The origin of lineage correlations among single cells and the extent of heterogeneity in their intermitotic times (IMT) and apoptosis times (AT) remain incompletely understood. Here we developed single cell lineage-tracking experiments and computational algorithms to uncover correlations and heterogeneity in the IMT and AT of a colon cancer cell line before and during cisplatin treatment. These correlations could not be explained using simple protein production/degradation models. Sister cell fates were similar regardless of whether they divided before or after cisplatin administration and did not arise from proximity-related factors, suggesting fate determination early in a cell’s lifetime. Based on these findings, we developed a theoretical model explaining how the observed correlation structure can arise from oscillatory mechanisms underlying cell fate control. Our model recapitulated the data only with very specific oscillation periods that fit measured circadian rhythms, thereby suggesting an important role of the circadian clock in controlling cellular fates.

FOXO transcription factors are regulators of cellular homeostasis linked to increased lifespan and tumor suppression. FOXOs are activated by diverse cell stresses including serum starvation and oxidative stress. FOXO activity is regulated through post-translational modifications that control shuttling of FOXO proteins to the nucleus. In the nucleus, FOXOs upregulate genes in multiple, often conflicting pathways including cell-cycle arrest and apoptosis. How cells control FOXO activity to ensure the proper response for a given stress is an open question. Using quantitative immunofluorescence and live-cell imaging we found that the dynamics of FOXO nuclear shuttling are stimulus dependent and correspond with cell fate. H2O2 treatment leads to an all-or-none response where some cells show no nuclear FOXO accumulation, while other cells show strong nuclear FOXO signal. The time that FOXO remains in the nucleus increases with dose and is linked with cell death. In contrast, serum starvation causes low amplitude pulses of nuclear FOXO and predominantly results in cell-cycle arrest. The accumulation of FOXO in the nucleus is linked with low AKT activity for both H2O2 and serum starvation. Our findings suggest the dynamics of FOXO nuclear shuttling is one way in which the FOXO pathway dictates different cellular outcomes. [Media: see text] [Media: see text] [Media: see text]

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Background: Time-lapse microscopy has been widely used in biomedical experiments because it can visualize the molecular activities of living cells in real time. However, biomedical researchers are still conducting cell lineage analysis manually. Developing automatic lineage tracing algorithms is a challenging task. In the past two decades, deep neural networks (DNNs) became have shown outstanding performance on computer vision tasks. They can learn complex visual features, capture long-range temporal dependencies, and have the potential to be used for automatic cell lineage analysis. Methods: In this study, we propose a multi-task spatio-temporal feature based deep neural network for cell lineages analysis (Cell-STN). The Cell-STN extracts spatio-temporal features from microscopy image sequences by leveraging our convolutional long short-term memory based core block. And the proposed Cell-STN utilized a task specific network to predict the cell location, the mitosis event, and the apoptosis event in a multi-task manner. Results: We evaluated the Cell-STN on three in-house datasets (MCF7, U2OS, and HCT116) and one public dataset (Fluo-N2DL-HeLa). For cell tracking, we used peak-wise precision, track-wise precision, end-peak precision, and spatial distance as metrics. The overall results showed the Cell-STN models outperform other state-of-the-art cell trackers. For mitosis and apoptosis tasks, we used accuracy, F1-score, temporal distance, and spatial distance as metrics. The Cell-STN models achieved the highest performance on all datasets. Conclusion: This study presented a novel DNNs approach for cell lineage analysis in microscopy images. The Cell-STN showed outstanding performance on the four datasets. Additionally, the Cell-STN required minimal training data and can be adapted to new biological event detection tasks by appending task-specific layers. This algorithm has the potential to be used in real-world biomedical research.