Name: natural product discovery with lc-ms/ms diagnostic fragmentation filtering: application for microcystin analysis
Uploaded: 2019-05-31T14:00:00.000+00:00
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Description: Watch this Scientific Journal Video about Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis at JoVE.com

The authors thank Heather Roshon (Canadian Phycological Culture Centre, University of Waterloo for providing the cyanobacteria culture studied and Sawsan Abusharkh (Carleton University) for technical assistance.

1. Preparation of non-targeted liquid chromatography (LC)-MS/MS datasets
NOTE: DFF can be performed using any high-resolution mass spectrometer and analytical method optimized for a target class of analytes. MC optimized LC-MS/MS conditions on Orbitrap mass spectrometer are listed in the Table of Materials.
<ol>
	<li>Downloading MZmine 2 (http://mzmine.github.io/) 
	NOTE: Example data CPCC300.raw can be found at https://drive.google.com/open?id=1HHbLdvxCMycSasyNXPRqIe5pkaSqQoS0.
	<ol>
		<li>Under the Raw data methods drop down menu, select the Raw data import option.</li>
		<li>Choose the data file(s) to be analyzed. Single or multiple files may be imported.</li>
	</ol>
	</li>
	<li>(Optional) If vendor data format is not supported by MZmine, use Proteowizard16 to generate centroided .mzml data files.
	<ol>
		<li>Choose the Peak Picking filter to apply vendor-supplied centroiding algorithm.</li>
	</ol>
	</li>
</ol>
2. Diagnostic fragmentation filtering of imported DDA files
<ol>
	<li>Using the cursor, select and highlight the data file(s) in the Raw data files column of the main MZmine screen.</li>
	<li>Under the Visualization drop down menu, select the Diagnostic fragmentation filtering option.</li>
	<li>In the DFF dialogue box that appears (Figure 3), input the following options:
	<ol>
		<li>Retention time &#8211; use Auto range or define the range of retention times in minutes when the targeted class of analytes will elute.</li>
		<li>Precursor m/z - use Auto range or define the m/z range of the targeted class of analytes, including the possibility for multiple charged compounds when appropriate.</li>
		<li>m/z tolerance &#8211; Input the achievable MS/MS mass accuracy of the MSinstrument; 0.01 m/z or 3.0 ppm is appropriate for an Orbitrap platform. If only diagnostic product ions will be investigated, input 0.0 into the Diagnostic neutral loss value (Da) option. Conversely, if only diagnostic neutral losses will be investigated, input 0.0 into the Diagnostic product ions (m/z) option.</li>
		<li>Diagnostic product ions (m/z) &#8211; Input the class specific product ion(s) m/z. Separate multiple product ions with a comma. 
		NOTE: Inputting multiple product ions will visualize spectra that contain all listed product ions.</li>
		<li>Diagnostic neutral loss value (Da) &#8211; Input the class specific neutral loss(es). Separate multiple neutral losses with a comma. 
		NOTE: Inputting multiple neutral losses will visualize spectra that contain all listed neutral losses. Inputting both diagnostic product ions and neutrals losses will visualize spectra that satisfy all the criteria.</li>
		<li>Minimum diagnostic ion intensity (% base peak) &#8211; As a % of the base peak of the MS/MS spectra, define the minimum intensity for diagnostic product ions and/or neutral losses to be considered.</li>
		<li>Peaklist output file &#8211; Select a path and filename to output the results.</li>
		<li>Click the OK button to start the DFF analysis. A DFF plot will appear upon successfully completing the above steps 
		NOTE: Two .csv data files will be generated. {Peaklist output file}.csv contains the precursor m/z, scan numbers, and retention times of the scans. This can be used in existing MZmine modules including Raw data methods &#62; Peak detection &#62; Targeted peak detection to generate extracted ion chromatograms of precursors that met the defined DFF criteria. {Peaklist output file}_data.csv contains the precursor m/z, product ion m/z and retention times to allow generation of DFF plots outside of MZmine.</li>
	</ol>
	</li>
</ol>
3. Example use of DFF for microcystin analysis
<ol>
	<li>Sample preparation
	<ol>
		<li>Sterilize 250 mL Erlenmeyer flasks containing 30 mL of sterile MA media17 or other cyanobacteria growth media (BG-11) fitted with a foam stopper.</li>
		<li>Inoculate sterilized growth media with a cyanobacteria culture to approximately 5 &#215; 105 cells mL-1 under aseptic conditions. Monitor cell density with a hemocytometer. In this example, grow M. aeruginosa strain CPCC300 photoautotrophically at 27 &#176;C, illuminated with cool white fluorescent light (30 &#181;E m-2 s-1) using a 12 h light: 12 h dark regime. Swirl the cells once per day.</li>
		<li>Separate the cells from the culture medium after 26 days by vacuum filtration using 47 mm diameter GF/C glass microfiber filter papers.</li>
		<li>Add 3 mL of 80% methanol (aq) to harvested cells in 14 mL test tube(s).</li>
		<li>Vortex and subsequently sonicate the test tube(s) containing cyanobacteria cells for 30 s each. Store the test tube(s) at -20 &#176;C for 1 h. Return the test tube to room temperature and allow the sample(s) to thaw for 15 min.</li>
		<li>Repeat step 3.1.5 two additional times to effectively lyse the cells.</li>
		<li>Filter the resulting cyanobacteria cell extract(s) through a 0.22 &#956;m PTFE syringe filter(s).</li>
		<li>Dry extract(s) with an evaporator at a temperature of 30 &#176;C using a gentle stream of nitrogen gas. Store the extract dry at -20 &#176;C until LC-MS/MS analysis.</li>
		<li>Reconstitute the dried residue with 500 &#181;L of 90% methanol (aq) and vortex for 30 s in an amber HPLC vial prior to analysis.</li>
	</ol>
	</li>
	<li>Analyze the cyanobacteria extract using a DDA acquisition method on a high-resolution mass spectrometer. 
	NOTE: The optimized LC-MS conditions for MC analysis used here are listed in Table of Materials.</li>
	<li>Prepare the DDA datafile(s) and import into MZmine following steps 1.1 and 1.2.</li>
	<li>Select the datafiles and start the DFF modules following steps 2.1-2.2.</li>
	<li>For MC analysis, use the following settings within the DFF module (Figure 3).
	<ol>
		<li>Retention time &#8211; Input the range of 2.00 to 6.00 min.</li>
		<li>Precursor m/z &#8211; Input m/z range of 430.00 to 1200.00.</li>
		<li>m/z tolerance &#8211; Apply m/z tolerance of 0.01 m/z or 3.0 ppm.</li>
		<li>Diagnostic product ions (m/z) &#8211; Input m/z of 135.0803, 163.1114 as the diagnostic product ions</li>
		<li>Diagnostic neutral loss value (Da) &#8211; Input 0.0 to define that no diagnostic neutral losses are being used.</li>
		<li>Minimum diagnostic ion intensity (% base peak) &#8211; Use 15.00 as the minimum intensity threshold</li>
		<li>Peaklist output file &#8211; Define the output file as putative_MCs.csv.</li>
	</ol>
	</li>
	<li>Click the OK button to start the DFF analysis. A DFF plot (Figure 4) will appear upon successfully completing the above steps</li>
</ol>

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The authors have nothing to disclose

DFF is a straight-forward and rapid strategy for detecting entire classes of compounds, especially relevant for natural product compound discovery. The most important aspect of DFF is defining the specific MS/MS fragmentation criteria for the targeted class of compounds. In this representative example, DFF was used to detect all Adda residue containing MCs present in an M. aeruginosa cellular extract. Although the vast majority of MCs contain an Adda residue, other residues at this position have been known, nota...

Tandem mass spectrometry (MS/MS) is a widely used mass spectrometry method that involves isolating a precursor ion and inducing fragmentation via application of activation energy such as collision induced dissociation (CID)1. The manner in which an ion fragments is intimately linked to its molecular structure. Natural products are often biosynthesized as mixtures of structurally similar compounds rather than as a single unique chemical2. As such, structurally related compounds that are part of the same biosynthetic class often share key MS/MS fragmentation characteristics, including shared product ions and/or neutral losses. The ability to screen complex samples for compounds that possess class-specific product ions and/or neutral losses is a powerful strategy to detect entire classes of compounds, potentially leading to the discovery of new natural products3,4,5,6. For decades, mass spectrometry methods such as neutral loss scanning and precursor ion scanning performed on low resolution instruments have allowed ions with the same neutral loss or product ions to be detected. However, the specific ions or transitions needed to be defined prior to performing the experiments. As high-resolution mass spectrometers have become more popular in research laboratories, complex samples are now commonly screened using non-targeted, data-dependent acquisition (DDA) methods. In contrast to traditional neutral loss and precursor ion scanning, structurally related compounds can be identified by post-acquisition analysis7. In this work, we demonstrate a strategy we have developed termed diagnostic fragmentation filtering (DFF)5,6, a straight-forward and user-friendly approach to detect entire classes of compounds within complex matrices. This DFF module has been implemented into the open-source, MZmine 2 platform and available by downloading MZmine 2.38 or newer releases. DFF allows users to efficiently screen DDA datasets for MS/MS spectra which contain product ion(s) and/or neutral loss(es) that are diagnostic for entire classes of compounds. A limitation of DFF is characteristic product ions and/or neutral losses for a class of compounds must be defined by the analyst.
For example, each of the more than 60 different fumonisin mycotoxins identified8,9 possess a tricarballylic side chain, that generates a m/z 157.0142 (C6H5O5-) product ion upon fragmentation of the [M-H]- ion4. Therefore, all putative fumonisins in a sample can be detected using DFF by screening all MS/MS spectra within a DDA dataset that contain the prominent m/z 157.0142 product ion. Similarly, sulfated compounds can be detected by screening DDA datasets for MS/MS spectra that contain a diagnostic neutral loss of 79.9574 Da (SO3)3. This approach has also been successfully applied for detecting new cyclic peptides5 and natural products that contain tryptophan or phenylalanine residues6.
To demonstrate the effectiveness of DFF and its ease of use within the MZmine platform10, we have applied this approach to the analysis of microcystins (MCs); a class of over 240 structurally related toxins produced by freshwater cyanobacteria11,12,13.
The most commonly reported cyanotoxins are MCs, with the MC-LR (leucine [L]/arginine [R]) congener most frequently studied (Figure 1). MCs are monocyclic non-ribosomal heptapeptides, biosynthesized by multiple cyanobacteria genera including Microcystis, Anabaena, Nostoc, and Planktothrix12,13. MCs are composed of five common residues and two variable positions occupied by L-amino acids. Nearly all MCs possess a characteristic &#946;-amino acid 3-amino-9-methoxy-2,6,8-trimethyl-10-phenyldeca-4,6-dienoic acid (Adda) residue at position 511. &#160;The MS/MS fragmentation pathways of MCs are well described14,15; the Adda residue is responsible for the prominent MS/MS product ion, m/z 135.0803+ (C9H11O+) as well as other product ions including m/z 163.1114+ (C11H15O+) (Figure 2). Non-targeted DDA datasets of Microcystis aeruginosa cellular extracts can be screened for all microcystins present using these diagnostic ions, granted that the microcystins have an Adda residue.

<table><tbody><tr><td>&lt;strong&gt;Cyanobacteria&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>&lt;em&gt;Microcystis aeruginosa&lt;/em&gt;CPCC300</td><td>CANADIAN PHYCOLOGICAL CULTURE CENTRE</td><td>CPCC300</td><td>https://uwaterloo.ca/canadian-phycological-culture-centre/</td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Proteowizard (software)</td><td></td><td>software</td><td>http://proteowizard.sourceforge.net/</td></tr><tr><td>Mzmine 2</td><td></td><td>software</td><td>http://mzmine.github.io/</td></tr><tr><td>&lt;strong&gt;LC-MS&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Q-Exactive Orbitrap</td><td>Thermo</td><td>-</td><td>Equipped with HESI ionization source</td></tr><tr><td>1290 UHPLC</td><td>Agilent</td><td></td><td>Equipped with binary pump, autosampler, column compartment</td></tr><tr><td>C18 column</td><td>Agilent</td><td>959757-902</td><td>Eclipse Plus C18 RRHD column (2.1 &amp;times; 100 mm, 1.8 &amp;mu;m)</td></tr><tr><td>&lt;strong&gt;Solvents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Optima LC-MS grade Methanol</td><td>Fisher</td><td>A456-4</td><td></td></tr><tr><td>OptimaLC-MS grade Acetonitrile</td><td>Fisher</td><td>A955-4</td><td></td></tr><tr><td>OptimaLC-MS grade Water</td><td>Fisher</td><td>W6-4</td><td></td></tr><tr><td>LC-MS grade Formic Acid</td><td>Fisher</td><td>A11710X1-AMP</td><td></td></tr><tr><td>Vortex-Genie 2</td><td>Scientific Industries</td><td>SI-0236</td><td></td></tr><tr><td>Centrifuge Sorvall Micro 21</td><td>Thermo Scientific</td><td>75-772-436</td><td></td></tr><tr><td>&lt;strong&gt;Other&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Amber HPLC vials 2 mL/caps</td><td>Agilent</td><td>5182-0716/5182-0717</td><td></td></tr><tr><td>0.2-&amp;mu;m PTFE syringe filters</td><td>Pall Corp.</td><td>4521</td><td></td></tr><tr><td>Whatman 47mm GF/A glass microfiber filters</td><td>Sigma-Aldrich</td><td>WHA1820047</td><td></td></tr><tr><td>&lt;strong&gt;Media&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>&lt;em&gt;MA media (pH 8.6) ( quantity / L)&lt;/em&gt;</td><td></td><td></td><td>Watanabe, M. F. &amp;amp; Oishi, S. Effects of environmental factors on toxicity of a cyanobacterium (Microcystis aeruginosa) under culture conditions. Applied and Environmental microbiology. 49 (5), 1342-1344 (1985).</td></tr><tr><td>Ca(NO&lt;sub&gt;3&lt;/sub&gt;)&amp;middot;4H&lt;sub&gt;2&lt;/sub&gt;O, 50 mg</td><td>Sigma-Aldrich</td><td>C2786</td><td></td></tr><tr><td>KNO&lt;sub&gt;3&lt;/sub&gt;, 100 mg</td><td>Sigma-Aldrich</td><td>P8291</td><td></td></tr><tr><td>NaNO&lt;sub&gt;3&lt;/sub&gt;, 50 mg</td><td>Sigma-Aldrich</td><td>S5022</td><td></td></tr><tr><td>Na&lt;sub&gt;2&lt;/sub&gt;SO&lt;sub&gt;4&lt;/sub&gt;, 40 mg</td><td>Sigma-Aldrich</td><td>S5640</td><td></td></tr><tr><td>MgCl&lt;sub&gt;2&amp;middot;&lt;/sub&gt;6H&lt;sub&gt;2&lt;/sub&gt;0, 50 mg</td><td>Sigma-Aldrich</td><td>M2393</td><td></td></tr><tr><td>Sodium glycerophosphate, 100 mg</td><td>Sigma-Aldrich</td><td>G9422</td><td></td></tr><tr><td>H&lt;sub&gt;3&lt;/sub&gt;BO&lt;sub&gt;3&lt;/sub&gt;, 20 mg</td><td>Sigma-Aldrich</td><td>B6768</td><td></td></tr><tr><td>Bicine, 500 mg</td><td>Sigma-Aldrich</td><td>RES1151B-B7</td><td></td></tr><tr><td></td><td>&lt;strong&gt;P(IV) metal solution, 5 mL&lt;/strong&gt;</td><td></td><td></td></tr><tr><td>&lt;em&gt;Bring the following to 1 L with ddH&lt;sub&gt;2&lt;/sub&gt;O&lt;/em&gt;</td><td></td><td></td><td></td></tr><tr><td>NaEDTA&amp;middot;2HO</td><td>Sigma-Aldrich</td><td>E6635</td><td></td></tr><tr><td>FeCl&lt;sub&gt;3&lt;/sub&gt; &amp;middot;6H2O</td><td>Sigma-Aldrich</td><td>236489</td><td></td></tr><tr><td>MnCl&lt;sub&gt;2&lt;/sub&gt;&amp;middot;4H&lt;sub&gt;2&lt;/sub&gt;O</td><td>Baker</td><td>2540</td><td></td></tr><tr><td>ZnCl&lt;sub&gt;2&lt;/sub&gt;</td><td>Sigma-Aldrich</td><td>Z0152</td><td></td></tr><tr><td>CoCl&lt;sub&gt;2&lt;/sub&gt;&amp;middot;6H2O</td><td>Sigma-Aldrich</td><td>C8661</td><td></td></tr><tr><td>Na&lt;sub&gt;2&lt;/sub&gt;MoO&lt;sub&gt;4&lt;/sub&gt;&amp;middot;2H&lt;sub&gt;2&lt;/sub&gt;O</td><td>Baker</td><td>3764</td><td></td></tr><tr><td>&lt;em&gt;Cyanobacteria BG-11 50X Freshwater Solution&lt;/em&gt;</td><td>Sigma-Aldrich</td><td>C3061-500mL</td><td></td></tr></tbody></table>

natural product discovery with lc-ms/ms diagnostic fragmentation filtering: application for microcystin analysis

Natural products are often biosynthesized as mixtures of structurally similar compounds, rather than a single compound. Due to their common structural features, many compounds within the same class undergo similar MS/MS fragmentation and have several identical product ions and/or neutral losses. The purpose of diagnostic fragmentation filtering (DFF) is to efficiently detect all compounds of a given class in a complex extract by screening non-targeted LC-MS/MS datasets for MS/MS spectra that contain class specific product ions and/or neutral losses. This method is based on a DFF module implemented within the open-source MZmine platform that requires sample extracts be analyzed by data-dependent acquisition on a high-resolution mass spectrometer such as quadrupole Orbitrap or quadrupole time-of-flight mass analyzers. The main limitation of this approach is the analyst must first define which product ions and/or neutral losses are specific for the targeted class of natural products. DFF allows for the subsequent discovery of all related natural products within a complex sample, including new compounds. In this work, we demonstrate the effectiveness of DFF by screening extracts of Microcystis aeruginosa, a prominent harmful algal bloom causing cyanobacteria, for the production of microcystins.

The DFF plot generated following the analysis of M. aeruginosa CPCC300 is shown in Figure 4. The x-axis of this plot is the m/z of the precursor ions that satisfied the defined DFF criteria while the y-axis shows the m/z of all product ions within the MCs MS/MS spectra. For this analysis, the criteria for MC detection included precursor ions within the m/z range of 440-1200, retention times between 2.00&#8...

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Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis

Diagnostic fragmentation filtering, implemented into MZmine, is an elegant, post-acquisition approach to screen LC-MS/MS datasets for entire classes of both known and unknown natural products. This tool searches MS/MS spectra for product ions and/or neutral losses that the analyst has defined as being diagnostic for the entire class of compounds.

Natural products are often biosynthesized as mixtures of structurally similar compounds, rather than a single compound. Due to their common structural ...

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Chemistry

8.7K Views.  Carleton University Ottawa. Diagnostic fragmentation filtering, implemented into MZmine, is an elegant, post-acquisition approach to screen LC-MS/MS datasets for entire classes of both known and unknown natural products. This tool searches MS/MS spectra for product ions and/or neutral losses that the analyst has defined as being diagnostic for the entire class of compounds.

Video: Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. The approach offers an unbiased and in-depth analysis that can enable the development of diagnostic tests, novel therapies, and further our understanding of disease processes. The inherent chemical diversity of the metabolome creates significant analytical challenges and there is no single experimental approach that can detect all metabolites. Additionally, the biological variation in individual metabolism and the dependence of metabolism on environmental factors necessitates large sample numbers to achieve the appropriate statistical power required for meaningful biological interpretation. To address these challenges, this tutorial outlines an analytical workflow for large scale non-targeted metabolite profiling of serum by UPLC-MS. The procedure includes guidelines for sample organization and preparation, data acquisition, quality control, and metabolite identification and will enable reliable acquisition of data for large experiments and provide a starting point for laboratories new to non-targeted metabolite profiling by UPLC-MS.

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. This article outlines a typical workflow utilized for non-targeted metabolite profiling of serum including sample organization and preparation, data acquisition, data analysis, quality control, and metabolite identification.

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to ...

Sampling Human Indigenous Saliva Peptidome Using a Lollipop-Like Ultrafiltration Probe: Simplify and Enhance Peptide Detection for Clinical Mass Spectrometry

Although human saliva proteome and peptidome have been revealed 1-2 they were majorly identified from tryptic digests of saliva proteins. Identification of indigenous peptidome of human saliva without prior digestion with exogenous enzymes becomes imperative, since native peptides in human saliva provide potential values for diagnosing disease, predicting disease progression, and monitoring therapeutic efficacy. Appropriate sampling is a critical step for enhancement of identification of human indigenous saliva peptidome. Traditional methods of sampling human saliva involving centrifugation to remove debris 3-4 may be too time-consuming to be applicable for clinical use. Furthermore, debris removal by centrifugation may be unable to clean most of the infected pathogens and remove the high abundance proteins that often hinder the identification of low abundance peptidome.
Conventional proteomic approaches that primarily utilize two-dimensional gel electrophoresis (2-DE) gels in conjugation with in-gel digestion are capable of identifying many saliva proteins 5-6. However, this approach is generally not sufficiently sensitive to detect low abundance peptides/proteins. Liquid chromatography-Mass spectrometry (LC-MS) based proteomics is an alternative that can identify proteins without prior 2-DE separation. Although this approach provides higher sensitivity, it generally needs prior sample pre-fractionation 7 and pre-digestion with trypsin, which makes it difficult for clinical use. 
To circumvent the hindrance in mass spectrometry due to sample preparation, we have developed a technique called capillary ultrafiltration (CUF) probes 8-11. Data from our laboratory demonstrated that the CUF probes are capable of capturing proteins in vivo from various microenvironments in animals in a dynamic and minimally invasive manner 8-11. No centrifugation is needed since a negative pressure is created by simply syringe withdrawing during sample collection. The CUF probes combined with LC-MS have successfully identified tryptic-digested proteins 8-11. In this study, we upgraded the ultrafiltration sampling technique by creating a lollipop-like ultrafiltration (LLUF) probe that can easily fit in the human oral cavity. The direct analysis by LC-MS without trypsin digestion showed that human saliva indigenously contains many peptide fragments derived from various proteins. Sampling saliva with LLUF probes avoided centrifugation but effectively removed many larger and high abundance proteins. Our mass spectrometric results illustrated that many low abundance peptides became detectable after filtering out larger proteins with LLUF probes. Detection of low abundance saliva peptides was independent of multiple-step sample separation with chromatography. For clinical application, the LLUF probes incorporated with LC-MS could potentially be used in the future to monitor disease progression from saliva.

Considering saliva sampling for future clinical application, a lollipop-like ultrafiltration (LLUF) probe was fabricated to fit in the human oral cavity. Direct analysis of undigested saliva by NanoLC-LTQ mass spectrometry demonstrated the ability of LLUF probes to remove large proteins and high abundance proteins, and make low-abundant peptides more detectable.

Although human saliva proteome and peptidome have been revealed 1-2 they were majorly identified from tryptic digests of saliva proteins. Identification ...

Medicine

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the local to federal levels. The continuing evolution of PFAS chemistries presents a challenge to the environmental monitoring, where ongoing development of targeted methods necessarily lags the discovery of new chemical compounds. There is a need, therefore, to have forward-looking methodologies that can detect emerging and unexpected compounds, monitor these species over time, and resolve details of their chemical structure to enable future work in human health. To this end, non-targeted analysis by high-resolution mass spectrometry offers a broad base detection approach that can be combined with almost any sample preparation scheme and provides significant capabilities for compound identification after detection. Herein, we describe a solid-phase extraction (SPE) based sample concentration method tuned for shorter chain and more hydrophilic PFAS chemistries, such as per fluorinated ether acids and sulfonates, and describe analysis of samples prepared in this fashion in both targeted and non-targeted modes. Targeted methods provide superior quantification when reference standards are available but are intrinsically limited to expected compounds when performing analysis. In contrast, a non-targeted approach can identify the presence of unexpected compounds and provide some information about their chemical structure. Information about chemical features can be used to correlate compounds across sample locations and track abundance and occurrence over time.

Here, we present a protocol for the sequential targeted quantification and non-targeted analysis of fluorinated compounds in water by mass spectrometry. This methodology provides quantitative levels of known fluorochemical compounds and identifies unknown chemicals in related samples with semi-quantitative estimates of their abundance.

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the ...

Environment

Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics

Peptidoglycan is an important component of bacterial cell walls and a common cellular target for antimicrobials. Although aspects of peptidoglycan structure are fairly conserved across all bacteria, there is also considerable variation between Gram-positives/negatives and between species. In addition, there are numerous known variations, modifications, or adaptations to the peptidoglycan that can occur within a bacterial species in response to growth phase and/or environmental stimuli. These variations produce a highly dynamic structure that is known to participate in many cellular functions, including growth/division, antibiotic resistance, and host defense avoidance. To understand the variation within peptidoglycan, the overall structure must be broken down into its constitutive parts (known as muropeptides) and assessed for overall cellular composition. Peptidoglycomics uses advanced mass spectrometry combined with high-powered bioinformatic data analysis to examine peptidoglycan composition in fine detail. The following protocol describes the purification of peptidoglycan from bacterial cultures, the acquisition of muropeptide intensity data through a liquid chromatograph&#8212;mass spectrometer, and the differential analysis of peptidoglycan composition using bioinformatics.

This protocol covers a detailed analysis of peptidoglycan composition using liquid chromatography mass spectrometry coupled with advanced feature extraction and bioinformatic analysis software.

Peptidoglycan is an important component of bacterial cell walls and a common cellular target for antimicrobials. Although aspects of peptidoglycan ...

Biochemistry

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

The vast majority of mass spectrometry (MS)-based protein analysis methods involve an enzymatic digestion step prior to detection, typically with trypsin. This step is necessary for the generation of small molecular weight peptides, generally with MW &#60; 3,000-4,000 Da, that fall within the effective scan range of mass spectrometry instrumentation. Conventional protocols involve O/N enzymatic digestion at 37 &#186;C. Recent advances have led to the development of a variety of strategies, typically involving the use of a microreactor with immobilized enzymes or of a range of complementary physical processes that reduce the time necessary for proteolytic digestion to a few minutes (e.g., microwave or high-pressure). In this work, we describe a simple and cost-effective approach that can be implemented in any laboratory for achieving fast enzymatic digestion of a protein. The protein (or protein mixture) is adsorbed on C18-bonded reversed-phase high performance liquid chromatography (HPLC) silica particles preloaded in a capillary column, and trypsin in aqueous buffer is infused over the particles for a short period of time. To enable on-line MS detection, the tryptic peptides are eluted with a solvent system with increased organic content directly in the MS ion source. This approach avoids the use of high-priced immobilized enzyme particles and does not necessitate any aid for completing the process. Protein digestion and complete sample analysis can be accomplished in less than ~3 min and ~30 min, respectively.

A quick protocol for proteolytic digestion with an in-house built flow-through tryptic microreactor coupled to an electrospray ionization (ESI) mass spectrometer is presented. The fabrication of the microreactor, the experimental setup and the data acquisition process are described.

The vast majority of mass spectrometry (MS)-based protein analysis methods involve an enzymatic digestion step prior to detection, typically with trypsin. ...

Bioengineering

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

The chemical space covered by natural products is immense and widely unrecognized. Therefore, convenient methodologies to perform wide-ranging evaluation of their functions in nature and potential human benefits (e.g., for drug discovery applications) are desired. This protocol describes the combination of genome mining (GM) and molecular networking (MN), two contemporary approaches that match gene cluster-encoded annotations in whole genome sequencing with chemical structure signatures from crude metabolic extracts. This is the first step towards the discovery of new natural entities. These concepts, when applied together, are defined here as MS-guided genome mining. In this method, the main components are previously designated (using MN), and structurally related new candidates are associated with genome sequence annotations (using GM). Combining GM and MN is a profitable strategy to target new molecule backbones or harvest metabolic profiles in order to identify analogues from already known compounds.

A mass spectrometry-guided genome mining protocol is established and described here. It is based on genome sequence information and LC-MS/MS analysis and aims to facilitate identification of molecules from complex microbial and plant extracts.

The chemical space covered by natural products is immense and widely unrecognized. Therefore, convenient methodologies to perform wide-ranging evaluation ...

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of cyanobacteria and related cyanotoxins in water samples and in an organic matrix, such as bivalve extracts. FDS combines remote/proximal sensing techniques with analytical/bioinformatics analyses. Sampling spots are chosen through multi-disciplinary, multi-scale, and multi-parametric monitoring in a three-dimensional physical space, including remote sensing. Microscopic observation and taxonomic analysis of the samples are performed in the laboratory setting, which allows for the identification of cyanobacterial species. Samples are then extracted with organic solvents and processed with LC-MS/MS. Data obtained by MS/MS are analyzed using a bioinformatic approach using the online platform Global Natural Products Social (GNPS) to create a network of molecules. These networks are analyzed to detect and identify toxins, comparing data of the fragmentation spectra obtained by mass spectrometry with the GNPS library. This allows for the detection of known toxins and unknown analogues that appear related in the same molecular network.

A fast-multidisciplinarystrategy for early detection of cyanobacterial blooms and associated cyanotoxins is described here. It allows for the detection of cyanobacteria and related cyanotoxins in water samples and in organic matrices, such as bivalve samples, in 24 h.

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of ...

Ultra High-Performance Liquid Chromatography-Mass Spectrometry and Self-established Database Analysis of Chinese Herbal Medicine Components

Chinese herbal medicine is complex and has numerous unknown compounds, making qualitative research crucial. Ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) is the most widely used method in qualitative analysis of compounds. The method includes standardized and programmed protocols for sample pretreatment, MS tune, MS acquisition, and data processing. The sample pretreatments include collection, pulverization, solvent extraction, ultrasound, centrifugation, and filtration. Data post-processing was described in detail and includes data importing, self-established database construction, method establishment, data processing, and other manual operations. The above-ground part of the alpine yarrow herb, Achillea millefolium L., is used to treat inflammation, gastrointestinal disturbances, and pain and its 3-oxa-guaianolides could be useful leads for anti-inflammatory drug development. Three representative compounds in AML were identified, combining TOF-MS with a self-established database. Moreover, the differences from existing literature, liquid-phase parameter optimization, scan mode selection, ion source suitability, collision energy adjustment, isomer screening, method limitation, and possible solutions were discussed. This standardized analysis method is universal and can be applied to identify complex compounds in Chinese herbal medicine.

We describe a general protocol and systematic design that could be applied to separate and recognize complex components in alpine yarrow herb, Achillea millefolium L., a Chinese herbal medicine.

Chinese herbal medicine is complex and has numerous unknown compounds, making qualitative research crucial. Ultra-high-performance liquid chromatography ...

Classification of Tropane Alkaloids from Plant Metabolites Using LC-MS/MS

Applications of Liquid-Chromatography Tandem Mass Spectrometry in Natural Products Research: Tropane Alkaloids as a Case Study

Although many drugs utilized today are synthetic in origin, natural products still provide a rich source of novel chemical diversity and bioactivity, and can yield promising leads for resistant or emerging diseases. The challenge, however, is twofold: not only must researchers find natural products and elucidate their structures, but they must also identify what is worth isolating and assaying (and what is already known - a process known as dereplication). With the advent of modern analytical instrumentation, the pace of natural product discovery and dereplication has accelerated. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become an especially valuable technique for identifying and classifying chemical structures. Tropane alkaloids (TAs) are plant-derived compounds of great medicinal and toxicological significance. In this study, we developed an LC-MS/MS-based screening workflow utilizing the multiple MS/MS configurations available on a triple-quadrupole (QQQ) mass spectrometer to annotate and classify TA structures based on their distinct fragmentation patterns. By using a combination of data-dependent (DD) product ion scans, precursor ion scans (PrIS), and neutral loss scans (NLS), we applied this method to TA-rich extracts of the nightshades Datura stramonium and Datura metel. This method is rapid, sensitive, and was successfully employed for both preliminary dereplication of complex TA-containing samples and for the discovery of a novel candidate for isolation, purification (and eventual bioassay).

Author Spotlight: Discovering New Alkaloids in Plants with Advanced Mass Spectrometry Techniques

We present a method for rapid mass spectrometry (MS)/mass spectrometry (MS)-based annotation and classification of tropane alkaloids, useful for both preliminary dereplication of tropane-containing samples and discovery of novel alkaloids for isolation.

Although many drugs utilized today are synthetic in origin, natural products still provide a rich source of novel chemical diversity and bioactivity, and ...

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell&#39;s survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. Here, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.

The protocols describe high-performance liquid chromatography methods coupled to refractive index or mass spectrometric detection for studying metabolic reactions in complex lysate-based cell-free systems.

Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the ...

Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis

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