Mass Spec Strategies for Plant Metabolomics

Jun 22, 2016 | Blogs, Life Science Research, Metabolomics | 0 comments


The metabolome is the set of all low molecular weight compounds (typically less than ~2000 Da) that are present within an organism, tissue, or cell. Within the mass spectrometry (MS) community, the systematic and comprehensive analysis of the metabolome – i.e., metabolomics – has become an important and increasingly popular area of research. Because the metabolome is dynamic and ever-changing, it provides a snapshot into the state of the organism at the time of measurement. Compounds may appear or disappear, or quantities may change, depending on environmental factors and internal processes. Thus, the metabolome can be thought of as a unique chemical signature providing important information about the health and inner-workings of a biological system at any given moment in time.

While metabolomics has been used extensively to aid in our understanding of human health and disease, it has also become indispensable in the world of plant research. It has been estimated that plants produce more than 200,000 metabolites1 with enormous chemical diversity, such as carbohydrates, lipids, organic acids, volatile alcohols and ketones, and complex natural products. Metabolomics has been used to help find, identify, and characterize unique compounds from plants that have advantageous drug or health properties. Many drugs that are now available were derived from plant compounds. A study from 2008 estimated that natural products accounted for about 60% of the drugs that are now available, or were the inspiration for the synthesis of novel drug candidates.2 To aid in this endeavor, reference databases and software have been developed specifically to aid in plant metabolomics such as the Medicinal Plant Metabolomics Resource.3

When considering plants as food, metabolomics has had enormous benefits for understanding plant responses to environmental stressors such as drought, flooding, extreme temperature or light conditions, and resistance to pests and disease. Identifying and characterizing stress-responsive metabolites help scientists to develop new plant strains that can be more resilient. Additionally, by studying the metabolome, specific crop traits can be optimized to maximize production of nutritionally relevant metabolites.

Many techniques have been used for metabolomics studies depending upon the ultimate goal of the research. For discovery metabolomics, because the metabolome can consist of thousands of small molecules – many of which have similar mass – the use of liquid chromatography (LC) coupled with accurate mass, high-resolution mass spectrometers (MS), such as the SCIEX TripleTOF® System, are particularly advantageous. Additionally, the fast scan speed, high sensitivity, and broad dynamic range of the TripleTOF instrument make it ideal for metabolomics studies. 

Traditionally, discovery metabolomics experiments have been performed using a data-dependent analysis (DDA) workflow, where, with each cycle of the instrument, a subset of compounds eluting from the LC column at that moment in time are selected and fragmented for analysis within the MS. This produces a dataset containing accurate mass precursor information and accurate mass fragment data that can be mined for the identities and relative abundances of metabolite compounds, with identification typically accomplished through library searching and matching. But because the metabolome is so incredibly complex with many compounds often co-eluting, some compounds are missed for MS/MS analysis and are invariably not detected.

To address this problem, data-independent acquisition (DIA) methodologies have been developed. This technique bypasses the MS selection step and automatically fragments all precursors within each cycle. The SWATH® Acquisition on a TripleTOF System is a DIA technique and has been extensively employed in the area of proteomics. But it is also incredibly useful for metabolomics as well. As described by Professor Oliver Fiehn from the University of California Davis in a recent webinar4the SWATH Acquisition workflow provides a distinct advantage over other data-independent workflows. Whereas some data-independent workflows allow all precursors to be fragmented at once, SWATH employs MS “windows” to allow a smaller range of precursors through at a time. This window is then quickly stepped across the entire mass range to allow all precursors to be sequentially fragmented within the scan cycle. The advantage of using small windows is that the data are less complex, and the assignment of fragments to precursors is simplified. This enables better data quality and more confident identifications. And because of the high scan speed of the TripleTOF system, it can use many more small windows for mass isolation than other data independent workflows. 

In his webinar, Professor Fiehn demonstrates the use of SWATH with a TripleTOF 5600 system and MS DIAL software for metabolomics studies. As shown, SWATH increased the number of identified algae lipids by 66% compared to DDA MS/MS experiments. However, DDA still has some merit. As shown in Professor Fiehn’s recent paper5, although SWATH Acquisition identified three times more lipids vs. conventional DDA, there was a very small subset of lipids identified by DDA that were not identified with SWATH. These lipids were not in the SWATH library used for identification. However, once found, they can be added to the library enabling reproducible quantitation going forward. Thus, the two techniques are quite complimentary in nature to one another. Because the TripleTOF system is adept at both workflows, one system can provide the researcher with the choice of either workflow.

Another example shows the use of the TripleTOF 6600 system, XCMSPlus Software, and the use of the Accurate Mass Metabolite Spectral Library (AMMSL).6 In this study, researchers from SCIEX and the Universität Hohenheim identified and confirmed potential markers in rose and sunflower leaf extracts and their relative quantitative amounts. Combining XCMSPlus Software and the AMMSL was beneficial for the confirmation of several antioxidants and endogenous metabolites present in both rose and sunflower leaf extracts. Phenolic antioxidants, especially those levels present in rose petal extracts of deep color (intense red to mauve), may be responsible for decreasing oxidative stress which plays a significant role in many metabolic diseases, thus justifying their use in traditional medicines.7

Historically, liquid chromatography has been the separation technique of choice with mass spectrometry. However, capillary electrophoresis coupled with electrospray mass spectrometry, or CESI-MS, has also been used for metabolomics. In a recent webinar, Dr Stephen Lock explains the technique and its advantages and then applies CESI-MS using a TripleTOF 5600+ system to the analysis of polar metabolites found in plants.8 Several of the samples analyzed contained isomeric and isobaric compounds which can be problematic using conventional reverse phase LC. This is further demonstrated in a recent technical note.9However, Dr. Lock is quick to point out that CESI-MS is very complimentary to LC-MS and that the two techniques together provide the most coverage for omics research.

At the center of it all, is the SCIEX TripleTOF System. Whether using SWATH or DDA for acquisition, LC or CESI for separation, the TripleTOF instrument provides the power and flexibility to accomplish state-of-the-art plant metabolomics studies.


  1. Fiehn, O (2002) Metabolomics—The Link Between Genotypes And Phenotypes, Plant Molecular Biology, 48, no. 1-2, 155–171
  2. Newman, DJ (2008) Natural Products As Leads To Potential Drugs: An Old Process Or The New Hope For Drug Discovery? J Med Chem, 51, 2589–2599 
  4. Fiehn, O (2014) Solutions For Metabolomics And Lipidomics Research: Discovery Simplified: SWATH MS DIAL (Webinar)
  5. Tsugawa, H, Cajka, T, Kind, T, Ma, Y, Higgins, B, Ikeda, K, Kanazawa, M, VanderGheynst, J, Fiehn, O, Arita, M (2015) MS-DIAL: Data-Independent MS/MS Deconvolution For Comprehensive Metabolome Analysis, Nature Methods 12, 523–526
  6. Miller, JD, Papan, C, Klaiber, I, Ubhi, BK, Pfannstiel, J (2015) Identification And Confirmation Of Potential Markers In Rose And Sunflower Leaf Extracts: Using The Accurate Mass Metabolite Spectral Library And TripleTOF® 6600 System 
  7. Cunja, V, Mikulic-Petkovsek, M, Stampar, F, Schmitzer, V (2014) Compound Identification of Selected Rose Species and Cultivars: an Insight to Petal and Leaf Phenolic Profiles, JASHS 139 no.2, 157-166
  8. Lock, S (2015) Significantly Simplify Your Plant Metabolomics Workflow (Webinar)
  9. Lock, S, Ramautar, R (2015) A New Approach To The Analysis Of Anionic Metabolites By CESI-MS With Negative Ion Electrospray Ionization

ProteinPilot phosphopeptide library and DIA-NN

I have prepared a spectral library for a phosphopeptide enriched sample and I am generating my SWATH samples from similarly enriched samples. The problem is that when I use DIA-NN for the retention time alignment and quant, it doesn’t recognise the terminology of the spectral library annotated by ProteinPilot. DIA-NN recognises Unimod:21 for phosphorylation, but PP uses phospho(Tyr) etc. Other than changing the data dictionary to get around the mismatch, anyone have any suggestions for how I might resolve this? Thank you, Roz

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SCIEX Senior Market Development Manager - Life Sciences Research Americas Baljit has over 20 years of experience in the life science industry with respect to mass spectrometry. Baljit shares her insights on how metabolomics tackles some of the current issues associated with healthcare and influences how we define and quantitatively measure wellness and illness.



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