DOI: 10.5061/dryad.jwstqjqf2
title: Proteomic spectra of epipelagic copepods

description: We analyzed robustness of species identification based on proteomic
 composition to data processing and intraspecific variability, specificity
 and sensitivity of species-markers as well as discriminatory power of
 proteomic fingerprinting and its sensitivity to phylogenetic distance. Our
 analysis is based on MALDI-TOF MS data from 32 marine copepod species
 coming from 13 regions (North and Central Atlantic and adjacent seas). A
 random forest (RF) model correctly classified all specimens to species
 level with only small sensitivity to data processing, demonstrating the
 strong robustness of the method. Compounds with high specificity showed
 low sensitivity i.e., identification was based on complex
 pattern-differences rather than on presence of single markers.
description: Specimens were derived from
 ethanol samples (&gt; 96 %), which were collected during diverse
 monitoring programs or field campaigns and from a copepod culture. Age and
 storing conditions varied between samples, samples were stored at 4°C from
 2018 onwards. Adult female copepods were identified morphologically to
 species level and stored in ethanol until further processing at 4°C. In
 total, 752 specimens from 32 species, and 13 different regions were used
 for proteomic fingerprinting analyses. Proteomic profiles were determined for all 752
 specimens. For small copepods (&lt; 2 mm), the whole specimen, and for
 larger copepods, a piece of the cephalosome was shortly dried at room
 temperature and kept in an Eppendorf tube. Depending on sample size 5–10
 µl matrix solution (α-Cyano-4-hydroxycinnamic acid as saturated
 solution in 50% acetonitrile, 47.5% LC-MS grade water, and 2.5%
 trifluoroacetic acid) was added. After at least 10 min extraction, 1.2 µl
 of each sample was added onto the target plate, with 2–3 replicates.
 Protein mass spectra were measured from 2 to 20 kDa using a linear-mode
 MALDI-TOF System (Microflex LT/SH, Bruker Daltonics). Peak intensities
 were analyzed during random measurement in the range between 2 and 20 kDa
 using a centroid peak detection algorithm, a signal-to-noise threshold of
 2 and a minimum intensity threshold of 400 with a peak resolution higher
 than 400 for mass spectra evaluation. Proteins/Oligonucleotide method was
 employed for fuzzy control with a maximal resolution 10 times above the
 threshold. For each sample, 240 satisfactory shots were summed up.
 Spectra in the range of 2–20 kDa were processed with
 MALDIquant and MALDIquantForeign using square root transformation,
 Savitzky-Golay smoothing with a half window size of 10, baseline removal
 by the statistics-sensitive non-linear iterative peak-clipping algorithm
 and normalization setting the total ion current set to 1. Normalized
 spectra of technical replicates were averaged. 
 Optimal peak detection
 parameters were derived by varying the signal-to-noise ratio (SNR)
 thresholds for peak identification and the half window size (HWS) of peak
 picking, both in the range of 3–15 with species classification success of
 the random forest model (method see below) as the target variable. Also,
 we determined the proportion of closely spaced peaks after binning (i.e.
 mass difference between peaks &lt; 6 Da), that have a risk of being
 mis-assigned to a specific m/z value. The highest classification success
 was reached with a SNR of 4 and a HWS of 3, however, the proportion of
 closely spaced peaks was still high and strongly decreased at SNR and HWS
 values of 8. These peak detection parameters (SNR=8, HWS=8) were applied
 to the final data set. Picked peaks were repeatedly binned to compensate
 for small variation in the m/z values between measurements until the
 intensity matrix reached a stable peak number (with a tolerance of 2000
 ppm, strict approach). All signals below the SNR were set to zero in the
 final peak matrix. For all further analysis peak intensities were
 Hellinger transformed using the R package vegan.
 Species classification was performed by a random
 forest (RF) model using the R package randomForest using 2000 trees and
 the square root of peak number as randomly sampled variables at each
 split. To avoid overrepresentation of the most abundant species, the
 number of sub-sampled specimens per species in each of the 2000 decision
 trees was limited to the abundance of the least abundant species (N=4),
 respectively. Species with less than four specimens were excluded as we
 observed a strong increase in the out of bag with smaller sample size. For
 all other species, the classification RF model was applied to all
 specimens from 27 species.