Two-Photon Microscopy for Non-Invasive, Quantitative Monitoring of Stem Cell Differentiation

BACKGROUND: The engineering of functional tissues is a complex multi-stage process, the success of which depends on the careful control of culture conditions and ultimately tissue maturation. To enable the efficient optimization of tissue development protocols, techniques suitable for monitoring the effects of added stimuli and induced tissue changes are needed. METHODOLOGY/PRINCIPAL FINDINGS: Here, we present the quantitative use of two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) as a noninvasive means to monitor the differentiation of human mesenchymal stem cells (hMSCs) using entirely endogenous sources of contrast. We demonstrate that the individual fluorescence contribution from the intrinsic cellular fluorophores NAD(P)H, flavoproteins and lipofuscin can be extracted from TPEF images and monitored dynamically from the same cell population over time. Using the redox ratio, calculated from the contributions of NAD(P)H and flavoproteins, we identify distinct patterns in the evolution of the metabolic activity of hMSCs maintained in either propagation, osteogenic or adipogenic differentiation media. The differentiation of these cells is mirrored by changes in cell morphology apparent in high resolution TPEF images and by the detection of collagen production via SHG imaging. Finally, we find dramatic increases in lipofuscin levels in hMSCs maintained at 20% oxygen vs. those in 5% oxygen, establishing the use of this chromophore as a potential biomarker for oxidative stress. CONCLUSIONS/SIGNIFICANCE: In this study we demonstrate that it is possible to monitor the metabolic activity, morphology, ECM production and oxidative stress of hMSCs in a non-invasive manner. This is accomplished using generally available multiphoton microscopy equipment and simple data analysis techniques, such that the method can widely adopted by laboratories with a diversity of comparable equipment. This method therefore represents a powerful tool, which enables researchers to monitor engineered tissues and optimize culture conditions in a near real time manner

Cardiovascular imaging 2011 in the International Journal of Cardiovascular Imaging

Cardiovascular Aspects of Radiolog

On the volume-ranking of opportunity sets in economic environments

We provide a characterization of the volume-ranking of oppor- tunity sets as de ned on the set of all polyconvex sets, i.e. nite unions of convex, compact, Euclidean sets. In fact, such a domain is large enough to encompass most of the opportunity sets typically encountered in economic environments, including non-linear or even non-convex budget sets, and opportunity sets arising from production sets. Our result relies on a valuation-based volume-characterization theorem due to Klain and Rota (Introduction to Geometric Probability, Cambridge University Press, Cambridge 1997) and helps to highlight some quite unusual conditions under which the volume-ranking can be justi ed as a freedom-ranking of opportunity sets. Therefore, it may also help to understand why the latter has been so conspicuously ignored in welfare analysis

A nonstandard characterization of sequential equilibrium, perfect equilibrium, and proper equilibrium

Sequential equilibrium, Perfect equilibrium, Proper equilibrium, Nonstandard probability,

Verification of General Markov Decision Processes by Approximate Similarity Relations and Policy Refinement

In this work we introduce new approximate similarity relations that are shown to be key for policy (or control) synthesis over general Markov decision processes. The models of interest are discrete-time Markov decision processes, endowed with uncountably-infinite state spaces and metric output (or observation) spaces. The new relations, underpinned by the use of metrics, allow in particular for a useful trade-off between deviations over probability distributions on states, and distances between model outputs. We show that the new probabilistic similarity relations can be effectively employed over general Markov decision processes for verification purposes, and specifically for control refinement from abstract models