Rapid model-guided design of organ-scale synthetic vasculature for biomanufacturing.

Our ability to produce human-scale bio-manufactured organs is critically limited by the need for vascularization and perfusion. For tissues of variable size and shape, including arbitrarily complex geometries, designing and printing vasculature capable of adequate perfusion has posed a major hurdle. Here, we introduce a model-driven design pipeline combining accelerated optimization methods for fast synthetic vascular tree generation and computational hemodynamics models. We demonstrate rapid generation, simulation, and 3D printing of synthetic vasculature in complex geometries, from small tissue constructs to organ scale networks. We introduce key algorithmic advances that all together accelerate synthetic vascular generation by more than 230 -fold compared to standard methods and enable their use in arbitrarily complex shapes through localized implicit functions. Furthermore, we provide techniques for joining vascular trees into watertight networks suitable for hemodynamic CFD and 3D fabrication. We demonstrate that organ-scale vascular network models can be generated in silico within minutes and can be used to perfuse engineered and anatomic models including a bioreactor, annulus, bi-ventricular heart, and gyrus. We further show that this flexible pipeline can be applied to two common modes of bioprinting with free-form reversible embedding of suspended hydrogels and writing into soft matter. Our synthetic vascular tree generation pipeline enables rapid, scalable vascular model generation and fluid analysis for bio-manufactured tissues necessary for future scale up and production.

High dynamic range proteome imaging with the structured illumination gel imager.

A current challenge for proteomics is detecting proteins over the large concentration ranges found in complex biological samples such as whole-cell extracts. Currently, no unbiased, whole-proteome analysis scheme is capable of detecting the full range of cellular proteins. This is due in part to the limited dynamic range of the detectors used to sense proteins or peptides. We present a new technology, structured illumination (SI) gel imager, which detects fluorescently labeled proteins in electrophoretic gels over a 1 000 000-fold concentration range. SI uses computer-generated masks to attenuate the illumination of highly abundant proteins, allowing for long exposures of low-abundance proteins, thus avoiding detector saturation. A series of progressively masked gel images are assembled into a single, very high dynamic range image. We demonstrate that the SI imager can detect proteins over a concentration range of approximately 1 000 000-fold, making it a useful tool for comprehensive, unbiased proteome-wide surveys.