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The secretome from mesenchymal stem cells (MSCs) have recently gained attention for new therapeutics. However, clinical application requires in vitro cell manufacturing to attain enough cells. Unfortunately, this process often drives MSCs into a senescent state that drastically changes cellular secretion activities. Antioxidants are used to reverse and prevent the propagation of senescence; however, their activity is short‐lived. Polymer‐stabilized crystallization of antioxidants has been shown to improve bioactivity, but the broad crystal size distribution (CSD) significantly increases the efficacy variation. Efforts are made to crystalize drugs in microdroplets to narrow the CSD, but the fraction of drops containing at least one crystal can be as low as 20%. To this end, this study demonstrates that in‐drop thermal cycling of hyaluronic acid‐modified antioxidant crystals, named microcrystal assembly for senescence control (MASC), can drive the fraction of microdrops containing crystals to >86% while achieving significantly narrower CSDs (13 ± 3 µm) than in bulk (35 ± 11 µm). Therefore, this approach considerably improves the practicality of CSD‐control in drops. In addition to exhibiting uniform release, MASC made with antioxidizing N‐acetylcysteine extends the release time by 40%. MASC further improves the restoration of reactive oxygen species homeostasis in MSCs, thus minimizing cellular senescence and preserving desired secretion activities. It is proposed that MASC is broadly useful to controlling senescence of a wide array of therapeutic cells during biomanufacturing.

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.

Multiple scattering and absorption limit the depth at which biological tissues can be imaged with light. In thick unlabeled specimens, multiple scattering randomizes the phase of the field and absorption attenuates light that travels long optical paths. These obstacles limit the performance of transmission imaging. To mitigate these challenges, we developed an epi-illumination gradient light interference microscope (epi-GLIM) as a label-free phase imaging modality applicable to bulk or opaque samples. Epi-GLIM enables studying turbid structures that are hundreds of microns thick and otherwise opaque to transmitted light. We demonstrate this approach with a variety of man-made and biological samples that are incompatible with imaging in a transmission geometry: semiconductors wafers, specimens on opaque and birefringent substrates, cells in microplates, and bulk tissues. We demonstrate that the epi-GLIM data can be used to solve the inverse scattering problem and reconstruct the tomography of single cells and model organisms.

Integration of conductive electrodes with 3D tissue models can have great potential for applications in bioelectronics, drug screening, and implantable devices. As conventional electrodes cannot be easily integrated on 3D, polymeric, and biocompatible substrates, alternatives are highly desirable. Graphene offers significant advantages over conventional electrodes due to its mechanical flexibility and robustness, biocompatibility, and electrical properties. However, the transfer of chemical vapor deposition graphene onto millimeter scale 3D structures is challenging using conventional wet graphene transfer methods with a rigid poly (methyl methacrylate) (PMMA) supportive layer. Here, a biocompatible 3D graphene transfer method onto 3D printed structure using a soft poly ethylene glycol diacrylate (PEGDA) supportive layer to integrate the graphene layer with a 3D engineered ring of skeletal muscle tissue is reported. The use of softer PEGDA supportive layer, with a 10⁵times lower Young's modulus compared to PMMA, results in conformal integration of the graphene with 3D printed pillars and allows electrical stimulation and actuation of the muscle ring with various applied voltages and frequencies. The graphene integration method can be applied to many 3D tissue models and be used as a platform for electrical interfaces to 3D biological tissue system.

Advancing biologically driven soft robotics and actuators will involve employing different scaffold geometries and cellular constructs to enable a controllable emergence for increased production of force. By using hydrogel scaffolds and muscle tissue, soft biological robotic actuators that are capable of motility have been successfully engineered with varying morphologies. Having the flexibility of altering geometry while ensuring tissue viability can enable advancing functional output from these machines through the implementation of new construction concepts and fabrication approaches. This study reports a forward engineering approach to computationally design the next generation of biological machines via direct numerical simulations. This was subsequently followed by fabrication and characterization of high force producing biological machines. These biological machines show millinewton forces capable of driving locomotion at speeds above 0.5 mm s⁻¹. It is important to note that these results are predicted by computational simulations, ultimately showing excellent agreement of the predictive models and experimental results, further providing the ability to forward design future generations of these biological machines. This study aims to develop the building blocks and modular technologies capable of scaling force and complexity of these devices for applications toward solving real world problems in medicine, environment, and manufacturing.