Microfluidic organ-on-chip devices constructed from polydimethylsiloxane (PDMS) have proven useful in studying both beneficial and adverse effects of drugs, supplements, and potential toxicants. Despite multiple advantages, one clear drawback of PDMS-based devices is binding of hydrophobic chemicals to their exposed surfaces. Chemical binding to PDMS can change the timing and extent of chemical delivery to cells in such devices, potentially altering dose–response curves. Recent efforts have quantified PDMS binding for selected chemicals. Here, we test a wider set of nineteen chemicals using UV-vis or infrared spectroscopy to characterize loss of chemical from solution in two setups with different PDMS-surface-to-solution-volume ratios. We find discernible PDMS binding for eight chemicals and show that PDMS binding is strongest for chemicals with a high octanol–water partition coefficient (log P > 1.85) and low H-bond donor number. Further, by measuring depletion and return of chemical from solution over tens to hundreds of hours and fitting these results to a first order model of binding kinetics, we characterize partitioning into PDMS in terms of binding capacities per unit surface area and both forward and reverse rate constants. These fitted parameters were used to model the impact of PDMS binding on chemical transport and bioavailability under realistic flow conditions and device geometry. The models predict that PDMS binding could alter in-device cellular exposures for both continuous and bolus dosing schemes by up to an order of magnitude compared to nominal input doses.

We compare the plasma and cavitation dynamics underlying pulsed laser microsurgery in water and in fruit fly embryos (in vivo)—specifically for nanosecond pulses at 355 and 532 nm. We find two key differences. First, the plasma-formation thresholds are lower in vivo —especially at 355 nm—due to the presence of endogenous chromophores that serve as additional sources for plasma seed electrons. Second, the biological matrix constrains the growth of laser-induced cavitation bubbles. Both effects reduce the disrupted region in vivo when compared to extrapolations from measurements in water.

To study the process of morphogenesis, one often needs to collect and segment time‐lapse images of living tissues to accurately track changing cellular morphology. This task typically involves segmenting and tracking tens to hundreds of individual cells over hundreds of image frames, a scale that would certainly benefit from automated routines; however, any automated routine would need to reliably handle a large number of sporadic, and yet typical problems (e.g., illumination inconsistency, photobleaching, rapid cell motions, and drift of focus or of cells moving through the imaging plane). Here, we present a segmentation and cell tracking approach based on the premise that users know their data best–interpreting and using image features that are not accounted for in any a priori algorithm design. We have developed a program, SeedWater Segmenter, that combines a parameter‐less and fast automated watershed algorithm with a suite of manual intervention tools that enables users with little to no specialized knowledge of image processing to efficiently segment images with near‐perfect accuracy based on simple user interactions. © 2012 International Society for Advancement of Cytometry

Morphogenetic events are driven by cell-generated physical forces and complex cellular dynamics. To improve our capacity to predict developmental effects from chemical-induced cellular alterations, we built a multicellular agent-based model in CompuCell3D that recapitulates the cellular networks and collective cell behavior underlying growth and fusion of the mammalian secondary palate. The model incorporated multiple signaling pathways (TGFβ, BMP, FGF, EGF, and SHH) in a biological framework to recapitulate morphogenetic events from palatal outgrowth through midline fusion. It effectively simulated higher-level phenotypes (e.g., midline contact, medial edge seam (MES) breakdown, mesenchymal confluence, and fusion defects) in response to genetic or environmental perturbations. Perturbation analysis of various control features revealed model functionality with respect to cell signaling systems and feedback loops for growth and fusion, diverse individual cell behaviors and collective cellular behavior leading to physical contact and midline fusion, and quantitative analysis of the TGF/EGF switch that controls MES breakdown–a key event in morphogenetic fusion. The virtual palate model was then executed with theoretical chemical perturbation scenarios to simulate switch behavior leading to a disruption of fusion following chronic (e.g., dioxin) and acute (e.g., retinoic acid) chemical exposures. This computer model adds to similar systems models toward an integrative “virtual embryo” for simulation and quantitative prediction of adverse developmental outcomes following genetic perturbation and/or environmental disruption.