Experimental and Computational Approaches to Optimizing Bovine Gamete Cryopreservation
Date
2021-08-31
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
0000-0001-9913-7071
Type
Thesis
Degree Level
Masters
Abstract
Cryopreservation uses freezing to suspend the metabolic activity of biological specimens
for increased longevity of biotic materials like gametes. Cryopreservation has been known
to affect both the functionality (performance of activities) and the viability (survival)
of biological specimens during the freezing and thawing process due to four types of
damage: (1) thermal, (2) ice, (3) osmotic stress, and (4) cytotoxic. Cryoprotective
agents (CPAs) are known to reduce thermal and ice damage but cause osmotic stress and cytotoxic
damage. Osmotic stress occurs when the addition of CPAs causes a rapid expulsion of
water from the cell as the extracellular environment has become hypertonic. Cytotoxic
damage occurs when a cell is exposed for too long to CPAs that may be damaging to the
cell at high temperatures, but aid in preservation at low temperatures. The purpose of my
project is to minimize osmotic stress in bovine embryos and cytotoxic damage in bovine
sperm caused by CPAs using novel algorithmically guided techniques. To minimize osmotic
stress in bovine embryos, I aim to facilitate the equilibration of embryos with
cryoprotective agents isochorically (constant volume). Isochoric cryoprotectant
equilibration, requires a feedback control system that in our case will use real-time
image analysis developed in this thesis to estimate current embryo volume and then
adjusts the concentration of CPAs being administered to the system. I implemented a
colour-based image analysis software that was able to process images of bovine embryos as
they were exposed to CPAs at a sub-second rate. The sub-second processing rates include
cell volume estimates that are comparable to manual cell volume estimates. To minimize
cytotoxic damage in bovine sperm, I optimized cryopreservation media (CPM) composition
to maximize post-thaw motility. The composition of CPM can contain many ingredients that
have the potential to interact and are infeasible to test only empirically. Here, I
combined empirical experiments, data-driven optimization algorithms, and machine learning
to optimize the composition of CPM. I used differential evolution and Gaussian process
regression to optimize CPM composition that are on par with commercial media after 9
iterations. During the optimization process I determined that Gaussian process
regression model was superior to artificial neural networks when predicting post-thaw
motility for a given CPM composition. By optimizing these cryopreservation processes,
cellular damage can be reduced, improving functionality and viability of gametes used in
assisted reproductive technology that can be applied across animal husbandry and
biomedical fields.
Description
Keywords
cryopreservation, cryoprotectant, machine learning, computer vision, optimization, sperm cryopreservation, oocyte, embryo
Citation
Degree
Master of Science (M.Sc.)
Department
Biology
Program
Biology