Fig. 1

Methodology for quantifying G0 arrest in cancer. a Workflow for evaluating G0 arrest from RNA-seq data; 139 genes differentially expressed in multiple forms of quiescence were employed to score G0 arrest across cancer tissues. b Receiver operating characteristic (ROC) curves illustrating the performance of the Z-score methodology on separating actively proliferating and G0 arrested cells in seven single-cell (continuous curves) and bulk RNA-seq (dotted curves) datasets. AUC area under the curve. c Compared classification accuracies of the G0 arrest Z-score approach and classic cell proliferation markers across the seven single-cell/bulk RNA-seq validation datasets. d G0 arrest levels of embryonic fibroblast cells under serum starvation for various amounts of time. Replicates are depicted in the same colour. e Representative images of lung cancer cell lines immunostained and analysed to detect the G0 arrest fraction. Hoechst (labels all nuclei) is in blue, phospho-Rb in green and EdU in red in the merged image. White dashed circles highlight G0 arrested cells that are negative for both phospho-Rb and EdU signals. Scale bar: 100 µm. f Graphs show single-cell quantification of phospho-Rb and EdU intensities taken from images and used to define the cut-off to calculate the G0 arrest fraction (green boxes). Images in e and graphs in f are taken from the A549 cell line. g–h Correlation between theoretical estimates of a G0 or G1 state and the fraction of cells entering G0 arrest in nine lung adenocarcinoma cell lines, as assessed through g phospho-Rb assays and h EdU assays. Mean of n = 3 is shown for the average percentage of G0 arrested cells