Biology subjects

Zandstra, P.

Publications and source records attributed to Zandstra, P..

5 recordsLinked to original sources

bioRxiv2023

Cell networks in the mouse liver during partial hepatectomy

In solid tissues homeostasis and regeneration after injury involve a complex interplay between many different cell types. The mammalian liver harbors numerous epithelial and non-epithelial cells and little is known about the global signaling networks that govern their interactions. To better understand the hepatic cell network, we isolated and purified 10 different cell populations from normal and regenerative mouse livers. Their transcriptomes were analyzed by bulk RNA-seq and a computational platform was used to analyze the cell-cell and ligand-receptor interactions among the 10 populations. Over 50,000 potential cell-cell interactions were found in both the ground state and after partial hepatectomy. Importantly, about half of these differed between the two states, indicating massive changes in the cell network during regeneration. Our study provides the first comprehensive database of potential cell-cell interactions in mammalian liver cell homeostasis and regeneration. With the help of this prediction model, we identified and validated two previously unknown signaling interactions involved in accelerating and delaying liver regeneration. Overall, we provide a novel platform for investigating autocrine/paracrine pathways in tissue regeneration, which can be adapted to other complex multicellular systems. HighlightsA platform predicting cell-cell interactions in liver regeneration was established This platform identified the BMP4 pathway antagonist Fstl1 as a stimulator of hepatocyte proliferation This platform also discovered the role of Wnt pathway inhibitor Sfrp1 delaying liver regeneration

cell biology↗

bioRxiv2022

Symmetry-breaking in adherent pluripotent stem cell-derived developmental patterns

The emergence of the anterior-posterior body axis during early gastrulation constitutes a symmetry-breaking event, which is key to the development of bilateral organisms, and its mechanism remains poorly understood. Two-dimensional gastruloids constitute a simple and robust framework to study early developmental events in vitro. Although spontaneous symmetry breaking has been observed in three dimensional (3D) gastruloids, the mechanisms behind this phenomenon are poorly understood. We thus set out to explore whether a controllable 2D system could be used to reveal the mechanisms behind the emergence of asymmetry in patterned cellular structures. We first computationally simulated the emergence of organization in micro-patterned mouse pluripotent stem cell (mPSC) colonies using a Turing-like activator-repressor model with activator-concentration-dependent flux boundary condition at the colony edge. This approach allows the self-organization of the boundary conditions, which results in a larger variety of patterns than previously observed. We found that this model recapitulated previous results of centro-symmetric patterns in large colonies, and also that in simulated small colony sizes, patterns with spontaneous asymmetries emerged. Model analysis revealed reciprocal effects between diffusion and size of the colony, with model-predicted asymmetries in small pattern sizes being dominated by diffusion, and centro-symmetric patterns being size-dominated. To test these predictions, we performed experiments on micro-patterned mPSC colonies of different sizes stimulated with Bone Morphogenetic Protein 4 (BMP4), and used Brachyury (BRA)-GFP expressing cells as pattern readout. We found that while large colonies showed centro-symmetric BRA patterns, the probability of colony polarization increased with decreasing sizes, with a maximum polarization frequency of 35% at [~]200m. These results indicate that a simple molecular activator-repressor system can provide cells with collective features capable of initiating a body-axes plan, and constitute a theoretical foundation for the engineering of asymmetry in developmental systems.

developmental biology↗

bioRxiv2019

Functional arrays of human pluripotent stem cell-derived cardiac microtissues

To accelerate the cardiac drug discovery pipeline, we set out to develop a platform that would be amenable to standard multiwell-plate manipulations and be capable of quantifying tissue-level functions such as contractile force. We report a 96-well-based array of 3D human pluripotent stem cell (hPSC)-derived cardiac microtissues - termed Cardiac MicroRings (CaMiRi) - in custom printed multiwell plates capable of contractile force measurement. Within each well, two elastomeric microcantilevers are situated above a ramp. The wells are seeded with cell-laden collagen which, in response to the slope of the ramp, self-organizes around tip-gated microcantilevers to form contracting CaMiRi. The contractile force exerted by the CaMiRi is measured and calculated using the deflection of the cantilevers. Platform responses were robust and comparable across wells and we used it to determine an optimal tissue formulation. We validated contractile force response of CaMiRi using selected cardiotropic compounds with known effects. Additionally, we developed automated protocols for CaMiRi seeding, image acquisition, and analysis to enable measurement of contractile force with increased throughput. The unique tissue fabrication properties of the platform, and the consequent effects on tissue function, were demonstrated upon adding hPSC-derived epicardial cells to the system. This platform represents an open-source contractile force screening system useful for drug screening and tissue engineering applications.

bioengineering↗

bioRxiv2018

Context-explorer: Analysis of spatially organized protein expression in high-throughput screens

A growing body of evidence highlights the importance of the cellular microenvironment as a regulator of phenotypic and functional cellular responses to perturbations. We have previously developed cell patterning techniques to control population context parameters, and here we demonstrate Context-explorer (CE), a software tool to improve investigation of microenvironmental variables through colony level analyses. We demonstrate the capabilities of CE in the analysis of human and mouse pluripotent stem cells (hPSCs, mPSCs) patterned in colonies of defined size and shape in multi-well plates.\n\nCE employs a density-based clustering algorithm to identify cell colonies within micropatterned wells. Using this automatic colony classification methodology, we obtain accuracies comparable to manual colony counts in a fraction of the time. Classifying cells according to their relative position within a colony enables statistical analysis of radial spatial trends in protein expression within multiple colonies in the same treatment group. When applied to colonies of hPSCs, our analysis reveals a radial gradient in the expression of the pluripotency inducing transcription factors SOX2 and OCT4, and a similar trend in the intra-colony location of different cellular phenotypes. We extend these analyses to colonies of different sizes and shapes and demonstrate how the metrics derived by CE can be used to asses the patterning fidelity of micropatterned plates.\n\nWe have incorporated a number of features to enhance the usability and utility of CE. To appeal to a broad scientific community, all of the softwares functionality is accessible from a graphical user interface, and convenience functions for several common data operations are included. CE is compatible with existing image analysis programs such as CellProfiler and extends the analytical capabilities already provided by these tools. Taken together, CE facilitates investigation of spatially heterogeneous cell populations in fundamental research and drug development validation programs.

cell biology↗

bioRxiv2018

Identifying the genetic basis of variation in cell behaviour in human iPS cell lines from healthy donors

Large cohorts of human iPSCs from healthy donors are potentially a powerful tool for investigating the relationship between genetic variants and cellular phenotypes. Here we integrate high content imaging, gene expression and DNA sequence datasets for over 100 human iPSC lines to identify the genetic basis of inter-individual variability in cell behaviour. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we identified genes that correlated in expression with intrinsic (genetic) and extrinsic (ECM) factors. However, variation in mRNA levels could not account for outlier cell behaviour. Instead, we identified rare, deleterious SNVs in the coding sequence of genes involved in ECM adhesion that occurred in cell lines that were outliers for one or more phenotypes such as cell spreading. These also correlated with altered germ layer differentiation on micropatterned surfaces. Our study thus establishes a strategy for integrating genetic and cell biological measurements for high-throughput analysis.

cell biology↗