Gralinska, E. | bioRxiv Science

bioRxiv2021

Bulkhead-like apical membrane structures between hepatocytes are required for anisotropic lumen expansion and liver tissue morphogenesis

Lumen morphogenesis is key to the function of organs and results from the integration of molecular pathways and mechanical forces1-3. The mechanisms governing anisotropic lumen expansion remain elusive4-6. In contrast to epithelial cells which have simple apico-basal polarity and form tubes, hepatocytes are multi-polar and form narrow lumina that grow anisotropically between adjacent cells, collectively generating a complex 3D network of bile canaliculi (BC)7,8. Here, we studied lumen elongation and BC morphogenesis in differentiating primary mouse hepatoblasts in vitro. Remarkably, we discovered a pattern of specific extensions of the apical membrane traversing the lumen between adjacent hepatocytes and sealed by tight junctions, reminiscent of the bulkheads of boats. These structures were also present in the developing liver. A targeted screen revealed that silencing of Rab35 caused loss of the bulkheads, conversion of hepatocyte into simple epithelial polarity and formation of spherical lumina in vitro. Strikingly, we could re-engineer hepatocyte polarity and tissue morphogenesis in vivo in the embryonic liver, converting BC into simple epithelial tubes. Our results suggest that the apical bulkheads of hepatocytes are cell-intrinsic anisotropic mechanical elements that ensure stability of the elongating lumen between two cells, thus determining the structure of BC during liver tissue morphogenesis.

cell biology↗

bioRxiv2020

Association Plots: Visualizing associations in high-dimensional correspondence analysis biplots

SO_SCPLOWUMMARYC_SCPLOWIn molecular biology, just as in many other fields of science, data often come in the form of matrices or contingency tables with many measurements (rows) for a set of variables (columns). While projection methods like Principal Component Analysis or Correspondence Analysis can be applied for obtaining an overview of such data, in cases where the matrix is very large the associated loss of information upon projection into two or three dimensions may be dramatic. However, when the set of variables can be grouped into clusters, this opens up a new angle on the data. We focus on the question which measurements are associated to a cluster and distinguish it from other clusters. Correspondence Analysis employs a geometry geared towards answering this question. We exploit this feature in order to introduce Association Plots for visualizing cluster-specific measurements in complex data. Association Plots are two-dimensional, independent of the size of data matrix or cluster, and depict the measurements associated to a cluster of variables. We demonstrate our method first on a small data set and then on a genomic example comprising more than 10,000 conditions. We will show that Association Plots can clearly highlight those measurements which characterize a cluster of variables.

bioinformatics↗