Figure S1.
Analysis of single-cell morphological features during quiescence entry, Cdc10 signal quantification algorithm workflow, TPML algorithm workflow, cluster number analysis, mitotic induction of the APC/C-Cdh1 activity sensor MET3p-mNeonGreen (mNG)-Ase1(R632-I885), and extended Spc42 foci quantification.(A) Comparison of morphological parameters between budded and unbudded Q-cells. (B) Algorithm to detect cell cycle progression based on the SD of Cdc10-mCyOFP1 fluorescence at the cell periphery or “Cdc10 Signal a.u.” (C) Generation of a machine learning algorithm to sort cells into clusters depending on the pattern of cell cycle arrest indicated by Cdc10. Left: Representative single-cell time series for Cdc10-mCyOFP1 signal (OAM421). (1) Time series are normalized per biological replicate, and their cell index is randomized. (2) Time series data are split into 50% training time series, 30% validation time series, and 20% test time series. (3) Training time series are used to establish different clustering models depending on parameters such as clustering method (k-means, k-medoids), time window (number of time points in time series), number of clusters, and distance metric. (4) Validation time series are labeled by experienced cell biologists who assign different cluster affiliations to each time series manually. (5) The preliminary clustering models are optimized by comparing the unsupervised clustering solutions to the human clustering solution using a multiclass Matthews’s coefficient. (6a) The best-performing clustering model is tested using the (6b) test time series. The best-performing algorithm is used to classify cells in future experiments. (D) Evaluation of the number of clusters using the silhouette method or by inspecting increasing number of clusters (k values). k = 3 is the minimum number of clusters that recapitulate the major cell cycle arrest patterns in the population. (E) Average time series for Cdc10 clusters in different S. cerevisiae strain backgrounds (Up, BY4743, OAM857; down, SK1, OAM856) bearing Cdc10-mCyOFP1. (F) Average time series in haploid (left, OAM846) and diploid (right, OAM840) strains bearing Cdc10-mCyOFP1 and Far1-mNeonGreen (n = 3; at least 400 cells per strain). (G) Percentage of each Cdc10 cluster in a representative population undergoing quiescence upon acute stress (n = 9; 1,600 cells, OAM425). (H) Percentage of Non-G1 Q-cells in three S. cerevisiae strain backgrounds (n = at least 4 biological replicates). (I) Induction of the APC/C-Cdh1 activity sensor, mNeonGreen-Ase1(R632-I885), upon depletion of methionine (MET) in rich medium (green arrowhead); OAM487 cells were aligned in silico according to their Cdc10 signal, and the average times series was plotted, Cdc10 (left) and APC/C-Cdh1 sensor (right). (J) Percentage of Non-G1 Q-cells displaying one or two Spc42-mTFP1 foci at starvation onset. (K) Percentage of Non-G1 Q-cells displaying a distance separation between Spc42-mTFP1 foci of 0 (single foci), <1.5 (S-G2), 1.5–4 (metaphase), or >4 (anaphase) µM. Solid lines with shaded area = average ± 95% confidence intervals. Box plots display data from biological replicates: central mark, median; box bottom and top limit, 25th and 75th percentiles; whiskers, most extreme nonoutlier values. Bar plots = mean + SD.

Analysis of single-cell morphological features during quiescence entry, Cdc10 signal quantification algorithm workflow, TPML algorithm workflow, cluster number analysis, mitotic induction of the APC/C-Cdh1 activity sensor MET3p-mNeonGreen (mNG)-Ase1(R632-I885), and extended Spc42 foci quantification. (A) Comparison of morphological parameters between budded and unbudded Q-cells. (B) Algorithm to detect cell cycle progression based on the SD of Cdc10-mCyOFP1 fluorescence at the cell periphery or “Cdc10 Signal a.u.” (C) Generation of a machine learning algorithm to sort cells into clusters depending on the pattern of cell cycle arrest indicated by Cdc10. Left: Representative single-cell time series for Cdc10-mCyOFP1 signal (OAM421). (1) Time series are normalized per biological replicate, and their cell index is randomized. (2) Time series data are split into 50% training time series, 30% validation time series, and 20% test time series. (3) Training time series are used to establish different clustering models depending on parameters such as clustering method (k-means, k-medoids), time window (number of time points in time series), number of clusters, and distance metric. (4) Validation time series are labeled by experienced cell biologists who assign different cluster affiliations to each time series manually. (5) The preliminary clustering models are optimized by comparing the unsupervised clustering solutions to the human clustering solution using a multiclass Matthews’s coefficient. (6a) The best-performing clustering model is tested using the (6b) test time series. The best-performing algorithm is used to classify cells in future experiments. (D) Evaluation of the number of clusters using the silhouette method or by inspecting increasing number of clusters (k values). k = 3 is the minimum number of clusters that recapitulate the major cell cycle arrest patterns in the population. (E) Average time series for Cdc10 clusters in different S. cerevisiae strain backgrounds (Up, BY4743, OAM857; down, SK1, OAM856) bearing Cdc10-mCyOFP1. (F) Average time series in haploid (left, OAM846) and diploid (right, OAM840) strains bearing Cdc10-mCyOFP1 and Far1-mNeonGreen (n = 3; at least 400 cells per strain). (G) Percentage of each Cdc10 cluster in a representative population undergoing quiescence upon acute stress (n = 9; 1,600 cells, OAM425). (H) Percentage of Non-G1 Q-cells in three S. cerevisiae strain backgrounds (n = at least 4 biological replicates). (I) Induction of the APC/C-Cdh1 activity sensor, mNeonGreen-Ase1(R632-I885), upon depletion of methionine (MET) in rich medium (green arrowhead); OAM487 cells were aligned in silico according to their Cdc10 signal, and the average times series was plotted, Cdc10 (left) and APC/C-Cdh1 sensor (right). (J) Percentage of Non-G1 Q-cells displaying one or two Spc42-mTFP1 foci at starvation onset. (K) Percentage of Non-G1 Q-cells displaying a distance separation between Spc42-mTFP1 foci of 0 (single foci), <1.5 (S-G2), 1.5–4 (metaphase), or >4 (anaphase) µM. Solid lines with shaded area = average ± 95% confidence intervals. Box plots display data from biological replicates: central mark, median; box bottom and top limit, 25th and 75th percentiles; whiskers, most extreme nonoutlier values. Bar plots = mean + SD.

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