Long-term time series analysis of quantum dot encoded cells by deconvolution of the autofluorescence signal.

The monitoring of cells labeled with quantum dot endosome-targeted markers in a highly proliferative population provides a quantitative approach to determine the redistribution of quantum dot signal as cells divide over generations. We demonstrate that the use of time-series flow cytometry in conjunction with a stochastic numerical simulation to provide a means to describe the proliferative features and quantum dot inheritance over multiple generations of a human tumor population. However, the core challenge for long-term tracking where the original quantum dot fluorescence signal over time becomes redistributed across a greater cell number requires accountability of background fluorescence in the simulation. By including an autofluorescence component, we are able to continue even when this signal predominates (i.e., >80% of the total signal) and obtain valid readouts of the proliferative system. We determine the robustness of the technique by tracking a human osteosarcoma cell population over 8 days and discuss the accuracy and certainty of the model parameters obtained. This systems biology approach provides insight into both cell heterogeneity and division dynamics within the population and furthermore informs on the lineage history of its members.