Difference between revisions of "Luke Whiley"

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(Created page with "thumb| Luke Whiley (BSc, PhD) ==Short Biography== ''' Biography''' I am a researcher based at Murdoch University, Perth, Australia and specialis...")
 
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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?'''
  
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.
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I first started down the path of metabolomics during my PhD research in 2009. My research aim was to identify blood-based biomarkers of Alzheimer’s disease. Before my PhD, I had already had some experience in analytical chemistry and small molecule LC-MS, and the PhD project had access to an LC-QToF-MS, so it was a natural fit that kicked off a metabolomics and lipidomics journey!
  
 
===Question 2===
 
===Question 2===
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''' 2. What have you been working on recently? '''
 
''' 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 [1][2] and are applying it to plant science.
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I’m currently based in Australia. I moved over from the UK in 2019 to help set up the Australian National Phenome Centre (ANPC) in Perth. Since I have been here, I have been working on building up metabolomics mass spectrometry methods and creating new collaborations, both nationally and internationally, as the centre goes through its formative years.  
I am also involved in the performance evaluation and application development of a new MSI instrument, the iMScope QT (Shimadzu, Kyoto, Japan).
 
  
===Question 3===
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My primary research interest in metabolomics remains in the neurodegenerative space, specifically - studying the mechanism and metabolism of neurodegeneration. The aim of this research is two fold; first, can we identify early mechanistic pathways that contribute to disease; and second to build an understanding of the wider impact of neurodegeneration on systemic metabolism.
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A recent interesting project that highlights this is the application of mass spectrometry metabolomics to serum collected from models of traumatic brain injury, with the aim to identify possible blood-based prognosis predictors of injury.
  
''' 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.
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===Question 3===
  
===Question 4===
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''' 3. You are actively involved in neurodegenerative disease-related research, such as Alzheimer's and dementia. What are the advantages of involving metabolomics in this field?  '''
  
''' 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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Our knowledge of Alzheimer’s disease is rapidly advancing all the time, but there are still some major gaps – particularly in our understanding of those who are most at risk of developing the disease and why they develop it. Although we now are adept at identifying genetic risk factors, how those risk factors translate to disease incidence and detailing the mechanisms that underpin them often remains unclear.
  
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.  
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Metabolomics gives us the opportunity to investigate this from a metabolic pathway and mechanistic viewpoint. I think over the coming years we will see much more research investigating the specific metabolic mechanisms of genetic and environmental risk of disease. This will really help us build the specific pathways that influence disease and the subsequent mechanistic picture as to why certain individuals go on to develop the disease and then perhaps we can then modify these pathways, and delay the progression of disease.
  
===Question 5===
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===Question 4===
  
''' 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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''' 4. You often use urine or serum as the samples for untargeted metabolomics studies for phenotyping Alzheimer's and dementia diseases. What are the challenges in translating the results found using this kind of samples into a more local understanding of the nervous system once samples like CSF and brain tissue are very unavailable?  '''
  
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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This is certainly a huge challenge in the field – and something that is really interesting to consider when performing such research. Typically, such analysis reveals systemic changes, rather than direct metabolites of the neurodegeneration itself. The challenge to unpick these patterns that we see in the data and determine if they are in anyway causative factors in the disease or if they are a response to disease pathology itself.  
  
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.
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One such way we can attempt to address this is to work on longitudinal cohorts, and to study the metabolism of “healthy” populations, before they develop neurodegenerative diseases. By retrospectively looking at this data in combination with up-to-date current clinical data of the participants diagnostic outcome, we can try to observe metabolic patterns in populations that could indicate those who later go on to develop neurodegenerative conditions.
 +
  
===References===
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===Question 5===
1. E. Takeo, Y. Sugiura, Y. Ohnishi, H. Kishima, E. Fukusaki , S. Shimma*, Mass spectrometric enzyme histochemistry for choline acetyltransferase reveals de novo acetylcholine synthesis in rodent brain and spinal cord. ACS Chem Neurosci, 2021, 12, 2079-2087.
 
  
2. E. Takeo, E. Fukusaki, S. Shimma*, Mass Spectrometric Enzyme Histochemistry Method Developed for Visualizing In Situ Cholinesterase Activity in Mus musculus and Drosophila melanogaster. Anal Chem, 2020, 92, 12379-12386.
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''' 5. Would you mind sharing any interesting findings in the application of metabolomics in neurodegenerative disease?  '''
  
3. E. Sato, Y. Tsunokuni, M. Kaneko, D. Saigusa, R. Saito, S. Shimma, A. Sekimoto, Y. Kawana, Y. Oe, S. Ito, H. Sato, N. Takahashi, Metabolomics of a mouse model of preeclampsia induced by overexpressing soluble fms-like tyrosine kinase 1. Biochem Biophys Res Commun, 2020, 527 1064-1071.
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We collaborate with a group in Singapore who are interested in the influence of the gut microbiome on the host neurological system. We recently helped them by applying mass spectrometry metabolomics platforms to measure specific plasma metabolites. The project was able to demonstrate that these metabolites, secreted by gut-microbes, go on to influence adult neurogenesis. The data indicates that a symbiotic gut–brain coregulatory axis exists, connecting the metabolic status of gut microbes to the control of neurogenesis in the brain.  
  
4. S. Jantrapirom, Y. Enomoto, J. Karinchai, M. Yamaguchi, H. Yoshida, E. Fukusaki, S. Shimma*, M. Yamaguchi*, The depletion of ubiquilin in Drosophila melanogaster disturbs neurochemical regulation to drive activity and behavioral deficits. Sci Rep, 2020, 10, 5689-5689.
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The gut-brain axis is a really interesting area of research, and the concept that the microbes in our gut can influence our neurological system, mood and even regulate neurogenesis is fascinating.
  
5. E. Takeo, Y. Sugiura, T. Uemura, K. Nishimoto, M. Yasuda, E. Sugiyama, S. Ohtsuki, T. Higashi, T. Nishikawa, M. Suematsu, E. Fukusaki, S. Shimma*, Tandem Mass Spectrometry Imaging Reveals Distinct Accumulation Patterns of Steroid Structural Isomers in Human Adrenal Glands. Anal Chem, 2019, 91, 8918-8925.
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===Question 5===
  
6. E. Takeo, S. Shimma*, Development of quantitative imaging mass spectrometry (q-IMS) for drug visualization using animal tissues. Surf Interface Anal, 2019, 51, 21-26.
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''' 6. What you would say are next hot topics in the field of neurodegenerative diseases that early career researchers in the field of metabolomics could strongly contribute to?  '''
  
7. Y. Sugiura, E. Takeo, S. Shimma, M. Yokota, T. Higashi, T. Seki, Y. Mizuno, M. Oya, T. Kosaka, M. Omura, T. Nishikawa, M. Suematsu, K. Nishimoto, Aldosterone and 18-Oxocortisol Coaccumulation in Aldosterone-Producing Lesions. Hypertension (Dallas, Tex. : 1979), 2018, 72, 1345-1354.  
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I think data integration will become a hot (hotter?) topic in the field, both in terms of combining metabolomic data from cohorts from around the world to create larger datasets to combining different omic techniques, for example metabolomics, microbiomics, genomics.  
  
8. Y. Enomoto, PN. An, M. Yamaguchi, E. Fukusaki, S. Shimma*, Mass Spectrometric Imaging of GABA in the Drosophila melanogaster Adult Head. Anal Sci, 2018, 34, 1055-1059.
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To achieve this we will need early career researcher contributions in a variety of areas at every stage of the pipeline. For example – we will need analytical chemistry experts who specialise in the analytical platforms and data acquisition, as we will need robust and reproducible data that can be translated to collaborating groups. We will also need researchers who can contribute in the bioinformatics of such projects - to combine and match data from different cohorts, and then work on integrating data acquired from the different omic technologies. By achieving this we can build giant datasets, and mine them to finely detail the mechanism of systemic diseases.
  
9. S. Shimma*, E. Takeo, E. Fukusaki, Protocol for Quantitative Imaging Mass Spectrometry. BUNSEKI KAGAKU, 2016, 65, 745-750.
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Also, research into the metabolism of the human microbiome will grow (more!), we’ve already touched above on the gut-brain axis, and I think that research into host-microbe interactions at a metabolic level will be hot topics going forwards, as will combining these datasets and interpreting the interactions at a metabolite level through metabolomics, and the subsequent impact and influence on disease, will be a fascinating research area.
  
10. S. Shimma, HO. Kumada, H. Taniguchi, A. Konno, I. Yao, K. Furuta, T. Matsuda, S. Ito, Microscopic visualization of testosterone in mouse testis by use of imaging mass spectrometry. Anal Bioanal Chem, 2016, 408, 7607-7615.
 
https://doi.org/10.1371/journal.pcbi.1002375
 
  
 
==See also==
 
==See also==
 
   
 
   
 
[[Category:Expert Opinion]]
 
[[Category:Expert Opinion]]

Revision as of 00:28, 12 April 2022

Luke Whiley (BSc, PhD)

Short Biography

Biography

I am a researcher based at Murdoch University, Perth, Australia and specialise in metabolism and phenomics applications in healthy ageing and dementia. Specifically, my research interests lie in identifying the systemic metabolism that underpins disease risk. I’m particularly interested in the complex interactions between genetics, lifestyle, and environment and how these interactions influence the chance of developing neurodegenerative conditions and dementias including Alzheimer’s disease.

Previously, my research in the metabolic phenotyping has taken me to university research teams in the UK (King’s College London, Imperial College London) and Spain (Universidad CEU, Madrid) before arriving at Murdoch University in October 2019. I specialise in metabolic phenotyping and metabolomics using mass spectrometry, including both discovery and targeted workflows. I enjoy working and continued development of skills across the complete pipeline; from mass spectrometry method development, raw data acquisition, data pre-processing and multivariate and univariate statistical data analysis. In 2021 I was named one of the American Chemical Society’s Rising Stars in Metabolomics and Proteomics for my research.

I am passionate in public outreach programs and the communication of research, and I have been involved with social media campaigns for dementia charities, including the Dementia Revolution London Marathon campaign, and have appeared on ABC Perth Radio, Pint of Science and multiple SciComm podcasts, including the Naked Scientist and Avid research.

Expert Opinion

Question 1

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

I first started down the path of metabolomics during my PhD research in 2009. My research aim was to identify blood-based biomarkers of Alzheimer’s disease. Before my PhD, I had already had some experience in analytical chemistry and small molecule LC-MS, and the PhD project had access to an LC-QToF-MS, so it was a natural fit that kicked off a metabolomics and lipidomics journey!

Question 2

2. What have you been working on recently?

I’m currently based in Australia. I moved over from the UK in 2019 to help set up the Australian National Phenome Centre (ANPC) in Perth. Since I have been here, I have been working on building up metabolomics mass spectrometry methods and creating new collaborations, both nationally and internationally, as the centre goes through its formative years.

My primary research interest in metabolomics remains in the neurodegenerative space, specifically - studying the mechanism and metabolism of neurodegeneration. The aim of this research is two fold; first, can we identify early mechanistic pathways that contribute to disease; and second to build an understanding of the wider impact of neurodegeneration on systemic metabolism.

A recent interesting project that highlights this is the application of mass spectrometry metabolomics to serum collected from models of traumatic brain injury, with the aim to identify possible blood-based prognosis predictors of injury.


Question 3

3. You are actively involved in neurodegenerative disease-related research, such as Alzheimer's and dementia. What are the advantages of involving metabolomics in this field?

Our knowledge of Alzheimer’s disease is rapidly advancing all the time, but there are still some major gaps – particularly in our understanding of those who are most at risk of developing the disease and why they develop it. Although we now are adept at identifying genetic risk factors, how those risk factors translate to disease incidence and detailing the mechanisms that underpin them often remains unclear.

Metabolomics gives us the opportunity to investigate this from a metabolic pathway and mechanistic viewpoint. I think over the coming years we will see much more research investigating the specific metabolic mechanisms of genetic and environmental risk of disease. This will really help us build the specific pathways that influence disease and the subsequent mechanistic picture as to why certain individuals go on to develop the disease and then perhaps we can then modify these pathways, and delay the progression of disease.

Question 4

4. You often use urine or serum as the samples for untargeted metabolomics studies for phenotyping Alzheimer's and dementia diseases. What are the challenges in translating the results found using this kind of samples into a more local understanding of the nervous system once samples like CSF and brain tissue are very unavailable?

This is certainly a huge challenge in the field – and something that is really interesting to consider when performing such research. Typically, such analysis reveals systemic changes, rather than direct metabolites of the neurodegeneration itself. The challenge to unpick these patterns that we see in the data and determine if they are in anyway causative factors in the disease or if they are a response to disease pathology itself.

One such way we can attempt to address this is to work on longitudinal cohorts, and to study the metabolism of “healthy” populations, before they develop neurodegenerative diseases. By retrospectively looking at this data in combination with up-to-date current clinical data of the participants diagnostic outcome, we can try to observe metabolic patterns in populations that could indicate those who later go on to develop neurodegenerative conditions.


Question 5

5. Would you mind sharing any interesting findings in the application of metabolomics in neurodegenerative disease?

We collaborate with a group in Singapore who are interested in the influence of the gut microbiome on the host neurological system. We recently helped them by applying mass spectrometry metabolomics platforms to measure specific plasma metabolites. The project was able to demonstrate that these metabolites, secreted by gut-microbes, go on to influence adult neurogenesis. The data indicates that a symbiotic gut–brain coregulatory axis exists, connecting the metabolic status of gut microbes to the control of neurogenesis in the brain.

The gut-brain axis is a really interesting area of research, and the concept that the microbes in our gut can influence our neurological system, mood and even regulate neurogenesis is fascinating.

Question 5

6. What you would say are next hot topics in the field of neurodegenerative diseases that early career researchers in the field of metabolomics could strongly contribute to?

I think data integration will become a hot (hotter?) topic in the field, both in terms of combining metabolomic data from cohorts from around the world to create larger datasets to combining different omic techniques, for example metabolomics, microbiomics, genomics.

To achieve this we will need early career researcher contributions in a variety of areas at every stage of the pipeline. For example – we will need analytical chemistry experts who specialise in the analytical platforms and data acquisition, as we will need robust and reproducible data that can be translated to collaborating groups. We will also need researchers who can contribute in the bioinformatics of such projects - to combine and match data from different cohorts, and then work on integrating data acquired from the different omic technologies. By achieving this we can build giant datasets, and mine them to finely detail the mechanism of systemic diseases.

Also, research into the metabolism of the human microbiome will grow (more!), we’ve already touched above on the gut-brain axis, and I think that research into host-microbe interactions at a metabolic level will be hot topics going forwards, as will combining these datasets and interpreting the interactions at a metabolite level through metabolomics, and the subsequent impact and influence on disease, will be a fascinating research area.


See also