Back

Long-term live imaging of epithelial organoids and corresponding multiscale analysis reveal high heterogeneity and identifies core regulatory principles

Hof, L.; Moreth, T.; Koch, M.; Liebisch, T.; Kurtz, M.; Tarnick, J.; Lissek, S. M.; Verstegen, M. M.; van der Laan, L. J.; Huch, M.; Matthaeus, F.; Stelzer, E. H.; Pampaloni, F.

2020-07-13 cell biology
10.1101/2020.07.12.199463 bioRxiv
Show abstract

Organoids are morphologically heterogeneous three-dimensional cell culture systems. To understand the cell organisation principles of their morphogenesis, we imaged hundreds of pancreas and liver organoids in parallel using light sheet and bright field microscopy for up to seven days. We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field macroscale analyses of entire cultures. Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying core regulatory principles of organoid morphogenesis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=182 HEIGHT=200 SRC="FIGDIR/small/199463v2_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1df3118org.highwire.dtl.DTLVardef@104dc7org.highwire.dtl.DTLVardef@62512forg.highwire.dtl.DTLVardef@bff752_HPS_FORMAT_FIGEXP M_FIG C_FIG Created with BioRender.com.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.