Periodic Reporting for period 4 - MechBiolRep (Mechanobiology of Bovine Reproduction)
Berichtszeitraum: 2020-12-01 bis 2021-11-30
The maturation of oocytes and preimplantation embryo development are reproductive processes that are implicated with the application of mechanical forces and changes in physical properties of the surrounding microenvironments. Nevertheless, the mechanobiological aspects of female reproductive physiology is poorly understood. This ERC project aims at revealing the relationship between the physical processes and cell-fate decision-making that regulate mammalian reproduction. Due to obvious ethical reasons, we focus on a bovine model which is an excellent mimic of human preimplantation development. In addition, bovine reproduction by itself is of great importance in terms of global food supply. Both the dairy industry and the beef industry depend on bovine fertility (only pregnant cos produce milk). While worldwide demand of dairy products is surging, cow fertility is declining, where oocyte developmental potential is a major factor. We expect to elucidate mechanistic understanding of bovine reproduction, which will contribute to the dairy industry by making it more efficient, thus increasing food production by fewer cows. The outcome of this project is expected to be further implemented in human clinical treatments to improve in vitro fertilization – embryo transfer procedures. The objectives of this ERC project are to reveal the mechanobiological aspects of bovine reproduction. We aim at understanding how changes in the mechanical properties of the ovary affect follicle growth and oocyte maturation. Such mechanical changes are implicated with normal ageing and disease. We aim at understanding how changes in the mechanical properties of the oocytes correlate and direct their potential to become fertilized. Finally, we aim at understanding how the changes in the mechanical properties of the embryos correlate and regulate their potential to develop and to implant within the uterus.
We defined the viscoelastic properties of the ovarian cortex in which follicles reside and revealed that ovaries obtained from young (fertile) heifers are fourfold stiffer than ovaries from aged (infertile) cows that were sent to slaughter. We discovered that during maturation, the mechanical properties of the oocytes changes from a solid-like (SLS model) to a fluid-like (Burger’s model) viscoelastic material. Next, we revealed a distinctive mechanical response of the ooplasm to applied load. Ooplasm resistance to applied load was associated with increased developmental potential. Mechanistically, we identified the Arp-2/3 branching nucleator of actin filaments at the ooplasm cortex to be responsible for providing mechanical integrity. We generalized our studies of bovine oocytes and generalized it to bovine zygotes. We also demonstrated the validity of our findings using a seasonal model of hot-season low quality oocytes versus winter oocytes. During the period-4 we published several papers from our ERC project. (1) We developed the best-performing classifiers to date that perform fully automatic prediction of the developmental potential of human embryos to reach blastulation and to implant within the uterus. This work, which has clinical implications on IVF treatments, was published: Kan-Tor et al. Adv Intell Syst-Ger 2, 2020. (2) We performed automatic morphokinetic annotation of human embryo preimplantation development using convolutional neural network. This work is currently in review and can be found here: 2022(öffnet in neuem Fenster). (3) Prediction of first term miscarriage using machine learning. This work is currently in review and can be found here: 2020(öffnet in neuem Fenster). During period-4 we encountered unforeseen circumstances due to the COVID-19 world pandemic, which have slowed down our progress. My team members were unable to conduct their work as expected since access to the campus was limited and interactions between team members reduced. We overcame this problem by kindly receiving a six-month extension to period-4.
We defined the viscoelastic properties of the ovarian cortex in which follicles reside and revealed that ovaries obtained from young (fertile) heifers are fourfold stiffer than ovaries from aged (infertile) cows that were sent to slaughter. We discovered that during maturation, the mechanical properties of the oocytes changes from a solid-like (SLS model) to a fluid-like (Burger’s model) viscoelastic material. Next, we revealed a distinctive mechanical response of the ooplasm to applied load. Ooplasm resistance to applied load was associated with increased developmental potential. Mechanistically, we identified the Arp-2/3 branching nucleator of actin filaments at the ooplasm cortex to be responsible for providing mechanical integrity. We generalized our studies of bovine oocytes and generalized it to bovine zygotes. We also demonstrated the validity of our findings using a seasonal model of hot-season low quality oocytes versus winter oocytes. During the period-4 we published several papers from our ERC project. (1) We developed the best-performing classifiers to date that perform fully automatic prediction of the developmental potential of human embryos to reach blastulation and to implant within the uterus. This work, which has clinical implications on IVF treatments, was published: Kan-Tor et al. Adv Intell Syst-Ger 2, 2020. (2) We performed automatic morphokinetic annotation of human embryo preimplantation development using convolutional neural network. This work is currently in review and can be found here: 2022(öffnet in neuem Fenster). (3) Prediction of first term miscarriage using machine learning. This work is currently in review and can be found here: 2020(öffnet in neuem Fenster). During period-4 we encountered unforeseen circumstances due to the COVID-19 world pandemic, which have slowed down our progress. My team members were unable to conduct their work as expected since access to the campus was limited and interactions between team members reduced. We overcame this problem by kindly receiving a six-month extension to period-4.
Developing the MechanoPLATE:
We have designed a new device-based technology termed the MechanoPLATE. It that facilitate continuous assessment of the viscoelastic properties of oocytes during in vitro maturation and embryos during preimplantation development. Using this device, mechanical assessment of embryo quality can be performed in parallel to time-lapse recording of preimplantation development which allow further evaluation of the developmental quality using machine learning-based classifiers. These methods are non-invasive and are performed inside incubators that maintain optimal culture conditions that are used for example in IVF clinics.
Oocyte and embryo mechanics:
Using the MechanoPLATE, we tested the role of whole-oocyte and embryo mechanics in predicting developmental potential but unfortunately it proved not to correlate with functional parameters. However, we further investigated and found discovered that the deformation dynamics of the ooplasm, which is the internal cellular mass of the oocytes and the embryos, responds to applied forces in a very interesting fashion. Low quality oocytes/embryos demonstrate a continuous deformation starting from the time at which forces were applied. However, high quality oocytes/embryos resist the applied loads for a few seconds before yielding. We further studied the mechanisms that are responsible for this interesting phenomenon. We found that the cortical actin cytoskeleton, and specifically a branching initiator of actin filaments termed the Aep-2/3 complex, was responsible for rendering mechanical resilience to the ooplasm of high quality oocytes/embryos.
Assessing embryo quality using machine learning:
One of our project's main effort was to develop machine-learning based algorithms that will be able to improve IVF treatments by evaluating the developmental potential of embryos during preimplantation stages based on their continuous visual appearance. To this end, we assembled the a large dataset of time-lapse imaging of human embryos that we collected from four medical centers in Israel. Each embryo is clinically labeled and the transferred embryos are annotated by outcome. Using this expansive database, consisting of ~70,000 embryos, we developed machine-learning algorithms for improving IVF treatments as described below.
Assessing the potential of embryo to implant in the uterus:
We harnessed advanced deep learning tools to generate fully automated embryo classification algorithms. One algorithm utilizes the "raw" images of the embryo to assess embryo quality. A second algorithm is used to perform fully automated annotation of how the embryos develop (this is termed morphokinetic annotation). Using these classifiers, we predict the potential of each embryo to implant within the uterus and generate pregnancy - a property which most embryos fail to perform. Our algorithms are more accurate than existing classifiers, and provide a standardized fully automated evaluation of the developmental potential of each embryo.
Predicting 1st trimester miscarriage
First trimester miscarriage is a major clinical problem accounting for ~one out of nine pregnancies. To date, there are available options for assessing the potential of an embryo during preimplantation development before it is selected to be miscarried. Hence, we trained a machine-learning model to predict the potential of the transferred embryo to reach live birth or to undergo 1st trimester miscarriage. This task was supported by the fact that our database is sufficiently large to include transferred embryos that were miscarried and to learn differential visual markers as compared with live embryos that reached live birth.
Impact on IVF treatments:
With the support of the ERC, we developed both mechanical tools and computational algorithms that assess multiple developmental functions of preimplanted embryos. Furthermore, we developed a device that can combine these tasks. The expected impact of this work is to serve as decision-making algorithms that will allow the clinical staff to better select which embryos optimize live birth rates and to select these embryos for transfer. This advancement is relevant not only to human IVF treatments and to fertility preservation treatments, but also to cattle IVF procedures that underlie the dairy industry and are targeting existing unmet needs.
We have designed a new device-based technology termed the MechanoPLATE. It that facilitate continuous assessment of the viscoelastic properties of oocytes during in vitro maturation and embryos during preimplantation development. Using this device, mechanical assessment of embryo quality can be performed in parallel to time-lapse recording of preimplantation development which allow further evaluation of the developmental quality using machine learning-based classifiers. These methods are non-invasive and are performed inside incubators that maintain optimal culture conditions that are used for example in IVF clinics.
Oocyte and embryo mechanics:
Using the MechanoPLATE, we tested the role of whole-oocyte and embryo mechanics in predicting developmental potential but unfortunately it proved not to correlate with functional parameters. However, we further investigated and found discovered that the deformation dynamics of the ooplasm, which is the internal cellular mass of the oocytes and the embryos, responds to applied forces in a very interesting fashion. Low quality oocytes/embryos demonstrate a continuous deformation starting from the time at which forces were applied. However, high quality oocytes/embryos resist the applied loads for a few seconds before yielding. We further studied the mechanisms that are responsible for this interesting phenomenon. We found that the cortical actin cytoskeleton, and specifically a branching initiator of actin filaments termed the Aep-2/3 complex, was responsible for rendering mechanical resilience to the ooplasm of high quality oocytes/embryos.
Assessing embryo quality using machine learning:
One of our project's main effort was to develop machine-learning based algorithms that will be able to improve IVF treatments by evaluating the developmental potential of embryos during preimplantation stages based on their continuous visual appearance. To this end, we assembled the a large dataset of time-lapse imaging of human embryos that we collected from four medical centers in Israel. Each embryo is clinically labeled and the transferred embryos are annotated by outcome. Using this expansive database, consisting of ~70,000 embryos, we developed machine-learning algorithms for improving IVF treatments as described below.
Assessing the potential of embryo to implant in the uterus:
We harnessed advanced deep learning tools to generate fully automated embryo classification algorithms. One algorithm utilizes the "raw" images of the embryo to assess embryo quality. A second algorithm is used to perform fully automated annotation of how the embryos develop (this is termed morphokinetic annotation). Using these classifiers, we predict the potential of each embryo to implant within the uterus and generate pregnancy - a property which most embryos fail to perform. Our algorithms are more accurate than existing classifiers, and provide a standardized fully automated evaluation of the developmental potential of each embryo.
Predicting 1st trimester miscarriage
First trimester miscarriage is a major clinical problem accounting for ~one out of nine pregnancies. To date, there are available options for assessing the potential of an embryo during preimplantation development before it is selected to be miscarried. Hence, we trained a machine-learning model to predict the potential of the transferred embryo to reach live birth or to undergo 1st trimester miscarriage. This task was supported by the fact that our database is sufficiently large to include transferred embryos that were miscarried and to learn differential visual markers as compared with live embryos that reached live birth.
Impact on IVF treatments:
With the support of the ERC, we developed both mechanical tools and computational algorithms that assess multiple developmental functions of preimplanted embryos. Furthermore, we developed a device that can combine these tasks. The expected impact of this work is to serve as decision-making algorithms that will allow the clinical staff to better select which embryos optimize live birth rates and to select these embryos for transfer. This advancement is relevant not only to human IVF treatments and to fertility preservation treatments, but also to cattle IVF procedures that underlie the dairy industry and are targeting existing unmet needs.
Our project highlights a mechanobiological perspective of mammalian reproductive biology. Our approach is “beyond the state of the art” in the field. We developed novel devices and instruments that allow real time mechanical evaluation of oocytes and embryos and provided a proof of concept that demonstrates how the developmental potential of oocytes and embryos can be evaluated based on their physical properties in a noninvasive manner. This approach is compatible with microscopic visualization of preimplantation embryo development. Hence, we also generated deep learning tools to evaluate the potential of embryos to generate a pregnancy. We identified specific mechanical markers that are indicative of high developmental quality. The clinical implications of this project are far-reaching. Using our tools, clinicians will be able to select the embryos that will implant in the uterus and generate a live birth. In this manner, IVF treatments will be shorter and safer both for the newborn and for the mother. The financial costs that these tools save can then be invested in other important medical treatments.