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).

Switchable hydrogel-layered composite membranes can be used for the analysis of particle size distributions. This functionality is provided by pores with controllable diameter. In order to obtain a device that can be used to measure the cell size distribution in native biological samples, lots of switchable pores are required. In the current work, we model and simulate the mechanical behavior of active composite membranes with switchable pores. This is done in order to find the maximum number of pores that can be integrated into a membrane without cross-influencing effects on the actuation of the pores. Therefore, we investigate (1) the interaction of active pores inside the multifunctional composite and (2) the membrane bending under microfluidic pressure load. We show that through miniaturization, sufficient pores can be added to a permeation control membrane for processing native blood samples. The envisioned device allows a parallelized measurement of cell sizes in a simple lab-on-a-chip setup.

Switchable hydrogel-layered composite membranes can be used for the analysis of particle size distributions. This functionality is provided by pores with controllable diameter. In order to obtain a device that can be used to measure the cell size distribution in native biological samples, lots of switchable pores are required. In the current work, we model and simulate the mechanical behavior of active composite membranes with switchable pores. This is done in order to find the maximum number of pores that can be integrated into a membrane without cross-influencing effects on the actuation of the pores. Therefore, we investigate (1) the interaction of active pores inside the multifunctional composite and (2) the membrane bending under microfluidic pressure load. We show that through miniaturization, sufficient pores can be added to a permeation control membrane for processing native blood samples. The envisioned device allows a parallelized measurement of cell sizes in a simple lab-on-a-chip setup.

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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).

The process of cell-sorting is essential for development and maintenance of tissues. With the Differential Adhesion Hypothesis, Steinberg proposed that cellsorting is determined by quantitative differences in cell-type-specific intercellular adhesion strengths. An implementation of the Differential Adhesion Hypothesis is the Differential Migration Model by Voss-Böhme and Deutsch. There, an effective adhesion parameter was derived analytically for systems with two cell types, which predicts the asymptotic sorting pattern. However, the existence and form of such a parameter for more than two cell types is unclear. Here, we generalize analytically the concept of an effective adhesion parameter to three and more cell types and demonstrate its existence numerically for three cell types based on in silico time-series data that is produced by a cellular-automaton implementation of the Differential Migration Model. Additionally, we classify the segregation behavior using statistical learning methods and show that the estimated effective adhesion parameter for three cell types matches our analytical prediction. Finally, we demonstrate that the effective adhesion parameter can resolve a recent dispute about the impact of interfacial adhesion, cortical tension and heterotypic repulsion on cell segregation. / Der Prozess der Zellsortierung ist für die Entwicklung und Erhaltung von Geweben unerlässlich. Mit der Differentiellen Adhäsionshypothese schlug Steinberg vor, dass die Zellsortierung durch quantitative Unterschiede in den zelltypspezifischen interzellulären Adhäsionsstärken bestimmt wird. Eine Umsetzung der Differentiellen Adhäsionshypothese ist das Differentielle Migrationsmodell von Voss-Böhme und Deutsch. In diesem wurde für Systeme mit zwei Zelltypen ein effektiver Adhäsionsparameter analytisch hergeleitet, der das asymptotische Sortiermuster vorhersagt. Die Existenz und Form eines solchen Parameters für mehr als zwei Zelltypen ist jedoch unklar. Hier verallgemeinern wir analytisch das Konzept eines effektiven Adhäsionsparameters für drei und mehr Zelltypen und zeigen numerisch seine Existenz für drei Zelltypen auf der Basis von in silico Zeitreihendaten, die von einem zellulären Automaten des Differentiellen Migrationsmodells erzeugt werden. Darüber hinaus klassifizieren wir das Segregationsverhalten mithilfe statistischer Lernverfahren und zeigen, dass der geschätzte effektive Adhäsionsparameter für drei Zelltypen mit unserer analytischen Vorhersage übereinstimmt. Schließlich zeigen wir, dass der effektive Adhäsionsparameter eine kürzlich aufgekommene Diskussion über den Einfluss von Grenzflächenadhäsion, Kortikalspannung und heterotypischer Abstoßung auf die Zellsegregation lösen kann.