error, indicating that this method can effectively eliminate incorrect traces. The analyses depicted in the figures in this manuscript were therefore performed using the trace removal feature of DCELLIQ. A potential cost of this trace removal feature, however, is that it will also tend to exclude abnormal mitotic divisions, potentially contributing to the underestimate of mitotic duration in the population. Supporting Information Figure S1 The choice of imaging frequency influences auto- mated and manual measurement of mitotic duration equally. HeLa H2B-GFP cells were imaged every 4 minutes for 24 hours and all images (4 min interval) or selected images (8 and 12 min intervals) were analyzed manually and with the automated approach. An example of changes in Area and average intensity for a nucleus are presented for 4, 8 and 12 min analysis (a, b and c, respectively). Mitotic entry and anaphase were determined using the corresponding nucleus images (arrows in a–d). (PDF) Figure S2 DCELLIQ detects dose-dependent increases in mitotic duration upon treatment with higher doses of nocodazole. HeLa cells expressing H2B-GFP were treated as indicated and imaged as in Figure 3. Image series were analyzed using DCELLIQ with the time series approach. Cumulative frequency curves of mitotic duration for the cell populations are provided. The number of events in each sample (N), event duration median and mean as well as p values for the Mann Whitney Wilcoxon statistical comparisons are provided. (PDF) Figure S3 The TSA approach detects dose-dependent increases in interphase duration upon treatment with higher doses of cycloheximide. HeLa H2B-GFP cells were treated with indicated concentrations of cycloheximide or untreated. Cumulative fre- quency curves of interphase duration for the cell populations are provided. The number of events in each sample (N), event duration median and mean as well as p-values for the Mann Whitney Wilcoxon statistical comparisons are provided. (PDF) Figure S4 Time Series Analysis algorithm. (a) Flow chart displaying the algorithm used to identify interphase-prophase transition (IPT) and metaphase-anaphase transition (MAT) points using only area and intensity features. See Methods for detailed description. (b) Example of a trace in which changes in intensity References 1. Kanda T, Sullivan KF, Wahl GM (1998) Histone-GFP fusion protein enables sensitive analysis of chromosome dynamics in living mammalian cells. Curr Biol 8: 377–385. 2. Jones JT, Myers JW, Ferrell JE, Meyer T (2004) Probing the precision of the mitotic clock with a live-cell fluorescent biosensor. Nat Biotechnol 22: 306–312. 3. Shi Q, King RW (2005) Chromosome nondisjunction yields tetraploid rather than aneuploid cells in human cell lines. Nature 437: 1038–1042. 4. Meraldi P, Draviam VM, Sorger PK (2004) Timing and checkpoints in the regulation of mitotic progression. Dev Cell 7: 45–60. 5. Gascoigne KE, Taylor SS (2008) Cancer cells display profound intra- and interline variation following prolonged exposure to antimitotic drugs. Cancer Cell 14: 111–122. 6. Harder N, Mora-Bermudez F, Godinez WJ, Wunsche A, Eils R, et al. (2009) Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time. Genome Res 19: 2113–2124. 7. Chen X, Zhou X, Wong ST (2006) Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans Biomed Eng 53: 762–766. 8. Gambe AE, Ono RM, Matsunaga S, Kutsuna N, Higaki T, et al. (2007) Development of a multistage classifier for a monitoring system of cell activity based on imaging of chromosomal dynamics. Cytometry A 71: 286–296. Automated Cell Cycle Analysis are used to identify the IPT. (c) Example of a trace in which changes in intensity are not sufficient to identify the IPT. In this instance, changes in area are used to identify the IPT. Parameters that can be modified by the user are indicated by a red number. The parameter order corresponds to the order they appear in the program input window. The parameters are: (1) Minimum area (A) threshold for primary identification of divisions in a trace (pixels); (2) Search window duration around local minima frames (included) in which to find area minima below the Area threshold; (3) Number of preceding frames in which to find the highest A value; (4) Intensity threshold above which to use the intensity data to identify the Interphase to Prophase Transition (IPT) frame (5) Change of area threshold for primary detection of entry into prophase (pixels); (6) Change of area threshold for refined detection of entry into prophase (% of previous frame), (7)Threshold number of frames between current frame and frame with lowest A (Fmin) for subsequent Area change analysis, (8) Change of area threshold for refined detection of entry into prophase (% of area from any subsequent frame until frame Fmin); (9) Change of area threshold for refined detection of entry into prophase (% of area from any subsequent frame until frame Fmin); (10) Number of subsequent frames in which to identify the frame with lowest area; (11) Change in area threshold for MAT frame decision (% of area from previous frame). (PDF) Figure S5 Correct and incorrect traces can be separated by a threshold in change in Area (dA). (a) Tracking accuracy of HeLa H2B-GFP traces (n = 173). Some traces contained multiple types of errors. (b) Cumulative percent of maximum slope of area (dA) for each nucleus tracked over all frames, manually categorized into correct and incorrect traces. Based on this analysis, 95% of correct traces contain a dA value less than 130. In contrast, 80% of incorrect traces contain a dA value greater than 130. By using this threshold, 80% of incorrect traces can be removed while removing only 5% of correct traces. (PDF) Author Contributions Conceived and designed the experiments: JFH FDS. Performed the experiments: FDS JFH. Analyzed the data: FDS JFH. Wrote the paper: FDS JFH RWK. Wrote the Time Series Analysis algorithm: JFH FDS. Integrated the algorithm in the DCellIQ platform: FL XZ. Contributed comments to the paper: STW. Managed the running of the project: STW RWK. 9. 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IEEE Trans Med Imaging. 15. Nath SK, Palaniappan K, Bunyak F (2006) Cell segmentation using coupled level sets and graph-vertex coloring. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 9: 101–108. PLoS ONE | www.plosone.org 13 September 2011 | Volume 6 | Issue 9 | e25511