Search

During liver development, bipotential progenitor cells called hepatoblasts differentiate into hepatocytes or cholangiocytes. Hepatocyte differentiation is uniquely associated with multi-axial polarity, enabling the anisotropic expansion of apical lumina between adjacent cells and formation of a three-dimensional network of bile canaliculi. Cholangiocytes, the cells forming the bile ducts, exhibit the vectorial polarity characteristic of epithelial cells. Whether cell polarization feeds back on the gene regulatory pathways governing hepatoblast differentiation is unknown. Here, we used primary mouse hepatoblasts to investigate the contribution of anisotropic apical expansion to hepatocyte differentiation. Silencing of the small GTPase Rab35 caused isotropic lumen expansion and formation of multicellular cysts with the vectorial polarity of cholangiocytes. Gene expression profiling revealed that these cells express reduced levels of hepatocyte markers and upregulate genes associated with cholangiocyte identity. Timecourse RNA sequencing demonstrated that loss of lumen anisotropy precedes these transcriptional changes. Independent alterations in apical lumen morphology induced either by modulation of the subapical actomyosin cortex or by increased intraluminal pressure caused similar transcriptional changes. These findings suggest that cell polarity and lumen morphogenesis feed back to hepatoblast-to-hepatocyte differentiation. [ABSTRACT FROM AUTHOR]

In molecular biology, just as in many other fields of science, data often come in the form of matrices or contingency tables with many observations (rows) for a set of variables (columns). While projection methods like principal component analysis or correspondence analysis (CA) 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 of which observations are associated to a cluster and distinguish it from other clusters. CA employs a geometry geared towards answering this question. We exploit this feature in order to introduce Association Plots for visualizing cluster-specific observations in complex data. Regardless of the data matrix dimensionality Association Plots are two-dimensional and depict the observations associated to a cluster of variables. We demonstrate our method on two small data sets and then use it to study a challenging genomic data set comprising >10,000 samples. We show that Association Plots can clearly highlight those observations which characterise a cluster of variables. [ABSTRACT FROM AUTHOR]