Learning cytometers and beyond



開催日時 2023年7月18日(火)17:00~18:00
開催場所 1号館講堂 ハイブリッド開催 Zoom URL:Join Zoom Meeting https://weillcornell.zoom.us/j/93155345117 ミーティングID: 931 5534 5117 (パスコード 230426)
講師 太田 禎生
所属・職名 東京大学先端科学技術センター・准教授 システム生物医学ラボラトリー・ディレクター
演題 Learning cytometers and beyond
世話人 主たる世話人 岩間 厚志(幹細胞分子医学分野)       
       高橋   聡(臨床精密研究基盤社会連携研究部門)


Conventionally, cytometry has been a tool used based on human hypotheses. Typically, we use a biomarker to label cells and measure the intensities of markers and other representative values for defining the cells to be sorted. This approach is often described as "finding a needle in a haystack." In other words, we are actually dismissing cells as hays because they are difficult to label.
I started thinking about this about 10 years ago. What if we don't have good markers to define the cells of interest? What if we don't know which cells to label? What if we don't know how to recognize image patterns in the case of high-content cytometry? My idea was to reverse this line of thinking and develop a data-driven learning cytometry technology. In today's talk, I will introduce several of these learning cytometry techniques.
First, I will talk about ghost cytometry (GC). GC can be used to classify cell types, states, differentiation, etc. (as long as the morphological information is discernible) and selectively sort cells with high throughput. I show you GC-based machines that integrate various data analysis methods to robustly discriminate cells without markers. I also hope to introduce further new technologies, such as very sensitive and high throughput exosome analysis technology, as well as a label-free bacterial cell analyzer, which is expected to be applied to sepsis research.