CellTrans: An R Package to Quantify Stochastic Cell State Transitions

Abstract

Many normal and cancerous cell lines exhibit a stable composition of cells in distinct states which can, e.g., be defined on the basis of cell surface markers. There is evidence that such an equilibrium is associated with stochastic transitions between distinct states. Quantifying these transitions has the potential to better understand cell lineage compositions. We introduce CellTrans, an R package to quantify stochastic cell state transitions from cell state proportion data from fluorescence-activated cell sorting and flow cytometry experiments. The R package is based on a mathematical model in which cell state alterations occur due to stochastic transitions between distinct cell states whose rates only depend on the current state of a cell. CellTrans is an automated tool for estimating the underlying transition probabilities from appropriately prepared data. We point out potential analytical challenges in the quantification of these cell transitions and explain how CellTrans handles them. The applicability of CellTrans is demonstrated on publicly available data on the evolution of cell state compositions in cancer cell lines. We show that CellTrans can be used to (1) infer the transition probabilities between different cell states, (2) predict cell line compositions at a certain time, (3) predict equilibrium cell state compositions, and (4) estimate the time needed to reach this equilibrium. We provide an implementation of CellTrans in R, freely available via GitHub (https://github.com/tbuder/CellTrans).

Stem cell-associated heterogeneity in Glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment

“…To quantify state transitions in time between the different phenotypes and predict the time of equilibrium, we applied CellTrans, a stochastic compartment model based on the Markov chain 40 , which represents an established tool for assessment of state transitions based on FACS data 41 . We assumed that the phenotypic state compositions could be determined by two main processes: (i) stochastic state transitions due to cellular plasticity and (ii) different proliferation rates reflecting selection.…”

“…Markov model principles: To quantify the transitions between the 16 phenotypes we applied Markov chain modeling implemented in the freely available R package CellTrans (http://github.com/tbuder/CellTrans) 41 . The model is based on the assumptions that cell state alterations occur due to stochastic cell state transitions only depending on the current state of the cell and possibly the experimental environment (e.g., hypoxia) and that proliferation rates of the involved phenotypes are approximately equal.…”

“…Hence, we obtained the three transition matrices , and in normoxia which needed to be regularized in order to obtain stochastic matrices, see ref. 41 for details. These matrices were averaged to obtain a final estimate for the transition matrix P , i.e., .…”

“…This resulted in 2 × 2 cell state proportion matrixes per time point. This coarse-grained data could be utilized to derive corresponding 2 × 2 cell state transition matrixes and corresponding steady states by applying CellTrans 41 . The steady states of these 2 × 2 cell state transition matrices could then be compared with the fractions of the corresponding phenotype when considering all four markers in the analysis.…”

“…The scRNA-seq data are available in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo) under the accession number GSE128195. Scripts supporting mathematical modeling are available in the R package CellTrans (http://github.com/tbuder/CellTrans) 41 . Remaining datasets supporting the findings are available from the corresponding author on reasonable request.…”