Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. However, few studies have tested species identification across different orders and classes. In this study, we collected data from 1,246 specimens and 198 species to test species identification in a diverse dataset. We also evaluated different specimen preparation and data processing approaches for machine learning and developed a workflow to optimize classification using random forest. Our results showed high success rates of over 90%, but we also found that the size of the reference library affects classification error. Additionally, we demonstrated the ability of the method to differentiate marine cryptic-species complexes and to distinguish sexes within species.

Tissue for measurements was taken mainly from the marine organisms tissue bank of the Senckenberg am Meer, German Centre for Marine Biodiversity Research, which was established using samples from numerous studies (Knebelsberger and Thiel, 2014; Knebelsberger et al., 2014; Markert et al., 2014; Gebhardt and Knebelsberger, 2015; Raupach et al., 2015; Barco et al., 2016; Laakmann et al., 2016; Rossel et al., 2020b) (supplementary table S1 for accession numbers) on North Sea metazoans. The material from this collection was taken from specimens processed for COI-barcoding to create reference libraries for a variety of marine animal groups. During this process, tissue samples of the respective specimens were stored in ethanol at -80°C. Tissue samples were available for Bivalvia (muscle, 18 species), Cephalopoda (muscle from arm, 12 species), Gastropoda (muscle from foot, 24 species), Polyplacophora (muscle from foot, 2 species), Ascidiacea (tissue, 1 species), Teleostei (muscle, 67 species), Elasmobranchii (muscle, 7 species), Malacostraca (muscle from foot or chelae, 39 species), Thecostraca (muscle from foot, 1 species), Pycnogonida (leg fragment, 1 species), Asteroidea (tube feet, 10 species), Ophiuroidea (tissue from arm, 10 species) and Echinoidea (tissue from the base of the tubercle, 6 species) (n_species= 198, n_specimens=1,246).

Sample preparation

The basic protocol of sample preparation was the same for all analyzed tissue samples. A very small tissue fragment (< 1 mm³) was incubated for 5 minutes in α-cyano-4-hydroxycinnamic acid (HCCA) as a saturated solution in 50% acetonitrile, 47.5% molecular grade water and 2.5% trifluoroacetic acid. Tissue from crustacean Cancer pagurus Linnaeus, 1758, the fish Clupea harengus Linnaeus, 1758, the cephalopod Eledone cirrhosa (Lamarck, 1798) and the echinoderm Stichastrella rosea (O.F. Müller, 1776) was used to find an optimal tissue to HCCA matrix ratio. Tissue was weighted on a METTLER TOLEDO XS3DU micro-balance and the amount of matrix was adjusted to tissue weight to obtain the desired ratios ranging from 0.012 µg µl^-1 to 200 µg µl^-1. After incubation, 1.5 µl of the solution was transferred to 10 spots on a target plate, respectively. Mass spectra were measured with a Microflex LT/SH System (Bruker Daltonics) using method MBTAuto. Peak evaluation was carried out in a mass peak range between 2 k – 10 k Dalton (Da) using a centroid peak detection algorithm, a signal to noise threshold of 2 and a minimum intensity threshold of 600. To create a sum spectrum, 160 satisfactory shots were summed up.

Resulting from observations during this initial test, a fast applicable protocol was developed without the need to weigh each tissue sample. Matrix volume was added to tissue samples depending on tissue volume, i.e. tissue samples were always completely covered by HCCA matrix with a small layer (ca. 1 mm) of supernatant. Samples were incubated for 5 minutes and 1.5 µl of the solution were transferred to a single spot on a target plate for measurement. Each spot was measured between two to three times.

Data can be analyzed using Bruker proprietary software such as Bruker Flex analysis or Bruker Biotyper.

Alternatively, data can be analyzed in R using R-packages MaldiQuant and MaldiQuantForeign following the vignette that can be found via:

https://cran.r-project.org/web/packages/MALDIquant/index.html