images acquired in multiple planes, limiting the duration or throughput of experiments [6]. Furthermore, current analysis methods depend on supervised learning, such as support-vector machine (SVM)-based image classification [6,7,8,9,10,11,12], which is computationally intensive, requires extensive training, and may not be robust when applied across different cell lines or under changing experimental conditions. Our goal in this study was to develop a fully automated method that could measure changes in interphase and mitotic duration using simple wide-field fluorescence imaging. Because most cultured cells have a cell cycle duration of more than 18 hours, we utilized a single-plane, wide-field (206) fluorescence imaging approach that enables long-term imaging of cells. Based on these imaging parameters, we developed a time-series approach to determine cell cycle phase duration, which does not require a training data set, and is computationally rapid. The software is integrated into a complete analysis platform that is publicly available. We show that this approach can accurately determine small changes in mitosis or interphase duration induced by a variety of different perturbations. Results Identification of image features that correlate with mitotic transitions DCELLIQ (Dynamic Cell Image Quantitator) is a computer package that we have developed for automated analysis of time- lapse movies of cells expressing the fluorescent nuclear marker H2B-GFP [10,11,13,14]. This program automatically segments the images and identifies nuclei by local adaptive thresholding and seeded watershed segmentation with fragment merging [7,11]. This process yields a binary image that represents the location of each nucleus, designating the region for subsequent feature extraction. Nuclei are then tracked from frame to frame by finding best matches for each nucleus based upon area, grey value histogram, XY displacement, speed, direction, shape similarity and Delaunay triangulation [7,14,15]. A trace is then defined as a single nucleus tracked over time, with each trace including only one daughter cell when divisions occur (Figure 1a). In our earlier approach, features were extracted and cell cycle phase determined for each image in a trace using an SVM approach [11]. Although the SVM-based approach identified mitotic and interphase cells with high accuracy in movies on which the SVM was trained, the approach did not work as well when applied to new movies and required developing a new training dataset for each new cell line analyzed. We therefore wanted to develop an alternative approach for determination of interphase and mitotic duration that did not require retraining for each new experiment. Because nuclei undergo dramatic changes in morphology as cells enter and exit mitosis, we reasoned that a time-series based approach should allow accurately identifying key transition points. In this approach, the goal was not to individually classify each object in each image, but instead to identify key transition points based on how features of each traced object changed as a function of time. To determine the duration of interphase and mitosis, it is necessary to identify the frame at which a cell enters mitosis (the interphase-prophase transition or IPT), and the frame at which a cell exits mitosis (defined in this case as the metaphase-anaphase transition, or MAT). These transition points were chosen because they represent the two key steps in mitotic progression that are regulated by the cell cycle machinery. We first determined which features showed reproducible and dramatic changes near these transition points. Of 211 features that can be extracted by DCELLIQ [11], only changes in area (A) and average intensity (I) Automated Cell Cycle Analysis were reproducibly associated with the IPT and MAT (Figure 1b). A typically decreased at the IPT, reaching a local minimum near the MAT. In contrast, I typically increased rapidly near the IPT. We developed an algorithm that allowed us to first identify the IPT, and subsequently the MAT, based solely on how A and I changed over time in a given trace (see details in the Methods section and Figure S4). Mitotic duration was defined as the time between the IPT and MAT, whereas interphase duration was defined as the time between MAT of the first division in a trace and the IPT of the next division for the same trace. Determination of optimal imaging frequency A major advantage of automating the analysis of live cell imaging experiments is that one can test many more conditions in one experiment than if the data were manually analyzed. An important consideration in such experiments is the frequency of imaging. More frequent imaging provides better temporal resolution, but fewer positions can be analyzed at once, and thus fewer conditions can be tested in a given experiment. We therefore assessed how changing the frequency of image acquisition influences the ability of the time-series method to accurately measure mitotic duration as compared to manual analysis. We imaged HeLa H2B-GFP cells with an imaging interval of 4 minutes for 24 hours. This short imaging interval enables many images to be captured during mitosis (typically 12–15 during the one hour period of mitosis). Under these conditions, manual and automated analysis measured identical median mitotic durations of 52 minutes, with the automated method measuring a slightly shorter mean mitotic duration (55.4 minutes for automated analysis versus 59.1 minutes for manual analysis; Figure 2). This difference was not statistically significant (p = 0.21), but we explore the basis for this trend below. To model what would happen when the imaging frequency is decreased, we repeated manual and automated analysis on the same movies, but used every other frame or every third frame to reflect imaging intervals of 8 minutes or 12 minutes, respectively. In this case, the mean and median mitotic duration increased as imaging frequency decreased. Because manual and automated analysis provided very similar results, the change in imaging frequency, rather than the analysis method, must be responsible for the difference. We determined that the increase in measured duration results from the fact that the interphase-prophase transition point is chosen as the last frame of interphase when early prophase is not imaged. As the imaging interval increases, the likelihood that early prophase will be missed increases, leading to a slight over- measurement of sample median and mean (See Figure S1 for example). Thus there is an inherent trade-off between imaging frequency and accuracy of measurement of mitotic duration. However, this trade-off is inherent to time-lapse imaging approaches, and is not a consequence of implementation of the automated analysis method per se. Based on this analysis we used frequent imaging (4 minutes) for perturbations that were expected to shorten mitotic duration, or less frequent imaging (12 minutes) for cases that were predicted to extend mitotic duration. TSA can identify small increases in mitotic duration We first measured the accuracy of the Time Series Analysis (TSA) in measuring mitotic duration of cells that were successfully tracked by the program. We treated HeLa H2B-GFP cells with DMSO or low concentrations of the microtubule depolymerizer nocodazole and imaged the cells every 12 minutes for 48 hours (Figure 3). The TSA method identified the IPT accurately in 93% of nuclei (N = 43), compared to manual inspection. In the remaining 7% of cells, the TSA identified the IPT one frame PLoS ONE | www.plosone.org 2 September 2011 | Volume 6 | Issue 9 | e25511