Computational Approaches and Models for Ovarian Ageing: From 2D to 4D
Author(s)
Skodras, Angelos A.
Type
Thesis
Abstract
The theme of the work presented in this multi-disciplinary PhD is the
development of new computational tools and techniques to study and understand
spatio-temporal follicle growth in neonatal mouse ovaries. The female ovary is
endowed at birth with a finite, non-renewable supply of oocytes, each enclosed in a
layer of supporting somatic (granulosa) cells to form a quiescent follicle. From birth,
a steady trickle of follicles initiate growth to maintain a supply of mature oocytes for
regular ovulation. Disruption in the regulation of initiation of follicle growth can
result in various pathologies, such as premature ovarian failure and polycystic ovary
syndrome.
The mechanism of regulation of the initiation of follicle growth remains
unclear, but may involve inter-follicle signaling via paracrine growth factors. To
investigate this hypothesis, a new technique for quantifying and analyzing spatial
distributions of quiescent and growing follicles in the adult human has been
developed, as an extension of a novel technique previously developed in neonatal
mice in our laboratory. As in the mouse study, we have found evidence that in the
human ovary neighbouring quiescent follicles inhibit follicle growth, at a small range.
This approach has been further extended to cultured neonatal mouse ovaries, which in
vitro lack a systemic blood supply, to investigate the relative contributions of inter-follicle
paracrine signaling and endocrine growth factor/nutrient signaling to the
regulation of initiation of follicle growth.
Accurate counts of the numbers of follicles in ovaries are important for a wide
variety of studies of ovarian physiology, including investigating the effects of age,
toxins, chemotherapeutics, endocrine disruptors and specific genes (knock
out/transgenic studies) on follicle formation, endowment and development. Many
published studies use frequent sampling of a small number of ovaries (often as few as
three) to obtain estimates of the number of follicles. We have tested the validity of
this approach by generating 3D spherical simulated ovaries which contain realistic
numbers of follicles at different stages and which are realistically positioned within
these ovaries. The number and position of follicles is based on real biological data.
This model enables us to rapidly ‘virtually’ section the ovary in silico and obtain
computer-generated counts of the numbers of follicles in sections at different
frequencies, such as one every fifth section (1/5), 1/20 or 1/50. As we know precisely how many follicles each simulated ovary contains, we can compare the accuracy
using different sampling frequencies of varying numbers of ovaries. This has enabled
us to demonstrate that the error is smaller when infrequent sampling of a large
number of ovaries (≥8) is carried out, and that this actually involves analyzing fewer
sections overall. We have gone on to generate simulated ovaries from knockout mice,
with more or fewer follicles, and can predict how many ovaries are required to make
robust comparisons between knockout and control animals. This has shown that
biological variability contributes more to counting error than the method of sampling.
These simulated ovaries provide a unique resource to model large studies.
Currently follicle counts are obtained by fixing and serially sectioning ovaries,
and manually counting the follicles in sections. This is laborious and time-consuming.
Faster methods of obtaining follicle estimates are required. With the use of confocal
microscopy and immunohistochemistry for an oocyte-specific protein, we were able
to establish a protocol that allows us to image and computationally reconstruct a
whole neonatal mouse ovary in 3D. Follicle number can be estimated rapidly using a
stereologic method. The stereologic technique error was estimated using the simulated
ovary model, leading to the conclusion that the method can be safely used to obtain
rapid estimates of follicle number. The time required can be further reduced by using
image processing to detect the stained follicles on the sections. We have developed an
algorithmic technique that can instantaneously identify stained oocytes, count them,
and calculate their spatial distribution.
A fundamental unanswered question is whether follicles move in the ovary,
particularly as they grow. This question has arisen from the observation that small
follicles tend to be situated close to the ovarian surface, while large ones are closer to
the medulla. This question has implications for interfollicle signaling. We have
developed a protocol to image the ovary while in culture using timelapse confocal and
live lipid stains to visualize the follicles. Results show that small follicles are not
moving significantly over a period of 12h. This project can be extended in the future
with the use of transgenic mice for GFP tagging, to accurately monitor changes in
structures of interest within cultured ovaries.
development of new computational tools and techniques to study and understand
spatio-temporal follicle growth in neonatal mouse ovaries. The female ovary is
endowed at birth with a finite, non-renewable supply of oocytes, each enclosed in a
layer of supporting somatic (granulosa) cells to form a quiescent follicle. From birth,
a steady trickle of follicles initiate growth to maintain a supply of mature oocytes for
regular ovulation. Disruption in the regulation of initiation of follicle growth can
result in various pathologies, such as premature ovarian failure and polycystic ovary
syndrome.
The mechanism of regulation of the initiation of follicle growth remains
unclear, but may involve inter-follicle signaling via paracrine growth factors. To
investigate this hypothesis, a new technique for quantifying and analyzing spatial
distributions of quiescent and growing follicles in the adult human has been
developed, as an extension of a novel technique previously developed in neonatal
mice in our laboratory. As in the mouse study, we have found evidence that in the
human ovary neighbouring quiescent follicles inhibit follicle growth, at a small range.
This approach has been further extended to cultured neonatal mouse ovaries, which in
vitro lack a systemic blood supply, to investigate the relative contributions of inter-follicle
paracrine signaling and endocrine growth factor/nutrient signaling to the
regulation of initiation of follicle growth.
Accurate counts of the numbers of follicles in ovaries are important for a wide
variety of studies of ovarian physiology, including investigating the effects of age,
toxins, chemotherapeutics, endocrine disruptors and specific genes (knock
out/transgenic studies) on follicle formation, endowment and development. Many
published studies use frequent sampling of a small number of ovaries (often as few as
three) to obtain estimates of the number of follicles. We have tested the validity of
this approach by generating 3D spherical simulated ovaries which contain realistic
numbers of follicles at different stages and which are realistically positioned within
these ovaries. The number and position of follicles is based on real biological data.
This model enables us to rapidly ‘virtually’ section the ovary in silico and obtain
computer-generated counts of the numbers of follicles in sections at different
frequencies, such as one every fifth section (1/5), 1/20 or 1/50. As we know precisely how many follicles each simulated ovary contains, we can compare the accuracy
using different sampling frequencies of varying numbers of ovaries. This has enabled
us to demonstrate that the error is smaller when infrequent sampling of a large
number of ovaries (≥8) is carried out, and that this actually involves analyzing fewer
sections overall. We have gone on to generate simulated ovaries from knockout mice,
with more or fewer follicles, and can predict how many ovaries are required to make
robust comparisons between knockout and control animals. This has shown that
biological variability contributes more to counting error than the method of sampling.
These simulated ovaries provide a unique resource to model large studies.
Currently follicle counts are obtained by fixing and serially sectioning ovaries,
and manually counting the follicles in sections. This is laborious and time-consuming.
Faster methods of obtaining follicle estimates are required. With the use of confocal
microscopy and immunohistochemistry for an oocyte-specific protein, we were able
to establish a protocol that allows us to image and computationally reconstruct a
whole neonatal mouse ovary in 3D. Follicle number can be estimated rapidly using a
stereologic method. The stereologic technique error was estimated using the simulated
ovary model, leading to the conclusion that the method can be safely used to obtain
rapid estimates of follicle number. The time required can be further reduced by using
image processing to detect the stained follicles on the sections. We have developed an
algorithmic technique that can instantaneously identify stained oocytes, count them,
and calculate their spatial distribution.
A fundamental unanswered question is whether follicles move in the ovary,
particularly as they grow. This question has arisen from the observation that small
follicles tend to be situated close to the ovarian surface, while large ones are closer to
the medulla. This question has implications for interfollicle signaling. We have
developed a protocol to image the ovary while in culture using timelapse confocal and
live lipid stains to visualize the follicles. Results show that small follicles are not
moving significantly over a period of 12h. This project can be extended in the future
with the use of transgenic mice for GFP tagging, to accurately monitor changes in
structures of interest within cultured ovaries.
Date Issued
2010
Date Awarded
2010-08
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Hardy, Kate
Franks, Stephen
Stark, Jaroslav
Publisher Department
Surgery and Cancer
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)