Celebrating 50 Years of MS Innovation - User interviews June 2020

1. How did you become familiar with Shimadzu?

I first heard about Shimadzu when Koichi Tanaka won the Nobel Prize for Chemistry in 2002. At the same time, Masatoshi Koshiba was awarded the Nobel Prize for Physics. He was at the University of Tokyo, where I was studying fundamental particle physics. Two Japanese researchers winning the Nobel Prize in the same year was an event that has remained in my memory as worthy of commemoration.

One year later, I decided to change my main area of study from physics to biology, and I entered the lab of Prof. Mitsutoshi Setou at Mitsubishi Kagaku Institute of Life Sciences as a research student. At first I was carrying out basic experiments in molecular biology, but with some luck I was able to begin some research using mass spectrometry. At the same time, I embarked on research on MS imaging (MSI), a technology which was then in its infancy, and was involved in the development of specialist MSI instruments. In order to produce instruments domestically, we submitted a proposal together with various Shimadzu engineers to the JST-SENTAN program [a program promoting the development of technology to advance analytical measurements and systems], which began in 2004. That was the beginning of our collaboration with Shimadzu Corporation.

4. What trends are you seeing in your field with regards to the use of mass spectrometry?

The potential applications of MSI are unlimited. Every day, MSI is being used in all sorts of fields. Researchers are increasingly able to create visualizations of molecular distributions to “see inside” samples. Not only can we visualize target molecules, but using statistical methods, we are becoming able to comprehensively extract trace molecular distributions of non-target molecules in the sample. That is, we are able to apply statistical methods to reduce high-dimensional data to fewer dimensions. In general, principal component analysis, a type of linear clustering, is the most common, but in recent years there are reports of analyses using non-linear clustering such as t-SNE (t-distributed Stochastic Neighbor Embedding) or UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction).

When I consider that back in 2004, we were carrying out manual data analysis on a huge number of peaks, it’s clear that we have made great progress. These dimensional reduction methods have contributed greatly to data processing in the analysis of medical specimens (mainly cancerous tissues). In this area, I have particular expectations for Shimadzu’s new data processing software IMAGEREVEAL MS.

5. What are your expectations for Shimadzu and for MS technology in general in the future?

In the field of MSI, my expectations come down to improvements in sensitivity and quantitivity. Initially it was thought that we would only be able to view phospholipids with MSI, but with the advancement of technology (both instruments and pretreatment methods), we are able to see a range of molecules, from metabolites to proteins. But I believe that by upping the sensitivity even further, we will be able to increase the numbers of molecules we can detect and process the results with non-target analysis, thereby making new discoveries that have so far eluded us. Of course, in order to interpret the data, collaborations between researchers in different fields are of great importance. I think imaging results will be among the most effective in uniting chemists and researchers with other specialties.

Meanwhile, there have been numerous reports with regards to quantitivity, but the situation is still rather chaotic. If we could achieve the kind of high-level quantitative analysis with laser ionization technology that we can with ICP-MS, it would become a much more powerful tool.

In terms of expansion, it would be good to see the iMScope TRIO and successive instruments become widely used in Asia and around the world. I hope to see Shimadzu become a world leader as a provider of both MSI instruments and new technologies.