Biology subjects

Heilmann, S.

Publications and source records attributed to Heilmann, S..

3 recordsLinked to original sources

bioRxiv2023

Real time quantification of apical polarity protein reveals novel dynamic processes in luminal network establishment and remodeling in the pancreas

During embryogenesis dynamic changes in tissue architecture transform primitive anlages to functional organs. Here we document in real time how pancreatic lumens are derived and transformed using a new apical-polarity mouse reporter. Our 4D imaging data reveals dynamic remodeling of apical proteins and lumens to primarily drive each stage of pancreatic duct development. Furthermore, we pinpoint two unique transitions during lumenogenesis. Contrary to current "de novo" models of polarity acquisition, we show that expansion and rearrangement of the pre-existing central primary lumen drives early network growth. We also document how the endocrine promoting niche - a "plexus" of interconnected ducts - is resolved. We show that an arborized network forms by gradual closing of ductal loops, rather than via pruning. These novel tissue dynamics provide a new framework within which cell and molecular signaling can be investigated to better understand the interplay between organ architecture and cell fate.

developmental biology↗

bioRxiv2020

Cell state diversity promotes metastasis through heterotypic cluster formation in melanoma

In melanoma, transcriptional profiling has revealed multiple co-existing cell states, including proliferative versus invasive sub-populations that have been posited to represent a "go or grow" tradeoff. Both of these populations are maintained in tumors, but how they physically interact to promote metastasis is unknown. We demonstrate that these subpopulations form spatially structured heterotypic clusters that cooperate in the seeding of metastasis. We unexpectedly found that INV cells were tightly adherent to each other, and formed clusters with a rim of PRO cells. Intravital imaging demonstrated cooperation between these populations, in which the INV cells facilitated the spread of less metastatic PRO cells. We identified the TFAP2 neural crest transcription factor as a master regulator of both clustering and the PRO/INV states. Our data suggest a framework for the co-existence of these two divergent cell populations, in which differing cell states form heterotypic clusters that promote metastasis via cell-cell cooperation.

cancer biology↗

bioRxiv2019

A New Mathematical Model for Tumor Growth, Reduction and Metastasis, Validation with Zebrafish Melanoma and Potential Implications for Dormancy and Recurrence

The genetic and environmental heterogeneity associated with tumors makes cancer treatment and recovery a difficult and unpredictable process. Patients with initially similar disease can experience vastly different outcomes including sustained recovery, refractory disease or, remarkably, recurrence years after treatment. Mathematical models informed with animal and human data provide tools for theoretical and clinical understanding of cancer progression and of the causes of highly variant disease outcomes. This work postulates a population balance model to describe how populations of a large ensemble of tumors of different sizes evolve in time. Each tumor can grow or reduce in size and metastasize. Gender-segregated, immune-competent and immune-suppressed translucent zebrafish (Casper variant) were inoculated with a transgenic melanoma cell line expressing human BRAF V600E and GFP and observed for tumor progression and metastasis. The model describes both these fish data sets, full histograms of population number vs size at multiple times for both immune states, and a human hepatocellular carcinoma data set also consisting of multiple time histograms very well with a minimum of cancer-specific parameters. The only zebrafish parameter to show strong gender-dependence was the host-dependent tumor reduction (immunity) parameter. This result significantly predicts that men should have far worse outcomes than females, yet similar metastasis rates, which are both indeed the case in human melanomas. Moreover the dynamic growth - reduction interplay, for certain relationships between these processes parameters in the model provides a potential mechanism for apparent cancer dormancy and recurrence. Although fish melanoma parameters are not in this range, the model guides future work to try to access it.

cancer biology↗