Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning
Fig 6
Oncogene-driven morphological alterations in organoids.
A) Representative organoid images from each cluster with transgene and dyes indicated. B) 2-dimensional Sammon projection of feature space for all organoids, C) clustering result and D) heat map of proportion of transgene expressing MCF10A organoids in each cluster. E) Clustering after extraction of 2D morphological features results in low information separation of the data. Morphological features assessed included 2D Area, Major Axis Length, Minor Axis Length, Eccentricity, Equivalent Diameter, Solidity, Extent, Convex Area, and Perimeter from maximum-projected 2D regions. As above, affinity propagation was used to clustering the 2D morphological features. Phindr3D methods for determining meaningful cluster numbers by affinity propagation clustering were used as in panels a-c) and resulted in six distinct clusters for the 2D data. However, clusters 2 and 3 and to a lesser extent cluster 1 contained the majority of organoids while only PIK3CAH1047R-expressing organoids formed a distinct cluster (cluster 6). F) After 508 3D texture features were calculated from the nuclear and organoid areas identified using the 3D segmentation algorithm in CellProfiler 3.0.0, the data were clustered by affinity propagation using the Phindr3D method for determining meaningful cluster numbers. Clustering again resulted in six distinct clusters, with clusters 2, 3, 4 and 6 containing large proportions of the organoids. As in D) PIK3CAH1047R-expressing organoids formed a distinct cluster (cluster 5). Empty Vector-expressing organoids were present in all clusters except cluster 3 in similar proportions. G) Computation time for 3D feature extraction was measured on a high performance laptop computer. Feature extraction alone required ~2 hours with Phindr3D and ~8 hours with CellProfiler. In addition, CellProfiler required reformatting the data files to TIFF stacks, which took ~3 hours. H) The utility of the extracted features for clustering to identify unique morphologies within the data was quantified using mutual information (MI). Low MI (i.e. values closer to 0) indicates that clustering using 2D morphological features results poorly predicted allocation of organoids while high MI indicates high probability that clustering using 3D features accurately reflects real differences in the data for images of oncogene-expressing organoids driving allocation to specific clusters. For Sammon plot (B,C): colors indicate transgenes (from D-F), each data point represents one 3D image stack, cluster centers are shown as pie charts indicating the proportion of treatment points within each cluster. The diameter of the pie charts represents the number of data points in that cluster with lines linking data points to cluster.