secondary antibody (1:1000, A-11029, Invitrogen). Negative control samples were subjected to the same processing steps, but only the secondary antibody was added. Data were obtained using a FACSymphony (BD Biosciences) flow cytometer and Diva software (BD Biosciences) and analyzed in FlowJo™ v10.6.2 Software (Tree Star). At least three biological replicates were performed, and at least 10,000 events were measured; dead cells were excluded by Hoechst 33258 staining. The gating strategy for the percentage of positive cells was determined from the unstained controls for each cell population. The relative median fluorescence intensity (rMFI) was determined by dividing the median fluorescence intensity of the sample by that of the unstained control. The values for double staining with antibodies CD133-PE/Cyanine7 and MAP2 were calculated by summing the individual rMFI values.

Immunofluorescence and imaging

To detect CD133 and MAP2 proteins via immunofluorescence, DAOY adherent cells were grown on glass coverslips, and DAOY spheres were harvested on slides using cytospin. All samples were fixed in 4% paraformaldehyde for 15 min and then permeabilized with Triton X-100 (0.1% for adherent cells; 0.5% for spheres) for 15 min. Blocking was performed for two hours in a mixture of 5% NGS (Jackson ImmunoResearch), 5% BSA (Sigma), and 0.1% Triton X-100. Samples were incubated overnight at 4 °C with the first primary antibody, the mouse monoclonal antibody CD133 (Prominin-1), clone 17A6.1 (1:100, MAB4399-I, Millipore; supplemented with 0.1% Triton X-100 and 1% BSA). The second primary antibody, rabbit MAP2 polyclonal antibody - Neuronal Marker (1:1000, ab32454, Abcam; supplemented with 0.1% Triton X-100 and 1% BSA), was added next day and incubated two hours at room temperature. Staining was visualized by goat mouse IgG Alexa Fluor 488 secondary antibody (1:1000, A-11029, Invitrogen), and goat rabbit IgG Alexa Fluor 568 secondary antibody (1:1000, A-11036, Invitrogen) at an incubation time of two hours. DAPI (Sigma) was used to visualize the cell nuclei. Images were acquired using an Andor Dragonfly 503 spinning disk confocal microscope, 40x/1.25 oil objective, and an Andor Zyla sCMOS camera. The intensity of the immunofluorescence staining was optimized for the spheres and the same intensity settings were subsequently applied to the adherent cells. The system was controlled by Fusion acquisition software (version 2.1.0.80). After acquisition, images were deconvolved in Huygens Professional software (version 22.04) and the final images were created using ImageJ software. Only linear adjustments (brightness/contrast) were used for imaging, and the same setup was used for both the adherent cells and the spheres.

Immunoblotting

Protein samples from an equal number of cells were separated by SDS-PAGE using 4–15% precast gradient polyacrylamide gels (Biorad). Proteins were transferred onto PVDF transfer membranes (Thermo Fisher Scientific) which were subsequently blocked in a solution containing 5% non-fat milk dissolved in TBS and 0.05% Tween-20 (TBST). Filters were incubated in primary antibody (rabbit polyclonal CD133 antibody, D2V8Q, 1:800, 64326 Cell Signaling; rabbit polyclonal actin antibody, 1:1000, A2066, Sigma-Aldrich; mouse monoclonal MAP2 antibody, 1:2000, MA5-12826, Thermo Fisher Scientific) diluted in 1% milk/TBST overnight and then washed and incubated with secondary antibody (ECL kit, GE Life Sciences). The images were captured using the Uvitec Cambridge instrument.

Cell irradiation

Adherent DAOY cells were seeded into T25 TPP tissue culture flasks at a density of 0.35 × 104 cells/cm2 three days before irradiation. DAOY spheres were seeded three days before irradiation at a concentration of 0.2 × 105 cells/ml into T25 cell culture flasks for cell suspension. Immediately before irradiation, spheres were transferred into 15 ml conical TPP centrifuge tubes. Cells were irradiated with single doses of 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 Gy using a 60Co γ-ray source at the Authorized Metrology Center of the Nuclear Physics Institute. Irradiation of the TPP flasks and centrifuge tubes was performed at a water depth of 5 cm. The dose rate was calculated before each experiment, and increasing doses were achieved by prolonging the irradiation time.

Cell survival assay

Cell survival analysis is one of the most important tools in radiation biology and is widely used to analyze responses to ionizing radiation both in vitro and in vivo. The cell survival curve expresses the relation between the proportion of cells that retain their reproductive integrity and the absorbed radiation dose. The linear-quadratic model (LQ model) is most commonly used to construct the cell survival curve. In this model, \(\:S=\:{e}^{-\alpha\:D-\:\beta\:{D}^{2}}\), S is the survival probability of a cell after exposure to a single dose of radiation, α and β are parameters describing the radiosensitivity of the cell, D is the dose, and the ratio α / β indicates how resistant the cells are to radiation damage.

Immediately after irradiation (or under control conditions without irradiation, 0 Gy), adherent DAOY cells and spheres were trypsinized, resuspended into single-cell suspension, and counted using the Muse Cell Analyzer (Millipore) and Muse Count &Viability Assay Kit (Millipore). Viable cells from both adherent and sphere cultures were then seeded. Plating efficiency for each condition was calculated based on the number of colonies formed in the preliminary experiments. Cells were plated into TPP tissue culture 6-well plates in IMEM containing 10% FBS and 100 U penicillin/ml and 100 µg streptomycin/ml. Seven days after seeding, formed colonies were fixed and visualized using crystal violet (Sigma-Aldrich) in 95% methanol. The number of colonies in each well was counted manually. Relative cell survival was plotted on a logarithmic scale and survival curves were calculated using the linear-quadratic model in Gnuplot software.

Analysis of cell viability after irradiation

After irradiation, DAOY adherent cells and spheres were trypsinized and seeded into TPP tissue culture 6-well plates at a density of 0.35 × 104 cells/cm2 (adherent cells) or into 6-well suspension culture plates (CELLSTAR, Greiner bio-one) at a concentration of 0.2 × 105 cells/ml (spheres). On the second and third days after irradiation, one well per dose was trypsinized and resuspended into a single-cell suspension. The number of viable cells was determined using the Muse Cell Analyzer (Millipore) and the Muse Count &Viability Assay Kit (Millipore). In each experiment, untreated (non-irradiated) cells were included as controls. The scatter plot of these control cells was used to define the gating strategy, which was subsequently applied to all other samples (irradiated cells) within the same experiment.

Statistical analysis

Results were processed using GraphPad Prism software version 6, Excel, and Gnuplot software version 5.2. Statistical analysis included a t-test performed in GraphPad Prism software and a two-way analysis of variance (ANOVA) performed in STATISTICA version 12.0.

Results

DAOY cells can be grown as adherent monolayer cell culture, but can also be propagated as floating spheres

In most cases, the MB cell line DAOY is cultured in vitro as an adherent cell monolayer. However, in serum-free medium supplemented with growth factors EGF and FGF-2 and under conditions of low adherence, these cells can form spheres. The size of these spheres depends on the day of cultivation and increases with increasing cultivation time (Fig. 1A).

Fig. 1
figure 1

DAOY MB cells grown as adherent monolayers or as spheres. (A) Representative images of DAOY adherent cells and spheres acquired by phase contrast microscopy (magnification 100×) in particular days after seeding. (B) Growth curves of DAOY cells in adherent or sphere-propagating conditions. Each spot represents the mean of four independent experiments with error bars indicating standard deviation. (C) The size of DAOY adherent cells and cells from DAOY spheres in single-cell suspension after trypsinization. Each column represents the mean of five independent experiments with error bars indicating standard deviation. The level of statistical significance **** p < 0.0001

Cells cultured as spheres can be dissolved into a single-cell suspension using enzymes such as trypsin, acutase, or/and mechanical action, and reseeded to obtain the second, third, and so on passages of spheres. Zanini et al. [15] found that DAOY cells can be successfully amplified as spheres for more than 10 passages, with varying amounts of spheres obtained at each passage. In a preliminary experiment, we compared spheres from the first to the fifth passage, counting cells and identifying spheres as aggregates of over 100 cells. The first passage had high cell numbers but low sphere counts due to many floating single cells and small aggregates. The second passage had low cell numbers. The third and fourth passages showed similar cell and sphere counts (Figure S1).

Based on these results, the third passage of spheres formed for two or three days was chosen for all subsequent experiments. The size of these spheres offered several advantages. Smaller, homogeneous spheres provided greater uniformity in the experiments and, importantly, reduced central necrosis. Under these conditions, the spheres exhibited high viability (~ 95%) as determined by the Muse Cell Analyzer.

DAOY cells cultured under adherent conditions grow slightly faster than DAOY cells grown as spheres (Fig. 1B). In addition, adherent DAOY cells show a flat and spread phenotype, whereas spheres consist of smaller round cells with condensed cytoplasm; the average size of cells in a single-cell suspension after trypsinization of adherent monolayers is 19.82 μm ± 0.14, the average size of cells in a single-cell suspension after trypsinization of spheres is 16.05 μm ± 0.15, p < 0.0001 (Fig. 1C). These measurements were consistently performed on the third day of cell growth following the third cell passage.

In DAOY cells grown as spheres, expression of neural stem cell marker PROM1 and marker for differentiated neurons MAP2 is increased compared to adherent cells

DAOY cells exhibit an undifferentiated phenotype and express certain neural stem/progenitor cell markers to varying degrees. In addition, DAOY cells can differentiate under certain conditions [27]. To analyze the differences between DAOY cells cultured as adherent monolayers and DAOY cells grown as spheres, we performed qRT-PCR analysis. We focused not only on neural stemness markers such as PROM1 and SOX2, but we also measured the expression levels of glial (CSPG4) and neural progenitor (DLX2) genes, as well as the expression of genes typical of differentiated neurons (TUBB3 and MAP2). Figure 2 and Table 1 show increased expression of all genes presented in DAOY spheres compared to adherent cells.

Fig. 2
figure 2

Neural gene expression differs in DAOY cells grown as adherent monolayers and in cells cultured as spheres. Quantitative RT-PCR analysis of mRNA levels of PROM1, SOX2, MAP2, DLX2, TUBB3 and CSPG4 genes in adherent DAOY cells and DAOY spheres two and three days after seeding. GAPDH was used as a reference gene. Each column represents the mean of five independent experiments with error bars indicating standard deviation. The level of statistical significance: * p < 0.05, ** p < 0.01, *** p < 0.001

Table 1 Relative expression of PROM1, SOX2, MAP2, DLX2, TUBB3, and CSPG4 genes in DAOY cells grown as adherent monolayers and in DAOY cells cultured as spheres

We found a significant increase in the expression of the PROM1 gene, a very early stemness marker, in the spheres. The expression of SOX2 was also increased. Interestingly, the MAP2 gene, which is a marker of highly differentiated neurons and is expressed at very low levels in adherent DAOY cells, was strikingly expressed in the spheres. It is equally important to highlight that the expression levels of the neural genes under investigation showed only slight variations when comparing the cells cultured as adherent monolayers in standard medium to those grown under identical conditions but with the addition of growth factors to the medium. These relatively minor differences in gene expression suggest that the supplementation of growth factors alone has a limited impact on the neural gene expression profile in this context, as illustrated in the supplementary figure (Figure S2).

Overall, our qRT-PCR results showed that both the stem cell marker PROM1 and the differentiated cell marker MAP2 were increased in the DAOY sphere-grown cell population. Moreover, the differences in neural gene expression between cells cultured as adherent monolayers and those cultured as spheres are not unique to DAOY cells. Comparable observations were also made with the MB cell lines D341 MED and D425 MED, both of which belong to molecular group 3. These results confirm the trends observed with the DAOY cell line and are shown in the accompanying supplementary figure (Figure S3).

Based on these results, we investigated the expression levels of neural markers in DAOY cells at the protein level. We initiated our analysis with immunofluorescence staining, in which adherent DAOY cells and DAOY spheres were stained with antibodies against CD133 and MAP2 proteins three days after seeding. Using 0.5% Triton X-100 to permeabilize the cells, spheres with a diameter of 100–150 μm were stained uniformly and efficiently, showing good accessibility and penetration of the staining reagents. Confocal microscopy showed weak expression of both CD133 and MAP2 proteins in adherent cells, confirming our qRT-PCR results (Fig. 3A). In contrast, staining of DAOY spheres showed that the majority of cells were strongly stained with both CD133 and MAP2 antibodies (Fig. 3B). This heightened expression of both markers in the spheres was further corroborated by western blot analysis (Fig. 3C and Figure S4).

Fig. 3
figure 3

Levels of MAP2 and CD133 proteins are increased in DAOY spheres. Representative immunofluorescence images of DAOY adherent cells (A) and spheres (B) stained with CD133 (clone 17A6.1, green) and MAP2 (Neuronal Marker, red) antibodies. DAPI (blue) was used to stain the nuclei. Cells were fixed and stained three days after seeding. Images were acquired using confocal microscopy (magnification 400×). (C) Levels of CD133 (D2V8Q) and MAP2 (MA5-12826) proteins confirmed by western blot. Actin served as a loading control. (D) Flow cytometry analysis of the percentage of cells positive for the surface AC133 protein (130-113-670, upper right), cells positive for the surface protein CD133 (clone 7-PE/Cyanine7, lower left). (E) Relative MFI values in adherent and sphere DAOY cell measured by flow cytometry for the CD133 (clone 7-PE/Cyanine7) protein expressed both on the cell surface and intracellularly (lower center) and for the MAP2 protein (MA5-12826, lower right). Each scatter plot in the graph represents the mean of 3–4 independent experiments, with the error bars indicating the standard deviation. The level of statistical significance: * p < 0.05, *** p < 0.001

The aforementioned findings were further validated through flow cytometry analysis. In these experiments, cell populations were gated based on forward and side scatter area parameters, followed by the exclusion of doublets and non-viable cells. Each population of antibody-positive cells was gated relative to unstained controls (Figure S5).

We began our analysis by utilizing of the widely used monoclonal antibody AC133, which targets a specific glycosylated epitope within human CD133. Consistent with prior studies [25, 28], a low proportion of CD133-positive cells (7.59%) were observed in the adherent cell population. In contrast, a significantly higher proportion of positive cells was observed in DAOY spheres (13.23%) (Fig. 3D-upper right and Table 2).

Table 2 Percentage of positive cells in adherent and sphere DAOY cell cultures measured by flow cytometry

Given that the CD133 protein undergoes extensive post-translational glycosylation modification [29], we also employed an antibody capable of recognizing both glycosylated and non-glycosylated epitopes of the CD133 protein. Staining with CD133 clone 7-PE/Cyanine7 demonstrated 28.25% CD133-positive cells in the adherent cell population and 48.85% in DAOY spheres (Fig. 3D-lower left and Table 2). For intracellular CD133 and MAP2 analysis by flow cytometry, additional steps involving fixation and permeabilization of cells were performed to facilitate the epitope-antibody interaction. Due to challenges in distinguishing between positive and negative cell populations during intracellular staining with CD133 and MAP2 antibodies, we utilized the relative median fluorescence intensity (rMFI) to quantify staining intensity in individual cell cultures. The relative MFI values for CD133 antibody staining were 4.08 in adherent cells and 12.69 in DAOY spheres (Fig. 3E-lower center and Table 3).

Table 3 rMFI values in adherent and sphere DAOY cell cultures measured by flow cytometry

Similarly, analysis of intracellular MAP2 protein revealed an increase in rMFI from 2.51 in adherent cells to 3.46 in spheres (Fig. 3E-lower right and Table 3). Flow cytometry analysis of intracellular double staining for CD133 and MAP2 confirmed the previously mentioned results and showed a greater number of cells with increased expression of both CD133 and MAP2 within the spheres (Figure S6 and Table 3).

The co-expression of both CD133, a well-established marker of undifferentiated cells, and MAP2, a marker indicative of late neuronal differentiation, appears to be a common within cancer cell populations. In the context of our study, we observed notably high levels of expression for both the PROM1 and MAP2 genes, as well as their corresponding proteins, in another MB cell line, CHLA-01-MED. This particular cell line is classified as belonging to group 4 of medulloblastoma subtypes and is characterized by its ability to proliferate in suspension culture. Furthermore, double staining for CD133 and MAP2 revealed that a significant proportion of the cells within this cell line were simultaneously positive for both proteins, as demonstrated in Figure S7 and S8.

DAOY cells cultured as spheres are more radioresistant than DAOY cells grown as adherent monolayers

To further investigate the differences between DAOY cells cultured under adherent conditions and DAOY cells grown as floating spheres, we decided to study the cell response to DNA damage by ionizing radiation. Cells grown in monolayers or in spheres for three days were irradiated with gamma rays and seeded for cell survival analysis.

Using the LQ model, we calculated the survival curves separately for adherent cells and for spheres. Figure 4 shows the fraction of surviving cells on a logarithmic scale and plotted on the y-axis against the radiation dose on the x-axis. Our results show a highly significant difference in response to radiation damage between DAOY cells grown in adherent conditions and DAOY spheres (Fig. 4; Table 4, p < 0.000001).

Fig. 4
figure 4

DAOY cells cultured as spheres are more resistant to DNA damage caused by gamma radiation than DAOY cells grown as adherent monolayers. Three days after growth as monolayers or as spheres, DAOY cells were irradiated with gamma rays (doses: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 Gy) and reseeded for cell survival analysis. Survival of DAOY adherent cells (A) and DAOY spheres (B) is on a logarithmic scale and plotted on the y-axis against radiation dose on the x-axis. Each spot represents the mean of four independent experiments with error bars indicating standard deviation. Survival curves and α and β parameters were calculated using the LQ model. The level of statistical significance for each dose: 4 Gy*, 5 Gy**, 6 Gy*, 7 Gy**, 8 Gy**, 9 Gy*, 10 Gy*; * p < 0.05, ** p < 0.01

Table 4 Cell survival (%) of adherent DAOY cells and DAOY spheres after gamma irradiation

The difference is also significant from 4 Gy for each single dose, and the difference in response to radiation damage grows with increasing dose. From the survival curves we also calculated α and β parameters for adherent cells and for spheres and determined the α / β ratio (Table 5).

Table 5 α and β parameters and α / β ratio for gamma-irradiated adherent DAOY cells and DAOY spheres determined from survival curves using the linear quadratic model

For spheres, the α / β ratio (9.25) was distinctly higher than the α / β ratio determined for adherent cells (3.32), suggesting that cells in the spheres are more radioresistant than adherent cells.

After irradiation, cell viability decreased more in DAOY cells grown adherently than in DAOY spheres; MAP2 and PROM1 gene expression decreased in the spheres

The results in previous paragraph showed that the cultivation conditions affect the survival of DAOY cells after irradiation. In further experiments, we decided to measure the viability of DAOY cells and the changes in gene expression of two selected genes, PROM1 and MAP2, in DAOY cells grown in monolayers or in spheres and analyzed on the second and third days after gamma irradiation.

Counting viable cells after irradiation confirmed our finding that cells in spheres are more radioresistant than adherent cells. In Fig. 5A, each value represents the average of three independent biological replicates, with each sample measured twice. The data show that on the second day after irradiation, the number of viable cells in the spheres is higher than in adherent monolayers (the slope of the decrease in viability after irradiation is 0.05 for monolayers and 0.04 for spheres); this difference is much more pronounced on the third day after irradiation (the slope of the decrease in viability after irradiation for monolayers is 0.12, for spheres it is 0.09).

Fig. 5
figure 5

The viability of adherent DAOY cells is lower than that of spheres on days two and three post-irradiation. PROM1 and MAP2 expression remains unchanged in adherent cells, while a dose-dependent decrease occurs in spheres. (A) The relative number of viable cells two and three days after gamma irradiation (IR). Left – DAOY cells grown as adherent monolayers, right – DAOY cells grown as spheres. Each column represents the mean of three independent experiments with error bars indicating standard deviation. (B) Quantitative RT-PCR analysis of mRNA levels of MAP2 and PROM1 genes in adherent DAOY cells and in DAOY spheres two and three days after gamma irradiation (IR). GAPDH was used as a reference gene. Left – DAOY cells grown as adherent monolayers, right – DAOY cells grown as spheres. Each column represents the mean of three independent experiments with error bars indicating the standard deviation

In further experiments, we measured the changes in mRNA levels of PROM1 and MAP2 genes in DAOY cells after irradiation. Consistent with our previous results, the expression levels of PROM1 and MAP2 differed significantly between adherent cells and spheres, and were much higher in cells grown as spheres for both non-irradiated cells and cells irradiated with different doses and analyzed on the second and third days after irradiation (Fig. 5B, p < 0.000001). In adherent cells, the expression of PROM1 and MAP2 was low and did not change significantly after irradiation. In contrast, the expression of MAP2 and PROM1 genes in the spheres decreased after gamma irradiation on both analyzed days.

Discussion

MB is the most common malignant brain tumor in children. Molecular classification has identified key developmental signaling pathways that control tumor development, and MB has been classified into several subgroups. Primary tumor cell lines derived from surgically removed tumors represent the endpoint of tumor development; nevertheless, they do not recapitulate the heterogeneity observed in the original tumor [9]. The SHH subgroup, in which the SHH signaling pathway is constitutively active, comprises approximately 30% of all MB. DAOY is a cell line derived from MB of a 4-year-old boy which belongs to the SHH subgroup. DAOY is the oldest MB cell line established [11], and it is the cell line most frequently cited in MB research.

To better understand the biology and behavior of MB, it is important to use an appropriate culturing system. Spheroid cultures have been an established method in cancer research for over 25 years, enabling the growth of tumor cells in three-dimensional in vitro systems. Cultivation in spheres mimics the tumor milieu much better and provides a more informative model of the tumor that better corresponds to the situation in vivo. MB research has predominantly been based on adherently grown cell lines, and there are only a limited number of studies using cells cultured in spheres [15, 30,31,32]. When DAOY cells are cultured in standard serum-containing medium, they form a single-layer adherent culture. However, when cultured in serum-free medium enriched in growth factors EGF and FGF-2 and under conditions of low adherence, these cells spontaneously form floating multicellular aggregates - spheres - that can be cultured for up to several weeks [24]. Because MB cell lines are frequently used for drug screening assays, the choice of the culturing system is important. For example, it has been shown that there is a significant difference in response to standard drugs between cells grown adherently and in spheres, with spheres being more resistant to treatment compared to monolayer culture in almost all cell lines tested [23].

To analyze the differences between DAOY cells cultured adherently and in spheres, we performed qRT-PCR analysis. For all experiments presented, we used the third passage of the spheres. Compared with adherent cells, we found increased expression of stemness markers PROM1 and SOX2 in the spheres. The DLX2, MAP2, and TUBB3 genes, which are markers of neurons, were also strongly expressed in the spheres. Expression of glial marker CSPG4 was increased in the spheres, but expression of the other glial markers, SLC1A3 and OLIG2, was very low in both spheres and adherent cells (not shown). We further investigated the expression levels of CD133 (PROM1) and MAP2 by immunofluorescence staining. Confocal microscopy showed weak expression of CD133 and MAP2 proteins in adherent cells. In contrast, staining of the spheres showed increased levels of both CD133 and MAP2 antibodies. Additionally, CD133 protein levels in the cells were determined through flow cytometry analysis using the AC133 monoclonal antibody, revealing 7.59% of adherent cells as CD133-positive. This percentage of positive cells increased to 13.23% in the spheres. Other authors also reported low CD133 expression in adherent cells, ranging from undetectable levels to 6% positive cells [15, 25, 33, 34]. Furthermore, some studies have observed an increase in CD133 expression in the spheres [15, 30, 31]. Since monoclonal antibody AC133 targets a specific glycosylated epitope within human CD133, we decided to also employ CD133 hybridoma clone 7, which recognizes both glycosylated and non-glycosylated epitopes of the CD133 protein. CD133 expression is in MB research typically assessed using antibodies that target the AC133 epitope situated within one of the extracellular domains of membrane-bound CD133 but evidence suggests that the glycosylation status of the CD133 protein plays a crucial role in determining the binding of the AC133 antibody to CD133. Therefore, it is crucial to note that CD133 expression does not always correspond to immunoreactivity for AC133 [35]. In our study, we demonstrated a significantly higher percentage of positive cells when utilizing the CD133 hybridoma clone 7 compared to the AC133 antibody, both in adherently grown cells and cells forming spheres. When cells were stained intracellularly, enabling the detection of CD133 expression throughout the cell rather than solely on the surface, increased CD133 expression was confirmed within the spheres compared to adherent DAOY cells.

Therefore, our results demonstrate that experimental outcomes may vary depending on the antibodies used, as their binding may rely on the glycosylation status and/or tertiary structure of the protein, as well as the availability of the corresponding epitope.

In addition to increased expression of stemness markers, we also found increased MAP2 levels in the spheres. The expression of microtubule-associated proteins is known to increase during neuronal differentiation [36], and MAP2 is specifically considered a marker of differentiated neurons [37]. Increased expression of MAP2 has been described in some brain tumors, especially in gliomas [38], (https://www.proteinatlas.org/ENSG00000078018-MAP2/pathology). In MB, MAP2 has been detected in the D283 MB cell line and in a few primary explants [39, 40]. However, our results indicate that the MAP2 protein is highly expressed in DAOY cells when grown in spheres. DAOY cells, based on their molecular characteristics belong to the SHH subgroup [9] with mutated TP53 gene [10]. The primary tumor showed evidence of both glial and neuronal differentiation, with retention of neuronal characteristics observed in nude mouse tumors. However, minimal neuronal differentiation is evident in DAOY cells cultured in vitro as adherent monolayers in serum-containing medium [11, 12]. Our findings thus demonstrate that culturing cells in spheres may recapitulate the original tumor characteristics more accurately.

Furthermore, we observed elevated expression of the neuronal marker MAP2 in other MB cell lines when cultured in sphere conditions. Notably, the suspension-cultured CD133-positive line CHLA-01-MED exhibited significant MAP2 expression as well.

To further investigate the differences between DAOY spheres and DAOY cells grown as adherent monolayers, we examined the response of cells to ionizing radiation and found that DAOY spheres were more radioresistant than DAOY cells grown adherently. This result was confirmed by our subsequent finding that the cell viability after irradiation decreased more in adherently grown DAOY cells than in DAOY spheres and is consistent with the finding of Blazek et al., who discovered that CD133-positive MB cells were more radioresistant than CD133-negative MB cells [28]. CD133 positivity also confers radioresistance on gliomas, which led to the conclusion that CD133 positivity could be the source of tumor recurrence after radiation therapy [41]. We further demonstrated that the expression of MAP2 and PROM1 genes in DAOY spheres decreased when analyzed on the second and third days after irradiation. It is known that exposure to ionizing radiation affects cell cycle progression. Radiation causes DNA damage that is followed by an arrest in the cell cycle as the cell activates DNA repair mechanisms. Studies in normal and cancer stem cell lines, as well as neural stem cells, have shown that CD133 protein and mRNA levels fluctuate throughout the cell cycle, with the highest CD133 levels found in the S/G2/M phase and CD133 down-regulation in the G0/G1 phase of the cell cycle [42, 43]. The presence of CD133-positive cells within tumors has been linked to increased resistance to radiotherapy, which is driven by several mechanisms. These cells demonstrate an enhanced ability to repair DNA damage induced by ionizing radiation, facilitated by the overexpression of DNA repair proteins such as RAD51 and EXO1, key components of homologous recombination repair pathways. Furthermore, CD133-positive cells exhibit heightened activation of DNA damage response pathways following radiation exposure, including increased phosphorylation of critical proteins such as ataxia-telangiectasia mutated (ATM), CHK1, and CHK2 [44]. In addition to robust DNA repair, CD133-positive cells often possess elevated levels of glutathione, a key antioxidant that mitigates oxidative stress by neutralizing reactive oxygen species generated during radiation therapy. This reduction in oxidative stress protects against radiation-induced cell death and contributes to their survival [45]. Collectively, these mechanisms enable CD133-positive cells to withstand radiotherapy, thereby promoting tumor recurrence and progression. However, our results also demonstrate a decrease in PROM1/CD133 levels following irradiation of DAOY spheres, underscoring the complexity of radioresistance and highlighting the need for further investigation into the dynamics of CD133 expression under radiation stress. CD133 is commonly used as a marker for the detection and isolation of cancer stem cells (CSCs) from various solid tumors. The use of CD133 to identify CSCs in brain tumors was first described by Singh [46] in MB and gliomas. However, there is conflicting evidence regarding the accuracy associated with the use of CD133 as a marker for CSCs, as some studies have shown that populations of CD133-negative cells are also able to recapitulate the morphology of the original tumor [47]. The cell cycle dependence on CD133 expression should also be considered when used CD133 to identify and isolate CSCs.

In summary, our findings reveal that DAOY cells cultured as spheres exhibit distinct characteristics compared to those grown in monolayers. Sphere-cultured cells display elevated levels of the stem cell marker CD133 and the neuronal marker MAP2. Additionally, these cells demonstrate increased radioresistance and enhanced post-irradiation viability, correlating with their higher CD133 expression. The size of our spheres, offers significant experimental advantages. Smaller, homogeneous spheres ensure greater reproducibility and minimize central necrosis, which can otherwise compromise drug penetration and data interpretation. Notably, our experiments showed a high percentage of viable cells (~ 95%) in the spheres, as assessed by the Muse Cell Analyzer. However, we recognize that smaller spheres cannot fully replicate the hypoxic conditions and metabolic gradients present in larger spheres, which are crucial for certain cancer research applications and therapeutic evaluations [48].

While sphere cultures more closely model tumor biology than traditional monolayer cultures, it still falls short of fully recapitulating the complexity of the in vivo tumor microenvironment. The tumor microenvironment consists of a variety of cell types, including cancer-associated fibroblasts, immune cells, and endothelial cells that dynamically interact with tumor cells. Standard sphere models usually consist of tumor cells only, thus lacking this cellular diversity and associated intercellular interactions. In addition, the extracellular matrix (ECM) provides structural support and biochemical signals in vivo that are important for regulating cell behavior. Spheres models are often unable to replicate the complex composition and mechanical properties of the native ECM. In addition, sphere models lack functional vasculature, leading to differences in nutrient and oxygen distribution compared to in vivo tumors [49]. Finally, the absence of immune components in sphere models eliminates critical immune-tumor interactions, further limiting their ability to faithfully recapitulate the tumor microenvironment in vivo [50, 51].

Overall, our findings underscore the differing properties of MB cells under various culture conditions and highlight the importance of selecting the appropriate culture system for specific research objectives, such as studying the effects of drugs on MB radiosensitivity. Such investigations are essential for advancing our understanding of MB biology and developing more effective therapeutic strategies for this aggressive pediatric brain tumor.

Data availability

The experimental parameters were uploaded to https://www.mispheroid.org and received the MISpheroID string 1125+bhkXUS+6681.

References

  1. Northcott PA, et al. Medulloblastoma. Nat Rev Dis Primers. 2019;5:11. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41572-019-0063-6.

    Article  PubMed  Google Scholar 

  2. Northcott PA, et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature. 2012;488:49–56. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nature11327.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Cavalli FMG et al. Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell. 2017;31:737–754 e736. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ccell.2017.05.005.

  4. Mulhern RK, et al. Neurocognitive consequences of risk-adapted therapy for childhood medulloblastoma. J Clin Oncol. 2005;23:5511–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1200/JCO.2005.00.703.

    Article  PubMed  Google Scholar 

  5. Yeole U, et al. What happens after Therapy? Quality of life and neurocognitive functions of children with malignant posterior Fossa tumors after Adjuvant Therapy. Neurol India. 2021;69:1293–301. https://doiorg.publicaciones.saludcastillayleon.es/10.4103/0028-3886.329599.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Schroeder K, Gururangan S. Molecular variants and mutations in medulloblastoma. Pharmgenomics Pers Med. 2014;7:43–51. https://doiorg.publicaciones.saludcastillayleon.es/10.2147/PGPM.S38698.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Skowron P, et al. The transcriptional landscape of shh medulloblastoma. Nat Commun. 2021;12:1749. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41467-021-21883-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhukova N, et al. Subgroup-specific prognostic implications of TP53 mutation in medulloblastoma. J Clin Oncol. 2013;31:2927–35. https://doiorg.publicaciones.saludcastillayleon.es/10.1200/JCO.2012.48.5052.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Ivanov DP, Coyle B, Walker DA, Grabowska AM. In vitro models of medulloblastoma: choosing the right tool for the job. J Biotechnol. 2016;236:10–25. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jbiotec.2016.07.028.

    Article  CAS  PubMed  Google Scholar 

  10. Saylors RL 3, et al. Infrequent p53 gene mutations in medulloblastomas. Cancer Res. 1991;51:4721–3.

  11. Jacobsen PF, Jenkyn DJ, Papadimitriou JM. Establishment of a human medulloblastoma cell line and its heterotransplantation into nude mice. J Neuropathol Exp Neurol. 1985;44:472–85. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/00005072-198509000-00003.

    Article  CAS  PubMed  Google Scholar 

  12. Hai Sang U, Banaie A, Rigby L, Chen J. Mutant p53 may selectively suppress glial specific proteins in pluripotential human neuroectodermal tumor cells. Neurosci Lett. 1998;244:41–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/s0304-3940(98)00061-5.

    Article  CAS  PubMed  Google Scholar 

  13. Wick W, et al. Prevention of irradiation-induced glioma cell invasion by temozolomide involves caspase 3 activity and cleavage of focal adhesion kinase. Cancer Res. 2002;62:1915–9.

    CAS  PubMed  Google Scholar 

  14. Salaroli R, et al. Radiobiologic response of medulloblastoma cell lines: involvement of beta-catenin? J Neurooncol. 2008;90:243–51. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11060-008-9659-5.

    Article  CAS  PubMed  Google Scholar 

  15. Zanini C, et al. Medullospheres from DAOY, UW228 and ONS-76 cells: increased stem cell population and proteomic modifications. PLoS ONE. 2013;8:e63748. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0063748.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Singh SK, et al. Identification of a cancer stem cell in human brain tumors. Cancer Res. 2003;63:5821–8.

    CAS  PubMed  Google Scholar 

  17. Neradil J, Veselska R. Nestin as a marker of cancer stem cells. Cancer Sci. 2015;106:803–11. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/cas.12691.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sutter R, et al. Cerebellar stem cells act as medulloblastoma-initiating cells in a mouse model and a neural stem cell signature characterizes a subset of human medulloblastomas. Oncogene. 2010;29:1845–56. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/onc.2009.472.

    Article  CAS  PubMed  Google Scholar 

  19. Pizer BL, Clifford SC. The potential impact of tumour biology on improved clinical practice for medulloblastoma: progress towards biologically driven clinical trials. Br J Neurosurg. 2009;23:364–75. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/02688690903121807.

    Article  PubMed  Google Scholar 

  20. Ivanov DP, et al. Multiplexing spheroid volume, resazurin and acid phosphatase viability assays for high-throughput screening of tumour spheroids and stem cell neurospheres. PLoS ONE. 2014;9:e103817. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0103817.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Neve A, Santhana Kumar K, Tripolitsioti D, Grotzer MA, Baumgartner M. Investigation of brain tissue infiltration by medulloblastoma cells in an ex vivo model. Sci Rep. 2017;7:5297. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-017-05573-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Schonholzer MT, et al. Real-time sensing of MAPK signaling in medulloblastoma cells reveals cellular evasion mechanism counteracting dasatinib blockade of ERK activation during invasion. Neoplasia. 2020;22:470–83. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.neo.2020.07.006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Roper SJ, Linke F, Scotting PJ, Coyle B. 3D spheroid models of paediatric SHH medulloblastoma mimic tumour biology, drug response and metastatic dissemination. Sci Rep. 2021;11:4259. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-021-83809-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Roper SJ, Coyle B. Establishing an in vitro 3D spheroid model to Study Medulloblastoma Drug Response and Tumor Dissemination. Curr Protoc. 2022;2:e357. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/cpz1.357.

    Article  CAS  PubMed  Google Scholar 

  25. Srivastava VK, Nalbantoglu J. Flow cytometric characterization of the DAOY medulloblastoma cell line for the cancer stem-like phenotype. Cytometry A. 2008;73:940–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/cyto.a.20633.

    Article  PubMed  Google Scholar 

  26. García-López R, et al. Sonic hedgehog inhibition reduces in vitro tumorigenesis and alters expression of Gli1-target genes in a desmoplastic medulloblastoma cell line. J Cancer Res Therapy. 2013;1:11–23. https://doiorg.publicaciones.saludcastillayleon.es/10.14312/2052-4994.2013-3.

    Article  CAS  Google Scholar 

  27. Li XN, et al. Phenylbutyrate and phenylacetate induce differentiation and inhibit proliferation of human medulloblastoma cells. Clin Cancer Res. 2004;10:1150–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/1078-0432.ccr-0747-3.

    Article  CAS  PubMed  Google Scholar 

  28. Blazek ER, Foutch JL, Maki G. Daoy medulloblastoma cells that express CD133 are radioresistant relative to CD133- cells, and the CD133 + sector is enlarged by hypoxia. Int J Radiat Oncol Biol Phys. 2007;67:1–5. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ijrobp.2006.09.037.

    Article  CAS  PubMed  Google Scholar 

  29. Swaminathan SK, et al. Identification of a novel monoclonal antibody recognizing CD133. J Immunol Methods. 2010;361:110–5. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jim.2010.07.007.

    Article  CAS  PubMed  Google Scholar 

  30. Yang MY, Lee HT, Chen CM, Shen CC, Ma H. I. Celecoxib suppresses the phosphorylation of STAT3 protein and can enhance the radiosensitivity of medulloblastoma-derived cancer stem-like cells. Int J Mol Sci. 2014;15:11013–29. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms150611013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Casciati A, et al. Human medulloblastoma cell lines: investigating on cancer stem cell-like phenotype. Cancers (Basel). 2020;12. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/cancers12010226.

  32. Douyere M, et al. NRP1 inhibition modulates radiosensitivity of medulloblastoma by targeting cancer stem cells. Cancer Cell Int. 2022;22:377. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-022-02796-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bonfim-Silva R, et al. Biological characterization of the UW402, UW473, ONS-76 and DAOY pediatric medulloblastoma cell lines. Cytotechnology. 2019;71:893–903. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10616-019-00332-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gu C, et al. Gene expression of growth signaling pathways is up-regulated in CD133-positive medulloblastoma cells. Oncol Lett. 2011;2:357–61. https://doiorg.publicaciones.saludcastillayleon.es/10.3892/ol.2011.235.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Barrantes-Freer A, et al. CD133 expression is not synonymous to immunoreactivity for AC133 and fluctuates throughout the cell cycle in Glioma Stem-Like cells. PLoS ONE. 2015;10:e0130519. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0130519.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zikova M, Sulimenko V, Draber P, Draberova E. Accumulation of 210 kDa microtubule-interacting protein in differentiating P19 embryonal carcinoma cells. FEBS Lett. 2000;473:19–23. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/s0014-5793(00)01488-5.

    Article  CAS  PubMed  Google Scholar 

  37. Johnson GV, Jope RS. The role of microtubule-associated protein 2 (MAP-2) in neuronal growth, plasticity, and degeneration. J Neurosci Res. 1992;33:505–12. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/jnr.490330402.

    Article  CAS  PubMed  Google Scholar 

  38. Yan T, et al. Neuronal markers are expressed in human gliomas and NSE knockdown sensitizes glioblastoma cells to radiotherapy and temozolomide. BMC Cancer. 2011;11:524. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/1471-2407-11-524.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Vinores SA, Herman MM, Katsetos CD, May EE, Frankfurter A. Neuron-associated class III beta-tubulin, tau, and MAP2 in the D-283 Med cell line and in primary explants of human medulloblastoma. Histochem J. 1994;26:678–85. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/BF00158293.

    Article  CAS  PubMed  Google Scholar 

  40. Ghantasala S, et al. Multiple reaction monitoring-based targeted assays for the validation of protein biomarkers in brain tumors. Front Oncol. 2021;11:548243. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fonc.2021.548243.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bao S, et al. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature. 2006;444:756–60. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nature05236.

    Article  CAS  PubMed  Google Scholar 

  42. Jaksch M, Munera J, Bajpai R, Terskikh A, Oshima RG. Cell cycle-dependent variation of a CD133 epitope in human embryonic stem cell, colon cancer, and melanoma cell lines. Cancer Res. 2008;68:7882–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/0008-5472.CAN-08-0723.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sun Y, et al. CD133 (prominin) negative human neural stem cells are clonogenic and tripotent. PLoS ONE. 2009;4:e5498. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0005498.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhou T, et al. Review: Mechanisms and perspective treatment of radioresistance in non-small cell lung cancer. Front Immunol. 2023;14:1133899. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fimmu.2023.1133899.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Song Y, et al. Sulfasalazine attenuates evading anticancer response of CD133-positive hepatocellular carcinoma cells. J Exp Clin Cancer Res. 2017;36. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13046-017-0511-7.

  46. Singh SK, et al. Identification of human brain tumour initiating cells. Nature. 2004;432:396–401. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nature03128.

    Article  CAS  PubMed  Google Scholar 

  47. Glumac PM, LeBeau AM. The role of CD133 in cancer: a concise review. Clin Transl Med. 2018;7:18. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40169-018-0198-1.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Friedrich J, Seidel C, Ebner R, Kunz-Schughart LA. Spheroid-based drug screen: considerations and practical approach. Nat Protoc. 2009;4:309–24. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nprot.2008.226.

    Article  CAS  PubMed  Google Scholar 

  49. Rodrigues DB, Reis RL, Pirraco RP. Modelling the complex nature of the tumor microenvironment: 3D tumor spheroids as an evolving tool. J Biomed Sci. 2024;31:ARTN 13. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12929-024-00997-9.

  50. Mu P, et al. Newly developed 3D in vitro models to study tumor-immune interaction. J Exp Clin Cancer Res. 2023;42:81. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13046-023-02653-w.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Chen JY et al. Molecular profile reveals immune-associated markers of medulloblastoma for different subtypes. Front Immunol. 2022:13. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fimmu.2022.911260.

Download references

Acknowledgements

We thank Šárka Takáčová for proofreading the manuscript and Ivan Novotný for help with microscopy and imaging.

Funding

This work was supported by the Ministry of Health, Czech Republic - conceptual development of research organization (NHH, 174701). We thank Petr Bartůněk for acquisition of the funding RVO: 68378050-KAV-NPUI, and LM2023052. Financial support of Šárka Jarošová was in part provided by a PhD student fellowship from Charles University in Prague.

Author information

Authors and Affiliations

Authors

Contributions

M.Z., M.D., J.K. and S.J. proposed the experiments. J.K., S.J., I.D., M.F., M.D., J.N. and M.Z. performed the experiments. J.K., S.J., and M.Z. analyzed the data. M.Z., J.K., S.J. and M.D. prepared the manuscript. All authors discussed the results and reviewed the manuscript.

Corresponding authors

Correspondence to Marie Davídková or Martina Zíková.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Klementová, J., Jarošová, Š., Danilová, I. et al. Comparative analysis of pediatric SHH medulloblastoma DAOY spheres and adherent monolayers: implications for medulloblastoma research. Cancer Cell Int 25, 22 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03646-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03646-9