Geoffrey Schiebinger

Developmental trajectory inference is the task of estimating the paths followed by cells over time as they develop (divide, die and differentiate) in a biological population. In this work we consider the problem of inferring developmental trajectories at single-cell resolution from time courses of dynamic populations which contain observations of cell developmental state, as such gene expression from single-cell RNA sequencing (scRNA-seq), and shared ancestry through lineage tracing. We focus on the setting in which shared ancestry data is obtained from static DNA barcodes, such as those inserted via lentiviral vectors, observed over multiple time points. DNA barcode data allows us to cluster cells across the time course into clones (cells with a common ancestor at some earlier time), and hence they are referred to as multi-time clonal barcodes. Our research reveals that in populations with heterogeneous growth rates, sampling can induce a bias in the cell type proportions represented in the multi-time clonal barcodes. We prove the existence of this effect by simple analysis of probabilities, and validate our arguments with proportions from simulations. Furthermore, we show using simulated data that it is possible for this bias to impact fate probability predictions from state-of-the-art methods for trajectory inference which incorporate multi-time clonal barcode information and cell state. There is only one current method in the literature, CoSpar [1], of this type. However in our research we have also developed an extension of another method, LineageOT [2], which uses only cell state and clonal barcode information from single time points, adapting the method to use both single- and multi-time clonal barcode data. Though this extension improves the performance of the original method, evaluated on simulated data, we find that the performance gains may be impacted by the bias effect we have uncovered. Given the potential for application of trajectory inference results to biomedical technologies and treatments, understanding and improving the accuracy of these methods is crucial. Our goal for these contributions is to inform researchers of this bias and stimulate the development of methods related to reducing its impact on trajectory inference.

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