DOI: 10.5061/dryad.cnp5hqc8q
title: Data from: Potential of MALDI−TOF MS-based proteomic fingerprinting for species identification of Cnidaria across classes, species, regions and developmental stages

description: Morphological identification of cnidarian species can be difficult
 throughout all life stages due to the lack of distinct morphological
 characters. Moreover, in some cnidarian taxa genetic markers are not fully
 informative, and in these cases combinations of different markers or
 additional morphological verifications may be required. Proteomic
 fingerprinting based on MALDI-TOF mass spectra was previously shown to
 provide reliable species identification in different metazoans including
 some cnidarian taxa. For the first time, we tested the method across four
 cnidarian classes (Staurozoa, Scyphozoa, Anthozoa, Hydrozoa) and included
 different scyphozoan life-history stages (polyp, ephyra, medusa) into our
 dataset. Our results revealed reliable species identification based on
 MALDI-TOF mass spectra across all taxa with species-specific clusters for
 all 23 analyzed species. In addition, proteomic fingerprinting was
 successful for distinguishing developmental stages, still by retaining a
 species specific signal. Furthermore, we identified the impact of
 different salinities in different regions (North Sea and Baltic Sea) on
 proteomic fingerprints to be negligible. In conclusion, the effects of
 environmental factors and developmental stages on proteomic fingerprints
 seem to be low in cnidarians. This would allow using reference libraries
 built up entirely of adult or cultured cnidarian specimens for the
 identification of their juvenile stages or specimens from different
 geographic regions in future biodiversity assessment studies.
description: In total, 278 specimens of
 Cnidaria belonging to 23 different species from four classes were
 analyzed. Field specimens were morphologically identified to
 species level by taxonomic experts immediately after collection, before
 complete specimens or subsamples were preserved in undenatured ethanol (80
 - 96%).  From each
 specimen, a small tissue fragment (max. 1 mm³) was incubated for 5 minutes
 with 5 µl of alpha-cyano-4-hydroxycinnamic acid (HCCA) matrix. Of this
 incubated solution, 1 to 1.5 µl were transferred to a target plate on one
 to nine spots for co-crystallization of matrix and analytes. Each spot was
 measured one to three times using a Microflex LT/SH System (Bruker
 Daltonics). Employing the flexControl 3.4. (Bruker Daltonics) software,
 molecule masses were measured from 2 to 20k Dalton (kDA). A centroid peak
 detection algorithm was carried out for peak evaluation by analyzing the
 mass peak range from 2 to 20 kDa. Furthermore, peak evaluation was carried
 out by a signal-to-noise threshold of two and a minimum intensity
 threshold of 600 with a peak resolution higher than 400. To validate fuzzy
 control, the proteins/oligonucleotide method was employed by maximal
 resolution of ten times above the threshold. To create a sum spectrum, a
 total of at least 120 laser shots were applied to a spot. Measurements
 were carried out using the same instrument at different occasions between
 2013 and 2019. <strong>MALDI-TOF data
 processing</strong> MALDI-TOF raw data were imported to R, Version
 4.1.0 (R-Core-Team,
 2022) and processed using
 R packages MALDIquantForeign, Version 0.12 (Gibb, 2015) and MALDIquant, Version 1.20
 (Gibb and Strimmer,
 2012). Spectra were
 square-root transformed, smoothed using the Savitzky Golay method
 (Savitzky and Golay,
 1964), baseline corrected
 using the SNIP method (Ryan et al., 1988) and spectra normalized using the TIC method.
 Repeated measurements were averaged by using mean intensities. Peak
 picking was carried out using a signal to noise ratio (SNR) of 12 and a
 half window size of 13. Mass peaks smaller than a SNR of 12 were however
 retained, if they occurred in other mass spectra as long as these were
 larger than a SNR value of 1.75, which is assumed as a lower detection
 limit. Repeated peak binning was carried out to align homologous mass
 peaks. Resulting data was Hellinger transformed (Legendre and Gallagher,
 2001) and used for further
 analyses.
description: 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
description: Readme file for data uploaded to data dryad project: Data from: Potential
 of MALDITOF MS-based proteomic fingerprinting for species identification
 of Cnidaria across classes, species, regions and developmental stages
 Potential of MALDITOF MS-based proteomic fingerprinting for species
 identification of Cnidaria across classes, species, regions and
 developmental stages in Molecular Ecology Resources DOI:ADD UPON
 ACCEPTANCE This dataset contains a .csv file called RefTable.csv and a
 .zip file containing raw Bruker mass spectrometry files for 562
 measurements from a variety of Cnidaria species. These folder are build up
 according to the default Bruker flex container system and contain one
 folder for each spot that was measured for a specimen. Each "spot
 folder" consists of folders for each technical replicate measurement
 on the respective spot. Within these folders the actual data produced by
 the instrument can be found. For each specimen, one to three folders can
 be found here. The RefTable.csv file contains information on which folders
 belong to which specimen and species. Data in this submission: \|-
 README.md #Readme file containing information on included data and how to
 process these \|- 01_Species_clustering.R #R script to replicate results
 from the publication (Species clustering) \|- 02_Ontogeny.R #R R script to
 replicate results from the publication (tSNE Plots, important peaks for
 differentiation) \|- 03_Stage_classification.R #R script to replicate
 results from the publication (Random Forest model for classification of
 specimens to species level based on a reference library not including the
 respective ontogenetic stage) \|- Meta_Data_for_submission.csv #Meta
 information on spcimens processed in this workt including sampling
 information as well as taxonomic information -Specimen: The name with
 which the specimen was measured -Specimen number: A specimen number to
 combine measurements from different specimens -Class: Class of the
 measured specimen -Species: Species name of that measurement -Stage:
 Developmental stage of the specimen the measurement was obtained from
 -Region: NS = North Sea; BS = Baltic Sea -Locality: A locality name where
 a specimen was obtained. Cruise: Specimen was taken during a cruise with
 more information in the sbsequent columns -Cruise: Cruise number during
 which the specimen was sampled. WH III = Walther Herwig III, Senckenberg =
 Cruise with research vessel Senckeneberg -Station: Station during that
 cruise -Coordinates: Coordinates of the respective sampling site -Sample
 or culture: If specimen was obrtained from a culture or sampled. S =
 sampled; C = cultured -Sampling date: Date of the sampling occasion
 -Fixation period: duration of fixation period in months -Fixation period2:
 duration of fixation period in months grouped in 3 groups by duration
 -Replicates: Number of measurement replicates for the respective specimen
 -Number of peaks: Amount of peaks for the respective specimen \|-
 RefTable.csv #Reference table for processing the data in R. Importing and
 application is included in all R scripts -id: Name of that measurement
 -species: Species name of that measurement -specimen: A number to combine
 different measurements of a single specimen -region: NS = North Sea; BS =
 Baltic Sea -stage: Developmental stage of the specimen the measurement was
 obtained from -origin: S = sampled, C= cultured -fixper: duration of
 fixation period in months -phylum: Phylum of the measured specimen -class:
 Class of the measured specimen -fixper2: duration of fixation period in
 months grouped in 3 groups by duration \|- Mass_Spectra.zip #Contains raw
 data in the Bruker file container format. Each folder within this is a
 measurement. Measurements from the same specimen are combined using the
 reference table in R -Mass spectra were obtained from different
 measurement occassions and thus have different names withouth a pattern
 applying to all of them. All relevant information about the measurements
 can be found in the file Meta_Data_for_submission.csv. 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: 
 To get an initial impression of the data in R, data can be imported
 following these commands: install.packages("MALDIquantForeign")
 install.packages("MALDIquant")
 library("MALDIquantForeign") library("MALDIquant")
 setwd("set working directory to Mass spectra location") #set wd
 to the folder in which the folder containing the mass spectra is located
 in rawSpectra &lt;-
 importBrukerFlex("Mass_Spectra",removeEmptySpectra=TRUE)#import
 raw spectra This imports all data to R. I you want to look at them, you
 can either do: plot(rawSpectra[[1]]) #view the first mass spectrum.
 Replace the 1 by any number you would like to view. or you can plot all
 mass spectra into a pdf: names&lt;- sapply(rawSpectra,
 function(x)metaData(x)$id) #get measurement IDs from the metaData \#Export
 raw Spectra to PDF pdf(file= "01_raw_spectra.pdf", height=14,
 width=24) for (i in seq(along=rawSpectra)) { plot(rawSpectra[[i]],
 main=names[i]) } dev.off() If you want to inspect the meta information of
 each mass spectrum you can use: metaData(rawSpectra[[1]])#View meta data
 of the first mass spectrum. Replace the 1 by any number you would like to
 view.