experiments [20,21,22,23,24]. Our imaging software and hard- ware settings were optimized to keep fluorescence light exposure to a minimum. These conditions produce no difference in mitotic or interphase duration compared to cells imaged by phase contrast microscopy (data not shown). The fact that our method requires only a single exposure for each time point (a single image plane) also helps limit overall exposure compared to another method that uses confocal microscopy and requires multiple planes to be acquired for each time point [6]. Most automated image analysis platforms use SVM-based approaches to classify individual images [7,8,9,10,11,12,13,25]. Most SVM approaches, however, do not exploit how individual features change as a function of time in the initial step of classifying objects, potentially losing valuable information. Instead, time information is used afterwards to reconstruct how class member- ship of a nucleus changes as a function of time, based on input of allowed phase transitions. Furthermore, accuracy of SVM approaches may require high-resolution images that limit the number of cells that can be analyzed in a given experiment [6] or concomitant analysis of multiple markers, thereby increasing light exposure [12]. In our own experience, we have found that extensive training is required for SVM approaches to function efficiently. Training sets are very dependent on similar experi- mental conditions and may not be easily transferred from one cell line to another. Because TSA analyzes how extracted features change as a function of time, the approach is more tolerant of initial variation in the absolute values of these parameters between cells. For example, cells often vary in the amount of H2B-GFP protein that is expressed, leading to variation in fluorescence intensity. Because the time-series algorithm only measures how intensity changes as a function of time, the approach is less sensitive to variation in absolute levels of the reporter protein. In our approach, the only program parameters that need to be adjusted between cell lines and conditions include the frequency of imaging (depending on the minimum mitotic length that one needs to detect), and the average nuclear size. Other parameters that can be adjusted within the program include the area threshold for initial detection of a division even, the rate of intensity change required for detecting the interphase-prophase transition, and thresholds for rates of change in area. However, we found that these parameters did not need to be altered for the time-series approach to be able to detect changes in mitotic duration in another cell line. Analysis time becomes limiting when high-throughput imaging experiments are performed. Feature extraction is the rate-limiting step in our analysis platform. Our time-series algorithm requires only 2 features, both of which are in the geometric feature extraction category of DCELLIQ, leading to a total of 11 features extracted per nucleus. In contrast, the SVM approach uses features from multiple classes, meaning that in many cases all 211 features need to be extracted. Segmentation, tracking and feature extraction of the 11 geometric features required an average of 1.2 hours per movie. Similar analysis with all 211 features for SVM processing required an average of 3.4 hours per movie. Therefore, the time-series approach is almost three times as fast as the SVM-based approach when including time needed for feature extraction. The principal limitation of our approach is the selection bias that is imposed by the need to accurately track nuclei over long periods of time. We observed that the TSA can detect the IPT and MAT very accurately in nuclei that are successfully tracked by the program. We found that nuclei that were not successfully tracked showed a slightly longer average mitotic duration as compared to successfully tracked nuclei. However, despite this bias, DCELLIQ can successfully identify small perturbations in mitotic and Automated Cell Cycle Analysis interphase duration, because both tracked and non-tracked cell populations respond similarly to drug treatment. Thus, although DCELLIQ under-measures average mitotic duration, it accurately measures treatment effect size. Furthermore, reducing cell density, or imaging cells for a shorter period of time, will increase the fraction of cells that are accurately tracked and provide a more accurate measurement of mitotic duration in cases where such accuracy is paramount. Other groups have developed automated or semi-automated software packages for analysis of cell division. None available for download, however, provide the functionality or ease of use that we describe here. Some packages for analysis of phase contrast movies are not fully automated, requiring partial manual analysis [20,26]. Other software packages analyze cells expressing fluorescent markers such as H2B-GFP [9] and GFP-Histone1 [8], but no tracking function is reported by these groups. The software from Harder et al [6,25] comes closest to our package. However, their approach requires high magnification oil-objec- tives (40–636) and use of 3 to 5 confocal z-slice acquisitions, increasing light exposure and reducing the number of fields that can be imaged in a given experiment. Held et al. [12] also use an SVM approach to classify cells as interphase or sub-phases of mitosis, but the maximum duration of mitosis that is measured is 138 minutes, which may result in an underestimate of average mitotic duration under certain conditions. In contrast, our approach allows identification of mitotic events of longer duration, from 200 to 600 minutes, depending on imaging frequency. While Held et al. report high accuracy of their approach in determining mitotic duration, their manual analysis only included cells that were successfully tracked. Therefore, their method may be subject to the same type of selection bias that we report. Finally, DCELLIQ is the only automated analysis platform that can automatically determine interphase duration, as other methods do not track cells for a long enough period to be able to make this measurement. We conclude that, to our knowledge, our software package remains unique in terms of its ability to identify small changes in both mitotic and interphase duration using low fluorescence exposure imaging techniques in a platform that is convenient for the end user. We have shown that automated time-series analysis can be used to accurately measure mitotic and interphase duration with the need to extract far fewer features than needed with other methods. Our approach opens up new opportunities for time-lapse microscopy experiments that would otherwise be impossible to analyze due to the large amount of time necessary for manual analysis. Compared to fixed-cell analysis methods, automated analysis of time-lapse movies enables interphase and mitotic duration to be determined independently. Automated time-lapse analysis can therefore identify perturbations that affect both interphase and mitosis, which would be misinterpreted if a single measurement of mitotic index were performed on fixed cells. Materials and Methods Material Cycloheximide (Calbiochem, 239764) was resuspended to 10 mg/mL in milliQ water and 0.2 mm filter sterilized. Doxoru- bicin (Sigma, D1515) was resuspended to 10 mM in DMSO and 0.2 mm filter sterilized. Nocodazole (Sigma, M1404) was resus- pended to 10 mM in DMSO. Chemicals were aliquoted upon resuspension and stored frozen at 220uC. Cell Culture Parental HeLa, A549 cell lines were purchased from ATCC. HeLa cells stably expressing H2B-GFP, previously described [3] PLoS ONE | www.plosone.org 10 September 2011 | Volume 6 | Issue 9 | e25511