Engineered Test Tissues: A Model for Quantifying the Effects of Cryopreservation Parameters

Engineered tissues are showing promise as implants to repair or replace damaged tissues in vivo or as in vitro tools to discover new therapies. A major challenge of the tissue engineering field is the sample preservation and storage until their transport and desired use. To successfully cryopreserve tissue, its viability, structure, and function must be retained post-thaw. The outcome of cryopreservation is impacted by several parameters, including the cryopreserving agent (CPA) utilized, the cooling rate, and the storage temperature. Although a number of CPAs are commercially available for cell cryopreservation, there are few CPAs designed specifically for tissue cryostorage and recovery. In this study, we present a flexible, relatively high-throughput method that utilizes engineered tissue rings as test tissues for screening the commercially available CPAs and cryopreservation parameters. Engineered test tissues can be fabricated with low batch-to-batch variability and characteristic morphology due to their endogenous extracellular matrix, and they have mechanical properties and a ring format suitable for testing with standard methods. The tissues were grown for 7 days in standard 48-well plates and cryopreserved in standard cryovials. The method allowed for the quantification of metabolic recovery, tissue apoptosis/necrosis, morphology, and mechanical properties. In addition to establishing the method, we tested different CPA formulations, freezing rates, and freezing points. Our proposed method enables timely preliminary screening of CPA formulations and cryopreservation parameters that may improve the storage of engineered tissues

Bio-Inspired Cryo-Ink Preserves Red Blood Cell Phenotype and Function During Nanoliter Vitrification

Current red blood cell cryopreservation methods utilize bulk volumes, causing cryo-injury of cells, which results in irreversible disruption of cell morphology, mechanics, and function. An innovative approach to preserve human red blood cell morphology, mechanics, and function following vitrification in nanoliter volumes is developed using a novel cryo-ink integrated with a bio-printing approach

A deep learning framework for neuroscience

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress

A deep learning framework for neuroscience

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress