Rapid species level identification of fish eggs by proteome fingerprinting using MALDI-TOF MS.

Quantifying spawning biomass of commercially relevant fish species is important to generate fishing quotas. This will mostly rely on the annual or daily production of fish eggs. However, these have to be identified precisely to species level to obtain a reliable estimate of offspring production of the different species. Because morphological identification can be very difficult, recent developments are heading towards application of molecular tools. Methods such as COI barcoding have long handling times and cause high costs for single specimen identifications. In order to test MALDI-TOF MS, a rapid and cost-effective alternative for species identification, we identified fish eggs using COI barcoding and used the same specimens to set up a MALDI-TOF MS reference library. This library, constructed from two different MALDI-TOF MS instruments, was then used to identify unknown eggs from a different sampling occasion. By using a line of evidence from hierarchical clustering and different supervised identification approaches we obtained concordant species identifications for 97.5% of the unknown fish eggs, proving MALDI-TOF MS a good tool for rapid species level identification of fish eggs. At the same time we point out the necessity of adjusting identification scores of supervised methods for identification to optimize identification success. SIGNIFICANCE: Fish products are commercially highly important and many societies rely on them as a major food resource. Over many decades stocks of various relevant fish species have been reduced due to unregulated overfishing. Nowadays, to avoid overfishing and threatening of important fish species, fish stocks are regularly monitored. One component of this monitoring is the monitoring of spawning stock sizes. Whereas this is highly dependent on correct species identification of fish eggs, morphological identification is difficult because of lack of morphological features.

A reliable DNA barcode reference library for the identification of the North European shelf fish fauna.

Valid fish species identification is an essential step both for fundamental science and fisheries management. The traditional identification is mainly based on external morphological diagnostic characters, leading to inconsistent results in many cases. Here, we provide a sequence reference library based on mitochondrial cytochrome c oxidase subunit I (COI) for a valid identification of 93 North Atlantic fish species originating from the North Sea and adjacent waters, including many commercially exploited species. Neighbour-joining analysis based on K2P genetic distances formed nonoverlapping clusters for all species with a ≥99% bootstrap support each. Identification was successful for 100% of the species as the minimum genetic distance to the nearest neighbour always exceeded the maximum intraspecific distance. A barcoding gap was apparent for the whole data set. Within-species distances ranged from 0 to 2.35%, while interspecific distances varied between 3.15 and 28.09%. Distances between congeners were on average 51-fold higher than those within species. The validation of the sequence library by applying BOLDs barcode index number (BIN) analysis tool and a ranking system demonstrated high taxonomic reliability of the DNA barcodes for 85% of the investigated fish species. Thus, the sequence library presented here can be confidently used as a benchmark for identification of at least two-thirds of the typical fish species recorded for the North Sea.