Differentiating samples and experimental protocols by direct comparison of tandem mass spectra

Fish species authentication in commercial fish products using mass spectrometry and spectral library matching approach

Seafood fraud has become a global issue, threatening food security and safety. Adulteration, substitution, dilution, and incorrect labeling of seafood products are fraudulent practices that violate consumer safety. In this context, developing sensitive, robust, and high-throughput molecular tools for food and feed authentication is becoming crucial for regulatory purposes. Analytical approaches such as proteomics mass spectrometry have shown promise in detecting incorrectly labeled products. For the application of these tools, genome information is crucial, but currently, for many marine species of commercial importance, such information is unavailable. However, when combining proteomic analysis with spectral library matching, commercially important fish species were successfully identified, differentiated, and quantified in pure muscle samples and mixtures, even when genome information was scarce. This study further tested the previously developed spectral library matching approach to differentiate between 29 fish species from the North Sea and examined samples including individual fish, laboratory-prepared mixtures and commercial products. For authenticating libraries generated from 29 fish species, fresh muscle samples from the fish samples were matched against the reference spectral libraries. Species of the fresh fish samples were correctly authenticated using the spectral library approach. The same result was obtained when evaluating the laboratory-prepared mixtures. Furthermore, processed commercial products containing mixtures of two or three fish species were matched against these reference spectral libraries to test the accuracy and robustness of this method for authentication of fish species. The results indicated that the method is suitable for the authentication of fish species from highly processed samples such as fish cakes and burgers. The study shows that current and future challenges in food and feed authentication can efficiently be tackled by reference spectral libraries method when prospecting new resources in the Arctic.

MS1Connect: a mass spectrometry run similarity measure

MOTIVATION

Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols.

RESULTS

We propose a method, MS1Connect, that calculates the similarity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though these data are not used by MS1Connect.

AVAILABILITY AND IMPLEMENTATION

The MS1Connect software is available at https://github.com/bmx8177/MS1Connect.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets

It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.