Qutub, A. A. | bioRxiv Science

bioRxiv2019

A novel self-organizing embryonic stem cell system reveals signaling logic underlying the patterning of human ectoderm

During development, the ectoderm is patterned by a combination of BMP and WNT signaling. Research in model organisms has provided substantial insight, however, there are currently no systems to study this patterning in humans. Further, the complexity of neural plate border specification has made it difficult to transition from discovering the genes involved to deeper mechanistic understanding. Here, we develop an in vitro model of human ectodermal patterning, in which hESCs self-organize to form robust and quantitatively reproducible patterns corresponding to the dorsal-ventral axis of the embryo. Using this platform, we show that the duration of endogenous WNT signaling is a crucial control parameter, and that cells sense relative levels of BMP and WNT signaling in making fate decisions. These insights allowed us to develop an improved protocol for placodal differentiation. Thus, our platform is a powerful tool for studying human ectoderm patterning and for improving directed differentiation protocols.

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

Biowheel: interactive visualization and exploration of biomedical data

We introduce Biowheel (https://biowheel.dibsvis.com/), a web-based award-winning data visualization tool, for exploring high-dimensional and heterogeneous biomedical data. Through interactive sorting and filtering of data, Biowheel enables researchers to quickly detect data outliers, evaluate data consistency, and discover mixed trends. Its interactive data presentation, visually-engaging design, and friendly user interface opens the door to easier, faster and better high-dimensional data interpretation for biomedical professionals with and without programming training.

bioinformatics↗