Clustering Spectra from High Resolution
DI-MS/MS Data Using CluMSID
Tobias Depke
October 29, 2024
Introduction
Although originally developed for liquid chromatography-tandem mass
spectrometry (LC-MS/MS) data, CluMSID can also be used with direct
infusion-tandem mass spectrometry (DI-MS/MS) data.
Generally, the missing retention time dimension makes feature
annotation in metabolomics harder but if only direct infusion data is at
hand, CluMSID can help to get an overview of the chemodiversity of a
sample measured by DI-MS/MS.
In this example, we will use a similar sample (1uL Pseudomonas
aeruginosa PA14 cell extract) as in the General Tutorial, measured
on the same machine, a Bruker maxisHD qTOF operated in
ESI-(+) mode with auto-MS/MS but without chromatographic separation.
Data import
We load the file from the CluMSIDdata package:
library(CluMSID)
library(CluMSIDdata)
DIfile <- system.file("extdata",
"PA14_maxis_DI.mzXML",
package = "CluMSIDdata")
Data preprocessing
The extraction of spectra works the same way as with LC-MS/MS
data:
ms2list <- extractMS2spectra(DIfile)
length(ms2list)
#> [1] 373
Merging of redundant spectra is less straightforward when retention
time is not available. Depending on the MS/MS method it can be next to
impossible to decide whether two spectra with the same precursor
m/z and similar fragmentation patterns derive from the same
analyte or from two different but structurally similar ones.
In this example, we would like to merge spectra with identical
precursor ions only if they were recorded one right after another. We
can do so by setting rt_tolerance to 1 second:
featlist <- mergeMS2spectra(ms2list, rt_tolerance = 1)
length(featlist)
#> [1] 349
We see that we have hardly reduced the number of spectra in the list.
If we would decide to merge all spectra with identical precursor
m/z from the entire run, we could do so by setting
rt_tolerance to the duration of the run, in this case
approx. 250 seconds:
testlist <- mergeMS2spectra(ms2list, rt_tolerance = 250)
length(testlist)
#> [1] 75
The resulting number of spectra is drastically lower but the danger
of merging spectra that do not actually derive from the same analyte is
also very big.
Generation of distance matrix
In this very explorative example, we skip the integration of previous
knowledge on feature identities and generate a distance matrix right
away:
distmat <- distanceMatrix(featlist)
Data exploration
Starting from this distance matrix, we can use all the data
exploration functions that CluMSID offers. In this example
workflow, we look at a cluster dendrogram:
HCplot(distmat, cex = 0.5)
Figure 1: Circularised
dendrogram as a result of agglomerative hierarchical clustering with
average linkage as agglomeration criterion based on MS2
spectra similarities of the DI-MS/MS example data set. Each leaf
represents one feature and colours encode cluster affiliation of the
features. Leaf labels display feature IDs, along with feature
annotations, if existent. Distance from the central point is indicative
of the height of the dendrogram.
It is directly obvious that we have some spectra that are nearly
identical and thus most likely derive from the same analyte, e.g. the
many spectra with a precursor m/z of 270.19. But we still see
nice clustering of similar spectra with different precursor
m/z, e.g. the huge gray cluster that contains a lot of
different alkylquinolone type metabolites (see General Tutorial).
In conclusion, CluMSID is very useful to provide an overview of
spectral similarities within DI-MS/MS runs but wherever annotation is in
the focus, one should not do without the additional layer of information
created by chromatographic separation.
Session Info
sessionInfo()
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
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#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
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