Platforms that allow parallel, quantitative analysis of single cells will be integral to realizing the potential of postgenomic biology. In stem cell biology, the study of clonal stem cells in multiwell formats is currently both inefficient and time-consuming. Thus, to investigate low-frequency events of interest, large sample sizes must be interrogated. We report a simple, versatile, and efficient micropatterned arraying system conducive to the culture and dynamic monitoring of stem cell proliferation. This platform enables: 1) parallel, automated, long-term ( approximately days to weeks), live-cell microscopy of single cells in culture; 2) tracking of individual cell fates over time (proliferation, apoptosis); and 3) correlation of differentiated progeny with founder clones. To achieve these goals, we used microfabrication techniques to create an array of approximately 10,000 microwells on a glass coverslip. The dimensions of the wells are tunable, ranging from 20 to >500 microm in diameter and 10-500 microm in height. The microarray can be coated with adhesive proteins and is integrated into a culture chamber that permits rapid (approximately min), addressable monitoring of each well using a standard programmable microscope stage. All cells share the same media (including paracrine survival signals), as opposed to cells in multiwell formats. The incorporation of a coverslip as a substrate also renders the platform compatible with conventional, high-magnification light and fluorescent microscopy. We validated this approach by analyzing the proliferation dynamics of a heterogeneous adult rat neural stem cell population. Using this platform, one can further interrogate the response of distinct stem cell subpopulations to microenvironmental cues (mitogens, cell-cell interactions, and cell-extracellular matrix interactions) that govern their behavior. In the future, the platform may also be adapted for the study of other cell types by tailoring the surface coatings, microwell dimensions, and culture environment, thereby enabling parallel investigation of many distinct cellular responses.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

In this paper, we investigate the pharmacokinetics and effect of doxorubicin and cisplatin in vascularized tumors through two-dimensional simulations. We take into account especially vascular and morphological heterogeneity as well as cellular and lesion-level pharmacokinetic determinants like P-glycoprotein (Pgp) efflux and cell density. To do this we construct a multi-compartment PKPD model calibrated from published experimental data and simulate 2-h bolus administrations followed by 18-h drug washout. Our results show that lesion-scale drug and nutrient distribution may significantly impact therapeutic efficacy and should be considered as carefully as genetic determinants modulating, for example, the production of multidrug-resistance protein or topoisomerase II. We visualize and rigorously quantify distributions of nutrient, drug, and resulting cell inhibition. A main result is the existence of significant heterogeneity in all three, yielding poor inhibition in a large fraction of the lesion, and commensurately increased serum drug concentration necessary for an average 50% inhibition throughout the lesion (the IC50 concentration). For doxorubicin the effect of hypoxia and hypoglycemia (“nutrient effect”) is isolated and shown to further increase cell inhibition heterogeneity and double the IC50, both undesirable. We also show how the therapeutic effectiveness of doxorubicin penetration therapy depends upon other determinants affecting drug distribution, such as cellular efflux and density, offering some insight into the conditions under which otherwise promising therapies may fail and, more importantly, when they will succeed. Cisplatin is used as a contrast to doxorubicin since both published experimental data and our simulations indicate its lesion distribution is more uniform than that of doxorubicin. Because of this some of the complexity in predicting its therapeutic efficacy is mitigated. Using this advantage, we show results suggesting that in vitro monolayer assays using this drug may more accurately predict in vivo performance than for drugs like doxorubicin. The nonlinear interaction among various determinants representing cell and lesion phenotype as well as therapeutic strategies is a unifying theme of our results. Throughout it can be appreciated that macroscopic environmental conditions, notably drug and nutrient distributions, give rise to considerable variation in lesion response, hence clinical resistance. Moreover, the synergy or antagonism of combined therapeutic strategies depends heavily upon this environment.