Computational Approaches and Models for Ovarian Ageing: From 2D to 4D

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

Computer-Generated Ovaries to Assist Follicle Counting Experiments

Precise estimation of the number of follicles in ovaries is of key importance in the field of reproductive biology, both from a developmental point of view, where follicle numbers are determined at specific time points, as well as from a therapeutic perspective, determining the adverse effects of environmental toxins and cancer chemotherapeutics on the reproductive system. The two main factors affecting follicle number estimates are the sampling method and the variation in follicle numbers within animals of the same strain, due to biological variability. This study aims at assessing the effect of these two factors, when estimating ovarian follicle numbers of neonatal mice. We developed computer algorithms, which generate models of neonatal mouse ovaries (simulated ovaries), with characteristics derived from experimental measurements already available in the published literature. The simulated ovaries are used to reproduce in-silico counting experiments based on unbiased stereological techniques; the proposed approach provides the necessary number of ovaries and sampling frequency to be used in the experiments given a specific biological variability and a desirable degree of accuracy. The simulated ovary is a novel, versatile tool which can be used in the planning phase of experiments to estimate the expected number of animals and workload, ensuring appropriate statistical power of the resulting measurements. Moreover, the idea of the simulated ovary can be applied to other organs made up of large numbers of individual functional units

Prominent microglial inclusions in transgenic mouse models of α-synucleinopathy that are distinct from neuronal lesions.

Alpha-synucleinopathies are a group of progressive neurodegenerative disorders, characterized by intracellular deposits of aggregated α-synuclein (αS). The clinical heterogeneity of these diseases is thought to be attributed to conformers (or strains) of αS but the contribution of inclusions in various cell types is unclear. The aim of the present work was to study αS conformers among different transgenic (TG) mouse models of α-synucleinopathies. To this end, four different TG mouse models were studied (Prnp-h[A53T]αS; Thy1-h[A53T]αS; Thy1-h[A30P]αS; Thy1-mαS) that overexpress human or murine αS and differed in their age-of-symptom onset and subsequent disease progression. Postmortem analysis of end-stage brains revealed robust neuronal αS pathology as evidenced by accumulation of αS serine 129 (p-αS) phosphorylation in the brainstem of all four TG mouse lines. Overall appearance of the pathology was similar and only modest differences were observed among additionally affected brain regions. To study αS conformers in these mice, we used pentameric formyl thiophene acetic acid (pFTAA), a fluorescent dye with amyloid conformation-dependent spectral properties. Unexpectedly, besides the neuronal αS pathology, we also found abundant pFTAA-positive inclusions in microglia of all four TG mouse lines. These microglial inclusions were also positive for Thioflavin S and showed immunoreactivity with antibodies recognizing the N-terminus of αS, but were largely p-αS-negative. In all four lines, spectral pFTAA analysis revealed conformational differences between microglia and neuronal inclusions but not among the different mouse models. Concomitant with neuronal lesions, microglial inclusions were already present at presymptomatic stages and could also be induced by seeded αS aggregation. Although nature and significance of microglial inclusions for human α-synucleinopathies remain to be clarified, the previously overlooked abundance of microglial inclusions in TG mouse models of α-synucleinopathy bears importance for mechanistic and preclinical-translational studies

Experimental evidence for temporal uncoupling of brain Aβ deposition and neurodegenerative sequelae

Brain A beta deposition is a key early event in the pathogenesis of Alzheimer ' s disease (AD), but the long presymptomatic phase and poor correlation between A beta deposition and clinical symptoms remain puzzling. To elucidate the dependency of downstream pathologies on A beta, we analyzed the trajectories of cerebral A beta accumulation, A beta seeding activity, and neurofilament light chain (NfL) in the CSF (a biomarker of neurodegeneration) in A beta-precursor protein transgenic mice. We find that A beta deposition increases linearly until it reaches an apparent plateau at a late age, while A beta seeding activity increases more rapidly and reaches a plateau earlier, coinciding with the onset of a robust increase of CSF NfL. Short-term inhibition of A beta generation in amyloid-laden mice reduced A beta deposition and associated glial changes, but failed to reduce A beta seeding activity, and CSF NfL continued to increase although at a slower pace. When short-term or long-term inhibition of A beta generation was started at pre-amyloid stages, CSF NfL did not increase despite some A beta deposition, microglial activation, and robust brain A beta seeding activity. A dissociation of A beta load and CSF NfL trajectories was also found in familial AD, consistent with the view that A beta aggregation is not kinetically coupled to neurotoxicity. Rather, neurodegeneration starts when A beta seeding activity is saturated and before A beta deposition reaches critical (half-maximal) levels, a phenomenon reminiscent of the two pathogenic phases in prion disease. The poor correlation between brain A beta deposition and clinical symptoms in Alzheimer ' s disease remains puzzling. Here, the authors show a temporal dissociation of A beta deposition and neurodegeneration

Ratios between oocyte and the follicle diameter, <i>R</i>_o-f.

<p>Ratios between oocyte and the follicle diameter, <i>R</i>_o-f.</p

Details of 3D illustration of a simulated ovary.

<p>Part of a day 8 simulated ovary, a) and its corresponding cross section, b). Primordial (red) and primary (green) follicles numbered respectively in both a) and b) are virtually sectioned. Yellow arrows point at follicles in the immediate vicinity of the numbered ones, albeit not appearing in the 2D section. Oocyte, nucleus and nucleolus have been added to the model for illustrative purposes, c). These structures can also be simulated based on real measurements, in order to be used in the stereological counting. d) shows an example of a secondary, a primary and two primordials (blue arrows) with all internal profiles visible (day 12 ovary).</p

Average follicle numbers in whole neonatal C57Bl/6 mouse ovaries.

<p>Average follicle numbers in whole neonatal mouse ovaries (mean ± standard error of the mean): data reported by Kerr <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120242#pone.0120242.ref012" target="_blank">12</a>]. 6 mice were used to estimate day 7 follicle numbers and 7 mice to estimate day 12 follicle numbers.</p><p>Average follicle numbers in whole neonatal C57Bl/6 mouse ovaries.</p