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. Author manuscript; available in PMC: 2016 Jul 1.
Published in final edited form as: Dev Biol. 2015 Apr 16;403(1):69–79. doi: 10.1016/j.ydbio.2015.04.006

Follicle dynamics and global organization in the intact mouse ovary

Mehlika Faire 1,*, Amanda Skillern 1,*, Ripla Arora 1, Daniel H Nguyen 1, Jason Wong 2, Chester Chamberlain 2, Michael S German 2, Jennifer C Fung 1, Diana J Laird 1,#
PMCID: PMC4469539  NIHMSID: NIHMS681889  PMID: 25889274

Abstract

Quantitative analysis of tissues and organs can reveal large-scale patterning as well as the impact of perturbations and aging on biological architecture. Here we develop tools for imaging of single cells in intact organs and computational approaches to assess spatial relationships in 3D. In the mouse ovary, we use nuclear volume of the oocyte to read out quiescence or growth of oocyte-somatic cell units known as follicles. This in-ovary quantification of non-growing follicle dynamics from neonate to adult fits a mathematical function, which corroborates the model of fixed oocyte reserve. Mapping approaches show that radial organization of folliculogenesis established in the newborn ovary is preserved through adulthood. By contrast, inter-follicle clustering increases during aging with different dynamics depending on size. These broadly applicable tools can reveal high dimensional phenotypes and age-related architectural changes in other organs. In the adult mouse pancreas, we find stochastic radial organization of the islets of Langerhans but evidence for localized interactions among the smallest islets.

Keywords: whole tissue labeling, confocal imaging, ovary, primordial follicle, pancreas, islet of Langerhans

INTRODUCTION

Recent advances in biological imaging and 3D analysis enable visualization of entire embryos and organs at subcellular resolution. The synthesis of microscopic detail at a macroscopic level is revealing features of tissue architecture such as cell orientation and cell shape as they relate to global properties such as morphogenesis (Le Garrec et al., 2013; Veeman and Smith, 2012). Comparison of such phenotypes across development, genetic backgrounds and different environments will begin to link tissue architecture to mechanism. Technical obstacles to wholemount imaging include penetration of the tissue with fixative and fluorescent labeling agents, optical clearing, and digital selection of the structures of interest in 3D reconstructed images. Algorithms have been developed for wholemount analysis of branching structures such as the kidney glomeruli (Short et al., 2014), but not for discrete units such as ovarian follicles.

Study of the ovary has been limited by information that can be obtained from histologic sections and the associated labor-intensive processing. Female reproductive lifespan and ovarian function depend upon the reserve oocyte population. Oocyte-somatic cell complexes termed primordial follicles (PFs) are established perinatally in mice with the encapsulation of a meiotically-arrested oocyte by an epithelial layer of granulosa cells (Pepling, 2001). Activation of cohorts of PFs for growth begins immediately following their formation, with the proliferation of granulosa cells and concomitant expansion of the oocyte. Although it remains unclear whether oocyte or granulosa cells initiate growth (Hirshfield, 1992), morphometric analyses of mice and humans have revealed an increase in the diameter of the oocyte as well as the oocyte nucleus accompanying PF activation and early growth (Moore et al, 1974; Westergaard et al, 2007). Folliculogenesis progresses through primary, secondary and pre-antral stages and, before puberty, culminates in follicle demise; thereafter, endocrine signals cyclically rescue further follicle growth and promote meiotic resumption and ovulation (McGee and Hsueh, 2000). When the number of remaining PFs falls below a critical threshold, ovulation ceases and menopause ensues (Gosden et al., 1983; Richardson et al., 1987). Exhaustion of the ovarian reserve depends upon multiple factors: initial endowment of follicles, rate of their loss, and rate of follicle activation (Nelson et al., 2013). Despite the importance of the quiescent PF reserve to fertility and ovarian function, clinical assessment is indirect, using growing antral follicle counts and levels of hormones produced by the pituitary and granulosa cells as proxies (Rosen et al., 2012). Experimentally, the number of PFs is estimated in adults by manual counting in selected histologic sections and applying a multiplication constant (Bristol-Gould et al., 2006a; Bristol-Gould et al., 2006b).

Here we developed methodology to image oocytes in the intact mouse ovary using wholemount immunofluorescence. We use oocyte nuclear volume to distinguish non-growing from growing follicles and obtain absolute counts as well as early follicle growth dynamics. We develop approaches for spatial analysis and extend previous observations on the global patterning of quiescent and activated zones, which is established in neonatal ovaries; although this architecture endures throughout life, we find localized clustering of primordial and early growing follicles that increases in magnitude during aging. These approaches can be extended to other organ systems with a variety of markers, as we demonstrate in the adult mouse pancreas by imaging the islets of Langerhans.

RESULTS

Identification of primordial and growing follicles by nuclear volume in intact ovaries

We established permeabilization and optical clearing parameters in wholemount mouse ovaries using DNA stains. We then screened oocyte-specific antibodies using these conditions. Nuclear antigens were preferred for their resolvability in 3D imaging, and the previously established expansion of the oocyte nucleus with early follicle growth suggested that nuclear volume could be used to distinguish non-growing from growing follicles (Moore et al, 1974; Fig. S1). In wholemount ovaries at postnatal day one (PD1), immunolabeling with the oocyte-specific transcription factor Nobox appeared variable, with larger and more robustly stained objects deepest in the ovary (Fig. 1A); this staining pattern was consistent with the reported onset of Nobox expression in late fetal oocytes (Suzumori et al., 2002; Rajkovic et al., 2004). A different distribution was observed at PD1 with the marker Germ Cell Nuclear Antigen (GCNA), which labels pre-diplotene oocytes at the periphery of the neonatal ovary (Enders and May, 1994; Kerr et al., 2006): a shell of GCNA+ cells surrounded a concentration of Nobox+ cells at the center of PD1 ovaries (Fig. 1B). By PD5, the majority of oocytes are incorporated in follicles (Pepling, 2006) and accordingly we observed NOBOX immunofluorescence more uniformly throughout the ovary, with individually-labeled cells readily separable in 3D reconstructions of confocal stacks (Fig. 1C, Fig. S2A). Thus wholemount staining of the neonatal ovary recapitulates the temporal and spatial dynamics of a nuclear marker of meiotic prophase marker (GCNA) and the Nobox homeobox transcription factor, which is required for the postnatal differentiation of oocytes and folliculogenesis (Rajkovic et al, 2004).

Figure 1.

Figure 1

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Oocyte nuclear markers enable spatial resolution of follicles in wholemount neonatal mouse ovaries. Extended projections of Nobox immunostaining in an intact PD1 ovary (A). Optical z stack slices through a PD1 ovary stained with Nobox (red) and GCNA (blue) show surface localization of the GCNA+ cells and exclusive Nobox+ cells in the core (B). Extended z-stack projection of Nobox staining in an intact PD5 ovary (C). Selection of Nobox+ objects is indicated by separate colors, inset. Frequency distribution of Nobox volumes compared with GCNA at PD1 (D) shows cutoff for PFs at 3000 µm3. Comparison of Nobox frequency distributions reveals a stepwise increase of largest object sizes from birth to PD5 to PD7 by the lengthening of the right tail, and a concomitant loss of smallest objects (E). Scale bars represent 150 µm.

Volumes of objects defined by NOBOX and GCNA immunostaining were examined in frequency distribution. At PD1, near the onset of follicle formation (Pepling et al, 2006), comparison of these profiles reveals similar range of sizes but smaller average volume of Nobox+ objects (Fig. 1D); this difference, together with the shoulder in Nobox distribution above 1500 µm3 may reflect the earliest wave of NOBOX expression. As only oogonial cysts and PFs are present at PD1 (Pepling, 2001), we used this timepoint to set an upper limit for detectable Nobox volumes. A threshold of 3000 µm3 for PF nuclei was rationalized, as this includes >99.5% of both GCNA and Nobox volumes at PD1. Measurements of oocyte nuclear volume in PFs from thick histologic sections revealed a similar distribution: a mean of 1169 µm3 and 2604 µm3 representing 3 standard deviations from that mean (Fig. S1C,D). Discrepancies between nuclear volumes obtained in wholemount and section may arise from differences in fixation conditions, partial loss of nuclei in sections, as well as z-axis distortion inherent to confocal imaging (Murray et al, 2011). When examined at 1 week of age, the smoothness of Nobox frequency distributions supports the notion that immediate and continuous follicle growth follows PF formation (Fig. 1E). Elongation of the right tail >4500 µm3 of the distribution from PD5 to PD7 likely represents an increase in follicles recruited for growth, whereas the dramatic loss of small Nobox+ objects probably reflects more oocyte death than recruitment.

Wholemount staining in pre-pubertal and adult ovaries revealed greater size variation in Nobox+ objects than in neonates (Fig. 2A–C, Fig. S2B,C, Supplemental Movie 1). Nuclear volumes reached 40,000 µm3, with overlapping frequency distributions between bilateral ovaries of individuals (Fig. S3A). Similar smoothness and shapes of the Nobox distributions at PD21 as compared to adults (Fig. S3B) suggest that early stages of follicle growth proceed with similar kinetics before and after puberty. Decrease in the tail of the distribution by 24 weeks (Fig. 2D) suggests a slackening in folliculogenesis that accompanies middle and advanced reproductive age in mice (McGee and Hsueh, 2000). Following superovulation at PD21, we observed a dramatic increase in ovary size and appearance of large antral follicles (Supplementary movies 2–3). Nobox profiles revealed an increase in the largest objects (30,000–40,000 µm3; Fig. S4), possibly reflecting the surge in antral follicles (McGee and Hsueh, 2000). One week following treatment with the alkylating chemotherapeutic agent cyclophosphamide, ovaries from 6 week-old mice retained 37% of Nobox+ objects, with the greatest loss in the volumes over 10,000 µm3 (Fig. 2E, S3C). This upholds a recent demonstration that cyclophosphamide directly destroys growing follicles, especially secondary stages, and causes compensatory recruitment of PFs (Titus et al, 2013). Together these observations suggest that oocyte nuclear volume identified by 3D immunofluorescence can distinguish primordial from growing follicles using a cutoff of ~3,000 µm3; a threshold of ~10,000 µm3 may separate primary from secondary follicles (although the comparatively dimness of larger and deeper objects makes their detection less reliable than small Nobox+ nuclei).

Figure 2.

Figure 2

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WM imaging of oocyte nuclei in prepubertal and adult ovaries reveals similar growth dynamics. In 3D reconstruction of a PD21 ovary (A), a range of object sizes is visible and similarly at 6-weeks (B) nonspecific staining occurs in vasculature of the hylem (arrowhead). Nobox penetrates the adult ovary, as shown in optical z stack slices at 24 weeks (C). Nobox profiles are largely similar between ovaries of young (12 week) and old (24 week) adults (D). One week following cyclophosphamide treatment, the most significant losses occur in large Nobox+ objects (>10,000 µm3), although small objects are also decreased. Arrows in all graphs indicate size cutoffs at 3000 and 10,000 µm3 (E). Scale bars represent 150 µm in panels A–B, and 600 µm in C.

Quantification of PFs in wholemount ovaries across aging

Using the above criteria, we determined PF counts from oocyte nuclear volumes in ovaries from PD5 through 6 months (Fig. 3A). The most acute loss and largest variation in small Nobox+ objects occur between PD5 and PD7 (Fig. 3B), consistent with reported neonatal death during the period of follicle formation in the mouse (Pepling, 2012). Increased variation in the number of Nobox+ objects detected in neonates compared to adults possibly reflects this period of accelerated follicle loss. The absolute number of small Nobox+ objects drops again in early sexual maturity (5–6 weeks), and thereafter decreases more gradually, while the proportion of medium and large objects increases until 3 months and declines at 6 months (Fig. 3B inset). To model the PF population mathematically, small Nobox data were fitted to a curve. As shown in Figure 3C, the best fit was a power function in which the number of PFs decreases indirectly proportionally to the square root of time (t−0.5). This function predicts a PF endowment of 9400 at t=1 and accordingly, we identified 8,235 cells at PD1 that expressed either Nobox or GCNA (Fig. S5). Alternatively, if t=1 is defined as embryonic day 14 (E14), which corresponds to the maximal number of oocytes at the time of meiotic entry, this function predicts an original endowment of 15,700-- close to prior estimates of ~15,000 germ cells per ovary (Kerr et al., 2013). A logarithmic function also fits the data for small Nobox+ objects, however this intercepts 0 before 8 months, which is inconsistent with observed fertility of female mice until ~1 year of age.

Figure 3.

Figure 3

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Quantification of primordial follicles in WM mouse ovaries is consistent with a finite ovarian reserve. A, Nobox-positive objects in juvenile and adult ovaries are tabulated and represented graphically (B), with relative proportion of each size class in the inset. Volume cutoffs for small, medium and large classes of objects are shown. C, The mean number of small Nobox+ objects, representing PFs, is plotted versus time and fit to a logarithmic (grey) and power function (black). D, Examination of PD7 ovaries with the proliferation marker pHH3 (blue) reveals very rare colabeling with Nobox (red; 5/17,972 in n=6 ovaries). Scale bar, 20 µm.

Although these results corroborate the fixed ovarian reserve model, it remains unresolved whether postnatal oocyte renewal can occur in rare instances (Kerr et al, 2006; White et al., 2012). As wholemount imaging is ideal for infrequent cellular events, we examined Nobox in conjunction with markers of proliferation. Following pulses at E14.5 and E16.5 with thymidine analog, we did not observe incorporation within Nobox+ objects by PD7 (n=4). Immunostaining revealed colocalization of phospho-histone H3 (pHH3) with Nobox in 5 cells across 6 ovaries at PD7 (Fig. 3D, Fig. S6, Supplemental Movie 4). Reliable EdU and pHH3 detection was not achieved with later ovaries. Together these results most strongly support a static ovarian reserve but raise the possibility of rare oocyte proliferation in neonates.

Radial measurements show that global follicle organization is established in neonatal ovaries and persists in adults

To assess spatial organization of follicle quiescence and maturation, we measured radial 3D distances from each Nobox+ object to either the geometric center or to the surface of the ovary; this surface was mapped using the most peripheral objects detected. At PD1, 70% of Nobox+ objects (and all of the largest volumes) localized within 200 µm of the ovary centroid, whereas 70% of GCNA+ objects were excluded from this region (Fig. S5), suggesting that oocyte maturation occurs in the interior of the organ, either deep in the cortex or in the medullary region. The architecture established by PD5 remains through adulthood: most Nobox+ objects resided within 100–200 µm of the ovary surface (Fig. 4A–H) and we generally observed a positive correlation between size and depth of objects (Fig. S7; the converse was found when measured from the centroid, except in irregularly-shaped adult ovaries). Thus, consistent with observations in histologic sections (Fig. S8A,B; Byskov et al., 1997, Da Silva-Buttkus et al, 2009), 3D measurements show that PFs concentrate near the surface, while follicle growth initiates at greater cortical depths. Interestingly, medium-sized Nobox+ objects accumulate in a swath within 50–150 µm of the ovary surface from PD5 through 24 weeks (brown in Fig. 4B,D,F,H). Centroid measurements revealed absence of NOBOX from a region <50 µm from the core at PD5 (Fig. S8C) and up to 200 µm in juvenile and adult ovaries (Fig. S8D–F), and this region may correspond to the medulla. These 3D models capture an outside-in organization of follicle development that begins after birth in mice and continues throughout reproductive life; they further point to a potential radial zone of initial follicle growth at 50–150 µm depth.

Figure 4.

Figure 4

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Follicle spatial dynamics in neonatal ovaries persist through adulthood. Location of small Nobox+ objects (<3000 µm3, gold), medium-sized (3000–10,000 µm3, brown), and large (>10,000 µm3, turquoise) are shown in 3D opacity projections of ovaries at PD5 (A), 3 weeks (C), 6 weeks (E), and 24 weeks (G). For each ovary, measurements from the center of each Nobox object to the ovary surface (B, D, F, H) are shown with respect to object volume. At all stages, small oocyte nuclei (gold) skew toward the surface, whereas medium sized nuclei most frequently reside 50–150 µm from the surface. Median depths for each size class are indicated by grey bars. Exemplary aggregations of objects in 3D are indicated in panels E and G by arrowheads of the corresponding color. Scale bars represent approximately 150 µm.

Point pattern analysis shows increased clustering of follicles with age

Despite continuity in radial distribution of oocytes within the ovary from birth through aging, 3D reconstructions revealed size-dependent differences in the spacing of objects. Whereas neonatal ovaries appeared homogenous (Fig. 4A) and juveniles regular (Fig. 4C), aggregations of Nobox objects can be seen by 6 weeks of age (Fig. 4E,G). We measured spatial homogeneity using Ripley’s K function, a technique for point pattern analysis, which quantifies the degree of object clustering or dispersion as compared to a random distribution. At PD5 and PD7 Nobox object distributions did not deviate from randomness (Fig. S9). From 3 through 24 weeks, significant reorganization imposes an increasingly clustered distribution affecting all oocytes detected regardless of size (Fig. 5A–C). During this period, however, the dynamics of aggregation differed by object size. Large objects were initially randomized at 3–6 weeks before becoming as similarly clustered as smaller objects at 24 weeks (Fig. 5C–F). In contrast, these smaller Nobox+ objects were already aggregated in juvenile ovaries (3–6 weeks). Thus spatial statistics indicate that the magnitude of localized interactions between nongrowing follicles increases over reproductive lifespan and furthermore suggest that processes acting on the large class of growing follicles change with age.

Figure 5.

Figure 5

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Spatial analysis of follicular clustering with age and follicle size. The distribution of differently-sized Nobox objects was assessed using Ripley’s K-function to identify second-order properties. The K(d)−E(K(d)) curves depict deviations of the follicle distribution (K(d)) from randomness (E(K(d)) over a given distance (d) between Nobox+ object centroids. Positive values indicate clustering while negative values indicate dispersion. The change in distribution of small, medium, and large oocyte nuclei (A, B, C) was evaluated as ovaries matured from 1–24 weeks. Objects of all sizes displayed an increasing trend towards clustering with age. The comparative distributions of differently-sized follicles at a given stage was also investigated at 3, 6, and 24 weeks (D, E, F). Large Nobox+ objects are comparatively more dispersed than medium and small through 6 weeks of age, but become similarly aggregated by 24 weeks. Maximum diameters for primordial, primary and secondary follicles are indicated by arrowheads for comparison.

Analysis of the pancreas shows random radial organization and size-specific interactions between islets of Langerhans

We extended these imaging and analysis approaches to discrete structures in other organs. In the adult mouse pancreas, we immunolabeled endocrine β-cells of the islets of Langerhans with the lineage-specific transcription factor Nkx6.1+ (Sander et al, 2000). Low magnification permitted reassembly of the entire pancreas and coalescence of juxtaposed β–cells into a single object (Fig. 6A). Resulting Nkx6.1+ objects were co-labeled with anti-insulin antibody (data not shown) and 3D-selected in the dorsal (n=1511) and ventral pancreas (n=565). Volumes obtained spanned more than 4 orders of magnitude and exhibited similar frequency distribution profiles between dorsal and ventral pancreas (Fig. 6B). An additional parameter of shape factor provided a metric of 3D sphericity of Nkx6.1+ objects, and revealed a trend toward decreasing sphericity with increasing volume (Fig. 6C). Radial distance measurements from a surface defined by the most peripheral Nkx6.1+ objects revealed random depth with respect to volume (Fig. 6D). Finally, we examined distances between object centroids of small (4,000–20,000 µm3), medium (20,000-106 µm3), and large volumes (>106 µm3), as defined by breaks in the frequency distribution in Fig. 6B; by Ripley’s K function, small and medium objects exhibited greater departure from spatial randomness as compared to large (Fig. 6E). A ratio of the measured K(d) compared to an expected stochastic distribution E[K(d)] revealed maximal deviation at 40–75 µm for small objects, which exceeds the minimum packing distance of this size class (2r = 34 µm; Fig. 6F). Together these point pattern analyses suggest random dispersal of large islets and localized interactions between the smallest detectable aggregations of β–cells, which may reflect their proximity to ducts (Pictet and Rutter, 1972). Consistent with prior observations (Miller et al, 2009) our 3D shape analysis suggests an elongation of islets accompanies their growth.

Figure 6.

Figure 6

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3D analysis of islets of Langerhans in the adult mouse pancreas. Islets were visualized and selected in the pancreas by Nkx6.1 wholemount staining, as shown in extended projection (A). Nkx6.1 volume profiles are similar between dorsal and ventral pancreas (B), with breaks between small, medium and large classes shown by arrows. Distribution of Nkx6.1 object volume versus 3D shape factor (a measure of sphericity) shows a similar trend toward elongation with increasing size for both dorsal and ventral pancreas (C). Radial distance analysis of Nkx6.1+ objects from a surface defined by the same objects reveals an absence of correlation between volume and depth (D). Clustering analysis of Nkx6.1+ objects in the entire pancreas finds no difference between the largest class and random distribution, but a departure of the small and medium object relationships K(d) from the expected random distribution E(K(d)) (E). The ratio of these distributions reveals a maximal clustering behavior of small objects at a distance of 40–80 µm, which exceeds the maximal inter-centroid distance of 34 µm (grey arrowhead); a smaller magnitude of attraction occurs between medium size Nkx6.1 objects at a distance between their minimum (grey arrowhead) and maximum (green arrowhead) adjacent distance (F). Scale bar represents 1 mm.

DISCUSSION

Here we developed tools for wholemount imaging and 3D spatial analysis of the mouse ovary and adult pancreas. This enables more rapid quantification of ovarian follicles and islets of Langerhans than was previously possible by serial histologic sectioning. Furthermore, the visualization and positional analysis of these structures globally and with respect to one another provides quantitative bases for previously qualitative observations as well as revealing new insights.

At two different biological scales, we exploit volume to differentiate stage of development. In the ovary, this is predicated upon the observation that the cross-sectional area of the mouse and human oocyte nucleus scales with the growth of the entire follicle from primordial to antral stages (Moore et al, 1974; Westergaard et al, 2007). Uridine incorporation studies suggest that RNA synthesis in the oocyte underlies this nuclear expansion (Moore et al, 1974), however more recent work raises the possibility of contribution from coincident epigenetic changes. Genome-wide DNA methylation occurs postnatally in mouse oocytes, and methylation marks at imprinted loci are acquired between primary and secondary follicle stages (Tomizawa et al., 2012). Chromatin remodeling accompanies the burst of transcription in the early growing oocyte that ceases by the antral follicle stage, and may contribute to nuclear expansion. Interestingly, in Xenopus oocytes, nuclear size does not scale with ploidy, as previously thought, but depends upon the rate of import into the nucleus (Levy and Heald, 2010). Linking these observations is the possibility that nuclear expansion in the growing oocyte results from the influx of transcription factors and epigenetic modifiers as well as changing chromatin structure associated with meiosis and transcription. Despite established synchronization between follicle structure and expansion of the entire oocyte, it remains to be determined whether growth is initiated by oocyte or granulosa cells (Hirshfield, 1992). In this sense, the current method of analysis by oocyte nuclear volume does not precisely read out the state of follicle growth; on the other hand, such wholemount quantitative imaging of oocytes and granulosa cells simultaneously may resolve this question. At a different scale, in the pancreas we exploit the volume of β-cell clusters to analyze islet growth. Our observations that islet elongation increases with volume and that small islets are increasingly aggregated compared to large islets do not distinguish between the possibilities of islet fission and fusion (Miller et al, 2009). This question could be addressed with a combination of quantitative wholemount imaging and inducible lineage tracing.

Using this higher-throughput methodology to quantify non-growing follicles with greater precision, we find adequate endowment at birth and maintenance through middle age. A power function fits these data and predicts ~500 PFs remaining at 1 year, matching previous estimates (Bristol-Gould et al, 2006b). Given the prevailing belief that reproductive senescence in mice and menopause in humans occurs at a critical but non-zero PF threshold rather than complete exhaustion (Gosden et al., 1983; Richardson et al., 1987), the power function may be biologically relevant for describing the decline of the ovarian reserve. Wholemount ovary imaging with cell death markers will facilitate identification of mechanisms of oocyte loss such as atresia that may contribute to premature ovarian insufficiency (Nelson et al., 2013). Although many studies in mice including this one corroborate a non-renewing ovarian reserve (Begum et al., 2008, Zhang et al., 2012, Lei and Spradling, 2013), the origins and fate of rare proliferating postnatal oocytes observed (1 per ovary at PD7) and previously described (Kerr et al., 2006, White et al., 2012) can be further explored in wholemount. Understanding the biological underpinnings of such events could be informative to regenerative medicine.

Dynamic and spatial information from intact ovary imaging may yield insights into mechanisms underlying the activation or quiescence of follicles. We observed striking similarity between size profiles of oocyte nuclei in prepubertal, young and aging adult ovaries. Despite variation across ages in the rate of recruitment and early growth of follicles (and hence rightward movement on frequency distributions), identical shapes of these distributions could imply identical processes, irrespective of hormones present in adults. Previous work suggests that growing follicles inhibit the recruitment of PFs (Durlinger et al., 1999; Mizunuma et al., 1999). Consistent with this idea, the observed decline in PFs after chemotherapeutic ablation of growing follicles likely represents compensatory recruitment. Alternatively, the release of growth-inhibiting signals by PFs themselves was posited by a recent spatial analysis of neonatal ovaries, in addition to the secretion of growth-promoting factors from early growing follicles (Da Silva-Buttkus et al., 2009). Accordingly, we find that the smallest oocytes concentrate near the ovary surface, whereas larger, growing oocytes avoid the ~50 µm outer radius. Perhaps more importantly, we observe aggregations of small oocytes by puberty that become discrete islands in adults. These behaviors conform to the secreted growth inhibitor and activator predictions of Hardy and colleagues (Da Silva-Buttkus et al., 2009) and furthermore raise the possibility that localized interactions maintain a threshold of PFs through aging, perhaps in a quorum sensing mechanism (Bristol-Gould et al., 2006b; Tingen et al., 2009). Importantly, the extent of early follicle movement within the ovary is poorly understood, and such spatial models which predict the range of secreted factors can only assume stasis; however, the study of PF migration with live imaging of intact or sliced ovaries will make inroads to this question. By contrast, the absence of radial organization of islets in the pancreas reflects different biological constraints. Finally, our spatial analyses reveal discrepancies between pubertal and adult ovarian follicle activation that may reflect separate follicle origins. Consistent with the view that follicle formation in the medulla precedes that in the cortex (Byskov et al., 1997), we find Nobox in the largest oocytes at the center of the PD1 ovary. This pattern persists with the largest growing oocytes lying deep within the 3-week ovary; these likely represent the first wave of medullary follicles (Hirshfield, 1992), recently shown to derive from a separate population of granulosa cells (Mork et al., 2012; Zheng et al., 2014). Although rescue of medullary follicles by superovulation and in vitro fertilization confirms their functional capability, the biological significance of this first wave of follicles destined to die before adulthood remains to be determined.

In conclusion, wholemount imaging increases the throughput and accuracy of counting PFs in the ovary and can be applied to discrete structures across a range of sizes and organs. The resulting capability for quantitative phenotyping will advance characterization of mutants and enhance understanding of follicle pool dynamics over lifetime as related to genetic background, ovarian aging, and exogenous agents.

MATERIALS AND METHODS

Mice

All animal protocols were reviewed and approved by IACUC policies of University of California, San Francisco. Mice were maintained on a mixed genetic background comprising 50% C57BL/6 and 50% CD1 in an AAALAC approved facility. Chemotherapy consisted of a single intraperitoneal injection of 75 mg/kg cyclophosphamide given at 5 weeks of age, followed by euthanasia 1 week later. Superovulation was carried out at PD21 by intraperitoneal injection of 50 units of PMSG (Harbor-UCLA Research and Education Institute), followed by euthanasia 2 days later.

Immunofluorescence

Ovaries for sectioning were fixed in 4% paraformaldehyde overnight, cryoprotected in 30% sucrose, embedded and cryosectioned in OCT at 5 µm or 25 µm. Sections were washed, blocked with 1× phosphate buffered saline (PBS)/0.1% Triton X-100/5% goat serum, and incubated overnight in primary antibody. Following primary incubation, slides were washed with 1× PBS/0.1% Triton, incubated with secondary antibody, and mounted on slides in Vectashield (Vector Labs).

Ovaries for wholemount immunostaining were carefully removed from the animal and surrounding bursa under the dissecting microscope, fixed in 4:1 Methanol:dimethylsulfoxide (DMSO) and stored at −20°C overnight or longer. Subsequent processing steps were carried out in 0.5 or 1.5 mL eppendorf tubes while rocking. Prior to staining, ovaries were washed with 50% Methanol/1× PBS for 30 minutes at room temperature, then blocked with 3 one-hour consecutive washes of blocking solution (1× PBS/1% Triton/10% goat serum) at room temperature. Ovaries were incubated with primary antibody overnight at 4°C, washed with blocking buffer 3 × 1 hour and incubated with secondary antibody overnight at 4°C. Secondary antibody was removed and ovaries were serially dehydrated through increasing methanol concentrations (25%, 50%, 75%, 100%) for 1 hour at each. For PD1s, PD5s, and PD7s, tissues were cleared in Benzyl Alcohol:Benzoyl Benzoate (BABB,1:2) for minimum of 6 hours at room temperature. Ovaries PD21 and older were incubated in 3% H2O2 in methanol overnight at 4°C, followed by 2 × 1 hour 100% methanol washes, and stored in BABB overnight at room temperature. All tissues were cleared in 10 mm long glass cylinders (ACE Glass 3865-10) mounted onto coverslips (Fisherfinest Premium coverglass cat: 12-548-5P). Primary antibodies used were as follows: GCNA, a gift from George Enders (undiluted), pHH3 (1:100, Sigma H9908), Nobox (1:50 Abcam 41521), Vasa (RnD AF2030) and stains included DAPI (1:500, Roche 236276), Hoechst (1:100 Life Technologies H3569), and Topro (1:100, Invitrogen T3605). Alexa-555 and 633 secondary antibodies were purchased from Invitrogen and used at 1:200.

Pancreas was harvested from 12 week old adult mice which had been heart perfused with 4% paraformaldehyde (PFA). Pancreas was then incubated in 4% PFA over night, washed and dehydrated in methanol before processing as described above. To allow full antibody penetration, the membrane surrounding the pancreas was removed and the pancreas was vigorously shaken in large volumes throughout antibody incubations (20 mL/pancreas) and washes (45 mL/pancreas). Blocking solution was 1× PBS/0.5% Triton/5% donkey and Nkx6.1 primary antibody was used at 1:250 (Sigma HPA036774).

Image acquisition

Optical stacks were collected through the ovary using inverted Leica TCS SP5 confocal microscope with a pulsed white light laser. A 10× objective was used for PD21 and older ovaries and pancreas, while 20× objective was used for PD1, PD5, and PD7 ovaries, both at 1024 × 1024 pixel resolution. Stacks were acquired at system optimized z steps between optical sections (0.33 µm at 20× and 1.98 µm at 10×). Adult ovaries were tile-scanned in 3–4 stacks of approximately 300 images each and pancreas was reconstructed from 12 stacks. Gain and offset were manually adjusted during the scan to maximize the contrast between the stained objects and surrounding tissue.

Image processing and object selection

Image stacks were visualized in Volocity 6.2 (Improvision) by extended projection of the z-axis and 3D opacity projections, which renders dark voxels transparent. Processing included noise removal (fine filter) and contrast adjustment. Objects of interest were selected by signal intensity threshold, determined as the dimmest visible oocyte using the voxel spy function, typically ≥3 standard deviations above the mean intensity in the entire stack. Noise and fragmented objects were excluded by volume cutoff; for ovaries imaged with the 10× objective, minimum volume was 180–300 µm3, while 50 µm3 was used at PD1–7 owing to the higher resolution of the 20× objective. Volume thresholds were vetted in control tissues stained with secondary antibody. Selected objects were manually inspected by scrolling through the stack. Object data and xyz coordinates were exported to Excel for analysis. For double immunostaining, the intersection of objects was determined by (1) selecting the primary marked population (i.e. Nobox); (2) establishing intensity threshold for the dimmest objects identified by the second marker, as the minimum number of standard deviations from the mean intensity value (e.g. 3 for GCNA, 63 for pHH3); (3) excluding Nobox+ objects with signal in the second channel below this threshold; (4) applying a second size cutoff to the double positive objects (50 µm3 for pHH3); (5) manual inspection by scrolling through Z stacks to verify overlap of both channels. In the pancreas, Nkx6.1+ objects were selected by automatic threshold using an offset of −75% and minimum object size 4952 µm3. Subsequent operations included separate touching objects (size guide of 15,000,000 µm3), fill holes, and separate touching objects (size guide of 2 E +08 µm3).

Oocyte spatial analysis

Analysis of follicle distances from the centroid and ovary surface was performed using a custom MATLAB program (script is provided in the Supplemental Methods). For each dataset consisting of xyz coordinates and volumes of every Nobox+ object, the centroid of the entire object set was calculated by averaging x, y and z coordinate values for all oocytes. This reference point was then used to calculate the distance from the centroid of each oocyte to the ovary centroid. To probe the surface of the ovary, the smallest polygonal surface containing all the oocytes was generated by first calculating a 3D Delaunay triangulation of the complete set of follicle coordinates and then extracting the convex hull surface using the built-in convexHull() function. The distance of each oocyte from the surface was calculated by determining the distance from the Nobox object centroid to each facet of the polygonal convex hull and taking the minimum distance. Correlation coefficients were then calculated between the distances to either the surface or centroid and the follicle volume using MATLAB’s corrcoef() function.

Clustering analysis was performed on Nobox object coordinates using RipleyGUI (Hansson et al, 2013) to calculate Ripley’s K function in three dimensions. The K function describes second-order properties of a spatial point process to evaluate how events are distributed in relation to each other. Briefly, the K function is expressed as the number of objects within a distance d of an arbitrary object divided by the object density of the corresponding sampled volume with radius d. To assess the degree of clustering for a follicle distribution, we calculated the difference between the experimental K score (K(d)) and an expected K score (E(K(d)) for a Poisson distribution following complete spatial randomness. Clustering is indicated by a K(d)>E(K(d)). K functions were determined at one-micron intervals (d=1) to a maximum of 200 µm. Comparisons between groups were performed with bootstrapped K(d)−E(K(d)) values.

Statistics

Frequency distributions and graphs were generated in Excel. Statistical tests including Student’s T test, ANOVA, and correlation coefficients were performed in Excel, MATLAB and Prism; comparison of Ripley K functions using the between-treatments sum of squares was performed in RipleyGUI.

Supplementary Material

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Highlights.

  • Ovarian follicle growth state can be discerned in 3D imaging by oocyte nuclear volume

  • Wholemount imaging provides higher throughput method for counting follicles

  • Spatial analysis shows that radial organization of follicle growth is established at birth

  • Point pattern analysis of follicles in 3D finds increasing aggregation with age

  • Image analysis of the pancreas quantifies islets and shows size-dependent clustering

ACKNOWLEDGEMENTS

We thank Matthew Gormley, Paul Miller and David Castaneda for technical help and Marcelle Cedars, Marco Conti, Amy Laird, Walter Miller, Valerie Weaver and members of the Laird lab for useful discussions and critical feedback. Funding for this work was provided by NIH 1DP2OD007420 and the UCSF Research Allocation Program to D.J.L., NIH R01 GM097213 to J.C.F, California Institute of Regenerative Medicine Training Grant TB1-01194 to M.F. and TG2-01153 to R.A., NIH training grant T32 HD007263 to A.S. and an NSF Graduate Research Fellowship to D.N.

Footnotes

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Competing interests: none

AUTHOR CONTRIBUTIONS

A.S., D.J.L., and M.F. designed the experiments, A.S., M.F., R.A. C.C. and J.W. carried out the staining and imaging, D.H.N. carried out the clustering analysis, J.C.F. carried out surface and centroid modeling, D.J.L. and M.F. analyzed the data, made the figures and wrote the manuscript, all authors contributed to data interpretation.

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Supplementary Materials

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