Difference between revisions of "Shuichi Shimma"

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(Created page with "thumb| Shuichi Shimma ==Short Biography== ''' Biography''' Johannes studied Electrical and Biomedical Engineering at the Technical University...")
 
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''' Biography'''  
 
''' Biography'''  
  
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Johannes studied Electrical and Biomedical Engineering at the Technical University of Graz, Austria. He obtained his MSc in Bioinformatics in 2003 and after that conducted a PhD in Bioinformatics with a focus on cancer research in particular childhood leukemia. From 2007 to 2015 he was working, first as a Post Doc and then as a junior group leader for Bioinformatics, at the Medical University of Innsbruck, Austria. His main research areas during this time were transcriptomics and genomics in the field of childhood leukemia. In 2015 he moved to the Institute for Biomedicine of the Eurac Research in Bolzano, Italy and shifted his focus first on genetics and subsequently to metabolomics research. In 2018 he became the head of the Computational Metabolomics Team of the Institute for Biomedicine at Eurac Research. Johannes has a long-lasting experience in open software development in the fields of transcriptomics, genomics, and metabolomics. Since 2020 he got more involved in the Bioconductor project and is since a member of the Community Advisory Board, the Code of Conduct Committee, and the Package Review Working Group. He is author of, and contributor to more than 15 Bioconductor packages most of them providing functionality for the analysis of mass spectrometry and metabolomics data. In his free time, he enjoys designing stickers and logos, mountaineering and spending time with his family.
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Shuichi Shimma received his B.S. degree in 2001 and M.S. degree in 2003 from University of Tsukuba, Japan. He completed the doctoral course at the graduate university for advanced studies, and received his Ph.D in 2007. He served as JSPS post-doctoral fellow and assistant professor at Osaka University between 2007 and 2012. From 2012, he entered National Cancer Center Research Institute in Tokyo and started to apply imaging mass spectrometry for clinical pharmacology. From 2015, he started a position of associate professor at Osaka University, Japan. His research interest is to develop instruments and applications for mass spectrometry imaging in plant and food science, medical science to visualize biomolecules (biological metabolites) and drugs.
  
 
==Expert Opinion==
 
==Expert Opinion==
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''' 1. When and why did you start using metabolomics in your investigations?'''
 
''' 1. When and why did you start using metabolomics in your investigations?'''
  
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I first got in contact with metabolomics data when I joined the Institute for Biomedicine of Eurac Research. I had long lasting experience in the analysis of large-scale data sets (mostly microarray and RNA-seq data) and was thus appointed to help analyzing the metabolomics data sets that were generated at the Institute, in particular the untargeted LC-MS data. I started investigating and looking for tools to analyze that data and had the impression that the software available at that time, especially when compared to the software for the processing of transcriptome data, was sub-optimal. This was when I then first contacted Steffen Neumann and Laurent Gatto and discussed with them the possibility to join forces to update and improve MS-related software in R. In particular, I wanted to avoid the code-duplication being present in the various software packages and to unify the code base of R/Bioconductor packages for the analysis of mass spectrometry (MS) data (both for metabolomics and proteomics). The rest is history. We've updated since the xcms and MSnbase R packages to support also the analysis of very large data sets and from there, started to implement, together with an ever-growing number of collaborators and contributors, a large panel of other software packages that together, as we believe, provide a comprehensive and flexible infrastructure for MS data handling and analysis.
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I studied physics until my master’s degree. I collaborated with Shimadzu to develop a mass spectrometry imaging (MSI) system in my doctoral program. I initially aimed at protein imaging, but the detection sensitivity was low, so I started phospholipids imaging in mouse brains and cancer tissues. Therefore, I can say that I introduced metabolomics (especially phospholipids analysis) to evaluate the performance of the developed instrument during my doctoral course.
  
 
===Question 2===
 
===Question 2===
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''' 2. What have you been working on recently? '''
 
''' 2. What have you been working on recently? '''
  
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Recently, we've implemented a set of R packages providing established methods and core functionality for the annotation of untargeted metabolomics data. Rather than being a single application, these packages provide modular functions that can be used to create customized, flexible, and reproducible annotation workflows. In addition, we're currently analyzing the targeted and untargeted metabolomics data sets from our in-house population study.
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Recently, I have applied MSI in various fields (biology, medicine, botany, food science). Among them, I have realized new enzyme histochemistry method with MSI (Takeo et al. Anal Chem 2020 and Takeo et al. ACS Chem Neurosci 2021) and are applying it to plant science.
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I am also involved in the performance evaluation and application development of a new MSI instrument, the iMScope QT (Shimadzu, Kyoto, Japan).
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===Question 3===
 
===Question 3===
  
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''' 3. What are the main challenges you see on the data analysis of untargeted metabolomics data from populational studies?  '''
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''' 3. At the beginning of your career, you are involved in physics science and then changing into life sciences and biology, How do you overcome the challenges and dissimilarities between both of study?  '''
  
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It's their magnitude. On one hand this data is computationally intense, but that's something we can easily work on and fix by simply implement more efficient or less memory demanding software. The bigger problem for me is that such data tends to be so large that it becomes hard to do a proper and comprehensive quality control. And that is obviously essential if we want to evaluate whether the pre-processing (peak detection, alignment, and correspondence) actually worked for all files. Another important fact, which however also applies to targeted metabolomics data, is that data from population studies will always be less controlled than for example data in case-control or clinical studies. Hence, evaluating influences of potential confounding factors is in my opinion very important, especially for metabolomics data because, as we know, it is more affected by environmental factors than for example genetic, transcriptome or proteome data.
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I get asked that question all the time. It would be interesting to hear my background. In my case, I majored in particle physics and developed instruments. Therefore, I switched fields to life sciences starting with instrumental development. The fundamentals of instrumental development are the same in any field. It simply differs in its application. For this reason, I studied instrumentation and brain science, which I was considering as a field of application at the time. At that time, I felt a big difference from physics. As I have already mentioned, I was studying particle physics. In particle physics, all phenomena are described by a very simple quantum field theory and gauge theory (the formulas are beautiful). On the other hand, I rarely saw formulas in biology. Of course, there are many different fields within biology, so I am describing one aspect of molecular biology that I worked on. At first, I was puzzled by this point, but I got used to it while doing research. In addition, I learned experimental techniques of molecular biology through research, not textbooks.
  
 
===Question 4===
 
===Question 4===
  
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''' 4. As one of the people constantly working on software/packages development in R for metabolomics, could you share some recent updates that may be interesting for the community?  '''
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''' 4. You are actively involved in developing the mass spectrometry imaging (MSI) methods for various biological samples, what are the challenges in developing the MSI methods, and how did you overcome them?  '''
  
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This might be partially also answered by point 2 above. In addition, what we aim at present is to define an infrastructure that enables to access various reference libraries (such as spectral libraries and compound annotations from e.g., HMDB, MassBank etc) in a more standardized way. Ultimately, this should help the end user, as they would no longer loose time in converting, importing, and reformatting data. My vision would be to distribute such annotation resources in a user friendly and reproducible way. For genomic, transcriptomic and proteome annotations this is already possible through Biocondutor's AnnotationHub resource. We are now planning to do the same for metabolite or small compound annotations. In addition, we're working hard to better integrate some of these fantastic tools that are out there, like SIRIUS or MASST, into R which would enable to use them without the need to manually export, upload, execute and re-load the results again into R.  
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My group's motto is "Seeing is believing. I want to make the various molecules in different samples visible. However, I am not in a situation where I can see everything at this point. Furthermore, even if I could, the process would be a trial-and-error process. At present, there is no other solution. I am trying to say that no matter how good the instrument is, what is important is the sample preparation method. Fortunately, I have gained much know-how through my research so far. However, I think that is not enough. It is just my imagination, but I would like to propose optimal sample preparation using AI and other methods in the future. I do not know if it is possible.  
  
 
===Question 5===
 
===Question 5===
  
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''' 5. What tips/advices would you give for ECR who would like to start working with R in metabolomics?  '''
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''' 5. Mass spectrometry imaging is indeed an interesting field, what is your advice to the early-career researcher that wants to be involved in this field?  '''
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I think it's good that you are as interested in different fields as I am. I also did research at CERN in Geneva, Switzerland, during my master's period. The time spent at CERN allowed me to study instrumental development and theory in particle physics (Actually, I felt that the experimental and theoretical researchers were quite different!). The two years I spent immersed in research in Switzerland are still a treasure for me.
  
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The power of R is the possibility to create flexible, customized, and reproducible analysis workflows by using and integrating methods from this huge number of packages that are out there. For that, obviously, some understanding of R is needed. For people that don't have experience with R, one of the introductory courses/workshops from Data Carpentry (https://datacarpentry.org/) might be a good starting point. Also, each R package (should) provides documents describing how it can be used based on some use-cases (the so-called package "vignettes"). It's always a good thing to first go through these to get a feeling how a package can be used and what functionality it provides. In addition, there are a lot of other tutorials and workshops out there, also for the analysis of metabolomics data, that can be used as a starting point to set up own, custom, workflows. Most importantly, don't be afraid to get in contact with the package developers if something is unclear. Most will help you out if something is not working.
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Back on topic, I consider MSI to have a cross-disciplinary aspect as it can be applied to various samples. It is not just a matter of obtaining ion distributions but also of knowing the anatomy of the sample in order to interpret the data. Furthermore, in recent years, methods have been reported to analyze many mass spectra obtained by MSI, considering them as big data. I believe that anyone from basic life science researchers to information scientists can be involved in MSI research. This situation is especially remarkable in Europe and the United States.
  
 
==See also==
 
==See also==
 
   
 
   
 
[[Category:Expert Opinion]]
 
[[Category:Expert Opinion]]

Revision as of 00:16, 8 March 2022

Shuichi Shimma

Short Biography

Biography

Shuichi Shimma received his B.S. degree in 2001 and M.S. degree in 2003 from University of Tsukuba, Japan. He completed the doctoral course at the graduate university for advanced studies, and received his Ph.D in 2007. He served as JSPS post-doctoral fellow and assistant professor at Osaka University between 2007 and 2012. From 2012, he entered National Cancer Center Research Institute in Tokyo and started to apply imaging mass spectrometry for clinical pharmacology. From 2015, he started a position of associate professor at Osaka University, Japan. His research interest is to develop instruments and applications for mass spectrometry imaging in plant and food science, medical science to visualize biomolecules (biological metabolites) and drugs.

Expert Opinion

Question 1

1. When and why did you start using metabolomics in your investigations?

I studied physics until my master’s degree. I collaborated with Shimadzu to develop a mass spectrometry imaging (MSI) system in my doctoral program. I initially aimed at protein imaging, but the detection sensitivity was low, so I started phospholipids imaging in mouse brains and cancer tissues. Therefore, I can say that I introduced metabolomics (especially phospholipids analysis) to evaluate the performance of the developed instrument during my doctoral course.

Question 2

2. What have you been working on recently?

Recently, I have applied MSI in various fields (biology, medicine, botany, food science). Among them, I have realized new enzyme histochemistry method with MSI (Takeo et al. Anal Chem 2020 and Takeo et al. ACS Chem Neurosci 2021) and are applying it to plant science. I am also involved in the performance evaluation and application development of a new MSI instrument, the iMScope QT (Shimadzu, Kyoto, Japan).


Question 3

3. At the beginning of your career, you are involved in physics science and then changing into life sciences and biology, How do you overcome the challenges and dissimilarities between both of study?

I get asked that question all the time. It would be interesting to hear my background. In my case, I majored in particle physics and developed instruments. Therefore, I switched fields to life sciences starting with instrumental development. The fundamentals of instrumental development are the same in any field. It simply differs in its application. For this reason, I studied instrumentation and brain science, which I was considering as a field of application at the time. At that time, I felt a big difference from physics. As I have already mentioned, I was studying particle physics. In particle physics, all phenomena are described by a very simple quantum field theory and gauge theory (the formulas are beautiful). On the other hand, I rarely saw formulas in biology. Of course, there are many different fields within biology, so I am describing one aspect of molecular biology that I worked on. At first, I was puzzled by this point, but I got used to it while doing research. In addition, I learned experimental techniques of molecular biology through research, not textbooks.

Question 4

4. You are actively involved in developing the mass spectrometry imaging (MSI) methods for various biological samples, what are the challenges in developing the MSI methods, and how did you overcome them?

My group's motto is "Seeing is believing. I want to make the various molecules in different samples visible. However, I am not in a situation where I can see everything at this point. Furthermore, even if I could, the process would be a trial-and-error process. At present, there is no other solution. I am trying to say that no matter how good the instrument is, what is important is the sample preparation method. Fortunately, I have gained much know-how through my research so far. However, I think that is not enough. It is just my imagination, but I would like to propose optimal sample preparation using AI and other methods in the future. I do not know if it is possible.

Question 5

5. Mass spectrometry imaging is indeed an interesting field, what is your advice to the early-career researcher that wants to be involved in this field?

I think it's good that you are as interested in different fields as I am. I also did research at CERN in Geneva, Switzerland, during my master's period. The time spent at CERN allowed me to study instrumental development and theory in particle physics (Actually, I felt that the experimental and theoretical researchers were quite different!). The two years I spent immersed in research in Switzerland are still a treasure for me.

Back on topic, I consider MSI to have a cross-disciplinary aspect as it can be applied to various samples. It is not just a matter of obtaining ion distributions but also of knowing the anatomy of the sample in order to interpret the data. Furthermore, in recent years, methods have been reported to analyze many mass spectra obtained by MSI, considering them as big data. I believe that anyone from basic life science researchers to information scientists can be involved in MSI research. This situation is especially remarkable in Europe and the United States.

See also

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