Granulosa Cell

Fixed ovaries were processed for microscopy and subsequently the entire ovary was sectioned at 8 µm. Every 5^th ovarian section was stained with haematoxylin and eosin for morphometric analysis. In order to prevent multiple counts of the same follicle, only those follicles with a visible oocyte nucleus were included. Since oocyte nuclei measured between 20–30 µm in diameter, counting every 5th section of the ovary ensured a distance of 40 µm between analysed sections, preventing multiple counts of the same ovarian follicle. Follicle classification based on characteristics proposed by Hirshfield & Midgley [66] (link) was as follows: type 1: primordial follicle, one layer of flattened granulosa cells surrounding the oocyte; type 2: primary follicle, one to fewer than two complete layers of cuboidal granulosa cells; type 3: secondary follicle, an oocyte surrounded by greater than one layer of cuboidal granulosa cells, with no visible antrum; type 4: antral follicle, an oocyte surrounded by multiple layers of cuboidal granulosa cells and containing one or more antral spaces, cumulus oophorus and theca layer may also have been evident. Total volume of each section was calculated (area of the section x thickness of the section) and follicle counts for each animal were corrected for the total volume of ovarian tissue counted. All follicle counts were then expressed as number of follicles counted per mm³ of ovarian tissue counted.

To minimize discrepancies due to variables such as sample preparation, hybridisation conditions, staining, or array lot, the raw expression data were normalised using RMA background correction (Robust Multi-array Average [75 (link)]) with quantile normalisation, log base 2 transformation and mean probe set summarisation with adjustment for GC content which were performed in Partek Genomics Suite Software version 6.5 (Partek Incorporated, St Louis, MO, USA). All samples sent for analysis passed all quality controls. The 15 arrays were analysed as part of a larger set of CEL files (which additionally included samples of granulosa cell RNA from 4 large follicles as discussed elsewhere [18 (link)]) uploaded to the Partek GS software program. Before statistical analysis, the data were first subjected to PCA [76 ] and hierarchical clustering analysis to compare the gene expression patterns of the arrays in terms of our classification. Hierarchical clustering was performed using the Euclidian algorithm for dissimilarity with average linkage. The expression data were analysed by ANOVA using method of moments estimation [77 (link)] with post-hoc step-up FDR test for multiple comparisons. The fold change in expression for each gene was based on the non log-transformed values after correction and normalisation. These differentially expressed genes were further annotated and classified based on the Gene Ontology (GO) consortium annotations from the GO Bos taurus database (2010/02/24) [78 (link)] using GOEAST (Gene Ontology Enrichment Analysis Software Toolkit; [79 (link)]). Expression data were also exported to Excel and used to generate size-frequency distributions of the coefficient of variation for each probe set for the two sets of follicles, healthy and atretic. The microarray CEL files, normalised data and experimental information have been deposited in the Gene Expression Omnibus (GEO) database under series record GSE39589.
Pathway analyses of differentially-expressed genes were conducted using IPA software (Redwood City, CA, USA). Network eligible molecules derived from these datasets were overlaid onto a global molecular network developed from information contained in the Ingenuity Knowledge Base. Networks of these molecules were then generated algorithmically based on their connectivity (Ingenuity Systems, 2005). The network score is based on the hypergeometric distribution and is calculated with the right-tailed Fisher’s Exact Test. The score is the negative log of this P value. Canonical pathway analysis identified the pathways from the IPA library of canonical pathways that were most significant to the dataset in terms of the ratio of the number of molecules that mapped to the pathway from the dataset and a right-tailed Fisher’s exact t-test to determine the probability that the molecules mapped to the pathway by chance alone. We also used IPA Upstream regulator analysis to identify upstream transcriptional regulators. Upstream regulators were predicted using a Fisher’s exact t-test to determine the probability that genes from the dataset correspond with targets which are known to be activated or inhibited by those molecules based on current knowledge in the Ingenuity database.

For cAMP stimulation a validated protocol was followed [27] (link). The COS-7/LHCGR, hGL5/LHCGR and granulosa cells were seeded in triplicate in 24-well plates for cAMP dose-response experiments and serum starved 12 hours before the experiments. Cells were stimulated using increasing doses of r-hLH, r-hCG, ex-hLH or ex-hCG as appropriate (ranging between 0.1 pM-1 mM) in the presence of 500 µM IBMX (Sigma-Aldrich). Total cAMP was measured after 3 hours incubation and the cAMP ED₅₀ values for hLH and hCG were calculated. A total of 4 independent experiments were performed.
To evaluate the kinetics of response to continuous exposure to gonadotropins, time-course experiments were performed. The COS-7/LHCGR and hGLC were stimulated using the cAMP equipotent ED₅₀ doses of r-hLH or r-hCG, for different times ranging between 5 minutes and 36 hours in the presence of IBMX. The intracellular cAMP was measured after each incubation. Cell viability was also evaluated [29] (link). A total of 3 independent experiments were performed.
The quantitative detection of cAMP was performed in triplicate by a competitive ELISA kit and the data were entered into a curve fitting software which extrapolates the cAMP concentration against a standard curve. The data were represented using a log regression analysis.

Paraffin-embedded ovaries were serially sectioned. The first section containing ovarian tissue was collected. Every six sections, another section was collected until 20 sections were collected. HE staining was performed to evaluate the morphology of follicles. The criteria for the classification of follicles are as follows: primordial follicle, the oocyte was enclosed by a layer of squamous granulosa cells; primary follicle, the oocyte was enclosed by cuboidal granulosa cells; secondary follicle, the oocyte was enclosed by 2 or more layers of granulosa cells; and antral follicle, an antrum cavity was present. We avoided recording the same follicle more than once.

Serial 8-μm sections were cut from paraffin-embedded adult ovaries. All follicles or oocytes in VASA immunostained ovarian sections were counted. Follicle types were classified according to the following structural characteristics. As described previously [67 (link)], the following follicle types were distinguished: primordial follicles (type 1 and type 2), primary follicles (type 3), secondary follicles (type 4 and type 5), and antral follicles (types 6 − 8).
Serial 5-μm sections were obtained from paraffin-embedded ovaries from 5 dpp and newborn mice. The sections were stained with haematoxylin, and the following categories of oocytes and follicles were counted in every fifth section: primordial follicles (oocytes approximately 20 μm in diameter surrounded by 3 − 5 flat pregranulosa cells each); oocytes in cysts (two or more oocytes with shared cytoplasm); and naked oocytes (oocytes without any granulosa cells). As described previously [68 (link)], the sum of these counts was multiplied by five to estimate the total number of oocytes and follicles in each ovary.

To evaluate the presence of HL60 cells, their proliferation, and graft vascularization, sections were stained with CD43, Ki-67, and CD31 using an automated IHC assay on the BenchMark ULTRA (Ventana Medical Systems Inc., USA). The proportion of vessel area was calculated using ImageJ software (version 1.53e, Wayne Rasband, NIH, USA). Additionally, rabbit anti-cleaved caspase-3 was employed for apoptosis detection. After deparaffinization by Histosafe (Yvsolab SA, Beerse, Belgium) and rehydration by isopropanol (Merck, Darmstadt, Germany) and water, endogenous peroxidase activity was inhibited by 30 min incubation with 0.3% H₂O₂ in demineralized water at room temperature. Then, the sections were demasked in citrate buffer and Triton X-100 (pH 6) for 75 min in a water bath at 98°C, and non-specific binding sites were blocked by incubation with normal bovine serum albumin (1%) for 30 min. The sections were then incubated overnight at 4°C with the primary antibody. Table I lists the antibodies, incubation conditions, and positive and negative control tissues. The dilution solution without any primary antibody was provided as a negative control. The slides were subsequently incubated for 60 min at RT with an anti-rabbit secondary antibody. After 15 min incubation with chromogen-substrate (diaminobenzidine, DAB), Mayer’s hematoxylin was used as a counterstain. The slides were digitized using a Pannoramic P250 Flash digital slide scanner (3DHISTECH Ltd.).
Proliferative follicles were defined as those with at least one granulosa cell stained positive for Ki67. Follicles were classified as positive for caspase-3 immunostaining if more than 50% of granulosa cells and/or the oocyte were positive.