Recently, an important research completed by the interdisciplinary research team of the Technical University of Munich, the University of Helmholtz in Munich and the ETH Zurich was published in the journal Nature. The study proposed an innovative framework called Moscot (optimal transport of multiomic single cell) and successfully reconstructed the developmental trajectory of 1.7 million mouse embryonic cells at 20 time points. This achievement marks a major breakthrough in the field of single-cell genomics and provides a new perspective for understanding the dynamic processes of cell development.
The design of the Moscot framework is inspired by the 18th century optimal transmission theory, which aims to efficiently move objects from one place to another. The researchers achieved the integration of multimodal data by converting biological mapping and alignment tasks into optimal transmission problems and using a series of consistent algorithms to solve these problems. Compared with previous methods, Moscot not only improves the scalability of computing, but also unifies its application in the fields of time and space, solving several key challenges currently faced in single-cell genomics.
Traditional methods often only provide limited cell snapshots and cannot fully understand the dynamic changes of cells during development, says Dominik Klein, the lead author of the study. Through Moscot, the research team was able to more accurately depict the developmental trajectory of mouse embryos and reveal the interactions of cells in different spaces and times. For example, in their study of pancreatic development in mice, they successfully portrayed the development process of hormone-producing cells and discovered NEUROD2, a key regulator in human-induced pluripotent stem cells. This finding provides a new perspective for understanding the underlying mechanisms of diabetes.
In addition, Moscot's open source features make it available to a wider scientific research community. The research team hopes to use this framework to promote in-depth research on disease mechanisms to achieve more targeted treatment methods. This innovative tool not only brings new breakthroughs in the field of single-cell genomics, but also opens up new paths for future biomedical research.