What is MicroGrad?
MicroGrad is a project on a new generation of microfluidics-based methods to study genetic and functional effects of complex spatiotemporal gradients in tumor microenvironments.
Cancer remains one of the most serious threats to human health. While the genetics and phenotypes of cancer cells are well-studied, a crucial but much less understood aspect is the role of the tumor microenvironment. This unique milieu is characterised by heterogeneous, spatiotemporal gradients of environmental and signalling factors, including oxygen and pH. These gradients profoundly impact cancer progression, but their mode of action is not well understood.
A major unsolved problem in cancer research is, that while this dynamic milieu cannot be understood in vivo because of its complexity, it also cannot currently be accurately mimicked in vitro. Three challenges in particular limit in vitro analysis:
Methods for establishing precise multicomponent- and time-dependent gradients are lacking, and in particular, pH gradients are essentially unstudied.
It is currently impossible to analyze cell mobility across superimposed microenvironmental gradients – i.e. which environmental states are preferred by which cells, and how this impacts their phenotype.
It is currently not feasible to measure how cells respond to along gradients of environmental states beyond imaging experiments. Gene expression analysis of cells with precise gradient information preserved is necessary for in-depth characterization.
The ambitious aim of this project is to establish a new generation of microfluidics-based methods that will enable analysis of how multifactorial microenvironmental gradients impact cancer cell genetics and behavior.
Building on microfluidics, cell biophysics, biophotonics and spatial transcriptomics approaches, this highly synergistic project will deliver methods and pilot results beyond the current state-of-the-art. It will furthermore enable and provide proof-of-principle for a larger project in which the developed methods will be exploited to generate a first detailed understanding of genetic and phenotypical responses of cancer cells to microenvironmental gradients.