Image quantification was performed in CellProfiler, using custom-developed pipelines and MATLAB. Briefly, for each bioreactor, final image thresholds were manually set to reflect similar staining trends. These settings were conserved for each well of the bioreactor. For each well, nuclei were identified. The cell body was then approximated by use of a watershed around the cell nuclei to score non-nuclear staining. Each nucleus was then used to score its mean intensity of WT1 or GATA3, while its cell boundary was used to score its ECAD values. These values were then normalized by the mean intensity of the nuclear staining. Next, each cell was classified on the basis of manually set gating for each of the three markers used and were applied across each bioreactor. CellProfiler analyst density plots were generated to assist with thresholding. These values were set using several test images from random wells and bioreactor-wide histogram information. Cell classification images were then generated by combining the three binary maps generated by CellProfiler into a single image in MATLAB with GATA3+, WT1+, and ECAD+, represented by the colors green, red, and blue, respectively, as shown in Fig. 5 and fig. S7. This entire process is outlined in fig. S4.

This allowed each well to be scored on the basis of percentage of positive and double-positive cells. Larger aggregates were also scored in a similar fashion, based on a larger size along with an image blur step. This was done for the WT1-positive clusters and the GATA3 clusters independently. This allowed for larger structures to be identified and investigated for cell number and size. All identification steps were validated and checked by the output of the cell or structure outline overlaid on the original image.

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