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For phenotypic behavior to be understood in the context of cell lineage and local environment, properties of individual cells must be measured relative to population-wide traits. However, the inability to accurately identify, track and measure thousands of single cells via high-throughput microscopy has impeded dynamic studies of cell populations. We demonstrate unique labeling of cells, driven by the heterogeneous random uptake of fluorescent nanoparticles of different emission colors. By sequentially exposing a cell population to different particles, we generated a large number of unique digital codes, which corresponded to the cell-specific number of nanoparticle-loaded vesicles and were visible within a given fluorescence channel. When three colors are used, the assay can self-generate over 17,000 individual codes identifiable using a typical fluorescence microscope. The color-codes provided immediate visualization of cell identity and allowed us to track human cells with a success rate of 78% across image frames separated by 8 h. [ABSTRACT FROM AUTHOR]

Background: The inheritance of cellular material between parent and daughter cells during mitosis is highly influential in defining the properties of the cell and therefore the population lineage. This is of particular relevance when studying cell population evolution to assess the impact of a disease or the perturbation due to a drug treatment. The usual technique to investigate inheritance is to use time-lapse microscopy with an appropriate biological marker, however, this is time consuming and the number of inheritance events captured are too low to be statistically meaningful. Results: Here we demonstrate the use of a high throughput fluorescence measurement technique e.g. flow cytometry, to measure the fluorescence from quantum dot markers which can be used to target particular cellular sites. By relating, the fluorescence intensity measured between two time intervals to a transfer function we are able to deconvolve the inheritance of cellular material during mitosis. To demonstrate our methodology we use CdTe/ ZnS quantum dots to measure the ratio of endosomes inherited by the two daughter cells during mitosis in the U2-OS, human osteoscarcoma cell line. The ratio determined is in excellent agreement with results obtained previously using a more complex and computational intensive bespoke stochastic model. Conclusions: The use of a transfer function approach allows us to utilise high throughput measurement of large cell populations to derive statistically relevant measurements of the inheritance of cellular material. This approach can be used to measure the inheritance of organelles, proteins etc. and also particles introduced to cells for drug delivery. [ABSTRACT FROM AUTHOR]

We present a new approach to the handling and interrogating of large flow cytometry data where cell status and function can be described, at the population level, by global descriptors such as distribution mean or co-efficient of variation experimental data. Here we link the ''real'' data to initialise a computer simulation of the cell cycle that mimics the evolution of individual cells within a larger population and simulates the associated changes in fluorescence intensity of functional reporters. The model is based on stochastic formulations of cell cycle progression and cell division and uses evolutionary algorithms, allied to further experimental data sets, to optimise the system variables. At the population level, the in-silico cells provide the same statistical distributions of fluorescence as their real counterparts; in addition the model maintains information at the single cell level. The cell model is demonstrated in the analysis of cell cycle perturbation in human osteosarcoma tumour cells, using the topoisomerase II inhibitor, ICRF-193. The simulation gives a continuous temporal description of the pharmacodynamics between discrete experimental analysis points with a 24 hour interval; providing quantitative assessment of inter-mitotic time variation, drug interaction time constants and sub-population fractions within normal and polyploid cell cycles. Repeated simulations indicate a model accuracy of ±5%. The development of a simulated cell model, initialized and calibrated by reference to experimental data, provides an analysis tool in which biological knowledge can be obtained directly via interrogation of the in-silico cell population. It is envisaged that this approach to the study of cell biology by simulating a virtual cell population pertinent to the data available can be applied to ''generic'' cellbased outputs including experimental data from imaging platforms. [ABSTRACT FROM AUTHOR]