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\begin{document}

\title{A universal tool for marine metazoan species identification -- Towards
best practices in proteomic fingerprinting}



\author[1]{Sven Rossel}%
\author[2]{Janna Peters}%
\author[3]{Nele Charzinski}%
\author[4]{Angelina Eichsteller}%
\author[5]{Silke Laakmann}%
\author[6]{Hermann Neumann}%
\author[4]{Pedro Martinez}%
\affil[1]{Senckenberg am Meer Deutsches Zentrum fur Marine Biodiversitatsforschung}%
\affil[2]{Senckenberg am Meer Deutsches Zentrum für Marine Biodiversitätsforschung}%
\affil[3]{Carl von Ossietzky Universitat Oldenburg}%
\affil[4]{Senckenberg am Meer}%
\affil[5]{Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg}%
\affil[6]{Johann Heinrich von Thünen-Institut für Seefischerei}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\selectlanguage{english}
\begin{abstract}
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. And thus can be a vital tool for
biodiversity assessments in ecological studies. 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.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Correct and cost-effective species identification is crucial in various
research areas, including biodiversity assessments, where obtaining
reliable information on species' occurrences and distributions is
pivotal. If species cannot be morphologically identified to the species
level, they are often assigned to higher taxonomic levels, leading to
less detailed analyses and consequently imprecise conclusions (Bailey et
al., 2001; Timms et al., 2013). However, identification of samples using
COI-barcoding is expensive, time consuming (Rossel et al., 2019) and
therefore not feasible in large biodiversity assessments including large
numbers of specimens.

Matrix Assisted Laser Desorption/Ionization Time-of-flight mass
spectrometry (MALDI-TOF MS) is a rapid species identification method
that measures a proteome fingerprint to identify specimens using a
reference library. With few preparation steps, peptides and proteins are
extracted from tissue and embedded in a matrix absorbing laser radiation
while measuring ionized, intact compounds in a mass spectrometer
(Singhal et al., 2015). This method is routinely applied for the
identification of microorganisms such as bacteria, viruses and fungi
(Fenselau and Demirev, 2001; Sandrin et al., 2013; Calderaro et al.,
2014). It was also used for food fraud detection (Mazzeo et al., 2008;
Flaudrops et al., 2015) or to check food adulteration (Sassi et al.,
2015). In pilot studies, it was successfully applied for identification
of metazoans such as copepods (Laakmann et al., 2013; Kaiser et al.,
2018; Rossel and Mart\selectlanguage{ngerman}ínez Arbizu, 2019; Renz et al., 2021; Yeom et al.,
2021; Rossel et al., 2022; Peters et al., 2023), isopods (Kürzel et al.,
2022; Paulus et al., 2022), different groups of Cnidaria (Holst et al.,
2019; Park et al., 2021; Korfhage et al., 2022), molluscs (Wilke et al.,
2020), fish (Mazzeo et al., 2008; Volta et al., 2012) and especially
disease vectors such as ticks, sandflies or mosquitoes (Yssouf et al.,
2013; Chavy et al., 2019; Rakotonirina et al., 2020, 2022; Nabet et al.,
2021). Most studies only analyzed a few species or were limited to a
certain taxonomic group while studies across different classes and phyla
are still missing. Also, no gold standard protocol for metazoan
analytics has been established yet. Systematic tests, how data
processing will affect the identification success and whether and how
pipelines need to be adapted to higher-taxonomic-level identification
are also missing.

For the first time, we present a generalized workflow for species
identification of metazoans as well as the subsequent bioinformatics
using a wide spectrum of marine taxa. We emphasize the importance of
adjusting bioinformatics to the data set and finally prove the power of
proteomic fingerprinting for differentiation of morphologically cryptic,
closely related marine species and beyond mere species identification on
sex level, making it a promising tool for ecological research.

\textbf{Methods}

Sample material

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\textsubscript{species}= 198, n\textsubscript{specimens}=1,246).

Sample preparation

The basic protocol of sample preparation was the same for all analyzed
tissue samples. A very small tissue fragment (\textless{} 1
mm\textsuperscript{3}) was incubated for 5 minutes in HCCA as a
saturated solution in 50\% acetonitrile, 47.5\% molecular grade water
and 2.5\% trifluoroacetic acid. Tissue from crustacean \emph{Cancer
pagurus} Linnaeus, 1758, the fish \emph{Clupea harengus} Linnaeus, 1758,
the cephalopod \emph{Eledone cirrhosa} (Lamarck, 1798) and the
echinoderm \emph{Stichastrella rosea} (O.F. Müller, 1776) was used to
find an optimal tissue to HCCA matrix ratio. Tissue was weighed 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 \selectlanguage{greek}µ\selectlanguage{english}g
\selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1} to 200 \selectlanguage{greek}µ\selectlanguage{english}g \selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1}. After
incubation, 1.5 \selectlanguage{greek}µ\selectlanguage{english}l 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 \selectlanguage{greek}µ\selectlanguage{english}l of the solution were transferred to a single spot on
a target plate for measurement. Each spot was measured between two to
three times.

Mass spectra processing in R

Mass spectra data was imported to R (R-Core-Team, 2022) using
MALDIquantForeign (Gibb, 2015) and further processed using MALDIquant
(Gibb and Strimmer, 2012). Mass spectra were trimmed to an identical
length from 2 to 20 kDa. Subsequently, spectra were square root
transformed, smoothed using Savitzky Golay method (Savitzky and Golay,
1964), baseline corrected using SNIP approach (Ryan et al., 1988) and
normalized using total ion current (TIC) method.

Spectra were quality controlled using the command `screenSpectra' from
the R-package MALDIrppa (Palarea-Albaladejo et al., 2017). Mass spectra
with a notably high a-score were checked by eye and discarded if mass
spectra were of bad quality. If due to this, only a single specimen for
a certain species was retained, the remaining specimen was discarded
from the data set.

Evaluation of random forest model for identification

Besides initial sample preparation and subsequent data processing, we
tested how to improve a random forest (RF) model used for species
identification. Optimal number of trees and variables was tested in a
previous study (Rossel and Mart\selectlanguage{ngerman}ínez Arbizu, 2018a). Here we assessed the
effect of minimum number of specimens per species category on the
resulting model power. We sampled the dataset using two to 11 specimens
per species including only species with at least 11 specimens per class
(n=20). For each minimum number of specimens, 100 data sets were sampled
using `sample\_n' from R-package dplyr (Wickham et al., 2022), a RF
model was created and the OOB errors assessed accordingly.

Standardization of data processing

Based on literature research and own observations, three data processing
steps were identified, which may have a severe impact on data and the
resulting quality of a random Forest (RF) classification model
(Breimann, 2001). I) Iterations of baseline subtraction: this is a first
manipulation step to reduce chemical noise and is carried out
iteratively (Martínez Arbizu and Rossel, 2018). Increasing iterations
will result in loss of low intensity peaks. II) Signal to noise ratio
(SNR) during peak picking: an increase in SNR will exclude signals of
low intensity. The higher the SNR value, the less peaks will be kept.
III) Half window size (HWS) during peak picking: within the HWS the peak
with the highest intensity will be chosen as the resulting peak during
peak picking. The higher the HWS is chosen, the less peaks will be
picked across an entire mass spectrum range.

Interactive effects of these data processing steps were tested using the
classification success by a random forest model as target variable:
iterations of baseline estimation and peak detection HWS were varied
both between 5 to 30 and SNR from 3 to 20. In total, 12,186 analyses
were carried out. In all cases, peak binning using `binPeaks' from
R-package MaldiQuant was repeated until the number of variables in the
data did not further change. The RF model (ntree=2,000 and mtry=35) was
trained on the Hellinger transformed peak intensities as suggested by
Rossel and Martínez Arbizu (2018a). The RF out-of-box (OOB) error was
used as measure for classification success. For these analyses, based on
the results from RF-model evaluation, only species were included with at
least six specimens. To investigate the main drivers of classification
success, a generalized additive model (GAM, family: binomial; link
function: logit) was calculated.

Testing the classification success

In concordance with the results from the previous tests, only species
with at least six specimens were included in the model. Mass spectra
from these species were processed according to the results from the test
on variation of HWS (7), SNR (3) and baseline iteration (22). To test
the overall classification success on species level, single specimens
were separated from the RF training data set and subsequently identified
using this model. After classification, the \emph{post-hoc} test by
Rossel and Martínez Arbizu (Martínez Arbizu and Rossel, 2018; Rossel and
Martínez Arbizu, 2018a) using the R-package RFtools
(\emph{https://github.com/pmartinezarbizu/RFtools}) was applied to
verify RF classification. This \emph{post-hoc} test uses the empirical
distribution of RF assignment probabilities from the RF model and
compares the assignment probabilities of newly classified specimens to
this distribution. Whereas classified specimens with assignment
probabilities falling within this empirical distribution are considered
true positive (tp), specimens with probabilities of assignment
significantly different to this distribution considered false positive
(fp).

Case studies

In order to show the applicability of MALDI-TOF MS, we present two model
cases. First, data of the North Sea starfish \emph{Astropecten
irregularis} (Pennant, 1777) were investigated based on MALDI-TOF mass
spectra. This species was found to be genetically divergent (Laakmann et
al., 2016) while revealing a high morphological similarity.
Differentiation of species was tested using RF models. Furthermore, data
on the crustacean \emph{Euterpina acutifrons} (Dana, 1847) from Rossel
and Martínez Arbizu, 2019 was analyzed to show the applicability for sex
level differentiation using hierarchical clustering and RF. Based on the
Gini index the 30 most important peaks for species/sex differentiation
in a RF model were extracted and investigated to show the expression
within the respective groups.

Phyla and class models for identification

To test whether specimen can be identified on an above-species level, a
RF model containing only class- and phylum-categories was applied. All
spectra from species to be classified were excluded from the model to
evaluate its use for specimen not included in a library. The respective
specimens were identified using the model and the predicted class/phylum
was tested with the RF \emph{post-hoc} test. To test classification on
phylum level, 1,246 specimens from 198 species were included. On class
level, 1,227 specimens from 195 species were analyzed.

\textbf{Results}

The data set contained 1,246 specimens from 198 taxa including
echinoderms (Asteroidea, Echinoidea and Ophiuroidea), molluscs
(Bivalvia, Gastropoda, Polyplacophora and Cephalopoda), arthropods
(Crustacea, Pantopoda) and chordates (Tunicata, Vertebrata: Teleostei,
Elasmobranchii). For 1,139 specimens a published COI barcode or another
molecular identifier is available (supplementary table S1). The
remaining specimens were identified morphologically. For 226 specimens
attempts to obtain mass spectra either failed or were of minor quality
and discarded.

Sample preparation

To determine the concentration range for successful measurements,
weighted tissue samples were mixed with varying amounts of
\selectlanguage{greek}α-\selectlanguage{english}cyano-4-hydroxycinnamic acid (HCCA). In total, 15 different
tissue/matrix concentrations were tested ranging from 0.01 \selectlanguage{greek}µ\selectlanguage{english}g
\selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1} to 200 \selectlanguage{greek}µ\selectlanguage{english}g \selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1} (Fig. 1A).
Despite variations between samples, high quality mass spectra were
generally assessed in a concentration range from to 3.1 \selectlanguage{greek}µ\selectlanguage{english}g
\selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1} to 12.5 \selectlanguage{greek}µ\selectlanguage{english}g \selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1}. The largest
concentration range for successful measurements was recorded for the
echinoderm \emph{Stichastrella rosea} (sample MT03612), with successful
measurements across almost the entire concentration range. No
measurements were obtained for concentrations of 200 \selectlanguage{greek}µ\selectlanguage{english}g
\selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1}. It was only when concentrations reached 12.5
\selectlanguage{greek}µ\selectlanguage{english}g/\selectlanguage{greek}µ\selectlanguage{english}l or lower that results were obtained for all specimens.

Good measurements were obtained from small tissue samples, when these
were completely submersed in the HCCA solution within a 1.5 ml
microcentrifuge tube (Fig. 1B). Before samples/matrix ratio was too low
to detect a signal, an increase in baseline height in the lower masses
was recorded (Fig. 1C). When sample to matrix ratio was increased, an
increase in noise was observed (Fig. 1D). Quality improvement of spectra
from high tissue-to-HCCA matrix ratios was achieved by dilution. This
was tested using tissue from the crustacean \emph{Cancer pagurus}(sample
MT01453). Concentrations were diluted from an initial concentration of
200 \selectlanguage{greek}µ\selectlanguage{english}g \selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1} that resulted in no mass spectra at all.
The measurements from diluted preparations then showed similar results
as measurements made with the respective concentrations from undiluted
sample preparations (compare Fig. 1A brown and red results).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1: Results of the sample preparation test. All graphs show peak
intensity on the y-axis. In A, x-axis represents tissue:matrix ratio in
\selectlanguage{greek}µ\selectlanguage{english}g per \selectlanguage{greek}µ\selectlanguage{english}l. In B C and D m/z values (ratio of molecule mass and loading)
are depicted on the x-axis. A) Maximum intensities as a measure of
quality for the different sample to HCCA matrix ratios assessed for four
species. Additionally, for \emph{Cancer pagurus} a dilution series
(brown) was carried out. B) Good quality spectrum at a ratio of 3.12 \selectlanguage{greek}µ\selectlanguage{english}g
tissue per \selectlanguage{greek}µ\selectlanguage{english}l matrix. C) Lower quality spectrum at 0.39 \selectlanguage{greek}µ\selectlanguage{english}g
\selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1} showing a high baseline. D) Lower quality
spectrum at 25 \selectlanguage{greek}µ\selectlanguage{english}g \selectlanguage{greek}µ\selectlanguage{english}l\textsuperscript{-1} showing stronger noise.

Optimize Random Forest model for classification

For application of RF as a method for classification, we evaluated how
strongly the number of specimens per species influences model error. A
repeated (n=100) random sampling of two to eleven specimens for species
with at least 11 specimens in the data set (n=20) was carried out. This
data was then used to create RF models and the OOB error was assessed as
a quality criterion. Increasing the number of specimens per species
resulted in a decrease of OOB error (Fig. 2). With only two specimens
per species the OOB error ranges from 0 to 0.375 with a mean error of
0.18 (SD = 0.073). With eleven specimens per species, the error ranges
from 0.005 to 0.036 with a mean error of 0.019 (SD = 0.008). The
decrease in OOB error goes nearly into saturation for n
\textgreater{}10. For further analyses, we chose n = 6 because the
results show a strong decrease in OOB-error variability and a strong
decrease in maximum OOB error at this point.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2: Random forest model quality assessed with increasing number of
specimens per species. For each number of specimens, 100 data sets were
created by random sampling. The OOB error (y-axis) decreases with
increasing number of specimens (x-axis) and starts going into
saturation. Thus, around 10 specimens per species are generally
recommended to obtain a high quality model.

Standardization of data processing

Different steps throughout data processing can have a severe impact on
classification results. The effect of changing the different data
processing steps was evaluated using the RF OOB error as an indicator.
For each data set a RF model was trained and the OOB error recorded
(supplementary figure 1). Whereas alteration of baseline subtraction
iterations generally only had little impact on RF OOB error, changing
HWS and SNR had greater effects (supplementary figure 1). The GAM shows
that the OOB error is significantly influenced by alteration of the HWS
(Table 1, p-value: 0.007) and SNR (Table 1, p-value: 0.001). A
combination of 22 baseline estimation iterations, HWS of 7 and SNR of 3
resulted in the lowest OOB error of 0.032. These settings were used for
further analyses.

Table. 1: Results of the GAM analyses to detect the most important
variable for data processing optimization.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& \textbf{Estimate} & \textbf{Std.error} & \textbf{z value} &
\textbf{P-value} & \textbf{Significance}\tabularnewline
\textbf{Intercept} & -2.49 & 0.03 & -72.72 & 2e\textsuperscript{-16} &
***\tabularnewline
& \textbf{edf} & \textbf{Ref.df} & \textbf{chi.sq} & \textbf{P-value} &
\textbf{Significance}\tabularnewline
\textbf{Baseline iterations} & 1 & 1.001 & 0.125 & 0.72 &\tabularnewline
\textbf{Peak detection HWS} & 1 & 1.00 & 7.39 & 0.007 &
**\tabularnewline
\textbf{SNR} & 1 & 1.00 & 10.2 & 0.001 & *\tabularnewline
& R-sq.(adj) = 0.686 & R-sq.(adj) = 0.686 & Deviance explained = 68.7\%
& Deviance explained = 68.7\% & Deviance explained =
68.7\%\tabularnewline
\bottomrule
\end{longtable}

Classification success

Finally, we tested the identification success based on MALDI-TOF MS data
for each specimen in the data set by excluding the respective specimen
and using the remaining reference data to identify it.

Overall, 93\% of the specimens (n=775) were identified correctly and
86\% (n=721) were accepted as correctly classified by the\emph{post-hoc}
test (Fig. 3A). Identification for specimens of the classes Ascidacea,
Teleostei, Elasmobranchii, Echinoidea, Ophiuroidea, Asteroidea, Bivalvia
and Gastropoda resulted in success rates of more than 90\%. For classes
Cephalopoda and Thecostraca the identification success was still above
85\%. Success rates lower than 80\% were not recorded. Of the 61
misclassified specimens, 15 were assigned to the false species and
recorded as correct identifications by the\emph{post-hoc} test. Of all
misclassified specimens, two were assigned to congeneric classes and
rated as true positives by the \emph{post-hoc}test, meaning these would
have been misclassified and remain unrecognized.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Fig. 3A} : Identification rate on species level displayed for
all included phyla and classes separately. \textbf{3B} : Results of RF
specimen identification to phylum and class level. Bars are divided into
three categories relevant to the identification success. The darkest
color displays the fraction of incorrect identifications, the
intermediate color displays correct random forest identifications and
the light color represents the percentage of specimens recognized as
correct identifications by the \emph{post-hoc} test.

Case study - cryptic species

In the present data, the identification of the starfish\emph{Astropecten
irregularis} (Pennant, 1777) specimens from the North Sea serves as an
example for closely related species that are still distinguishable by
proteomic fingerprinting. In a previous study, this morphotype was found
to consist of two major genetic clades with inter-clade distances in COI
of up to 12\%. Morphological differences were not determined so far.
Both groups show different distribution patterns with overlaps (Laakmann
et al., 2016). Our data included specimens of both clades, \emph{A.
irregularis} 1 (n=8) and \emph{A. irregularis} 2 (n=27).

Data processing settings were optimized for the sub-set of data (HWS = 9
and SNR = 8). Within a RF model produced from the data, a clear
distinction between the two genetic groups was possible. None of the
specimens was misassigned to the respective other group. This RF model
was also used to find the most important variables for differentiation
of the two groups using the Gini index, which shows the degree of
dissimilarity of the respective variables (Han et al., 2016). The 30
most important variables are given in Fig. 4A. Whereas all peaks can be
found in specimens of both groups, the intensities differ strongly
allowing a clear differentiation of \emph{A. irregularis} clades using
proteome fingerprinting.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 4: \textbf{A} The 30 most important peaks for differentiation of
the starfish \emph{A. irregularis} groups within the random forest
model. Species according to COI delimitation are given on top. Molecule
masses sorted by size are given on the left hand side.
\textbf{B}Hierarchical clustering depicts differentiation of the copepod
\emph{E. acutifrons} specimens on sex level. Nodal bootstrap support is
displayed at the nodes of the tree. The heatmap below the clustering
results depicts the 30 most important mass peaks for sex-differentiation
using a random forest model with color-coded peak intensities. Data from
the marine copepod \emph{Microarthridion littorale} (Poppe, 1881) from
the same study was used here as an outgroup species. Relative
intensities are color coded.

Case study - sex determination

In previous research it was shown that sex determination may be possible
in some species by analyzing the proteomic fingerprint (Rossel and
Mart\selectlanguage{ngerman}ínez Arbizu, 2019), however the data was not analyzed any further
therein. In depth analyses emphasize these findings and show
sex-specific protein patterns in the crustacean copepod \emph{Euterpina
acutifrons} (Fig. 4B). Mass peaks such as m/z 2523, 2929 and 7417 are
female specific and not found in any of the male specimens. Others
however, predominantly occur in male specimens (m/z 3638, 3719). Further
mass peaks are evenly observed in measurements from both sexes but show
intensity-pattern differences.

Phyla and class models for identification

If a species is not part of a reference library, it may be desirable to
obtain a higher level classification. To test if this is possible based
on MALDI-TOF mass spectra of metazoans, species were systematically
taken out of the RF training data set and classified with a RF model
that was trained on higher taxonomic level but does not include any
information on the respective species to be classified. Regarding all
phyla together, a classification success of 81\% (77\% true positive
rate (tpr)) was achieved with phyla-wise success rates ranging from 73\%
(64 \% tpr) in Echinodermata to 95\% (92\% tpr) in Chordata (Fig. 3B).
On class level the combined success rate was 72\% (66\% tpr) ranging
from 7\% (0\% tpr) in Polyplacophora, for which only two species were
included in the data set, to 96\% (94\% tpr) in Teleostei.

For 31 taxa (n = 324), a congeneric species was included. Thus, it was
tested if species have a higher affinity to be classified as a
congeneric species in case the respective species is removed from the
training data. Of these 31 taxa, 30\% of specimens were classified as a
congeneric species.

\textbf{Discussion}

The aims of this study were (1) to evaluate the wide applicability of
proteomic fingerprinting for species identification in marine science
across different metazoan phyla and classes, (2) to identify critical
steps in sample preparation and data processing, and (3) to contribute
to the development of standard procedures and best practices for
MALDI-TOF MS based metazoan classification. The general applicability to
metazoans has been proven before (Mazzeo et al., 2008; Dieme et al.,
2014; Yssouf et al., 2014; Flaudrops et al., 2015; Mazzeo and Siciliano,
2016; Maász et al., 2017; Rossel and Martínez Arbizu, 2019; Rossel et
al., 2020a). However, here we show for the first time the applicability
of this method to a large taxonomic range using a comprehensive data set
with an overall species identification success rate of 93\%.

Similar high identification success rates on species level were observed
for individual metazoan groups (Hynek et al., 2018; Vega-Rúa et al.,
2018; Holst et al., 2019; Loaiza et al., 2019; Rakotonirina et al.,
2020; Rossel et al., 2020a). Additionally, our results show that
specimens absent from the reference library will be assigned to the
correct phyla or class with a high probability implying some kind of
phylogenetic signal on higher taxonomic level as was already reported
for congeneric \emph{Drosophila} before (Feltens et al., 2010). Testing
if species would be classified as a congeneric species in the absence of
the actual species was less promising in our study with only 30\% of
specimens being assigned to a congeneric species. This complies with
other studies that only show occasional similarity of congeneric species
e.g. in cluster analyses but without consistency across all congeneric
species (Laakmann et al., 2013; Chavy et al., 2019; Rossel and Martínez
Arbizu, 2019).

In closely related species, morphological identification can often be
complicated. Using proteomic fingerprinting, these problems can however
be resolved as indicated by the analysis of the \emph{A.
irregularis}complex. Even though mass spectra show high similarities,
distinct patterns of peak presence and absence as well as pronounced
differences in relative peak intensities serve as good markers for
species identification. Beyond mere species identification, the example
of\emph{E. acutifrons} shows the power of the method to differentiate
specimens even on a sex level. This has been shown before for e.g. the
fish species \emph{Alburnus alburnus} (Linnaeus, 1758) (Maász et al.,
2017). Whereas authors focused on presence and absence of peaks, we were
able to show that also relative intensities of certain mass peaks play
an important role in differentiation of sexes. Prior studies on larger
planktonic copepods have also shown a great potential for
differentiation of developmental stages based on a proteomic fingerprint
(Rossel et al., 2022).

Finally, we have shown the necessity of comprehensive reference
libraries. Low numbers of specimens per species in reference libraries
fail to provide sufficient information on species specific mass spectra
features and intraspecific variability. Only with around nine to ten
reference specimens per species, the identification error stabilizes on
a constantly low level. This supports findings by Rakotonirina et al
(2020) who found an increase of identification score with increasing
numbers of available main spectrum patterns. In general we would
recommend to use more than three specimens per species and preferably to
include around ten specimens for every species in a reference library.

MALDI-TOF MS can be used as a universal method for species
identification of metazoan species. Due to the short preparation time,
low costs (Tran et al., 2015; Rossel et al., 2019) and high
identification success it can be a valuable tool in biodiversity
assessments replacing time-intense morphological identification or
costly DNA barcoding. Especially in cases of closely related or very
similar species it can foster a rapid identification. The applicability
of proteome fingerprinting for the differentiation of cryptic species
was already shown and even in cases of morphologically very similar
species, still differences were found (Müller et al., 2013; Paulus et
al., 2022).

Tissue samples used in this work were obtained from specimens stored
between seven to 12 years under partly unknown storage conditions. We
assume working with fresh or recently fixed material would have resulted
in even higher identification success rates. This is supported by the
high mass spectra quality obtained from fish species, which were
extracted and put into freezer storage almost immediately after sampling
(personal communication Knebelsberger). The adverse effect of fixation
and storage on resulting mass spectra quality in metazoans was
investigated several times and supports this assumption (Rossel and
Martínez Arbizu, 2018b; Rakotonirina et al., 2020). We received good
results for storage at -20°C and also for long-term storage at -80°C,
thus we recommend cold storage of samples at -20°C, until further
systematic analyses will specify threshold temperatures for short-
(months) or long-term (years) storage.

Our tests have shown that sample concentration is pivotal to obtain good
quality mass spectra. While too low sample/matrix ratios will result in
lower intensities and a higher baseline, too much tissue will increase
the noise in the data and result in unsuccessful measurements. For all
investigated taxa, the same sample preparation method was used; however
attention must be paid to the correct ratio of matrix and compound to be
analyzed. This allows the wide application of this method without
adaptation of the protocol to a certain species as it would be necessary
for methods such as COI barcoding where certain groups would need highly
specific sets of amplification primers (Lohman et al., 2009; Toumi et
al., 2013) and adjustment of PCR settings.

Much effort is put into optimizing mass spectra quality by adjusting
different preparation protocols (Jeverica et al., 2018; Wang et al.,
2021) or developing methods for steps such as baseline correction,
smoothing or peak picking (Ressom et al., 2007; Shin et al., 2010).
Methods are adjusted either to increase classification success or to
obtain better mass spectra reproducibility. Here, we tested the
influence of certain steps during data processing on classification
success focusing on the important steps for peak detection. Whereas
baseline subtraction and adjustment of a SNR value both aim at reducing
noise within the data, adjusting the HWS influences the peak picking
resolution. Thus, by decreasing the HWS during peak detection, the
number of peaks will increase as the highest peak within the HWS will be
the detected. This will result in peaks of very similar size being
recognized as distinct peaks, rather than being put together in a single
bin. This does also explain the high effect of both parameters SNR and
HWS compared to baseline subtraction. Baseline subtraction is
constrained towards reducing instrument-dependent noise. Adjustment of
the SNR value will however, like HWS alteration, affect the number of
more dominant peaks and thus the general resolution of the mass spectra.
Hence, more species-specific information is retained and more
information is available for classification. Based on our results,
rather than testing all variables, adjusting SNR and HWS should be
adequate to optimize the data pipeline. However, it needs to be
emphasized that this pipeline aims at optimizing species identification
and may not be adequate for investigation of intraspecific variability
as was shown elsewhere\textsuperscript{16}.

In summary, we propose a workflow applicable for any metazoan species or
tissue sample to be identified: A comprehensive reference library is
needed with species level identification by morphological or molecular
approaches (Fig. 5a). In the lab, a small tissue (up to 1mm³) is
retrieved and incubated for at least 5 minutes in the HCCA-matrix
solution. Of the resulting extract, 1 to 1.5 \selectlanguage{greek}µ\selectlanguage{english}l are transferred to a
target plate for measurement. Data processing is carried out in R (Fig
5b). Mass spectra quality is done by eye and supported by R-packages
such as MALDIrppa (Palarea-Albaladejo et al., 2017). Finally, based on
previously assessed species identification, data processing can be
optimized to obtain ideal settings for classification. Depending on our
results this can be narrowed to adjustment of HWS- and SNR-value. Based
on the reference library, a RF model can be calculated for specimen
identification (Fig. 5c). Applying a \emph{post-hoc} test will provide
further support for the identification. If classification is not well
supported, a RF model on class or phyla level can be applied to obtain
higher-level classification.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig. 5: Proposed workflow from specimen to classification. A) Sampling
and sample preparation. A tissue sample from an identified specimen is
stored at cold temperatures until further processing. Especially for
long-term storage it should be aimed at low temperature storage. A
smaller subsample is incubated in HCCA Matrix and the solution is
transferred to target plate for measurement. B) Measurements are
imported to R and mass spectra are quality controlled. The data then
needs optimization for best model quality (e.g. adjust HWS, SNR, etc.).
C) Classification using the optimized RF model. Application of
the\emph{post-hoc} test to verify classification. If classification is
rejected, phylum/class/family level models may result in a higher
taxonomic classification. The R logo is \selectlanguage{ngerman}© 2016 The R Foundation and is
used under the CC-BY-SA 4.0 license.

\textbf{Conclusion}

MALDI-TOF MS was proven an easy to apply, cost-effective and time-saving
tool for identification across taxa. It is especially feasible in
applications where mere species identification is desired, for example
in biodiversity assessments. By the standardized workflow, a wide range
of marine metazoan specimens can be identified quantitatively and
effectively on species level thereby bypassing some of the high
requirements associated with genetic methods, such as access to special
laboratories, searching for primers etc. We want to highlight here that
proteomic fingerprinting will be due to its simplicity, reliability and
efficiency a valuable supplement to the molecular toolbox for taxonomy.

\textbf{Acknowledgement}

We thank Michael Raupach, Thomas Knebelsberger, Andrea Barco and their
collaborators, students and technicians for bringing the tissue database
into life and good documentation of their work as well as making their
work publicly available. Respectively, this work was supported by the
following grants: The Federal Ministry of Education and Research (Grant
No. 03F0499A) and the Land Niedersachsen. This is publication 14 of the
Senckenberg am Meer Proteomics Laboratory. This study was supported by
the DFG initiative 1991 ``Taxono-omics'' (grant number RE2808/3-1/2).
HIFMB is a collaboration between the Alfred-Wegener-Institute,
Helmholtz-Center for Polar and Marine Research, and the Carl-von
Ossietzky University Oldenburg, initially funded by the Ministry for
Science and Culture of Lower Saxony and the Volkswagen Foundation
through the `Niedersächsisches Vorab' grant program (grant no. ZN3285).

\textbf{Author contributions}

S.R, J.P, S.L and P.M.A conceived the study. H.N. carried out the
majority of morphological species identifications. S.R., N. C. and A. E.
carried out MALDI-TOF MS measurements. S.R. and J.P. analyzed the data
and wrote a first manuscript draft. All authors significantly
participated in critical revision of the manuscript draft.

\textbf{Data accessibility}

All mass spectra data will be made available at Data Dryad (DOI:XXXX)
upon manuscript acceptance. Relevant R-Scripts will be stored alongside
raw data.

\textbf{Benefit-Sharing Statement}

No data from foreign countries were acquired and thus, no benefit
sharing as demanded by the Nagoya protocol is carried out.

\textbf{Competing interests}

The authors declare to not have any competing interests.

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Supplementary Fig. 1: Out of box error of the random forest model (OOB
error on y-axis) for the different combinations of baseline subtraction
iterations, signal to noise ratio (SNR) and half window size (HWS)
during peak picking. Each box represents the number of baseline
iteration steps ranging from 5 to 30. The x-axis displays the SNR value
ranging from 3 to 20. Colors indicate different HWS ranging from 5 to
30. The results are shown for all 12,186 variable combinations.

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