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Authentication of closely related fish and derived fish products using tandem mass spectrometry and spectral library matching Merel Anne Nessen, Dennis Jorden van der Zwaan, Sander Greevers, Hans Dalebout, Martijn Staats, Esther Kok, and Magnus Palmblad J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.5b05322 • Publication Date (Web): 18 Apr 2016 Downloaded from http://pubs.acs.org on April 23, 2016
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Journal of Agricultural and Food Chemistry
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Authentication of closely related fish and derived fish products using tandem mass
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spectrometry and spectral library matching
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Merel A. Nessen†, Dennis J. van der Zwaan†, Sander Grevers‡, Hans Dalebout‡, Martijn
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Staats†, Esther Kok*†, Magnus Palmblad*‡
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† RIKILT Wageningen UR, P.O. Box 230, 6700 AE Wageningen, The Netherlands
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‡ Center for Proteomics and Metabolomics, Leiden University Medical Center, P.O. Box
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9600, 2300 RC Leiden, The Netherlands
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* Corresponding Authors: Tel. +31 317 480252, E-mail:
[email protected] and Tel.: +31 71
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5269582, E-mail:
[email protected]
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The authors declare no competing financial interest.
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Abstract
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Proteomics methodology has seen increased application in food authentication, including
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tandem mass spectrometry of targeted species-specific peptides in raw, processed or mixed
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food products. We have previously described an alternative principle that uses untargeted data
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acquisition and spectral library matching - essentially spectral counting - to compare and
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identify samples without the need for genomic sequence information in food species
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populations. Here we present an interlaboratory comparison demonstrating how a method
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based on this principle performs in a realistic context. We also increasingly challenge the
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method by using data from different types of mass spectrometers, by trying to distinguish
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closely related and commercially important flatfish, and by analyzing heavily contaminated
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samples. The method was found to be robust in different laboratories and 94-97% of the
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analyzed samples were correctly identified, including all processed and contaminated
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samples.
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Key Words
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Food authentication
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Species identification
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Mass spectrometry
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Proteomics
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Spectral libraries
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Introduction
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Fraud has likely occurred since the origin of trade, as it has always been lucrative to make
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more profit out of inferior products. Food products such as wine, beer and bread have been
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commonly subjected to adulteration in the past.1 Nowadays, globalization and
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industrialization allows distribution of (in principle) good quality food to a large part of the
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world’s population. At the same time it has become a daunting task to trace the origin of
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products and the composition of processed and “mixed” products. This was illustrated by
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recent incidents in Europe, where beef in processed foods such as lasagna and meatballs was
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adulterated or substituted by horse meat2 and studies in the United Kingdom and Ireland
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revealing cheaper substitutes were used in battered fish and marketed as the traditional “fish
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and chips” dish.3, 4
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Several studies on the authenticity of meat and fish products have revealed structural
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inadequacies in some food supply chains in this respect, showing between 17% and 68% of
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the analyzed samples to contain undeclared species.5-8 However, two surveys focusing on
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Western European countries (United Kingdom and France) show a lower rate of false
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labelling (5.5% and 3.7% respectively).9,
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significant difference in the occurrence of food fraud between countries, as suggested by
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Bénard-Capelle et al..10 Another reason may be that the increased enforcement of correct
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labelling after the recent food scandals in Western Europe has been successful in discouraging
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similar fraud, as recently described in a European study on the (mis)labeling in the seafood
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supply chain.11, 12
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This discrepancy could be explained by a
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For fishery and aquaculture products in the European Union, specific labelling
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instructions are given in the Regulation (EU) No 1169/2011 on the provision of food
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information to consumers, which entered into force on 13 December 2014. This regulation
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stipulates that the name of the food shall be its legal name, i.e. the scientific name of the
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species. Identification of fresh fish traditionally takes place by visual inspection, examining
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the anatomy and morphology of the fish. For closely related fish species this can be a
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challenge. Especially after removal of the skin, leaving the bare fillets, identification of the
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different (flat)fish species is troublesome. For further processed fish products authentication
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becomes a very difficult if not impossible task.
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Molecular and chemical methods have been developed to assist in the identification of
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fish species, such as immunochemistry and DNA-based methods. In particular the latter is
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standardly used for species identification in food safety and quality assessments, mainly
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utilizing real-time PCR and DNA barcoding methods.13 DNA barcoding is a method for
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assigning taxonomy to species using standardized short DNA sequences.14 More recently and
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upcoming is the application of DNA metabarcoding, which involves Next-Generation
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Sequencing (NGS) of DNA barcodes for the simultaneous detection of multiple species in
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complex samples.15 Advantages of DNA-based methods are the robustness, low detection
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limits and high specificity of the analysis. The drawback of DNA-based methods is that
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species can only be identified by specific, known target sequences. Furthermore, due to the
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low detection limits even small traces that are introduced unintentionally, either in the food
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production process or in the analysis process, will be identified.
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In recent years, proteomics is increasingly applied to assess the quality and safety of 16
and 17). Proteomics is the large-scale study
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fish and fish derived products (for reviews see
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of the expression, structure and function of proteins in specific cells, tissues or organisms.
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Mass spectrometry is the most commonly used technology in proteomics, especially in so-
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called ‘shot-gun’ or ‘bottom-up’ proteomics, and allows identification and quantification of
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thousands of proteins. In this bottom-up proteomics, proteins are typically enzymatically
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cleaved into peptides and the obtained digest is analyzed by liquid-chromatography tandem
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mass spectrometry (LC-MS/MS). Proteins can be identified by matching the peptide
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fragmentation spectra against a protein sequence database, comparing the experimental
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peptide spectra with the predicted peptide spectra generated from the sequence database.
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Protein identification is in proteomics is generally restricted by the availability of
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(genetic) sequence information. This is not a problem for human or common model organisms
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whose genomes are completely sequenced and well annotated. But most data analysis
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methods in proteomics are inapplicable when the species is unknown and no sequence
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information is available for that species.
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For authentication therefore, current methods mainly focus on the detection and
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quantification of species specific peptides of which the sequence information is available.18-24
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However, the (genetic) sequence information for many fish species is often limited to the
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mitochondrial cytochrome c oxidase I (COI) and cytochrome b genes, two regions that are
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often used for DNA barcoding.13 Selection of species specific protein or peptide biomarkers
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can therefore be a labor intensive task, requiring well-defined samples of the species of
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interest, as well of all closely related species. An example is the selection and de novo
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sequencing by mass spectrometry of a species specific, thermostable and allergenic protein,
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parvalbumin, allowing discrimination between 11 different species from the unsequenced
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Merlucciidae family.25-27
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To overcome the limited availability of genome sequences of many animal species,
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authentication by mass spectrometry and spectral library matching may be a robust and
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reliable alternative to current methods for the identification of species. For microorganisms in
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clinical settings Matrix Assisted Laser Desorption Ionization Time-of-Flight Mass
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Spectrometry (MALDI-TOF-MS) has become a common tool to rapidly identify species and
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define proper treatment.28 The MALDI-TOF-MS analysis results in a single spectrum (or
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technically the sum of multiple spectra from the same sample), consisting of m/z values of
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peptides, proteins (e.g. ribosomal proteins) and other cell components of the investigated
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microorganism. This mass spectral fingerprint can be matched against spectra from known
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isolates in a reference database, allowing identification of the species. This approach was
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recently applied to identify scallop species29 and to determine the origin of meat and gelatin.30
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MALDI-TOF allows fast analysis of untreated (intact) samples, though the information in the
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obtained profiles is limited and might be insufficient for more complex (processed) samples
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and closely related species. Furthermore, for the identification of microorganisms, standard
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culture conditions need to be used for the MALDI-TOF spectra to match the reference.
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Alternatively, proteome-wide tandem mass spectrometry (MS/MS) can be used.31-33
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These approaches typically use tandem mass spectrometry data from protein digests and, like
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the MALDI-TOF method, can use the spectra directly and therefore do not require any
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genome sequence information. Figure 1 shows an overview of the workflow for the
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identification of animal species. First, one spectral library is created for each species from the
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tandem mass data (ignoring the chromatographic dimension). For authentication, an unknown
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sample is analyzed using the same or a similar workflow, and the tandem mass data used to
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query all spectral libraries to find the best matching reference. Identification takes place by
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counting the number of shared tandem mass spectra between the investigated sample and the
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reference spectral libraries. In a previous study using this approach, we could correctly
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identify 22 analyzed fish samples.32 In addition, 47 additional fish samples, both fresh and
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processed (steamed, smoked, fried, autoclaved or canned) were analyzed. All samples, except
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one, a smoked salmon, were correctly identified simply by counting shared spectra. Three
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samples of a tuna species that was not included the reference libraries matched best to one of
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the two tuna species that was included. This demonstrated that this method allows
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identification on the genus or family level, using the closest relative in the reference database,
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when the correct species is not present in the database.
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In food safety and quality control, robust application of analysis methods is a
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prerequisite. Sample preparation and analysis at different laboratories, utilizing different
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preparation techniques and different types of tandem mass spectrometers, should ideally result
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in identical results. Furthermore, processed samples mixed with additional ingredients are
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common, and a method should allow identification of the correct species in these kinds of
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samples as well. We therefore challenged the previously developed method and applied it,
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without modification or fine tuning, to the identification and discrimination of closely related
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flatfish species in two different laboratories, using two types of common tandem mass
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spectrometers. In addition, we analyzed samples of a battered and fried fish product obtained
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from a local fish store to investigate the applicability to heavily contaminated samples.
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Materials and methods
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Samples
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Five different flatfish were prepared in this study: European plaice (Pleuronectes platessa),
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Rock sole (Lepidodsetta bilineata), turbot (Scophthalmus maximus) and common dab
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(Limanda limanda) were collected by IMARES-WUR in Den Helder. Yellowfin sole
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(Limanda aspera) was obtained from a local fish shop and originate from the Pacific Ocean.
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The identity of the five selected flatfish was determined by morphological experts from
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IMARES Wageningen UR and by DNA barcoding at RIKILT Wageningen UR. Samples
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were stored at -20°C until further preparation and analysis.
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For ion trap libraries, samples of one fish from the five selected flatfish were prepared
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in triplicate. For the Q-Exactive libraries, samples of the same fish were prepared in triplicate
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for European plaice, common dab, yellowfin sole and rock sole. For preparation of the
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“unknown” samples, samples of the same European plaice, common dab and yellowfin sole
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were prepared in triplicate at two laboratories, twice at two different days, obtaining a total of
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four sets of nine samples. 7
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A battered and fried fish, known as “kibbeling” in the Netherlands, was obtained from
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a local fish store. Samples containing either ~90% fish (and ~10% batter) or 10-20% fish (and
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80-90% batter) were taken from the fried battered fish and prepared in triplicate freshly before
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analysis.
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Sample preparation
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Of each fish sample, ~20 mg of muscle tissue was taken and proteins were extracted in 100
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µL urea buffer (8 M urea, 40 mM MgCl (lab 1) or MgSO4 (lab 2), 50 U/mL benzonase).
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Samples were homogenized either for 3 minutes using 0.5 mm zirconium oxide beads (Next
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Advance Inc., Averill Park, NY) in an air-cooled Bullet Blender® (Next Advance Inc.,
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Averill Park, NY) on speed 8 (lab 1) or twice at 6500 rpm for 2 times 20 seconds using 2.3
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mm zirconium silica beads (Precellys, Bertin, France) in a PRECELLYS® 24 homogenizer
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(Precellys, Bertin, France) (lab 2). After incubation for 12 minutes at 4°C, supernatant was
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collected after centrifugation of the samples at 16,000 x g for 30 minutes at 4°C and
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transferred to LoBind Eppendorf tubes (Eppendorf, Hamburg, Germany). Final protein
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concentration was determined using a micro bicinchoninic acid (BCA) protein assay kit
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(Thermo Fisher Scientific).
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Tryptic digestion
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To either 150 µg of proteins in 20 µL (volume adjusted by 50 mM ammonium bicarbonate)
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(lab 1) or 10 µL protein extract and 10 µL 50 mM ammonium bicarbonate (20 µL final
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volume) (lab 2), 4 µL 60 mM dithiothreitol (10 mM final concentration) was added. Cysteines
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were reduced for 45 minutes at 60 °C, after which alkylation of free thiols was achieved by
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addition of 6 µL 100 mM iodoacetamide (20 mM final concentration) and incubation for 1
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hour at room temperature. Excess of chemicals was removed by dilution of the sample 1:4
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with 50 mM ammonium bicarbonate and centrifugation for 30 minutes at 14.000xg in a
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Amicon Ultra-0.5 mL 3K centrifugal filter device (Merck Millipore, Billerica, MA). The
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concentrate was transferred to a Lobind Eppendorf tube (Eppendorf, Hamburg, Germany) and
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trypsin (Sequencing grade, Promega, Madison, WI) was added for digestion o/n at 37 °C
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(enzyme: protein ratio ~1:100). Tryptic digestion was stopped by addition of 8 µL
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trifluoroacetic acid (TFA). Samples were centrifuged for 10 minutes at 2500xg and either
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transferred to a 1.2 mL Ultra Recovery Clear MS-vial (Grace, Columbia, MD) for direct MS
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analysis or stored at -20 C until further analysis.
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Mass spectrometric analysis
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Ion Trap
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For ion trap analyses, 2 µL of sample (~10 µg protein digest) was loaded and desalted on a
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C18 PepMap 300 µm x 5 mm, 300 Å precolumn (Thermo Scientific), after which peptides
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were separated by reversed-phase liquid chromatography using two identical MicroLC
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columns (3 µm, ChromXP C18CL, 120Å, 150 x 0.3 mm) (Eksigent, Dublin, CA) coupled in
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parallel and connected to a splitless NanoLC-Ultra 2D plus system (also Eksigent) with a
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linear gradient of 45 minutes from 4 to 35% solvent B at a flow rate of 4 µL/min (solvent A:
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0.05% formic acid, solvent B: 95% acetonitrile, 0.05% formic acid). While the gradient was
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applied to one column after sample injection, the other column was being washed and
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equilibrated. A 6-port column selection valve was used to direct the eluent from the column
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running the gradient to the mass spectrometer and divert the wash from the other column to
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waste. The column selection valve was connected to an amaZon speed ETD ion trap (Bruker
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Daltonics) configured with an Apollo II ESI source. After each MS scan, up to 10 abundant
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multiply charged species in the mass range of m/z 300-1,300 were selected for tandem mass
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spectrometry and actively excluded for one minute after having been selected twice. The LC
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system was controlled by HyStar 3.2 and the ion trap by trapControl 7.1.
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Q-Orbitrap
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For Q-Orbitrap analyses, 5 µL of sample (~35 µg protein digest) was loaded on an UltiMate
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3000 UHPLC system (Thermo Scientific Dionex) equipped with a UPLC BEH300 C18 150
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mm x 1 mm column (Waters) placed in a column oven at 50°C. Peptides were separated with
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a linear gradient of 45 minutes from 5 to 35% solvent B at a flow rate of 0.1 mL/min (solvent
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A: 5% acetonitrile, 0.05% formic acid, solvent B: 95% acetonitrile, 0.05% formic acid). The
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LC system was coupled to a Q-Exactive Orbitrap MS (Thermo Scientific) configured with a
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heated electrospray ionization II source in positive mode. MS scans were recorded in a mass
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range of m/z 300-2,000 at a resolution of 70,000 with an AGC target of 3e6. After each MS
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scan up to 10 most abundant multiply charged ions were selected for fragmentation. MS2
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scans were recorded at a resolution of 17,500 with an AGC target of 1e5 and a maximum fill
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time of 50 ms, using either a fixed NCE of 25 or a stepped NCE of 20, 25 and 30 for
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fragmentation in the HCD cell.
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Data analysis
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LC-MS/MS datasets were converted to .mzXML34 format using compassXport (Bruker) for
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ion trap data and msconvert35 for Q-Exactive data. For conversion of the Q-Exactive .raw
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data, the vendors software Xcalibur (Thermo Scientific) needs to be installed as well.
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Generation of libraries
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Libraries of the five (Ion trap) or four (Q-Orbitrap) selected flatfish were generated as
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described by Wulff et al.32 and added to our database. Spectral libraries were generated using
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SpectraST 4.0 by first searching a randomized zebrafish protein sequence database with
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X!Tandem36 and including all results in the PeptideProphet analysis,37 as previously
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described.32 From version 5.0, SpectraST can directly build spectral libraries from
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unidentified peptides.
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Analysis query data
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Converted LC-MS/MS datasets were analyzed using SpectraST version 4.0 (as part of Trans-
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Proteomic Pipeline version 4.8.0 PHILAE, Build 201411201551-6764 (mingw-i686)) under
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Debian Linux. Each dataset was searched against 27 fish libraries present in our database. For
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the analysis against Q-Orbitrap libraries, an additional four (flat)fish libraries were present.
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The number of spectral hits with a dot product of 0.7 or higher were returned (dot products
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above 0.7 represent good SpectraST matches with typical false discovery rates below 1%).
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The exact number of spectral hits returned might depend on the specific TPP and SpectraST
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versions installed and used for the analysis.
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Results and discussion
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Spectral library matching has been shown to be valuable for species identification of both
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microorganisms and animal species.31-33 To assess applicability of this approach to food safety
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and quality control, an interlaboratory comparison was carried out and the compatibility of
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data from different mass spectrometers was investigated, applied to closely related flatfish
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species and contaminated processed fish samples.
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Selection of flatfish samples and libraries
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For the identification of closely related flatfish, three commercially common flatfish,
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European plaice, common dab and yellowfin sole were selected. European plaice and
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common dab are common to the seas of Northern Europe and the North-Eastern part of the
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Atlantic Ocean, while yellowfin sole mainly originates from the Pacific Ocean. Similar in
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taste, yellowfin sole is a common substitute, especially for the European plaice.
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Morphological discrimination of the species is readily feasible when the skin is still present on
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the fish, European plaice has a distinctive orange to red dots on its brownish skin, which is
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absent for the two Limanda species. However, fillets without skin are difficult to impossible
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to identify.
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Spectral libraries of tandem mass data from 22 fish species were already generated in a
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previous study, including from three flatfish: Atlantic halibut and Greenland halibut
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(Pleuronectidae) and common sole (Soleidae). In addition to the three selected flatfish for the
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analysis, two additional flatfish were chosen to add to the library database to further
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investigate the specificity of the approach. Rock sole is a species that, as the European plaice,
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common dab and yellowfin sole, belongs to the Pleuronectidae family, while turbot is part of
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the more distinct Scophthalmidae family. After preparation and analysis of all samples, for
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each species, the overall best scoring library (i.e. most spectral hits) was selected to be added
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to the existing species database. With the spectral libraries of the flatfish already present in the
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database a total of eight flatfish spectral libraries are now available.
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Identification of flatfish by spectral library matching
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Identification of the flatfish was accomplished by analysis of the obtained tandem mass
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spectrometry data of each sample against the spectral libraries of all species available at our
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laboratory, using SpectraST.38 For each query dataset, the total number of tandem mass
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spectra that have a good match to the tandem mass spectrometry data in each species library
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(dot-product > 0.7) is returned. The species library returning the highest number of spectral
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matches is considered the identity of the (unknown) fish sample.
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In Figure 2, results typically obtained for each of the three flatfish are presented for the
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ion trap data. The total number of spectral matches to each of the spectral libraries in the
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database is given. The European plaice (Figure 2A) matches to the correct library for all
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analyzed samples, with the second best library match at around 60% (55% - 65%) of the total
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spectral hits of the first library. A clear distinction can be made between this species and the
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other flatfish libraries in the database. For the two Limanda species, the common dab (Figure
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2B) and yellowfin sole (Figure 2C), the number of spectral hits clearly show that these two
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species are more related. The second best match for the species in this study was always
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found to be the other Limanda species, with around 90% (82% - 99%) of the total number of
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spectral matches of the best match. The single incorrect identification of the flatfish was a
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yellowfin sole sample, which was falsely identified as common dab (Supporting Information
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1, Figure S1). Overall, out of the 36 samples analyzed, high quality data was obtained from 34
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samples that could be used for the spectral library matching. Out of these 34 samples only one
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sample was incorrectly identified (yellowfin sole as common dab) and it was therefore
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possible to identify 97% of the samples correctly.
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Apart from the identity of the species, also information on the number of spectral
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matches to other species is obtained, with phylogenetically more closely related species
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having a higher number of spectral matches compared to less related species. Closely related
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species will have more shared proteins and more identical peptide sequences and therefore
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more common tandem mass spectra, which is reflected in our analyses as depicted in Figure 2.
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The flatfish matches best with the other flatfish libraries in the database (in green), after which
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the samples are closest to the other fish (in blue). The number of library matches to mammal
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(in red) and bird (in orange), however, show a shallow, almost flat descend in number of
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spectral hits. These hits are most likely to come from conserved regions of (abundant) muscle
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proteins that are highly conserved among metazoans. The last column in the graphs represents
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the spectral hits against the European squid (dark blue), a cephalopod mollusk that is
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phylogenetically most distantly related to the investigated flatfish. From these observations
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the evolutionary distance to a species can be estimated: when an unknown sample matches
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equally well to all species in one clade, the mammals in this case, then one can conclude the
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species does not belong to that clade (the mammals).
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Spectral library matching for species identification is robust
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The method was further investigated by comparison of the preparation and analysis of the
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flatfish samples at different laboratories and at different days, without any optimization of the
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method. As a measure for the repeatability and reproducibility the total number of spectral
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hits to the correct library is used. This will take into account both the quality of the sample
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itself and the tandem mass spectrometry data (and therewith performance of the mass
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spectrometer). Here, the repeatability has been defined as the variation in number of spectral
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hits of the samples prepared and analyzed on each single day (i.e. within-day variation). For
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the reproducibility, multiple days are taken into account. The within-lab reproducibility is
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determined by the variation in spectral hits between the two different days at each laboratory,
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while for the between-lab reproducibility the results of all analyses are taken into account. In
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Figure 3, the average number of spectral hits and the corresponding standard deviation is
316
given as a measure for the repeatability, within-lab and between-lab reproducibility.
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The variation in the number of spectral hits between the samples prepared on the same
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day was found to be good to very good to with variations between 1.0% and 27.9% (see
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Supporting Information 1, table S1). Earlier experiments showed that triplicate analyses of the
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same sample resulted in a variation of about 20% in number of spectral hits to the correct
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library32 and is in the same order of magnitude as the variation observed in this study between
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different samples of the same species. The samples of the common dab showed a variation of
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1.0% to 27.0%, with an average of 10.1%. The European plaice (6.3% - 20.5%, average of
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15.1%) and the yellowfin sole (3.0 – 27.9%, average of 16.6%) follow.
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The within-lab reproducibility was found to be comparable to the repeatability, with
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variations in total number of hits between 6.5% and 25.8%. The between-lab reproducibility
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was found to be slightly higher, but good with variations of the total number of spectral hits of
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21.7% for European plaice, 13.2% for common dab and 17.7% for yellowfin sole.
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In conclusion, the method was found to be reproducible and robust with a variation in
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total number of spectral hits in the same order of magnitude of replicate analyses of a single
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sample. Furthermore, the approach is easily implemented and applied at other laboratories,
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without the need of optimization of sample preparation or data analysis.
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Data of different mass spectrometers is compatible
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In addition to the reproducibility of the method the use of different types of mass
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spectrometers was investigated. In previous species identification studies that used spectral
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library matching with SpectraST, ion trap mass spectrometers have been used.31, 32, 39 In this
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study, tandem mass spectrometry data was also acquired on a Q-Orbitrap mass spectrometer.
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The two mass spectrometers implement collision induced dissociation differently, with the Q-
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Orbitrap making use of a higher energy collision induced dissociation (HCD) cell. In addition,
340
the ion trap ramps the gradient collision energy for fragmentation, while the Q-Orbitrap uses
341
either a fixed collision energy, or a three stepped collision energy. This will influence the ions
342
observed in the spectra and have an impact on how well the tandem mass spectra match.
343
To investigate the compatibility of the ion trap and Q-orbitrap data, libraries of four
344
flatfish, European plaice, common dab, yellowfin sole and rock sole, were generated as well
345
on the Q-Orbitrap using a three stepped collision energy. It was chosen to use a stepped
346
collision energy as this will generate fragmentation spectra that have a higher resemblance to
347
the ion trap fragmentation spectra and increases the number of spectral hits. All obtained data
348
(ion trap and Q-Orbitrap) was analyzed against both the database with ion trap libraries and
349
Q-Orbitrap libraries. In Figure 4C a representative example is given of peptide fragmentation
350
spectra generated by the ion trap and Q-Orbitrap mass spectrometer. In butterfly plots,
351
matching query tandem mass spectra (in blue on the top) and library tandem mass spectra (in
352
red on the bottom) generated by both mass spectrometers are shown. The butterfly plot in the
353
middle shows the difference in fragmentation pattern for the Q-Orbitrap (query data, on the
354
top) and ion trap (library data, on the bottom), which results in a lower resemblance and thus
355
dot product, though it still correctly matches the two spectra.
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356
An overview of the results of the spectral library matching can be found in Figure 4.
357
Both the average number of spectral hits to the first (correct) match with standard deviation
358
and the percentage of correct identifications is given. The ion trap data matches with an
359
average of around 3,500 spectral hits to the correct library in the ion trap database. This is
360
almost three times higher compared with the results for the Q-Orbitrap data matching to the
361
correct library in the Q-Orbitrap database (on average around 1,200 spectral hits), even
362
though the libraries contain a similar number of total tandem mass spectra (10,617±1,158 for
363
the ion trap libraries, 7,808±964 for the Q-Orbitrap libraries). The difference can be partially
364
explained by the use of different settings for the collision energy for the Q-Orbitrap data of
365
samples compared with the libraries. For the MS analysis of the samples a fixed collision
366
energy was used, while for the libraries a stepped collision energy was employed, resulting in
367
about 10% less spectral hits (Supporting Information 1, table S2). Regardless the difference in
368
total number of spectral hits, the number of correct identifications for the ion trap and Q-
369
Orbitrap are comparable, 97% (33 out of 34) and 94% (32 out of 34) respectively, when the
370
data is matched to the libraries of the same mass spectrometer.
371
To investigate the possibility to use a single database containing libraries of data
372
derived from one type of instrument, query data and libraries of different sources were
373
combined. Analysis of the ion trap query data to the Q-Orbitrap libraries shows a large
374
decrease in the total number of spectral hits compared to the ion trap libraries. A reduction of
375
more than five times is observed (624 vs 3,427) for the number of spectral hits. When fixed
376
collision energy libraries were used in the analysis, the number of spectral hits even dropped
377
to around 300 (Supporting Information 1, table S2).
378
Similar results are obtained for the analysis of Q-Orbitrap query data against ion trap
379
libraries. On average less than 100 spectra could be matched against the correct library, which
380
is more than ten times lower than the Q-Orbitrap query data against the Q-Orbitrap libraries
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(~1,200 spectral hits) and six times lower than the ion trap data versus the Q-Orbitrap libraries
382
(~600 spectral hits). Nevertheless 88% of the samples were correctly identified, 5% higher
383
than the ion trap data versus Q-Orbitrap libraries (82.5%).
384
Even though the absolute total number of spectral hits (to the correct library) seem to
385
be of less importance, it does give information about the quality of the sample and the quality
386
of the data (both query and library). As well, in case the species of the investigated sample is
387
not part of the database, the data will match to the most closely related species. In this case,
388
comparison to the (average) total number of spectra of a correct identification might be able to
389
reveal the incorrect identification. For very closely related species, such as the here
390
investigated Limanda species, this will be difficult though, as the variation in average spectral
391
hits between the two species is lower than the variation between samples of the same species.
392
Furthermore, a deviation of the total number of spectral hits (compared to a standard) can be
393
an indication the investigated sample is actually a mixture and the number of spectral matches
394
can be potentially used to calculate the ratio in which the species are present in the mixture.
395
We recently have shown this possibility for mixtures of cow and horse meat40, though for
396
closely related species this will be a challenge as will be further discussed in the future
397
perspectives.
398
Identification of contaminated processed fish
399
Like many fish, flatfish is often served à la Meunière (lightly floured and fried in butter) or
400
battered and deep fried. We therefore further challenged the method by analyzing battered and
401
fried fish sold as “kibbeling” at a local fish store. Traditionally, this Dutch snack made from
402
cod, although it is now common to use other species as well. The fried fish meat was mixed
403
with pieces of the fried batter from the same sample to determine the degree of contamination
404
at which the fish can still be correctly identified. In both cases, fried fish was correctly
405
identified as cod as is shown in Figure 5, even when the sample mostly consists of batter. The
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406
total number of spectral hits for the samples containing only 10% fish is about half of the total
407
number of spectral hits for the 90% fish samples (~2,000 hits vs ~3,500 hits), showing that the
408
quality of the sample does influence the total number of spectral hits, but not proportionally to
409
the purity. Furthermore, independent of the amount of fish in the sample, a similar trend in
410
number of spectral hits to successive species libraries is observed. Pollock and haddock
411
belong both to the same family as cod (Gadidae), and are the second and third best match,
412
respectively, with about 75% of the number of spectral hits to the cod library. Salmon,
413
included for comparison with Figure 3 in Wulff et al.32, is a far more distantly related species,
414
belonging to Protacanthopterygii, a different superorder of the infraclass Teleostei than cod (a
415
Neoteleost).41 The last common ancestor of cod and salmon lived ~240 Mya, which is clearly
416
reflected in the results with far fewer matched to the salmon reference library. This example
417
shows that the method also allows identification of species in processed fish samples, even
418
when the sample mostly consists of other components. And it mimics a forensic scenario
419
where only scraps or crumbs may be available for analysis.
420
Future perspectives
421
Here we have shown that untargeted tandem mass spectrometry and simple spectral library
422
matching can be applied reproducibly to identify closely related fish species and processed,
423
contaminated, fish products. The method can be optimized and improved to increase the
424
specificity and improve the reliability. The fish libraries could be extended by inclusion of
425
several individual fish, preferably caught at different locations and of different age, in each
426
species library. Library data files could be merged into a single, extended, library per species,
427
in a similar approach as described by Önder et al..39 This will produce richer libraries,
428
including information on variation within the populations from different geographic origins
429
for further analysis. Furthermore, the database should be extended by adding more flatfish
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430
species, by which a wider range of flatfish can be confidently identified and more insight
431
generated on the specificity of other closely related (flat)fish will be obtained.
432
Another topic that deserves detailed investigation is the discrimination of species in
433
mixtures of two or more closely related species without knowledge and targeting of species-
434
specific peptides. This is likely even more challenging than the identification of a single fish
435
species in a processed sample containing mainly plant material, which is easy to distinguish
436
from fish proteins. More distantly related species such as horse and cow can be identified and
437
relatively quantified in mixtures.40 However, in closely related species, the high fraction of
438
shared peptides and tandem mass spectra challenges the relative quantitation based solely on
439
spectral counting (essentially measuring a small difference between two large numbers). In
440
the current, simple, data analysis, only the total number of spectra matching the reference
441
samples are used. This simplicity emphasizes the inherent robustness and general nature of the
442
method, but detection of closely related species in mixtures may necessitate information on
443
species-specific peptides and tandem mass spectra.
444
Currently, DNA-based methods are most often used for the confirmation of species
445
identity. It has to be stressed that the here described and challenged method is not intended to
446
replace DNA-based methods, but is meant to complement existing ones. In specific situations,
447
such as for heavily processed samples or samples adulterated or contaminated by food
448
products containing none or little DNA, mass spectrometry can be a better solution. Another
449
promising application of spectral library matching is the identification of specific tissues, as
450
has been demonstrated in earlier work on zebrafish.42 Where DNA has a high specificity and
451
low detection limit, it cannot easily discriminate between different tissues derived from the
452
same species. The difference in expression (levels) of proteins in different tissues will allow
453
the our method to differentiate tissues and apply the method to products of organ meats.
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454
Tandem mass spectrometry and spectral library matching has been successfully
455
applied to identify closely related flatfish species. The method is simple, robust, easily
456
transferable to different laboratories and allows the use of different types of mass
457
spectrometers. One database, consisting of spectral libraries of different species, from one
458
type of mass spectrometer, also allows identification of species based on data derived from
459
other mass spectrometers, albeit at a slightly lower identification rate is obtained. In addition,
460
the method correctly identifies the species in heavily contaminated fish samples. In the field
461
of food quality and safety, the method is of particular value in cases where DNA based
462
methods have problems, such as (heavily) processed samples, foods adulterated with protein
463
products and differentiation of tissues.
464 465
Acknowledgements
466
The authors thank Marleen Voorhuijzen and Theo Prins at RIKILT Wageningen UR and
467
Hilde van Pelt-Heerschap at IMARES Wageningen UR for identification of the flatfish, Klaas
468
Wubs and Marco Blokland at RIKILT Wageningen UR for technical support and Rob
469
Marissen at LUMC for help with implementing the reference library searches on a Web server
470
and the MassIVE submission.
471 472
Associated Content
473
Supporting Information
474
Supporting Information Available: Supporting Information 1: Contains Figure S1 (Limanda
475
species are sometimes difficult to discriminate), Table S1 (Repeatability and reproducibility
476
of number of spectral hits for the identification of flatfish using tandem mass spectrometry
477
and spectral library matching) and Table S2 (Comparison of Q-Orbitrap libraries, recorded
478
with stepped (S) or fixed (F) collision energy). This material is available free of charge via the
479
Internet at http://pubs.acs.org. 20
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480
Data
481
The Q-Exactive Orbitrap and amaZon ion trap data from the flatfish used to generate and
482
query the libraries are available on MassIVE (ftp://
[email protected] , with
483
password ‘a’).
484 485 486
References
487
1.
488
2010, 112, 198-213.
489
2.
http://ec.europa.eu/food/food/horsemeat/.
490
3.
http://www.which.co.uk/news/2014/09/which-investigation-uncovers-fish-fraud-
491
379594/.
492
4.
http://cordis.europa.eu/news/rcn/32023_en.html.
493
5.
Cawthorn, D.-M.; Steinman, H. A.; Hoffman, L. C., A high incidence of species
494
substitution and mislabelling detected in meat products sold in South Africa. Food Control
495
2013, 32, 440-449.
496
6.
497
E., DNA barcoding reveals a high level of mislabeling in Egyptian fish fillets. Food Control
498
2014, 46, 441-445.
499
7.
500
V.; Kolovos, M.; Liakou, C.; Stasinou, V.; Mamuris, Z., What do we think we eat? Single
501
tracing method across foodstuff of animal origin found in Greek market. Food Research
502
International 2015, 69, 151-155.
503
8.
504
the U.S. commercial market using DNA-based methods. Food Control 2016, 59, 158-163.
Shears, P., Food fraud – a current issue but an old problem. British Food Journal
Galal-Khallaf, A.; Ardura, A.; Mohammed-Geba, K.; Borrell, Y. J.; Garcia-Vazquez,
Stamatis, C.; Sarri, C. A.; Moutou, K. A.; Argyrakoulis, N.; Galara, I.; Godosopoulos,
Kane, D. E.; Hellberg, R. S., Identification of species in ground meat products sold on
21
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 22 of 38
505
9.
Helyar, S. J.; Lloyd, H. A.; de Bruyn, M.; Leake, J.; Bennett, N.; Carvalho, G. R., Fish
506
product mislabelling: failings of traceability in the production chain and implications for
507
illegal, unreported and unregulated (IUU) fishing. PloS one 2014, 9, e98691.
508
10.
509
A., Fish mislabelling in France: substitution rates and retail types. PeerJ 2015, 2, e714.
510
11.
Bénard-Capelle, J.; Guillonneau, V.; Nouvian, C.; Fournier, N.; Le Loët, K.; Dettai,
http://ec.europa.eu/food/safety/official_controls/food_fraud/fish_substitution/index_en
511 512
.htm.
513
12.
514
Schröder, U.; Verrez-Bagnis, V.; Silva, H.; Vandamme, S. G.; Boufana, B.; Mendes, R.;
515
Shorten, M.; Smith, C.; Hankard, E.; Hook, S. A.; Weymer, A. S.; Gunning, D.; Sotelo, C. G.,
516
Low mislabeling rates indicate marked improvements in European seafood market operations.
517
Frontiers in Ecology and the Environment 2015, 13, 536-540.
518
13.
519
through DNA barcodes. Proceedings. Biological sciences / The Royal Society 2003, 270, 313-
520
21.
521
14.
522
Systematic biology 2005, 54, 852-9.
523
15.
524
R.; Dolman, P. M.; Woodcock, P.; Edwards, F. A.; Larsen, T. H.; Hsu, W. W.; Benedick, S.;
525
Hamer, K. C.; Wilcove, D. S.; Bruce, C.; Wang, X.; Levi, T.; Lott, M.; Emerson, B. C.; Yu,
526
D. W., Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding.
527
Ecology letters 2013, 16, 1245-57.
528
16.
529
safety of fishery products. Food Research International 2013, 54, 972-979.
Mariani, S.; Griffiths, A. M.; Velasco, A.; Kappel, K.; Jérôme, M.; Perez-Martin, R. I.;
Hebert, P. D.; Cywinska, A.; Ball, S. L.; deWaard, J. R., Biological identifications
Hebert, P. D.; Gregory, T. R., The promise of DNA barcoding for taxonomy.
Ji, Y.; Ashton, L.; Pedley, S. M.; Edwards, D. P.; Tang, Y.; Nakamura, A.; Kitching,
Carrera, M.; Cañas, B.; Gallardo, J. M., Proteomics for the assessment of quality and
22
ACS Paragon Plus Environment
Page 23 of 38
Journal of Agricultural and Food Chemistry
530
17.
Tedesco, S.; Mullen, W.; Cristobal, S., High-Throughput Proteomics: A New Tool for
531
Quality and Safety in Fishery Products. Current Protein & Peptide Science 2014, 15, 118-
532
133.
533
18.
534
based approach for detection of chicken in meat mixes. Journal of proteome research 2010, 9,
535
3374-83.
536
19.
537
monitoring for the fast identification of seafood species. Journal of chromatography. A 2011,
538
1218, 4445-51.
539
20.
540
allergen, parvalbumin, by selected MS/MS ion monitoring mass spectrometry. Journal of
541
proteomics 2012, 75, 3211-20.
542
21.
543
B. P., Proteomic analysis of sarcoplasmic peptides of two related fish species for food
544
authentication. Applied biochemistry and biotechnology 2013, 171, 1011-21.
545
22.
546
metabolic networks and potential bioactive peptides for nutritional inferences. Journal of
547
proteomics 2013, 78, 211-20.
548
23.
549
sensitive high-performance liquid chromatography-tandem mass spectrometry method for the
550
detection of horse and pork in halal beef. Journal of agricultural and food chemistry 2013, 61,
551
11986-94.
552
24.
553
MS/MS based method for the fast and sensitive detection of horse and pork in highly
554
processed food. Journal of agricultural and food chemistry 2014, 62, 9428-35.
Sentandreu, M. A.; Fraser, P. D.; Halket, J.; Patel, R.; Bramley, P. M., A proteomic-
Ortea, I.; Canas, B.; Gallardo, J. M., Selected tandem mass spectrometry ion
Carrera, M.; Canas, B.; Gallardo, J. M., Rapid direct detection of the major fish
Barik, S. K.; Banerjee, S.; Bhattacharjee, S.; Das Gupta, S. K.; Mohanty, S.; Mohanty,
Carrera, M.; Canas, B.; Gallardo, J. M., The sarcoplasmic fish proteome: pathways,
von Bargen, C.; Dojahn, J.; Waidelich, D.; Humpf, H. U.; Brockmeyer, J., New
von Bargen, C.; Brockmeyer, J.; Humpf, H. U., Meat authentication: a new HPLC-
23
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 24 of 38
555
25.
Carrera, M.; Canas, B.; Pineiro, C.; Vazquez, J.; Gallardo, J. M., Identification of
556
commercial hake and grenadier species by proteomic analysis of the parvalbumin fraction.
557
Proteomics 2006, 6, 5278-87.
558
26.
559
new parvalbumin isoforms using a novel combination of bottom-up proteomics, accurate
560
molecular mass measurement by FTICR-MS, and selected MS/MS ion monitoring. Journal of
561
proteome research 2010, 9, 4393-406.
562
27.
563
Fast monitoring of species-specific peptide biomarkers using high-intensity-focused-
564
ultrasound-assisted tryptic digestion and selected MS/MS ion monitoring. Analytical
565
chemistry 2011, 83, 5688-95.
566
28.
567
ionization-time of flight mass spectrometry: a fundamental shift in the routine practice of
568
clinical microbiology. Clinical microbiology reviews 2013, 26, 547-603.
569
29.
570
reliable species identification of scallops by MALDI-TOF mass spectrometry. Food Control
571
2014, 46, 6-9.
572
30.
573
origin of meat and gelatin by MALDI-TOF-MS. Journal of Food Composition and Analysis
574
2015, 41, 104-112.
575
31.
576
blood meals using unidentified tandem mass spectral libraries. Nature communications 2013,
577
4, 1746.
Carrera, M.; Canas, B.; Vazquez, J.; Gallardo, J. M., Extensive de novo sequencing of
Carrera, M.; Canas, B.; Lopez-Ferrer, D.; Pineiro, C.; Vazquez, J.; Gallardo, J. M.,
Clark, A. E.; Kaleta, E. J.; Arora, A.; Wolk, D. M., Matrix-assisted laser desorption
Stephan, R.; Johler, S.; Oesterle, N.; Näumann, G.; Vogel, G.; Pflüger, V., Rapid and
Flaudrops, C.; Armstrong, N.; Raoult, D.; Chabrière, E., Determination of the animal
Onder, O.; Shao, W.; Kemps, B. D.; Lam, H.; Brisson, D., Identifying sources of tick
24
ACS Paragon Plus Environment
Page 25 of 38
Journal of Agricultural and Food Chemistry
578
32.
Wulff, T.; Nielsen, M. E.; Deelder, A. M.; Jessen, F.; Palmblad, M., Authentication of
579
fish products by large-scale comparison of tandem mass spectra. Journal of proteome
580
research 2013, 12, 5253-9.
581
33.
582
sequence-independent shotgun proteomics workflow for strain-level bacterial differentiation.
583
Scientific reports 2015, 5, 14337.
584
34.
585
Pratt, B.; Nilsson, E.; Angeletti, R. H.; Apweiler, R.; Cheung, K.; Costello, C. E.; Hermjakob,
586
H.; Huang, S.; Julian, R. K.; Kapp, E.; McComb, M. E.; Oliver, S. G.; Omenn, G.; Paton, N.
587
W.; Simpson, R.; Smith, R.; Taylor, C. F.; Zhu, W.; Aebersold, R., A common open
588
representation of mass spectrometry data and its application to proteomics research. Nature
589
biotechnology 2004, 22, 1459-66.
590
35.
591
S.; Gatto, L.; Fischer, B.; Pratt, B.; Egertson, J.; Hoff, K.; Kessner, D.; Tasman, N.; Shulman,
592
N.; Frewen, B.; Baker, T. A.; Brusniak, M. Y.; Paulse, C.; Creasy, D.; Flashner, L.; Kani, K.;
593
Moulding, C.; Seymour, S. L.; Nuwaysir, L. M.; Lefebvre, B.; Kuhlmann, F.; Roark, J.;
594
Rainer, P.; Detlev, S.; Hemenway, T.; Huhmer, A.; Langridge, J.; Connolly, B.; Chadick, T.;
595
Holly, K.; Eckels, J.; Deutsch, E. W.; Moritz, R. L.; Katz, J. E.; Agus, D. B.; MacCoss, M.;
596
Tabb, D. L.; Mallick, P., A cross-platform toolkit for mass spectrometry and proteomics.
597
Nature biotechnology 2012, 30, 918-20.
598
36.
599
Bioinformatics 2004, 20, 1466-7.
600
37.
601
estimate the accuracy of peptide identifications made by MS/MS and database search.
602
Analytical chemistry 2002, 74, 5383-92.
Shao, W.; Zhang, M.; Lam, H.; Lau, S. C., A peptide identification-free, genome
Pedrioli, P. G.; Eng, J. K.; Hubley, R.; Vogelzang, M.; Deutsch, E. W.; Raught, B.;
Chambers, M. C.; Maclean, B.; Burke, R.; Amodei, D.; Ruderman, D. L.; Neumann,
Craig, R.; Beavis, R. C., TANDEM: matching proteins with tandem mass spectra.
Keller, A.; Nesvizhskii, A. I.; Kolker, E.; Aebersold, R., Empirical statistical model to
25
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 26 of 38
603
38.
Lam, H.; Deutsch, E. W.; Eddes, J. S.; Eng, J. K.; King, N.; Stein, S. E.; Aebersold,
604
R., Development and validation of a spectral library searching method for peptide
605
identification from MS/MS. Proteomics 2007, 7, 655-67.
606
39.
607
parasitic arthropods using shotgun proteomics and unidentified tandem mass spectral libraries.
608
Nature protocols 2014, 9, 842-50.
609
40.
610
Palmblad, M., Identification of meat products by shotgun spectral matching. Food chemistry
611
2016, 203, 28-34.
612
41.
613
Wainwright, P. C.; Friedman, M.; Smith, W. L., Resolution of ray-finned fish phylogeny and
614
timing of diversification. Proceedings of the National Academy of Sciences of the United
615
States of America 2012, 109, 13698-703.
616
42.
617
A.; Hoogendijk, J. L.; Henneman, A. A.; Deelder, A. M.; Spaink, H. P.; Palmblad, M.,
618
Identifying proteins in zebrafish embryos using spectral libraries generated from dissected
619
adult organs and tissues. Journal of proteome research 2014, 13, 1537-44.
Onder, O.; Shao, W.; Lam, H.; Brisson, D., Tracking the sources of blood meals of
Ohana, D.; Dalebout, H.; Marissen, R. J.; Wulff, T.; Bergquist, J.; Deelder, A. M.;
Near, T. J.; Eytan, R. I.; Dornburg, A.; Kuhn, K. L.; Moore, J. A.; Davis, M. P.;
van der Plas-Duivesteijn, S. J.; Mohammed, Y.; Dalebout, H.; Meijer, A.; Botermans,
620 621
Funding Sources
622
The research at RIKILT Wageningen UR has been financially supported by the Dutch
623
Ministry of Economic Affairs. MP has been financially supported by the Dutch Organization
624
of Scientific Research (NWO) via VIDI grant 917.11.398.
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Figures
Figure 1, Workflow for the identification of closely related flatfish by tandem mass spectrometry and spectral library matching.
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Figure 2, Identification of flatfish by tandem mass spectrometry and spectral library matching. Column graph representation of total number of spectral hits against all species spectral libraries (n=59) of representative samples of (A) European plaice, (B) common dab and (C) yellowfin sole. The samples match best to the flatfish spectral libraries in the database (in green), after which the other fish (blue), mammals (red), birds (orange) and European squid (dark blue) follow. Insert zooms in on the flatfish libraries. In dark green the Pleuronectidae family, to which the three investigated flatfish belong, in lighter greens the Scophthalmidae and Soleidae family. Columns indicated with an asterisk are the flatfish
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spectral libraries added to the database at a different moment from the other fish spectral libraries.
Figure 3, Variation of total number of spectral matches for the identification of (closely related) flatfish. On four days three samples of three different flatfish (European plaice (white), common dab (striped) and yellowfin sole (dotted)) were prepared at two different laboratories and analyzed on a Bruker Daltonics amaZon speed ion trap mass spectrometer. The number of spectral matches to the correct species was used to calculate the average number of spectral matches and the standard deviation. A. Within-day variation (n=3 per species*). B. Within-lab variation (n=6 per species*). C. Between-lab variation (n=12 per species*). *due to poor sample and data quality two yellowfin sole samples are missing (no spectral hits)
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Figure 4, Compatibility spectral library matching different fragmentation techniques. Results from a direct comparison of flatfish species identification from tandem mass spectra from a Bruker Daltonics amaZon speed ion trap and a Thermo Scientific Q-Exactive Orbitrap mass spectrometer. Both the query (’unknown’) and reference library data was varied. A. Pie chart of average number of spectral hits, including correct identification rates. B. Average number of spectral hits for each analysis. The liquid chromatography system, ion source and instrument settings are all important. This is not a competitive comparison between instruments, but a validation that query data can come from a different instrument than was used to generate the reference libraries, as long as it contains collision-induced dissociation tandem mass spectra from tryptic peptides. For the results presented here, SpectraST version 4.0 was used. Only correct identifications were used to calculate the average number of spectral hits. C. Typical example of matching tandem mass spectrum from Yellowfin sole samples. The spectra are almost certainly from a conserved region of tropomyosin 4
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(MEIQELQLK), as was identified using Mascot. With the method presented here, however, no peptide identifications are used to identify biological species - only information on shared fragmentation spectra of peptides is used for identification. From top to bottom: Matching amaZon Ion trap tandem mass spectra, Q-Exactive Orbitrap tandem mass spectrum (top) from Yellowfin sole best matching the amaZon ion trap library spectrum (bottom) and matching QExactive Orbitrap tandem mass spectra. In this case, the Orbitrap spectrum contains more signal at low m/z but less immediately below the precursor (neutral loss peaks). All spectra are plotted to relative intensity scale.
Figure 5, Positive identification of fish species of battered and fried fish at 10% fish content. A sample of battered and fried cod was mixed in a 9:1 and 1:9 ratio of fish to batter. Both at ~90% and ~10% fish, cod was positively identified, showing the method to be robust and applicable to fish samples contaminated with additional ingredients.
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Figure 1, Workflow for the identification of closely related flatfish by tandem mass spectrometry and spectral library 150x178mm (300 x 300 DPI)
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Figure 2. Identification of flatfish by tandem mass spectrometry and spectral library matching. Column graph representation of total number of spectral hits against all species spectral libraries (n=59) of representative samples of (A) European plaice, (B) common dab and (C) yellowfin sole. The samples match best to the flatfish spectral libraries in the database (in green), after which the other fish (blue), mammals (red), birds (orange) and European squid (dark blue) follow. Insert zooms in on the flatfish libraries. In dark green the Pleuronectidae family, to which the three investigated flatfish belong, in lighter greens the Scophthalmidae and Soleidae family. Columns indicated with an asterisk are the flatfish spectral libraries added to the database at a different moment from the other fish spectral libraries. 176x179mm (300 x 300 DPI)
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Figure 3. Variation of number of spectral matches for the identification of (closely related) flatfish. On four days three samples of three different flatfish (European plaice (white), common dab (striped) and yellowfin sole (dotted)) were prepared at two different laboratories and analyzed on a Bruker Daltonics amaZon speed ion trap mass spectrometer. The number of spectral matches to the correct species was used to calculate the average number of spectral matches and the standard deviation. A. Within-day variation (n=3 per species*). B. Within-lab variation (n=6 per species*). C. Between-lab variation (n=12 per species*). *due to poor sample and data quality two yellowfin sole samples are missing (no spectral hits) 93x102mm (300 x 300 DPI)
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Figure 4. Compatibility spectral library matching different fragmentation techniques. Results from a direct comparison of flatfish species identification from tandem mass spectra from a Bruker Daltonics amaZon speed ion trap and a Thermo Scientific Q-Exactive Orbitrap mass spectrometer. Both the query (’unknown’) and reference library data was varied. A. Pie chart of average number of spectral hits, including correct identification rates. B. Average number of spectral hits for each analysis. The liquid chromatography system, ion source and instrument settings are all important. This is not a competitive comparison between instruments, but a validation that query data can come from a different instrument than was used to generate the reference libraries, as long as it contains collision-induced dissociation tandem mass spectra from tryptic peptides. For the results presented here, SpectraST version 4.0 was used. Only correct identifications were used to calculate the average number of spectral hits. C. Typical example of matching tandem mass spectrum from yellowfin sole samples. The spectra are almost certainly from a conserved region of tropomyosin (MEIQELQLK), as was identified using Mascot. With the method presented here, however, no peptide identifications are used to identify biological species - only information on shared fragmentation spectra of peptides is used for identification. From top to bottom: Matching amaZon Ion trap tandem mass spectra, Q-Exactive Orbitrap tandem mass spectrum (top) from Yellowfin sole best matching the amaZon ion trap library spectrum (bottom) and matching Q-Exactive Orbitrap tandem mass spectra. In this case, the Orbitrap spectrum contains more signal at low m/z but less immediately below the precursor (neutral loss peaks). All spectra are plotted to relative intensity scale. 176x133mm (300 x 300 DPI)
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Figure 5, Positive identification of fish species of battered and fried fish at 10% fish content. A sample of battered and fried cod was mixed in a 9:1 and 1:9 ratio of fish to batter. Both at ~90% and ~10% fish, cod was positively identified, showing the method to be robust and applicable to fish samples contaminated with additional ingredients. 82x45mm (300 x 300 DPI)
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