Easy and efficient production of completely embryonic-stem-cell-derived mice using a micro-aggregation device.

Abstract

There is an increasing demand for genetically modified mice produced without crossing, for rapid phenotypic screening studies at the organismal level. For this purpose, generation of completely embryonic-stem-cell (ESC)-derived chimeric mice without crossing is now possible using a microinjection or aggregation method with 3i culture medium. However, the microinjection of ESCs into blastocyst, morula, or 8-cell-stage embryos requires a highly skilled operator. The aggregation method is an easier alternative, but the conventional aggregation protocol still requires special skills. To make the aggregation method easier and more precise, here we developed a micro-aggregation device. Unlike conventional 3-dimensional culture, which uses hanging-drop devices for aggregation, we fabricated a polystyrene funnel-like structure to smoothly drop ESCs into a small area (300-μm in diameter) at the bottom of the device. The bottom area was designed so that the surface tension of the liquid-air interface prevents the cells from falling. After aggregation, the cells can be recovered by simply exerting pressure on the liquid from the top. The microdevice can be set upon a regular 96-well plate, so it is compatible with multichannel pipette use or machine operation. Using the microdevice, we successfully obtained chimeric blastocysts, which when transplanted resulted in completely ESC-derived chimeric mice with high efficiency. By changing the number of ESCs in the aggregate, we found that the optimum number of co-cultured ESCs was around 90~120 per embryo. Under this condition, the efficiency of generating completely ESC-derived mice was the same or better than that of the injection method. These results indicated that our microdevice can be used to produce completely ESC-derived chimeric mice easily and with a high success rate, and thus represents a promising alternative to the conventional microinjection or aggregation method, especially for high-throughput, parallel experimental applications.