A novel model to study mechanisms of cholestasis in human cholangiocytes reveals a role for the SIPR2 pathway.

BACKGROUND: Ductular reactivity is central to the pathophysiology of cholangiopathies. Mechanisms underlying the reactive phenotype activation by exogenous inflammatory mediators and bile acids are poorly understood. METHODS: Using human extrahepatic cholangiocyte organoids (ECOs) we developed an injury model emulating the cholestatic microenvironment with exposure to inflammatory mediators and various pathogenic bile acids. Moreover, we explored roles for the bile acid activated Sphingosine-1-phosphate receptor 2 (S1PR2) and potential beneficial effects of therapeutic bile acids UDCA and norUDCA. RESULTS: Synergistic exposure to bile acids (taurocholic acid, glycocholic acid, glycochenodeoxycholic acid) and TNF-α for 24 hours induced a reactive state as measured by ECO diameter, proliferation, lactate dehydrogenase activity and reactive phenotype markers. While NorUDCA and UDCA treatments given 8 hours after injury induction both suppressed reactive phenotype activation and most injury parameters, proliferation was improved by NorUDCA only. Extrahepatic cholangiocyte organoid stimulation with S1PR2 agonist sphingosine-1-phosphate reproduced the cholangiocyte reactive state and upregulated S1PR2 downstream mediators; these effects were suppressed by S1PR2 antagonist JET-013 (JET), downstream mediator extracellular signal-regulated kinase 1/2 inhibitor, and by norUDCA or UDCA treatments. JET also partially suppressed reactive phenotype after bile acid injury. CONCLUSIONS: We developed a novel model to study the reactive cholangiocyte state in response to pathological stimuli in cholestasis and demonstrated a contributory role of S1PR2 signaling in both injury and NorUDCA/UDCA treatments. This model is a valuable tool to further explore the pathophysiology of human cholangiopathies.

Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC).

Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell-population heterogeneity using time-lapse fluorescence microscopy, which allows one to follow reaction kinetics in individual cells. The current and only CRRC workflow utilizes a single fluorescence image to manually identify cell contours which are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow is only reliable if cells maintain their positions during the time-lapse measurements. If the cells move, the original cell contours become unsuitable for evaluating intracellular fluorescence and the CRRC experiment will be inaccurate. The requirement of invariant cell positions during a prolonged imaging is impossible to satisfy for motile cells. Here we report a CRRC workflow developed to be applicable to motile cells. The new workflow combines fluorescence microscopy with transmitted-light microscopy and utilizes a new automated tool for cell identification and tracking. A transmitted-light image is taken right before every fluorescence image to determine cell contours, and cell contours are tracked through the time-stack of transmitted-light images to account for cell movement. Each unique contour is used to determine fluorescence intensity of cells in the associated fluorescence image. Next, time dependencies of the intracellular fluorescence intensities are used to determine each cell's rate constant and construct a kinetic histogram "number of cells vs rate constant." The new workflow's robustness to cell movement was confirmed experimentally by conducting a CRRC study of cross-membrane transport in motile cells. The new workflow makes CRRC applicable to a wide range of cell types and eliminates the influence of cell motility on the accuracy of results. Additionally, the workflow could potentially monitor kinetics of varying biological processes at the single-cell level for sizable cell populations. Although our workflow was designed ad hoc for CRRC, this cell-segmentation/cell-tracking strategy also represents an entry-level, user-friendly option for a variety of biological assays (i.e., migration, proliferation assays, etc.). Importantly, no prior knowledge of informatics (i.e., training a model for deep learning) is required.