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Details of Grant 

EPSRC Reference: EP/S001433/1
Title: Innovation Fellowship: Computational modelling of cryopreservation of biological tissue
Principal Investigator: Bauer, Dr R
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Asymptote Ltd CERN Newcells Biotech Limited
Department: Sch of Computing
Organisation: Newcastle University
Scheme: EPSRC Fellowship - NHFP
Starts: 29 June 2018 Ends: 28 June 2021 Value (£): 502,399
EPSRC Research Topic Classifications:
Bioinformatics
EPSRC Industrial Sector Classifications:
R&D
Related Grants:
Panel History:
Panel DatePanel NameOutcome
10 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 8 - 10 and 11 May 2018 Announced
Summary on Grant Application Form
The preservation of organs and biological tissue is currently in its infancy. The lack of preservation capacity results in 2/3 of donor hearts and 4/5 of lungs being rejected for transplant for logistical reasons. Current estimates of the incidence of diseases that could be treated by on-demand organ replacement amount to several millions in the United States and Europe combined. Hence, advances in biological tissue preservation would revolutionize medicine and biotechnology. Moreover, since synthetic tissue growth is time and labour intensive, such advances would also enable large-scale drug-screening and toxicology testing using organoids grown from human stem cells.

Cryopreservation, where very low temperatures are used, is a standard procedure for the preservation of embryos, oocytes and sperm. It allows tissue availability on demand (as opposed to waiting several months for fresh culture), allows economies of scale to reduce costs, facilitates quality control, and prevents wastage. However, state-of-the-art freezing and thawing techniques lead to tissue damage in anything larger than 1-3mm3. Above-freezing storage times of human organs range from approximately 3 to 24 hours - depending on organ - before becoming unviable.

Currently, cryopreservation is mainly an experimental process, and lacks a guiding computational component. The control of the spatial influence of cryoprotectant agents, their administered quantity, and the timing of induced temperature changes remain largely subject to a black-box approach. However, with the advent of modern computing technologies, the automated analysis of large datasets and detailed computer simulations have become possible.

In this fellowship, I aim at the computational analysis and modelling of cryogenic processing, to provide a systematic framework that generates novel protocols. Physical processes, such as the diffusion of chemicals, mechanical interactions, heat transfer and ice propagation will be incorporated in 3D computer models. This implementation will benefit from the BioDynaMo collaboration with CERN openlab, which aims at a cloud-based software platform for computer simulations of biological tissue dynamics. Cells will be modelled in an agent-based approach. Overall, this computational model will be able to predict tissue-specific cryogenic processing methodologies to ensure optimal tissue viability.

This fellowship will initially focus on the retina, which is part of the central nervous system, and is particularly well-suited because its function can be assessed relatively easily and cost-effectively. Mouse retinal tissue will be used as the model system, because of the consistency of retinal structure across vertebrates. Additionally, retinal organoids, which were synthetically grown in culture in the lab of Prof. Majlinda Lako, will be used. After successful retinal cryopreservation, other tissues (e.g. mouse kidney and cortex) will be cryogenically processed to demonstrate the power of the research approach. Asymptote Ltd (GE Healthcare) will provide expertise, equipment and support for the freezing and thawing processes, hence adding to a prestigious network of well-established collaborators for this fellowship.

Different cryopreservation protocols will be applied to generate samples for computational analysis. Serial block-face scanning electron microscopy, immunocytochemistry, quantitative polymerase chain reaction and multi-electrode array recordings will be used to quantify damage to the tissue after thawing. Based on these data, a 3D computational model will be informed to generate optimal cryogenic processing parameters. Ultimately, this fellowship will allow me to become an international leader in cryopreservation. I will also pursue commercial activities based on the research results. To this end, the computational approach will be used for consulting purposes, e.g. for pharmaceutical, cosmetics or cryopreservation companies.
Key Findings
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Organisation Website: http://www.ncl.ac.uk