Modeling Human Neural Tube Patterning with Microfluidic Gradients
31 August till 2 September 2016، Tehran - Iran
Presentation Type: Speech
Knowledge about structural brain development is almost purely derived from studies performed in rodents or smaller model organisms, despite the fact that the human brain is much more complex and 2000 times larger than that of the mouse. This research bias is caused by the impossibility of performing dynamic studies on anatomical brain patterning in human embryos, resulting in a significant lack of knowledge on human-specific neural development. Here, we build a simplified 3D model of the developing human neural tube in vitro using human embryonic stem cells (hESCs). Taking advantage of established knowledge on neural tube patterning, we have designed a closed microfluidic culturing chamber for differentiation of hESCs. In this chamber, the cells are exposed to a gradient of chemicals during the first days of differentiation to mimic the anatomical gradients of growth factors present in the embryo around the developing neural tube. We show that through differentiation of hESCs in the gradient chamber we are able to achieve progressive caudalization of neural identity, obtaining pure forebrain cells in the left side of the culture chamber to midbrain cells in the middle and hindbrain cells in the right side of the chamber, thereby mimicking the anatomical rostro-caudal organization of the neural tube. Remarkably, we find the gene WNT1 to be very highly expressed in the area of the midbrain-hindbrain boundary in the culture chamber, indicating the formation of an in vitro equivalent to the anatomical structure of the Isthmic Organizer (IsO). We apply this model to study region-specific effects of growth factors and key developmental genes, to show that the model can be used as a novel tool for studying anatomically relevant patterning of the early human brain. We envision that our model can be used to investigate how human brain development differs from that of other species in order to achieve an extraordinary degree of complexity.