Assoc. Prof. Berin Boughton

Berin obtained his BSc (Hons)(Chemistry and Pharmacology) and PhD (Chemistry) from the University of Melbourne (2010) and recently joined the Australian National Phenome Centre in 2020 as Associate Professor Magnetic Resonance Mass Spectrometry. His research interests are focused upon Bio-Analytical Chemistry in the fields of Metabolomics and Spatial Metabolomics using ultra-high mass resolution Magnetic Resonance Mass Spectrometry (MRMS or FT-ICR-MS) and Trapped Ion Mobility mass spectrometry applied to a wide range of biological systems. Specific areas of research include eye and neurological diseases, endometriosis, plant metabolomics and nutrition.

ORCID – https://orcid.org/0000-0001-6342-9814

Ultra-High Resolution Mass Spectrometry using MRMS

UHRMS using MRMS approaches allow extremely accurate measurement of the mass-to-charge of ions, enabling accurate identification of molecular formula. Ultra-high resolving powers allow separation of ions in the parts-per-billion which provides unsurpassed abilities for metabolite dereplication and identification in metabolomics experiments. This provides significant advantages when using Electrospray Ionisation (ESI) profiling or Matrix Assisted Laser Desorption/Ionisation mass spectrometry imaging approaches.

Flow Injection Analysis approaches for population wide metabolomics.

We are using the UHRMS capabilities to implement rapid Flow-Injection-Analyses on our SolariX MRMS where we are able to measure individual patient samples in a matter of minutes allowing for rapid measurement of population wide studies. FIA-MRMS allows for a wide cross section of metabolites to be measured at once providing a snapshot of metabolism. Additionally, we are using Continuous Accumulation of Selected Ion (or CASI) methods for deep profiling and accurate identification of molecular formula using Isotopic Fine Structure. An example below shows how the isotopic fine structure of an ion provides an unambiguous identification of the molecular formula based upon the isotopic peak profile at higher isotopologues.

Dias et al., Metabolites, 6 (4), 46, 2016. https://doi.org/10.3390/metabo6040046

Spatial Metabolomics

Spatial mapping using Mass Spectrometry Imaging (MSI) is an powerful technique that can provide unique insights into the functional roles of metabolites in higher organisms. Animals and plants are compartmentalized into specific tissues and cells that perform specialized metabolic processes; understanding the fine spatial distribution of metabolites is especially crucial to advance our understanding in physiology, medicine and biology. The process begins with selecting a suitable sample and preparing thin sections of tissue. For the most common MSI technique, Matrix Assisted Laser Desorption Ionisation (MALDI) a chemical matrix is deposited on the surface, then a series of position correlated mass spectra are acquired by rastering a UV laser across the surface. An image of any single ion can be generated by mapping the distribution as a function of signal intensity. One of the most important steps allowing biological interpretation is metabolite annotation. This is performed by using a variety of methods to determine specific metabolites contributing to the dataset. Downstream statistical analysis can be performed on single or multiple images using vendor or freeware software, enabling identification of specific ions or metabolites that contribute to different tissues or disease types that correlate with distinct changes in metabolism.

MALDI-MSI can be applied to a wide range of different sample types and questions in biology. We have successfully developed a range of methods and techniques that have allowed access to a wide range of different sample types with our collaborators. Different sample types have included a variety of mammalian systems and tissues, human health, host-parasite interactions, invertebrates, marine ecology, plant-environment interactions, plant nutrition and fundamental plant biology.

Mass Spectrometry Imaging can be applied to a wide range of biological samples. We have successfully used MSI to examine samples across a number of Kingdoms, including Plantae, Fungi, Bacteria and Animalia.

Jarvis et al., Nature, 542 (7641), 307, 2017. https://doi.org/10.1038/nature21370

Boughton et al., Phytochemistry Reviews, 15 (3), 445-488, 2016. https://doi.org/10.1007/s11101-015-9440-2

Sarabia et al., Metabolomics, 14 (5), 63, 2018. https://doi.org/10.1007/s11306-018-1359-3

Madio et al., Cellular and Molecular Life Sciences, 75 (24), 4511-4524, 2018. https://doi.org/10.1007/s00018-018-2897-6

Ritmejeryte el al., Annals of Botany, 126 (3), 387-400, 2020. https://doi.org/10.1093/aob/mcaa038

Boughton & Hamilton, Metabolomics: From Fundamentals to Clinical Applications, 291-321, 2017. https://doi.org/10.1007/978-3-319-47656-8_12

3-Dimensional Spatial Distributions

The MSI technique can also be expanded to measure the 3D distributions of metabolites in whole organs. This is achieved by measuring serial 2D cross sections then stacking these individual images to reconstruct a 3D volume. An exemplar of the 3D approach applied to the mouse eye is shown below. The eye is a complex organ containing multiple different tissue and cell types, ranging from the liquid-gel like anterior and vitreous humors, smooth muscle, the multiple cell layers of the retina, optic nerve and the hard lens. Serial sections and mapping of metabolite distributions enables determination of metabolite map of the eye and demonstrates unique distributions of ions in all tissue types, including within the vitreous humor.

<Insert 3D images here>

Boughton et al., Journal of Mass Spectrometry, 55 (4), e4460, 2020. https://doi.org/10.1002/jms.4460

Informatics for Spatial Metabolomics

We are actively developing specific computational methods for examining our spatial metabolomics data and actively contribute to community developments through the public METASPACE (https://metaspace2020.eu/) metabolite annotation platform.

Alexandrov et al., bioRxiv, https://doi.org/10.1101/539478.

Gustaffson el al., Gigascience, 7 (10), giy102, 2018. https://doi.org/10.1093/gigascience/giy102

Endometriosis

Endometriosis is a common disease defined by the presence of benign lesions of endometrial-like glands and stroma outside of the endometrial cavity of the uterus.(Giudice et al., Lancet 364 (9447), 1789-99, 2004) Endometriosis has been estimated to affect more than 11% of women in Australia, with a range of symptoms that include chronic pelvic pain, dysmenorrhea and infertility. (Rowlands et al., BJOG, 2020, doi: 10.1111/1471-0528.16447). The economic and social impacts are wide. At present, the current gold standard for diagnosis is expensive and invasive laparoscopic surgery, which has been shown to lead to lengthy delays in diagnosis. I actively collaborate with Dr Sarah Carson, University of Melbourne and Dr Jane Girling, University of Otago where we are using metabolomics, proteomics and mass spectrometry imaging to examine this disease with the aim of determining new diagnostic markers to provide non-invasive and earlier methods of diagnosis.

Further information can be found at Endometriosis Australia: https://www.endometriosisaustralia.org/

Mass spectrometry imaging data from Formalin Fized Paraffin Embedded endometriosis biopsies showing an increased signal intensity for stearic acid in diseased tissues.