The side population (SP) assay, a technique used in cancer and stem cell research, assesses the activity of ABC transporters on Hoechst staining in the presence and absence of transporter inhibition, identifying SP and non-SP cell (NSP) subpopulations by differential staining intensity. The interpretation of the assay is complicated because the transporter-mediated mechanisms fail to account for cell-to-cell variability within a population or adequately control the direct role of transporter activity on staining intensity. We hypothesized that differences in dye kinetics at the single-cell level, such as ABCG2 transporter-mediated efflux and DNA binding, are responsible for the differential cell staining that demarcates SP/NSP identity. We report changes in A549 phenotype during time in culture and with TGFβ treatment that correlate with SP size. Clonal expansion of individually sorted cells re-established both SP and NSPs, indicating that SP membership is dynamic. To assess the validity of a purely kinetics-based interpretation of SP/NSP identity, we developed a computational approach that simulated cell staining within a heterogeneous cell population; this exercise allowed for the direct inference of the role of transporter activity and inhibition on cell staining. Our simulated SP assay yielded appropriate SP responses for kinetic scenarios in which high transporter activity existed in a portion of the cells and little differential staining occurred in the majority of the population. With our approach for single-cell analysis, we observed SP and NSP cells at both ends of a transporter activity continuum, demonstrating that features of transporter activity as well as DNA content are determinants of SP/NSP identity.
The side population (SP) assay is used to identify stem cells by flow cytometry through the characteristic of enhanced dye efflux mediated via ATP-binding cassette (ABC) transporters [1]. The SP was first identified by Goodell et al. as hematopoietic stem cells in samples of murine bone marrow aspirate [2]. The role of the SP has since expanded to serve as a means to identify stem cell populations based, primarily, on ABCG2 activity [3], though additional ABC transporters, such as P-glycoprotein/ABCB1, can also mediate formation of a SP [4]. ABCG2, also known as breast cancer resistance protein (BRCP), can mediate multidrug resistance (MDR) in breast [5–9] and other cell lines [10–14]. The SP has been implicated in numerous cancers as a harbinger of MDR-mediated chemoresistance [15–18] and cancer stem cells (CSCs) [19–22] in in vitro cancer cell lines; thus the presence of a SP is understood as an undesirable indicator. SPs are identified by splitting samples into conditions with and without an ABC transporter inhibitor followed by Hoechst staining, which enables population-level comparison of differences in cell staining due to ABC transporter activity between the two conditions (Fig 1A). Blocking of transporter mediated Hoechst efflux by the inhibitor serves as a basis for comparison of cell staining in the condition without the transporter inhibitor (Fig 1B). When comparing the two conditions, SP cells are observed as a population with decreased staining in the lower left of the Hoechst Red and Blue staining plot (Fig 1A). The basis of differential staining is thought to be driven by impaired dye efflux in the presence of ABC transporter inhibitor, with SP cells exhibiting high-ABC transporter activity and decreased staining compared to low-ABC transporter-activity NSP cells with uninhibited transporter activity [3,23,24]. Although the kinetic, ABC transporter-mediated mechanism is universally accepted as the basis for differential staining of cell populations in the +inhibitor and -inhibitor conditions of the SP assay, differences in cell staining due to transporter activity have not been demonstrated from a kinetic perspective nor at the single-cell level. This gap in knowledge persists due to a technical limitation that precludes an individual cell from Hoechst staining in both +inhibitor and -inhibitor conditions, thereby preventing any measurement of shifts of individual cells within the population distribution. Therefore, it is unclear how the heterogeneity of ABC transporter activity within a population influences staining characteristics in the +inhibitor and -inhibitor conditions of the SP assay and unclear how the heterogeneity in transporter activity is reflected in individual cells of the SP and NSP. We hypothesize that specific distributions of ABC transporter activity in a population, representing the heterogeneity of transporter activity at the single-cell level, will exhibit differential cell staining as is observed in the SP assay. In this study, we employ experimental and computational approaches to demonstrate the kinetic nature of the SP assay and the dynamic nature of the SP/NSP phenotype. Experimentally, we investigated SP formation in the A549 lung carcinoma cell line with Hoechst 33342 staining and inhibition of ABCG2 with the inhibitor Fumitremorgin C (FTC) [25,26]. We present a novel computational approach for simulating heterogeneity in transport kinetics, using mass-action kinetic reactions inspired by the conceptual model of Hoechst staining the SP (Fig 1B and 1C), at the single-cell level across a population. The population-level model is used to demonstrate the validity of the transporter-mediated kinetic interpretation of the SP assay. The approach enables in silico staining of an identical cell population in both inhibitor-free (–FTC) and inhibitor-containing (+FTC) conditions with subsequent single-cell analysis of the role of transporter variability on cell staining. In this manner, we investigated the role of heterogeneity in transporter expression, activity, and kinetics at the single-cell level on the formation of the side population.
A549 lung carcinoma cells were expanded in culture for 4 days prior to measurement of the SP by the Hoechst staining assay (Fig 1A). We observed an initial 18% SP in A549 cells (S1A Fig), which was eliminated by treatment with TGFβ (S1B and S1C Fig). TGFβ treatment for 4 days resulted in epithelial-mesenchymal transition (EMT), indicated by down-regulation of E-cadherin and up-regulation of N-cadherin (Fig 2A and S2 Fig), as well as down-regulation of ABCG2 (Fig 2B and S2 Fig). SP percentage was correlated with ABCG2 expression (Fig 2C). We developed an algorithmic approach for objective quantification of SP size using a Hoechst staining threshold to define the gate that delineates SP and NSP regions (S3 Fig). Although suggestions have been made for standardizing the reporting of SP assay results [1], gating protocols vary considerable in the literature. Here, we defined Hoechst staining intensity as the x-y projection of z-score transformed Hoechst Red and Blue raw signals with a threshold set at the 1st percentile in the +FTC condition. This projection gating approach demonstrated a strong correlation (R2 = 0. 98, S4B Fig) between manual and automated algorithmic measurement of percentage SP. To further elucidate the role of TGFβ on SP size, we made serial measurements of SP size using our projection gating method. A549 cells were expanded in culture for 4 days without TGFβ treatment and then passaged. On the subsequent day (Day 0), treatment with 0,1, 10, and 100 pM TGFβ was started and SP size was measured at 2 day intervals (S4A Fig). On the day of passage, Day 4, the SP constituted 20% of the cell population; however, following cell passage, the SP consisted of 2% of the population (Fig 2D). In the following days, SP size increased and plateaued near 20% in the control condition. With increasing amounts of TGFβ exposure, the repopulation of the SP attenuated in a dose-dependent manner (Fig 2D). We derived clonal populations of A549 cells by expanding individual low- and high-ABCG2 expressing cells to determine whether subclones within the A549 cell line define either the SP or NSP subpopulations. Cells were labeled with anti-ABCG2 antibody and 96 high-ABCG2 expressing and 96 low-ABCG2 expressing cells were sorted into individual wells of a 96 well–plate (S5A Fig). Most cells failed to form colonies; however, following expansion of individual cells for 30 days, all surviving cells demonstrated both SP and NSP cells (S5B Fig). Collectively, these results indicate that the SP within the A549 cell line is dynamic with respect to cell phenotype (Fig 2A), cyclically variable in culture (Fig 2D), and is not genetically distinct from the NSP (S5 Fig). The study of side populations has been limited by the approach to define the SP, which relied on manually defined gates to define a boundary between SP and NSP regions within the Hoechst Red and Hoechst Blue plot. In the development of our automated projection gating approach to measure SP size, we closely examined numerous aspects associated with SP data. A key observation was that the SP and NSP are not clearly separated within the–FTC condition. We observed a continuous staining distribution in both Hoechst Red and Blue channels of the–FTC condition, which is redistributed towards lower signal intensities compared to the +FTC condition (S6A and S6B Fig). The decrease of mean Hoechst signal intensity in the–FTC condition, compared to the +FTC condition, correlated with increased SP size (S6C Fig). The intensity of both Hoechst Red and Blue staining is greatly reduced in the conventional binary assignment of cells into SP or NSP subpopulations. We sought to develop analytical methods to enable characterization of staining data from the SP assay with greater preservation of staining information. We processed the Hoechst Red and Blue flow cytometry signals into 2-dimensional population density functions (PDF) for both the +FTC (PDF+FTC) and–FTC (PDF-FTC) conditions, which converted raw data into normalized data sets (S3 Fig). This allowed us to directly compare spatial differences in staining intensity in the +FTC and–FTC conditions by subtracting the PDF+FTC from the PDF-FTC to define the ΔFTC density for a given sample (Fig 3A). Conversion of the raw data into PDFs was critical because it allowed for direct comparison of flow cytometry samples of un-equal event counts. An increased cell population density in the–FTC condition compared to the +FTC condition is visualized as a red signal whereas blue indicates decreased population density. By converting raw data into PDFs and thereby normalizing, we able to make quantitative comparisons in spatial staining intensities between +FTC and–FTC conditions of a particular sample. Furthermore, we are also able to compare the differences in staining redistribution (ΔFTC) between different samples by taking the difference between respective ΔFTC densities to compute the ΔSP density (Fig 3B). These methods enable visualization of the influence of ABCG2 inhibition on staining while preserving the quantitative aspects of staining intensity and density. We analyzed the influence of tert-butylhydroquinone (tBHQ) on ABCG2 activity and SP size using an imaging cytometer using these data analysis steps. Following 48 hours of 50 μM tBHQ treatment, A549 cells (4 days post-passage) were found to have and increased SP size from 6% to 9% (S7 Fig). The red signal within the ΔFTC plots reflected the presence of the SP in each sample; similarly, the red signal within the ΔSP plot reflected the increased SP size in the tBHQ-treated sample compared to the control (Fig 3C). Comparing differences in the TGFβ–treated samples shown in Fig 2 (S8 Fig), we observe decreased staining present in the–FTC condition without arbitrary segregation SP and NSP subpopulations from a continuous staining distribution. We developed an approach for modeling of the SP assay to investigate the role of transporter heterogeneity within the cell population on the generation of SP and NSP responses of individual cells. At the cellular scale, we considered the influence of transporter activity in a mass-action kinetic model to simulate Hoechst transport dynamics and cell staining. At the population scale, we considered the role of heterogeneity in the cell population, defined by variation in transporter properties and cell morphology. We defined an ensemble as the pairing of a particular set of kinetic constraints with all of the staining simulations for an in silico population. Having sampled M = 10,000 sets of kinetic rate constants (K), we have then had 10,000 ensembles with in silico staining results that we then compared to our experimental data to assess the ensemble for a SP response at the population level. We then analyzed the influence of transporter properties on staining results at the single-cell level for ensembles demonstrating a SP response. Each of the cells in the N = 1,000 cell in silico population was uniquely defined by parameters for cell volume, cell membrane surface area, nuclear volume, nuclear membrane surface area, DNA content, and transporter heterogeneity (Fig 4A). Morphological parameters for each cell were sampled from corresponding experimental distributions using Latin hypercube sampling (LHCS), which ensured that the resulting in silico population faithfully represented the experimental distributions (S9 Fig and S10 Fig). Staining of the in silico population was carried out for each individual cell under 4 different levels of transporter expression, T1-T4, corresponding to distributions derived from 0,1, 10, and 100 pM TGFβ-treated cells (S10 Fig), under +FTC and–FTC conditions. Single-cell staining simulations consisted of a compartmental mass-action kinetic model of intracellular dye transport processes, governed by kinetic rate constants for the given ensemble and the morphologic parameters associated with the cell (Fig 4A and Fig 1C). Following simulation of each of the cells in a population, the final dye concentrations of the samples were processed via in silico flow cytometry to determine Hoechst Red and Blue signals (Fig 4B and S11B, S11C and S11D Fig). Simulated +FTC and–FTC conditions were used to measure SP size, PDF+FTC, PDF-FTC, ΔFTC, and ΔSP distributions for each of the 4 transporter expression levels (T1-T4; Fig 4C), which were then compared to corresponding values from the experimental data (Fig 4D). Ensembles were determined to have an SP response if each of the following criteria were met: 1) the change in mean Hoechst Red Signal (ΔHRSmean) and Hoechst Blue Signal (ΔHBSmean) were negative when comparing the–FTC to the +FTC condition (indicating decreased staining in the–FTC condition); 2) PDF+FTC, PDF-FTC distributions overlapped (indicating the presence of a NSP); 3) the cross-correlation between experimental and in silico ΔFTC and ΔSP distributions was positive (indicating similarity in spatial response distributions); and 4) in silico SP sizes correlated with experimental SP sizes for the 4 transporter expression levels (indicating a consistent decrease in SP size with decreasing transporter expression levels). Ensembles producing a SP response were further analyzed at the single-cell level to investigate the influence of transporter properties on SP/NSP status and differential staining the +FTC and–FTC conditions. We independently implemented 3 different modes of transporter heterogeneity within the in silico cell population to assess the role of transporter heterogeneity in the formation of SP responses in the SP assay. In the first mode of transporter heterogeneity (I), the concentrations of transporter across the population is uniform, which due to the heterogeneity in cell size, leads to a distribution of transporter numbers within the population. In the second mode of heterogeneity (II), the number of transporters per cell across the population is uniform, generating a distribution of transporter densities within the population. For the first two modes, uniform number and densities of transporters for the T1-T4 expression levels were determined by mean values of ABCG2 expression from flow cytometry data (Fig 2B). We explicitly designed the third mode of heterogeneity (III) to be more expansive than the first two modes. In this mode, transporter expression within the populations were sampled from experimental distributions of ABCG2 expression (S10A Fig) as part of the LHCS approach in generating the in silico population. Further, we enabled a non-linear relationship between transporter expression levels and transporter activity levels at the single cell-level. This mimics cooperativity in which higher transporter levels led to greater transporter activity than would be accounted for by a simple 1-to-1 correspondence between expression and activity. We implemented this modeling approach to assess the relationship between transporter heterogeneity and SP responses for the 3 modes of transporter heterogeneity. In our modeling approach, the primary aim was to identify the ensembles in which an SP response resulted. Again, an ensemble consisted of the simulated staining of the in silico cell population using a specific set of governing kinetic rate constants (Kj) for a particular mode of transporter heterogeneity in both +FTC and–FTC conditions for each of the T1-T4 transporter expression levels. For each mode of heterogeneity, we assessed M = 10,000 kinetic rate constant sets (K) for an SP response with less than 5% of the simulation ensembles aborted due to timeout for long simulation time and with SP responses only identified in a small subset, ~5%, of successfully completed ensemble simulations (S15 Fig). In addition, the range of SP responses for each of the modes exhibited a similar quality of fit to experimental SP size (S16 Fig) and a wide range of kinetic rate constants were permissive of SP responses (S17 Fig). An increased frequency of ensembles with SP responses was observed in the concentration (I) and number (II) modes of heterogeneity with larger transporter expression slopes (k8; S17A and S17B Fig), in which a larger k8 value corresponds to greater discrepancy in expression between T1-T4 conditions. Similarly, in the experimental distribution mode of heterogeneity (III), an increased frequency of SP responses resulted in ensembles with greater non-linearity (k8, k9; S17C Fig). Having met the selection criteria, ensembles exhibiting an SP response were analyzed at the single-cell level for features. Experimentally, SP responses are apparent when a subpopulation of cells stains less intensely in the–FTC condition compared to the +FTC condition. Using our ensemble modeling approach, we were able to simulate SP responses in heterogeneous in silico cell populations. The simulated flow cytometry data generated during the simulations was analyzed using the same approaches for experimental SP data, yielding outputs of SP size, PDF+FTC, PDF-FTC, ΔFTC, and ΔSP distribution data for the T1-T4 conditions (Fig 5A). We observed subtle differences in the staining patterns of SP cells, in which some responses exhibit relatively fewer SP cells with a more significant decrease in cell staining (subsequently defined as a Subpopulation Response). In contrast, other responses had more SP cells with a less significant decrease in cell staining (subsequently defined as a Full Response) between +FTC and–FTC conditions. Experimentally, such differences could only be investigated at the population level due to technical limitations; however, we designed our modeling approach to circumvent these limitations. A key advantage to our modeling approach was in the consistency of the cell population for staining simulations, in which an identical cell population could be seeded for both +FTC and–FTC conditions. Furthermore, the same in silico population was consistent within an ensemble, with the exception of transporter expression by T1-T4 conditions, and from ensemble to ensemble, differing only by the respective set of governing kinetic rate constant sets. This permitted a level of comparison that would be impossible in an experimental setting. For example, we were able to reduce Hoechst Red and Blue scores onto an x-y projection, as is done in the projection gating method, to obtain a single staining metric for each cell. Because the same in silico cell population was used for both +FTC and–FTC conditions, we were then able to compute the difference in Hoechst staining projection scores (-ΔHproj) on a cell-by-cell basis. A larger -ΔHproj corresponds to a greater decrease in staining in the–FTC condition compared to the +FTC condition, thus indicating a greater influence of transporter activity on staining intensity. Next, we investigated the relationship between the distribution of single-cell -ΔHproj responses with a population and the staining pattern of SP cells for a given ensemble. In plotting histograms of -ΔHproj values we were able to appreciate patterns in responses (Fig 5B). In one pattern, we observed a -ΔHproj distribution with the greatest number of -ΔHproj values near zero and a tail of increasing -ΔHproj values (Fig 5B top). This indicates that transporter inhibition had little to no effect on staining in a majority of the cells but greatly influenced staining in a portion of the cells. We described such a scenario as a Subpopulation Response. Contrastingly, in the Full Response, the entire population exhibits non-zero -ΔHproj values and a more normal-like distribution (Fig 5B bottom), indicating that staining of each cell in the population was affected by inhibition of transporter activity as well as consistency in the magnitude of this effect. The nature of our modeling approach enabled us to characterize a great number of relationships not possible experimentally. We compared the influence of transporter inhibition on staining intensity (-ΔHproj) against staining intensity in the–FTC condition (Hproj-FTC; Fig 5C). In the Subpopulation Response, we observed SP cells with little influence of transporter inhibition on staining as well as NSP cells with significant influence of transporter inhibition (Fig 5C top). In the Full Response, we observed NSP cells with greater influence of transporter inhibition on staining (Fig 5C bottom). Similar comparisons were made to +FTC conditions and by transporter numbers (S19 Fig). While both were capable of generating SP sizes consistent with experimental data, the Subpopulation Responses (S13 Fig) and Full Responses (S14 Fig) differed. In the Subpopulation Response, the range of staining differences, apparent in the ΔFTC distribution, varied across transporter expression levels (S13D Fig), which was similar to experimental observations (ΔFTC; S8D Fig). However, in the Full Response, the range of staining in the (S14 Fig) ΔFTC distributions was consistent across the range of transporter expression levels, varying in intensity (S14 Fig). From a kinetic perspective, decreasing transporter levels in the Subpopulation Response was associated with a reduction in the portion of cells influenced by transporter inhibition (S18A Fig) while in the Full Response it was associated with a decrease in the magnitude and variability of the -ΔHproj magnitude within the population (S18B Fig). For most ensembles, the -ΔHproj response distributions were not clearly Subpopulation or Full Response in nature, rather most exhibited aspects of both. To more objectively characterize the -ΔHproj responses, the distributions were evaluated by standardized skewness and bimodality coefficient (Fig 6). Standard skewness is a metric that quantifies asymmetry of the distribution while the bimodality coefficient quantifies the similarity between the distribution and a purely bimodal distribution (S20 Fig). Both transporter number (I) and concentration (II) modes of heterogeneity had a greater tendency to generate -ΔHproj distributions more closely resembling the Full Response with greater distribution symmetry and bimodality coefficients similar to a normal distribution (blue and red; Fig 6). Ensembles with SP responses generated in the experimental distribution of transporter heterogeneity (III) were more varied, ranging from Full Responses to Subpopulation Responses (green; Fig 6). The overall spread of distribution mappings in this plot demonstrates a consistency of SP responses arising from -ΔHproj distributions that are roughly normal and have a right-sided tail of variable magnitude.
SP cells arise through ABC transporter activity; however, the technical limitations that prevent staining of specific cells in both inhibitor-free and inhibitor-containing conditions have prevented direct measurement of the influence of transporter activity on Hoechst staining a the single-cell level. Our novel computational approach to in silico staining of a cell population in numerous conditions, differing only by transporter activity, revealed that specific distributions of kinetic transport heterogeneity within a population can alone account for the formation of a SP, independent of genetic or other phenotypic traits, such as CSC. This biological insight was obtained by the pairing of flow cytometry distribution data with a novel computational approach for modeling dye kinetics at the single-cell level. This builds on similar approaches by You et al that demonstrated cell-to-cell variability and heterogeneity in gene expression can govern biphasic responses to extracellular cues [27] and that variability of bacterial uptake by hosts are governed in a probabilistic manner determined by host receptor expression levels [28]. It is increasingly understood, even within clonal/isogenic populations, that noisy or variable gene expression leads to heterogeneity within a population and can contribute to differences in phenotype [29–32]. A role for noisy gene expression in generating diverse phenotypic responses in clonal population has been supported both experimentally and computationally; further, it has been postulated as a mechanism of “bet hedging”, thereby conferring survival advantages in stressful or alternate environments [33–37]. The kinetic aspects involved in the SP assay caused us to more carefully consider the significance of SP/NSP discrimination. SP/NSP phenotype are not strictly defined by inheritance or genetic factors, as is indicated by the fact that SP and NSP cells emerge in clonal populations derived from single-cell sorting (S5 Fig) and by reports that describe the interconversion between SP and NSP phenotypes from isolated cell populations [20,38,39]. Further, in our modeling, we observed a range of transporter activities in SP and NSP cells (Fig 5C), suggesting that not all SP cells have high transporter activities and not all NSP cells have low transporter activities. This can be attributed to the interplay between heterogeneity in transporter activity within the population as well as relative differences in DNA content across the population, which alters that staining potential in the SP assay [40]. Thus, the SP phenotype of a specific cell is not discretely defined by membership in a homogenous subpopulation; rather it is a kinetically defined property, inextricably defined by the specific experimental conditions. We observed SP sizes ranging from 2% to 20% in the A549 cell line (Fig 2D), which is consistent with reported SP sizes of 2. 5% to 30% for A549 cells in the literature [21,38,41–45]. We observed consistent measures of SP size with reproducible trends with increasing time/passage in culture and decreasing exposure to TGFβ (Fig 2D). It should be noted that SP size is determined by the specific experimental conditions in the SP assay, the analytic technique employed by the investigators [1,46–48], and the conditions in which samples are maintained in culture [39,45]. Additional variability may arise from discrepancies in gating SP/NSP regions in cytometry data [21,38,41–45], which is highly subjective and inconsistent and complicated by the fact that SP and NSP staining regions are continuous with no self-evident border of separation (S6A and S6B Fig). Proper gating to exclude erroneous events is key to accurate SP measurement as cellular debris or dead cells may be mistaken as SP cells. Apoptotic cells, for instance, lie near the SP staining region with greater decreased Hoechst red signal than Hoechst blue signal [1]. Failure to properly exclude these events could result in misclassification of this population as SP when using manual gating methods thereby falsely elevating SP size. The automated SP measurement we described provides an objective measure of the SP without user bias; however, the reliability of the output, %SP, is highly dependent on the input, Hoechst red and Hoechst blue intensities of cytometry events. To ensure the quality of our input we sequentially excluded cellular debris, multiple events, and dead cells in the gating tree to define the final Hoechst Red and Hoechst Blue stained cells as recommended by Golebiewska et al. [1]. Further method development may be necessary to consider scenarios with large apoptotic populations in order to properly exclude such events from the final analysis. Encouragingly, the automated methods we have described may have potential to measure SP size despite the presence of apoptotic cells as these cells would be expected to stain similarly in -inhibitor and +inhibitor conditions. While an apoptotic population may underestimate SP size by falsely lowering the 1st percentile level in the projection gating method, the staining differences between +inhibitor and -inhibitor conditions would be negated in the ΔFTC distribution. Therefore, the Hoechst staining density distributions may be further developed for SP measurements. While automated methods hold promise for objective measurement of SP size, we emphasize that additional studies will be necessary to establish validity of such measurements in the presence of errant populations. Variability in biological, experimental, and analytical conditions likely account for the much of the variability in literature-reported SP sizes for the A549 cell line, which highlights the importance of reporting detailed experimental conditions and calls to question the utility of comparing SP size in different experimental and analytical conditions. For example, SP size in untreated A549 cells 4 days after passage had SP sizes of 20% (Fig 2D) and 6% (S7C Fig) when measured using a BD LSR II flow cytometer or Amnis FlowSight imaging cytometer, respectively, despite having identical Hoechst staining conditions and SP data analysis. Instrument settings such as excitation, emission, Hoechst Red and Blue channel windows, detector sensitivity, and detector scaling all greatly influence the recorded Hoechst signals. The kinetic interpretation of the SP may be applied to some immortalized cell lines, such as the one used in this study; however, SP may not arise strictly from kinetic properties in all situations, especially in samples of multicellular composition in situ. For example, in the original description of the SP, the SP corresponded to hematopoietic stem cells within bone marrow aspirate [2] and the SP in glioblastoma-derived samples were tumor stromal cells while the glioblastoma cells, including glioblastoma CSCs, did not contribute to the SP [49]. Our investigation adds to this body of evidence as it validates an ABCG2-dependent kinetic basis for the formation of the SP and provides a novel perspective on the distribution of single-cell ABCG2 activity across a population as well as within the SP itself. Independent of cell line, SP size is also a function of passage frequency, Hoechst staining conditions, fluorescence excitation/emission settings, and SP/NSP gating strategies. Therefore, to focus our investigation on the kinetic aspects of the SP assay, we performed the SP assay with attention to consistency of the culture conditions, Hoechst staining conditions, cytometry settings, and unbiased SP analysis. While this approach enabled investigation of assay kinetics based on differences in ABCG2 expression, other kinetic factors, such as Hoechst staining concentration and duration [46,48], influence SP measurement. A more refined and comprehensive modeling approach would be necessary to determine precise kinetic rate parameters to more definitively characterize the transport phenomena generating the SP. The SP has been the subject of many investigations, many with conflicting results. Some studies claim the SP to be a population of CSCs [20,50], or that ABCG2 is necessary to maintain a stem cell phenotype [51]. In contrast, other studies have identified stem cells lacking a SP phenotype or ABCG2 expression [49,52–54] or identified SP lacking stem cell phenotypes [55–58]. In A549 cells, TGFβ simultaneously leads to enhanced expression of CSC properties [38,43,59] and decreased SP size (Fig 2B) [38]. These findings suggest that SP and CSC phenotypes are nonequivalent; however, the phenotypes appear to be frequently co-expressed. The co-expression of SP and CSC phenotypes may have a significant role in tumorigenesis as ABCG2 expression can confer a MDR phenotype to CSCs [60]. Additional cellular processes, related to ABCG2 transporter activity, may contribute to resistance in the MDR phenotype. For instance, activity of Nrf2, a master regulator of the cellular redox environment is also a key regulator of ABCG2 expression [61–64]. ABCG2 has been implicated in antioxidant processes [65–71] and TGFβ signaling, down-regulates ABCG2 expression (Fig 2B) in addition to down-regulating multiple antioxidants [38,72–76]. Furthermore, population-level studies have demonstrated increased antioxidant expression in SP cells compared to NSP cells. [77] Therefore, the intracellular redox processes may act in concert with ABCG2-mediated drug efflux to promote the MDR phenotype. Targeting functional properties specific to high ABCG2-expressing cells may be a promising approach to overcome a MDR phenotype. For instance, addition of the tyrosine kinase inhibitor axitinib to topotecan enhanced topotecan-mediated apoptosis in A549 cells through inhibition of ABCG2-mediated transport, independent of ABCG2 expression. [78] Despite the fact that numerous chemotherapeutics are substrates of ABC transporters, evaluation of ABC transporter inhibitors in clinical trials has failed to demonstrate added benefit due to drug toxicities and the inability to achieve sufficient concentrations to effectively inhibit transporters [79]. Further investigation of the kinetic processes involving ABCG2 will refine our understanding of the functional consequences of ABCG2 activity in cancer cells and potentially inspire novel chemotherapeutic approaches.
This investigation is the first to demonstrate the kinetic mechanisms that form the basis of the SP assay. We validated the ABCG2 transporter-mediated differential staining between +FTC and–FTC conditions across multiple transporter concentrations in a heterogeneous cell population, accounting for differences in responses for SP and NSP cells. Our modeling approach leveraged these experimentally determined dynamic distributions of ABCG2 expression levels as a function of TGF-β treatment and culture time. The computational model tested influences of transporter properties on cell staining across a heterogeneous population, which would otherwise be impossible to achieve due to the technical limitations of the SP assay. Our results suggest that in particular distributions of transporter kinetics within a population, a subset of cells within the population exhibit marked enhancement of transporter activity compared to the main cell population. Analysis of thousands of single cell simulations provided unique insight that NSP and SP cells both lie along a spectrum of ABCG2 activity. Collectively, these results support our hypothesis that specific single-cell distributions of ABC transporter activity yield differential staining and serve as a kinetic basis in forming side populations.
A549 lung carcinoma cells were obtained from American Type Culture Collection (ATCC; CCL-185) and maintained in growth media, consisting of high glucose DMEM with L-glutamine (Sigma D5796), 10% FBS (Sigma F4135) and penicillin (50 IU/ml) -streptomycin (50 μg/ml) (Cellgro 30-001-CI). Cells were plated in flasks at density of 3,000 cells per well in growth media (15 ml per T-75/35 ml per T-175) and maintained at 37°C and supplemented with 5% CO2. TGFβ (Millipore, GF111) and tBHQ (ACROS Organics, tert-butylhydroquinone, AC15082) treatment took place in culture media. Hoechst staining and flow cytometry to measure the SP in A549 cells closely followed the protocol described in [23,48]. Cells were trypsinized and resuspended in CO2 conditioned DMEM+, consisting of high-glucose DMEM without phenol red, 10 mM HEPES, 2% FBS, and 2 mM EDTA, at a concentration of 1x106 cells/ml. Samples were split into +FTC and -FTC conditions, supplemented with DMSO-mobilized FTC (EMD Millipore) at a final concentration of 10 μM or DMSO alone, respectively. The solutions were incubated in a 37°C water bath for 30 minutes, after which they were supplemented with Hoechst 33342 (Life Technologies) at a final concentration of 5 μM for 90 minutes, with mixing at 30-minute intervals. The staining solutions were then centrifuged at 1,000 RCF for 10 minutes and resuspended in HBSS+, consisting of HBSS without phenol red, 10 mM HEPES, 2% FBS, and 10 mM EDTA. Cells were incubated with the viability stain SYTOX Blue (Life Technologies, 1: 1000) for 5 minutes prior to fluorescence measurement via flow cytometry. Positive controls for dead cell staining were obtained by incubating cells at 56°C for 45 minutes followed by SYTOX Blue staining. The BD LSR II flow cytometer was monitored using fluorescent beads to ensure optimization of system optics and detectors for quality control in polychromatic settings. Samples were excited with a 355 nm UV laser and the Hoechst Red signal was measured in a λem = 675/50 nm channel with linear scaling and the Hoechst Blue signal measured in a λem = 450/50 nm channel with linear scaling, collecting 100,000 events per flow sample. The Hoechst stained -FTC condition was initially used to tune Hoechst Red and Hoechst Blue photomultiplier tube settings, which were held constant for all subsequent studies. Additionally, samples were excited with a 445 nm violet laser with SYTOX blue emission measured in a λem = 473/10 nm channel with logarithmic scaling. Sample gating proceeded as follows: 1) Debris exclusion with FSC-area/SSC-area (λex = 488 nm) gating; 2) Single-cell selection with FSC-height/FSC-area; 3) Live cell selection with the violet-λem = 473/10 nm channel. Events retained through all 3 gates were used for subsequent SP analysis in the Hoechst Red and Blue channels. Manual selection of SP gates was determined using the +FTC conditions where a quadrant gate was placed as tight as possible such that greater than 99% of the cells in the +FTC condition were located in the upper right quadrant. The same gates were then applied to the -FTC condition where the two left gates were considered to be SP gates and the right two gates considered to be NSP gates. The measured %SP in the manual gating approach is the sum of the percent of parent population in the SP gates. For a given sample, the %SP was determined using the specific +FTC and -FTC conditions for that sample. Following 4 days of TGFβ treatment, surface marker expression of A549 cells was analyzed by flow cytometry following dissociation from culture flasks using non-enzymatic means. Following treatment, cells were dissociated with Enzyme Free Dissociation Solution (Millipore S-004-B) and resuspended in DMEM without phenol red supplemented with 2% fetal bovine serum, 10 mM EDTA, and 10 mM EGTA. The cells were then pelleted (1000 RCF, 10 minutes) and resuspended at a concentration of 1x107 cells per ml in HBSS without phenol red, Ca2+, & Mg2+ supplemented with 1 mM HEPES, 2% fetal bovine serum, 1 mM EDTA, and 1 mM EGTA. The cell solution was added to an equal volume of antibody staining solution and incubated for 30 minutes on ice with gentle rotation. Antibody solutions (mouse IgG1 anti-E-cadherin/PE-CF594, BD Biosciences, Clone 67A4,2x dilution; mouse IgG1 anti-N-cadherin/PE, BD Biosciences, Clone 8C11,2x dilution; mouse IgG2 anti-ABCG2/APC, BioLegend, Clone 5D3,2x dilution) were prepared in the aforementioned HBSS solution. Following incubation, cells were washed and resuspended in the HBSS solution. Next the solutions were stained with SYTOX Blue to select for live cells. A BD LSR II flow cytometer was used to analyze fluorescence of the stained cells with the following settings: FSC/SSC (λex = 488 nm), PE (λex = 488 nm, λem = 575/26 nm), PE-C594 (λex = 488 nm, λem = 610/20 nm), APC (λex = 633 nm, λem = 660/20 nm), SYTOX Blue (λex = 445 nm, λem = 473/10 nm). Unstained and single-stained control samples were prepared to determine compensation matrix corrections within FlowJo for each replicate. Heat killed control cells were mixed with live cells to establish live-dead discrimination with SYTOX Blue staining. We employed the following gating strategy: debris exclusion (FSC/SSC), single events (FSC-H/FSC-A), and live cells (SYTOX Blue). Upon gating for single, live-cell events, fluorescence intensities were measured as the geometric mean fluorescence. We imaged samples at 20X magnification using a FlowSight imaging cytometer with the Quantitative Imaging Upgrade (Amnis, Seattle, WA). Single color controls were used to set-up compensation matrices. For the SP assay, the compensation matrix was manually edited to allow collection of the Hoescht Blue (470/35 nm) and Red (694/51 nm) signals using the 405 nm laser. Images were analyzed with IDEAS analysis software (Amnis). Using the gradient root mean square feature for the brightfield channel, “Focused cells” were selected according to the manufacturer’s recommendation. Debris was eliminated by gating single cells using the area and aspect ratio features for the brightfield channel. Live cells were gated using the intensity feature in the green channel (533/27 nm) for SYTOX Green staining. For ABCG2 analysis, the intensity feature of the APC channel (694/51 nm; 642 nm excitation laser) was used to quantify the expression of ABCG2. For the side population assay, double positive cells from were selected by gating in the Hoechst Red and Hoechst Blue channels. Flow cytometry and Flow Sight imaging cytometry data were processed and analyzed using FlowJo for Mac OS X version 10. 0. 7, Tree Star, Inc. Statistical analyses of experimental data were performed within Graphpad Prism for Mac OS X version 6. 0e. Imaging Cytometry images were segmented using ImageJ and the SCIPY platform in python. Cytometry distribution analyses were performed using MATLAB version 2014a (64-bit), MathWorks Inc, in 64-bit Windows 8. 1. Side population simulations were implemented in MATLAB version 2014a for Linux and run in parallel on the PACE cluster at Georgia Tech, which consisted of 64 single core 3. 8 GHZ AMD processors with over 240 GB of total RAM available (10 GB per node). The following MATLAB File Exchange entries (accessed on 11/5/14) were implemented in MATLAB to analyze or display cytometry or simulation data: smoothhist2d (13352) [85], tight_subplot (27991) [86], suplabel (7772) [87], redblue (25536) [88], progress monitor (32101) [89], and distributionPlot (23661) [90].
A common method of evaluating stemness among pluripotent cells or cancer cells is the side population assay, a flow cytometry technique which identifies a subgroup of cells that exhibit differences in dye fluorescence upon blocking of a membrane transporter. A technical limitation of this assay is that it relies on two independent experimental conditions, with and without a transporter inhibitor, preventing evaluation of single cell characteristics that generate population-level shifts in fluorescence. Here, the computational implementation of various forms of cellular heterogeneity allows for ensemble single-cell simulations to be performed in order to assess the underlying properties that give rise to the population-level behavior. We simulated staining in 10,000 kinetic ensembles consisting of 1,000-cell populations with and without inhibitor to determine which cells respond in the assay. We quantitatively establish that a small, responsive subgroup of cells with nonlinear activities associated with transporter number are most likely to recapitulate observed behavior in the side population assay; however, a continuum of phenotypes at different stages of the cell cycle and with a range transporter expression levels will shift fluorescence. We present a new perspective on the phenotype of SP cells at the single-cell level that is determined by biological and experimental kinetic processes, and is not equivalent to a cancer stem cell phenotype.
flow cytometry
nuclear staining
radii
geometry
probability distribution
mathematics
cytometry
skewness
research and analysis methods
staining
specimen preparation and treatment
probability density
probability theory
spectrophotometry
cytophotometry
cell staining
cell biology
phenotypes
genetics
biology and life sciences
physical sciences
spectrum analysis techniques
Kinetic Modeling of ABCG2 Transporter Heterogeneity: A Quantitative, Single-Cell Analysis of the Side Population Assay