Introduction
In this tutorial, we would like to demonstate the use of CluMSID with
a publicly available LC-MS/MS data set deposited on MetaboLights. We chose
data set MTBLS433 that can be accessed on the MetaboLights web page (https://www.ebi.ac.uk/metabolights/MTBLS433) and which
has been published in the following article:
Kalogiouri, N. P., Alygizakis, N. A., Aalizadeh, R., & Thomaidis,
N. S. (2016). Olive oil authenticity studies by target and nontarget
LC–QTOF-MS combined with advanced chemometric techniques. Analytical
and bioanalytical chemistry, 408(28), 7955-7970.
The authors analysed olive oil of various providence using
reversed-phase ultra high performance liquid chromatography–electrospray
ionisation quadrupole time of flight tandem mass spectrometry in
negative mode with auto-MS/MS fragmentation.
As a representative pooled sample is not provided, we will combine
MS2 data from several runs and use the peak picking done by
the authors of the study for the merging of MS2 spectra. Some
metabolite annotations are also included in the MTBLS433 data set which
we will integrate into our analysis.
library(CluMSID)
library(CluMSIDdata)
Merge spectra with external peak list
First, we import the peak list by reading the respective table and
filtering for the relevant information. We only need the columns
metabolite_identification, mass_to_charge and
rentention_time and we would like to replace
"unknown" with an empty field in the
metabolite_identification column. Plus, the features do not
have a unique identifier in the table but we can easily generate that
from m/z and RT. Note that the retention time in the raw data
is given in seconds and in the data table it is in minutes, so we have
to convert. For the sake of consistency, we also change the column
names. We use tidyverse syntax but users can do as they
prefer.
raw_mtbls_df <- system.file("extdata",
"m_mtbls433_metabolite_profiling_mass_spectrometry_v2_maf.tsv",
package = "CluMSIDdata")
require(magrittr)
mtbls_df <- readr::read_delim(raw_mtbls_df, "\t") %>%
dplyr::mutate(metabolite_identification =
stringr::str_replace(metabolite_identification,
"unknown", "")) %>%
dplyr::mutate(id = paste0("M", mass_to_charge, "T", retention_time)) %>%
dplyr::mutate(retention_time = retention_time * 60) %>%
dplyr::select(id,
mass_to_charge,
retention_time,
metabolite_identification) %>%
dplyr::rename(mz = mass_to_charge,
rt = retention_time,
annotation = metabolite_identification)
This peak list, or its first three columns, can now be used to merge
spectra. We exclude spectra that do not match to any of the peaks in the
peak list. As we are not very familiar with instrumental setup, we set
the limits for retention time and m/z deviation a little wider.
To make an educated guess on mass accuracy, we take a look at an
identified metabolite, its measured m/z and its theoretical
m/z. We use arachidic acid [M-H]-, whose theoretical
m/z is 311.2956:
## Define theoretical m/z
th <- 311.2956
## Get measured m/z for arachidic acid data from mtbls_df
ac <- mtbls_df %>%
dplyr::filter(annotation == "Arachidic acid") %>%
dplyr::select(mz) %>%
as.numeric()
## Calculate relative m/z difference in ppm
abs(th - ac)/th * 1e6
#> [1] 28.91143
So, we will work with an an m/z tolerance of 30ppm (which
seems rather high for a high resolution mass spectrometer).
A 30ppm mass accuracy necessitates an m/z tolerance of
60ppm, because deviations can go both ways:
oillist <- mergeMS2spectra(raw_oillist,
peaktable = mtbls_df[,1:3],
exclude_unmatched = TRUE,
rt_tolerance = 60,
mz_tolerance = 3e-5)
Add annotations
To add annotations, we use mtbls_df as well, as
described in the General Tutorial:
fl <- featureList(oillist)
fl_annos <- dplyr::left_join(fl, mtbls_df, by = "id")
annolist <- addAnnotations(oillist, fl_annos, annotationColumn = 6)
Explore data
To explore the data, we have a look at a cluster dendrogram:
HCplot(distmat, h = 0.7, cex = 0.7)
Figure 1: Circularised
dendrogram as a result of agglomerative hierarchical clustering with
average linkage as agglomeration criterion based on MS2
spectra similarities of the MTBLS433 LC-MS/MS example data set. Each
leaf represents one feature and colours encode cluster affiliation of
the features. Leaf labels display feature IDs. Distance from the central
point is indicative of the height of the dendrogram.
Since it was not in the focus of their study, the authors identified
only a few metabolites. If we look at the positions of these metabolites
in the cluster dendrogram, we see that the poly-unsaturated fatty acids
alpha-linolenic acid and alpha-linolenic acid are nicely separated from
the saturated fatty acids stearic acid and arachidic acid. We would
expect the latter to cluster together but a look at the spectra reveals
that stearic acid barely produces any fragment ions and mainly contains
the unfragmented [M-H]- parent ion:
specplot(getSpectrum(annolist, "annotation", "Stearic acid"))
Figure 2: Barplot for the
feature M283.2642T14.62, identified as stearic acid, displaying fragment
m/z on the x-axis and intensity normalised to the maximum
intensity on the y-axis.
In contrast, arachidic acid produces a much richer spectrum:
specplot(getSpectrum(annolist, "annotation", "Arachidic acid"))
Figure 3: Barplot for the
feature M311.3046T15.1, identified as arachidic acid, displaying
fragment m/z on the x-axis and intensity normalised to the
maximum intensity on the y-axis.
Inspecting the features that cluster close to arachidic acid shows
that many of them have an exact m/z that conforms with other
fatty acids of different chain length or saturation (within the
m/z tolerance), e.g. the neighbouring feature M339.2125T15.32
that could be arachidonic acid [M+Cl]-.
Looking at oleic acid [M-H]-, we see that it clusters very
closely to M563.5254T13.93, whose m/z is consistent with oleic
acid [2M-H]- and some other possible adducts.
As a last example, the only identified metabolite that does not
belong to the class of fatty acids is acetosyringone, a phenolic
secondary plant metabolite. It forms part of a rather dense cluster in
the dendrogram, suggesting high spectral similarities to the other
members of the cluster. It would be interesting to try to annotate more
of these metabolite to find out if they are also phenolic compounds.
In conclusion, we demonstrated how to use CluMSID with a
publicly available data set from the MetaboLights repository and how to
include external information such as peak lists or feature annotations
into a CluMSID workflow. In doing so, we had a look on a
few example findings that could help to annotate more of the features in
the data set and thereby showed the usefulness of CluMSID
for samples very different from the ones in the other tutorials.