Biology subjects

Warmflash, A.

Publications and source records attributed to Warmflash, A..

21 records · Page 2Linked to original sources

bioRxiv2017

Morphogen dynamics control patterning in a stem cell model of the human embryo

During embryonic development, diffusible signaling molecules called morphogens are thought to determine cell fates in a concentration-dependent manner1-4, and protocols for directed stem cell differentiation are based on this picture5-8. However, in the mammalian embryo, morphogen concentrations change rapidly compared to the time for making cell fate decisions9-12. It is unknown how changing ligand levels are interpreted, and whether the precise timecourse of ligand exposure plays a role in cell fate decisions. Nodal and BMP4 are morphogens crucial for gastrulation in vertebrates13. Each pathway has distinct receptor complexes that phosphorylate specific signal transducers, known as receptor-Smads, which then complex with the shared cofactor Smad4 to activate target genes14. Here we show in human embryonic stem cells (hESCs) that the response to BMP4 signaling indeed is determined by the ligand concentration, but that unexpectedly, the expression of many mesodermal targets of Activin/Nodal depends on rate of concentration increase. In addition, we use live imaging of hESCs with GFP integrated at the endogenous SMAD4 locus to show that a stem cell model for the human embryo15 generates a wave of Nodal signaling. Cells experience rapidly increasing Nodal specifically in the region of mesendoderm differentiation. We also demonstrate that pulsatile stimulation with Activin induces repeated strong signaling and enhances mesoderm differentiation. Our results break with the paradigm of concentration-dependent differentiation and demonstrate an important role for morphogen dynamics in the cell fate decisions associated with mammalian gastrulation. They suggest a highly dynamic picture of embryonic patterning where some cell fates depend on rapid concentration increase rather than absolute levels, and point to ligand dynamics as a new dimension to optimize protocols for directed stem cell differentiation.

developmental biology↗

bioRxiv2017

cytoNet: Network Analysis of Cell Communities

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNets capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin 4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains. Availability and ImplementationQutubLab.org/how | cytoNet contact: cytoNetProject@gmail.com Brain Initiative Alliance Toolmaker cytoNet site: https://www.braininitiative.org/toolmakers/resources/cytonet/ Author / Lay SummarycytoNet provides an online tool to rapidly characterize relationships between objects within images and video frames. To study complex tissue, cell and subcellular topologies, cytoNet integrates vision science with the mathematical technique of graph theory. This allows the method to simultaneously identify environmental effects on single cells and on network topology. cytoNet has versatile use across neuroscience, stem cell biology and regenerative medicine. cytoNet applications described in this study include: (1) characterizing how sensing pain alters neural circuit activity, (2) quantifying how vascular cells respond to neurotrophic stimuli overexpressed in the brain after injury or exercise, (3) delineating features of fat tissue that may confer resistance to obesity and (4) uncovering structure-function relationships of human stem cells as they transform into neurons.

bioengineering↗

bioRxiv2017

Morphogen And Community Effects Determine Cell Fates In Response To BMP4 Signaling In Human Embryonic Stem Cells

Paracrine signals maintain developmental states and create cell-fate patterns in vivo, and influence differentiation outcomes in human embryonic stem cells (hESCs) in vitro. Systematic investigation of morphogen signaling is hampered by the difficulty of disentangling endogenous signaling from experimentally applied ligands. Here, we grow hESCs in micropatterned colonies of 1-8 cells (\"Colonies\") to quantitatively investigate paracrine signaling and the response to external stimuli. We examine BMP4-mediated differentiation in Colonies and standard culture conditions and find that in Colonies, above a threshold concentration, BMP4 gives rise to only a single cell fate, contrary to its role as a morphogen in other developmental systems. Under standard culture conditions, BMP4 acts as morphogen, but this effect requires secondary signals and particular cell densities. We further find that a \"community effect\" enforces a common fate within Colonies both in the state of pluripotency and when cells are differentiated, and that this effect allows more precise response to external signals. Using live cell imaging to correlate signaling histories with cell fates, we demonstrate that interactions between neighbors result in sustained, homogenous signaling necessary for differentiation.\n\nSummary StatementWe quantitatively examined signaling and differentiation in hESC colonies of varying size treated with BMP4. We show that secondary signals result in morphogen and community effects that determine cell fates.

developmental biology↗