Abstract 880: A computational and statistical approach for interpreting real-time in-vitro gene reporter data

Abstract

We have developed a new computational approach to track and analyze data from cells engineered with fluorescent cell cycle gene reporters. Gene reporters are a rapidly growing tool to gather information captured with time-lapse imaging, and can give insights to real-time metabolic conditions, cell cycle state, cancer stem cell state, and many other applications. Gene reporters may reflect cellular conditions that are cyclical rather than monotonic. Currently, the primary mechanism for interpretation of time lapse gene reporter data is reporting the mean fluorescence of images over time. This simplistic analysis discards information such as the cycle length and variation between of gene reporters between cells, and can be biased due to the overall fluorescence shift from population growth. More nuanced data acquisition and analysis approaches have been limited due to requirement of laborious hand-on data analysis. We have implemented a high-throughput computational pipeline for interpretation of cell cycle information using a combination of commercial and newly developed software to partition the activities of individual cells. The fluorescence information for each cell is smoothed and the cycle states for each cell are assigned adaptively. Our approach captures and quantifies the total time each individual cell spends in each presumptive state, and then analyses state information from the cohort of observed cells using censored survival time data methods such as the Cox Proportional Hazards Model to account for cell death or movement out of the observed area. As a proof of concept for our pipeline, we engineered 3T3 fibroblast cells with a cell cycle reporter construct including mCherry-CDT1 and mVenous-p27K- reporters which can discriminate cells in G0 phase from G1 phase. We calculated the sample size required for statistical power of 80% to detect a hazard ratio (HR) of 0.8 between groups at α = 0.05 at 386 tracked cells per group. As our treatment model, we used the Protein Phosphatase 2A inhibitor Okadaic Acid (OA) an agent known to promote departure from G0. We captured a distinct total of 601 vehicle cells and 479 treatment cells in a 24 hour monitoring period with 20 minute capture intervals. We were able to discern subtle differences between control and treatment G0 time, including a subtle, statistically significant decrease in G0 time after treatment with OA (p=0.0166, HR = 0.84). Here we have developed a computational pipeline with the capability for high-throughput image-stack data processing and nuanced quantification. Our approach assigns cycling cell states adaptively and the time until each cell departs its current state is tallied and interpreted statistically with survival time statistical analysis. The cell-level analysis affords a high degree of statistical power to discriminate group differences. Our pipeline is highly adaptable and has a multitude of applications in cancer biology research.

Citation Format: Alexander T. Pearson, Dan Sun, Zhengda Li, Jacques Nor, Qiong Yang, Laura Buttitta. A computational and statistical approach for interpreting real-time in-vitro gene reporter data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 880. doi:10.1158/1538-7445.AM2017-880