Figure 4 from Epigenetic Heritability of Cell Plasticity Drives Cancer Drug Resistance through a One-to-Many Genotype-to-Phenotype Paradigm

Transcriptional programs show plasticity after drug administration. A–D, UMAP of the 37,000 cells in the experiment after quality check filtering, colored, respectively, by experimental stage, drug, barcode, and cell-cycle phase. Cells for which a valid barcode could not be extracted or those with an abundance of less than 1% are shown in gray. Cells in the drug phase tend to strongly cluster by drug, whereas they tend to mix back with the parental cells during regrowth. E, Z-score distribution of adult colonic cell type markers from ref. 70 shows the presence of distinct differentiation programs inside the organoid. F, AA aims at decomposing the input dataset as a convex combination of extreme points by learning two matrices, A and B, which are representative of the archetype weights for each point in the dataset and the matrix that defines the archetypes starting from the input dataset, respectively. Here, we use a deep learning implementation of AA. G, We then exploit the weights of matrix A to quantify differences in transcriptional programs across conditions and genotypes. The Z-score distribution for the same genes as in E is computed by archetype. H, Archetype weight distribution over UMAP. I, Average archetype weight for different selected barcodes. The trend is consistent with cells going back to the parental phenotype after regrowth. Barcode colors are consistent with Fig. 2.