Fig 1.
Appearance of Granulosa Cells (GC) and Thecal Cells (TC) cultured under serum-free conditions for 48, 96 and 144 hours.
Fig 2.
Unsupervised PCA of granulosa cell (A) and theca interna (B) arrays. The graphs are scatter plots of the values for the first (X) and second (Y) principal components based on the correlation matrix of the total normalised array intensity data. The numbering of each sample enables the samples in the figure to be identified in S1 and S2 Figs. Abbreviations are GC (granulosa cell), TH (theca interna), CC (cultured under control conditions), CF (cultured and FSH treated), CL (cultured and LH treated) and V (in vivo freshly isolated cells).
Table 1.
Top 50 genes that were most differentially up regulated in vitro in granulosa cells.
Table 2.
Top 50 genes that were most differentially down regulated in vitro in granulosa cells.
Fig 3.
Top canonical pathways in granulosa cells mapped in IPA (A) and GO terms (B) classified under biological process. In (A) the bar chart on the left represents the percentage of genes from the data set that map to each canonical pathway showing those which are up regulated (in red) and down regulated (in blue) in vitro with respect to in vivo. The line chart on the right ranks these pathways derived for the same data set, from the highest to lowest degree of association based on multiple correction testing for the Benjamini-Hochberg False Discovery Rate. In (B) the bar chart on the left represents the percentage of genes from the data set that map to each GO term showing those which are differentially regulated (in blue) in vitro with respect to in vivo. The line chart on the right ranks these pathways derived for the same data set, from the highest to lowest degree of association using the Benjamini-Yuketeli test for multiple corrections (bottom to top in graphs on right).
Fig 4.
Regulator effect network analysis in IPA identifying altered regulators and networks in granulosa cells cultured in vitro.
Orange and blue are predicted up and down regulated regulators on top, and the pink and green represent up and down regulated genes from the array data with the intensity of colour reflecting the degree of change. The pathways altered are listed at the bottom and orange indicates they are up regulated in vitro.
Table 3.
Top 50 genes that were most differentially up regulated in vitro in thecal cells.
Table 4.
Top 50 genes that were most differentially down regulated in vitro in thecal cells.
Fig 5.
Top canonical pathways in thecal cells mapped in IPA (A) and GO terms (B) classified under biological process. In (A) on the left, the percentage of genes from the data set that map to each canonical pathway which are up regulated (in red) and down regulated (in blue) in vitro with respect to in vivo are shown. On the right, these pathways are then ranked from the highest to lowest degree of association based on multiple correction testing for the Benjamini-Hochberg False Discovery Rate (A). In (B) on the left, the percentage of genes from the data set that map to each GO term which are differentially regulated (in blue) in vitro with respect to in vivo are shown. These pathways are ranked from the highest to lowest degree of association using the Benjamini-Yuketeli test for multiple corrections (bottom to top in graphs on right).
Fig 6.
Regulator effect network analysis in IPA identifying altered regulators and networks in thecal cells cultured in vitro.
Orange and blue are predicted up and down regulated regulators on top, and the pink and green represent up and down regulated genes from the array data with the intensity of colour reflecting the degree of change. The pathways altered are listed at the bottom and orange indicates they are up regulated in vitro.