Abstract
BackgroundTime-lapse microscopy allows to monitor cell state transitions in a spatiotemporal context. Combined with single cell tracking and appropriate cell state markers, transition events can be observed within the genealogical relationship of a proliferating population. However, to infer the correlations between the spatiotemporal context and cell state transitions, statistical analysis with an appropriately large number of samples is required.ResultsHere, we present a method to infer spatiotemporal features predictive of the state transition events observed in time-lapse microscopy data. We first formulate a generative model, simulate different scenarios, such as time-dependent or local cell density-dependent transitions, and illustrate how to estimate univariate transition rates. Second, we formulate the problem in a machine-learning language using regularized linear models. This allows for a multivariate analysis and to disentangle indirect dependencies via feature selection. We find that our method can accurately recover the relevant features and reconstruct the underlying interaction kernels if a critical number of samples is available. Finally, we explicitly use the tree structure of the data to validate if the estimated model is sufficient to explain correlated transition events of sister cells.ConclusionsUsing synthetic cellular genealogies, we prove that our method is able to correctly identify features predictive of state transitions and we moreover validate the chosen model. Our approach allows to estimate the number of cellular genealogies required for the proposed spatiotemporal statistical analysis, and we thus provide an important tool for the experimental design of challenging single cell time-lapse microscopy assays.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0208-5) contains supplementary material, which is available to authorized users.
Highlights
Time-lapse microscopy allows to monitor cell state transitions in a spatiotemporal context
In the following, we use our generative model to simulate datasets from the simple cell state transition model (Fig. 2a) according to four different scenarios, where the transition rate λ depends on different features
We extend the set of relevant features and consider a scenario where the transition rate depends on time and on local cell density (λ ∝ density + time, Eq 8), this time modeled via a Gaussian kernel instead of a tophat kernel to illustrate the versatility of our method
Summary
Time-lapse microscopy allows to monitor cell state transitions in a spatiotemporal context. Combined with single cell tracking and appropriate cell state markers, transition events can be observed within the genealogical relationship of a proliferating population. To infer the correlations between the spatiotemporal context and cell state transitions, statistical analysis with an appropriately large number of samples is required. We present a model and analysis framework that can infer the spatiotemporal features predictive of state transitions and allows to estimate the number of samples required for this analysis. To validate the performance of our framework, we first simulate cellular genealogies from a generative spatiotemporal model for different scenarios of transition rate dependencies. We show that our method is able to correctly identify the transition rate as a multi-feature function and determine the number of required genealogies and allowed tracking errors for different scenarios. We use the correlations between cell siblings to validate the chosen approach and detect shortcomings – either due to non-considered features, or due to cell-internal effects that drive cell state transitions
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