Keisuke Goda has set the standard for sorting cells based on images. As a professor at the University of Tokyo leading a lab of 53 scientists, Keisuke is developing new tools to explore biology at different scales. During grad school at MIT, he studied gravitational waves in the LIGO group where a collaboration at Caltech led to an opportunity to move into biology at UCLA. By bringing LIGO technology, and a physicist's perspective, to cytometry, Keisuke has been able to pioneer the field of sorting cells by an image. In our conversation, we touch upon his career starting in Japan, 15 years in the US, and finally back to Japan.

Starting around 2007, Keisuke was working on high-speed imaging then moved toward cell sorting after trying to figure out how to integrate imaging into FACS. In short, previous sorting methods don't capture high-resolution spatial features and by adding morphological and other spatial features, we might be able to discover and isolate cells we haven't studied before. Molecular biology has a long history of physicists transforming biology - Keisuke is no different. He made a lot of friends and led a large team of collaborators bringing various fields together from engineering and machine learning to biology. The challenges and opportunities were in managing an interdisciplinary project that had technical roadblocks along the way but more importantly cultural ones. Just as much as Keisuke is world-class at translating different scales of biology, he is equally skilled at translating the languages of various scientific fields.

In 2018, the Goda Lab published research in Cell that established image-activated cell sorting (IACS). This led to the creation of an ecosystem of various groups around the world improving the technology and new applications. And the current state-of-the-art is at sorting thousands of cells per second with IACS with around a 10 ms lag time. Throughput can be increased with tradeoffs in purity and imaging success rate with current projects around developing better neural networks and imaging processing methods to increase the number of cells per second that can be sorted.

Keisuke pursues data-driven research and treats biology as an open field when new techniques and ideas can be mixed-and-matched together. His lab is pursuing new projects related to merging cytometry, microscopy, and sequencing. Essentially, 3 different languages with each at a different scale of time and space. A key lesson from Keisuke's career is making friends to bring different expertises in order to solve important problems. With his view of a long-term opportunity to integrate biological data at different scales, Keisuke and his group have been really good at making friends.

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