Package: xcms
Authors: Johannes Rainer, Michael Witting
Modified: 2020-10-27 13:15:19
Compiled: Wed Oct 28 00:18:59 2020

1 Introduction

Metabolite identification is an important step in non-targeted metabolomics and requires different steps. One involves the use of tandem mass spectrometry to generate fragmentation spectra of detected metabolites (LC-MS/MS), which are then compared to fragmentation spectra of known metabolites. Different approaches exist for the generation of these fragmentation spectra, whereas the most used is data dependent acquisition (DDA) also known as the top-n method. In this method the top N most intense m/z values from a MS1 scan are selected for fragmentation in the next N scans before the cycle starts again. This method allows to generate clean MS2 fragmentation spectra on the fly during acquisition without the need for further experiments, but suffers from poor coverage of the detected metabolites (since only a limited number of ions are fragmented).

Data independent approaches (DIA) like Bruker bbCID, Agilent AllIons or Waters MSe don’t use such a preselection, but rather fragment all detected molecules at once. They are using alternating schemes with scan of low and high collision energy to collect MS1 and MS2 data. Using this approach, there is no problem in coverage, but the relation between the precursor and fragment masses is lost leading to chimeric spectra. Sequential Window Acquisition of all Theoretical Mass Spectra (or SWATH [1]) combines both approaches through a middle-way approach. There is no precursor selection and acquisition is independent of acquired data, but rather than isolating all precusors at once, defined windows (i.e. ranges of m/z values) are used and scanned. This reduces the overlap of fragment spectra while still keeping a high coverage.

This document showcases the analysis of two small LC-MS/MS data sets using r Biocpkg("xcms"). The data files used are reversed-phase LC-MS/MS runs from the Agilent Pesticide mix obtained from a Sciex 6600 Triple ToF operated in SWATH acquisition mode. For comparison a DDA file from the same sample is included.

2 Analysis of DDA data

Below we load the example DDA data set using the readMSData function from the MSnbase package.


dda_file <- system.file("TripleTOF-SWATH/PestMix1_DDA.mzML",
                        package = "msdata")
dda_data <- readMSData(dda_file, mode = "onDisk")

The variable dda_data contains now all MS1 and MS2 spectra from the specified mzML file. The number of spectra for each MS level is listed below.

##    1    2 
## 1504 2238

For the MS2 spectra we can get the m/z of the precursor ion with the precursorMz function. Below we first filter the data set by MS level, extract the precursor m/z and call head to just show the first 6 elements. For easier readability we use the forward pipe operator %>% from the magrittr package.


dda_data %>%
    filterMsLevel(2L) %>%
    precursorMz() %>%
##  F1.S1570  F1.S1588  F1.S1592  F1.S1594  F1.S1595  F1.S1596 
## 130.96578 388.25426  89.93779  83.99569 371.22409 388.25226

With the precursorIntensity function it is also possible to extract the intensity of the precursor ion.

dda_data %>%
    filterMsLevel(2L) %>%
    precursorIntensity() %>%
## F1.S1570 F1.S1588 F1.S1592 F1.S1594 F1.S1595 F1.S1596 
##        0        0        0        0        0        0

Some manufacturers (like Sciex for the present test data) don’t define/export the precursor intensity and thus either NA or 0 is reported. We can however use the estimatePrecursorIntensity function to determine the precursor intensity for a MS 2 spectrum based on the intensity of the respective ion in the previous MS1 scan (note that with method = "interpolation" the precursor intensity would be defined based on interpolation between the intensity in the previous and subsequent MS1 scan). Below we estimate the precursor intensities, on the full data (for MS1 spectra a NA value is reported)

prec_int <- estimatePrecursorIntensity(dda_data)

We next set the precursor intensity in the spectrum metadata of dda_data. So that it can be extracted later with the precursorIntensity function.

fData(dda_data)$precursorIntensity <- prec_int

dda_data %>%
    filterMsLevel(2L) %>%
    precursorIntensity() %>%
##  F1.S1570  F1.S1588  F1.S1592  F1.S1594  F1.S1595  F1.S1596 
## 0.9691072 3.0772917 0.3885723 0.3215049 1.6329483 4.4057989

Next we perform the chromatographic peak detection on the MS level 1 data with the findChromPeaks method. Below we define the settings for a centWave-based peak detection and perform the analysis.

cwp <- CentWaveParam(snthresh = 5, noise = 100, ppm = 10,
                     peakwidth = c(3, 30))
dda_data <- findChromPeaks(dda_data, param = cwp)

In total 112 peaks were identified in the present data set.

The advantage of LC-MS/MS data is that (MS1) ions are fragmented and the corresponding MS2 spectra measured. Thus, for some of the ions (identified as MS1 chromatographic peaks) MS2 spectra are available. These can facilitate the annotation of the respective MS1 chromatographic peaks (or MS1 features after a correspondence analysis). MS2 spectra for identified chromatographic peaks can be extracted with the chromPeakSpectra method which returns a MSpectra object of MS2 spectra associated with a chromatographic peak (i.e. with their retention time within the retention time window of a chromatographic peak and their precursor m/z within the m/z range of the same chromatographic peak.

dda_spectra <- chromPeakSpectra(dda_data)
## MSpectra with 150 spectra and 1 metadata column(s):
##                    msLevel     rtime peaksCount |     peak_id
##                  <integer> <numeric>  <integer> | <character>
##   CP004.F1.S1812         2   237.869       1489 |       CP004
##   CP004.F1.S1846         2   241.299       1807 |       CP004
##   CP005.F1.S2446         2   326.583         21 |       CP005
##   CP006.F1.S2450         2   327.113         85 |       CP006
##   CP006.F1.S2502         2   330.273        168 |       CP006
##              ...       ...       ...        ... .         ...
##   CP100.F1.S5110         2   574.725        890 |       CP100
##   CP100.F1.S5115         2   575.255       1042 |       CP100
##   CP101.F1.S5272         2   596.584        196 |       CP101
##   CP102.F1.S5236         2   592.424         12 |       CP102
##   CP102.F1.S5266         2   596.054         88 |       CP102

The metadata column "peak_id" contains the identifiers of the chromatographic peaks the MS2 spectrum is associated with.

We next use the MS2 information to aid in the annotation of a chromatographic peak. As an example we use a chromatographic peak of an ion with an m/z of 304.1131 which we extract in the code block below.

ex_mz <- 304.1131
chromPeaks(dda_data, mz = ex_mz, ppm = 20)
##             mz    mzmin    mzmax      rt   rtmin   rtmax    into     intb
## CP057 304.1133 304.1126 304.1143 425.024 417.985 441.773 13040.8 12884.14
##           maxo sn sample
## CP057 3978.987 79      1

A search of potential ions with a similar m/z in a reference database (e.g. Metlin) returned a large list of potential hits, most with a very small ppm. For two of the hits, Flumazenil (Metlin ID 2724) and Fenamiphos (Metlin ID 72445) experimental MS2 spectra are available. Thus, we could match the MS2 spectrum for the identified chromatographic peak against these to annotate our ion. Below we extract all MS2 spectra that were associated with the candidate chromatographic peak using the ID of the peak in the present data set.

ex_id <- rownames(chromPeaks(dda_data, mz = ex_mz, ppm = 20))
ex_spectra <- dda_spectra[mcols(dda_spectra)$peak_id == ex_id]
## MSpectra with 5 spectra and 1 metadata column(s):
##                    msLevel     rtime peaksCount |     peak_id
##                  <integer> <numeric>  <integer> | <character>
##   CP057.F1.S3505         2   418.926         10 |       CP057
##   CP057.F1.S3510         2   419.306         30 |       CP057
##   CP057.F1.S3582         2   423.036        694 |       CP057
##   CP057.F1.S3603         2   423.966        783 |       CP057
##   CP057.F1.S3609         2   424.296        753 |       CP057

There are 5 MS2 spectra representing fragmentation of the ion(s) measured in our candidate chromatographic peak. We next reduce this to a single MS2 spectrum using the combineSpectra method employing the consensusSpectrum function to determine which peaks to keep in the resulting spectrum.

ex_spectrum <- combineSpectra(ex_spectra, method = consensusSpectrum, mzd = 0,
                              ppm = 20, minProp = 0.8, weighted = FALSE,
                              intensityFun = median, mzFun = median)
## MSpectra with 1 spectra and 1 metadata column(s):
##                    msLevel     rtime peaksCount |     peak_id
##                  <integer> <numeric>  <integer> | <character>
##   CP057.F1.S3505         2   418.926         24 |       CP057

Mass peaks from all input spectra with a difference in m/z smaller 20 ppm (parameter ppm) were combined into one peak and the median m/z and intensity is reported for these. Due to parameter minProp = 0.8, the resulting MS2 spectrum contains only peaks that were present in 80% of the input spectra.

A plot of this consensus spectrum is shown below.

Consensus MS2 spectrum created from all measured MS2 spectra for ions of chromatographic peak CP53.

Figure 1: Consensus MS2 spectrum created from all measured MS2 spectra for ions of chromatographic peak CP53

We could now match the consensus spectrum against a database of MS2 spectra. In our example we simply load MS2 spectra for the two compounds with matching m/z exported from Metlin. For each of the compounds MS2 spectra created with collision energies of 0V, 10V, 20V and 40V are available. Below we import the respective data and plot our candidate spectrum against the MS2 spectra of Flumanezil and Fenamiphos (from a collision energy of 20V).

flumanezil <- spectra(readMgfData(
    system.file("mgf/metlin-2724.mgf", package = "xcms")))
fenamiphos <- spectra(readMgfData(
    system.file("mgf/metlin-72445.mgf", package = "xcms")))

par(mfrow = c(1, 2))
plot(ex_spectrum[[1]], flumanezil[[3]], main = "against Flumanezil",
     tolerance = 40e-6)
plot(ex_spectrum[[1]], fenamiphos[[3]], main = "against Fenamiphos",
     tolerance = 40e-6)
Mirror plots for the candidate MS2 spectrum against Flumanezil (left) and Fenamiphos (right). The upper panel represents the candidate MS2 spectrum, the lower the target MS2 spectrum. Matching peaks are indicated with a dot.

Figure 2: Mirror plots for the candidate MS2 spectrum against Flumanezil (left) and Fenamiphos (right)
The upper panel represents the candidate MS2 spectrum, the lower the target MS2 spectrum. Matching peaks are indicated with a dot.

Our candidate spectrum matches Fenamiphos, thus, our example chromatographic peak represents signal measured for this compound. In addition to plotting the spectra, we can also calculate similarities between them with the compareSpectra method (e.g. using the dotproduct method as shown below).

compareSpectra(ex_spectrum[[1]], flumanezil[[3]], binSize = 0.02,
               fun = "dotproduct")
## [1] 0.005350294
compareSpectra(ex_spectrum[[1]], fenamiphos[[3]], binSize = 0.02,
               fun = "dotproduct")
## [1] 0.9092189

Clearly, the candidate spectrum does not match Flumanezil, while it has a high similarity to Fenamiphos. While we performed here the MS2-based annotation on a single chromatographic peak, this could be easily extended to the full list of MS2 spectra (returned by chromPeakSpectra) for all chromatographic peaks in an experiment. Respective code facilitating this will be implemented in future.

In the present example we used only a single data file and we did thus not need to perform a sample alignment and correspondence analysis. These tasks could however be performed similarly to plain LC-MS data, retention times of recorded MS2 spectra would however also be adjusted during alignment based on the MS1 data. After correspondence analysis (peak grouping) MS2 spectra for features can be extracted with the featureSpectra function which returns all MS2 spectra associated with any chromatographic peak of a feature.

Note also that this workflow can be included into the Feature-Based Molecular Networking FBMN to match MS2 spectra against GNPS. See here for more details and examples.

3 SWATH data analysis

In this section we analyze a small SWATH data set consisting of a single mzML file with data from the same sample analyzed in the previous section but recorded in SWATH mode. We again read the data with the readMSData function. The resulting object will contain all recorded MS1 and MS2 spectra in the specified file.

swath_file <- system.file("TripleTOF-SWATH",
                          package = "msdata")

swath_data <- readMSData(swath_file, mode = "onDisk")

Below we determine the number of MS level 1 and 2 spectra in the present data set.

##    1    2 
##  444 3556

As described in the introduction, in SWATH mode all ions within pre-defined isolation windows are fragmented and MS2 spectra measured. The definition of these isolation windows (SWATH pockets) is imported from the mzML files and stored in the object’s fData (which provides additional annotations for each individual spectrum). Below we inspect the respective information for the first few spectra. The upper and lower isolation window m/z can be extracted with the isolationWindowLowerMz and isolationWindowUpperMz.

head(fData(swath_data)[, c("isolationWindowTargetMZ",
                           "msLevel", "retentionTime")])
##          isolationWindowTargetMZ isolationWindowLowerOffset
## F1.S2000                  208.95                      21.95
## F1.S2001                  244.05                      14.15
## F1.S2002                  270.85                      13.65
## F1.S2003                  299.10                      15.60
## F1.S2004                  329.80                      16.10
## F1.S2005                  367.35                      22.45
##          isolationWindowUpperOffset msLevel retentionTime
## F1.S2000                      21.95       2       200.084
## F1.S2001                      14.15       2       200.181
## F1.S2002                      13.65       2       200.278
## F1.S2003                      15.60       2       200.375
## F1.S2004                      16.10       2       200.472
## F1.S2005                      22.45       2       200.569
## [1] 187.0 229.9 257.2 283.5 313.7 344.9
## [1] 230.9 258.2 284.5 314.7 345.9 389.8

In the present data set we use the value of the isolation window target m/z to define the individual SWATH pockets. Below we list the number of spectra that are recorded in each pocket/isolation window.

## 163.75 208.95 244.05 270.85  299.1  329.8 367.35 601.85 
##    444    445    445    445    445    444    444    444

We have thus 1,000 MS2 spectra measured in each isolation window.

3.1 Chromatographic peak detection in MS1 and MS2 data

Similar to a conventional LC-MS analysis, we perform first a chromatographic peak detection (on the MS level 1 data) with the findChromPeaks method. Below we define the settings for a centWave-based peak detection and perform the analysis.

cwp <- CentWaveParam(snthresh = 5, noise = 100, ppm = 10,
                     peakwidth = c(3, 30))
swath_data <- findChromPeaks(swath_data, param = cwp)

Next we perform a chromatographic peak detection in the MS level 2 data of each isolation window. We use the findChromPeaksIsolationWindow function employing the same peak detection algorithm reducing however the required signal-to-noise ratio. The isolationWindow parameter allows to specify which MS2 spectra belong to which isolation window and hence defines in which set of MS2 spectra chromatographic peak detection should be performed. While the default value for this parameter uses isolation windows provided by calling isolationWindowTargetMz on the object, it would also be possible to manually define the isolation windows, e.g. if the corresponding information is not available in the input mzML files.

cwp <- CentWaveParam(snthresh = 3, noise = 10, ppm = 10,
                     peakwidth = c(3, 30))
swath_data <- findChromPeaksIsolationWindow(swath_data, param = cwp)

The findChromPeaksIsolationWindow function added all peaks identified in the individual isolation windows to the chromPeaks matrix containing already the MS1 chromatographic peaks. These newly added peaks can be identified by the value of the "isolationWindow" column in the corresponding row in chromPeakData, which lists also the MS level in which the peak was identified.

## DataFrame with 368 rows and 6 columns
##        ms_level is_filled isolationWindow isolationWindowTargetMZ
##       <integer> <logical>        <factor>               <numeric>
## CP01          1     FALSE              NA                      NA
## CP02          1     FALSE              NA                      NA
## CP03          1     FALSE              NA                      NA
## CP04          1     FALSE              NA                      NA
## CP05          1     FALSE              NA                      NA
## ...         ...       ...             ...                     ...
## CP364         2     FALSE          601.85                  601.85
## CP365         2     FALSE          601.85                  601.85
## CP366         2     FALSE          601.85                  601.85
## CP367         2     FALSE          601.85                  601.85
## CP368         2     FALSE          601.85                  601.85
##       isolationWindowLowerMz isolationWindowUpperMz
##                    <numeric>              <numeric>
## CP01                      NA                     NA
## CP02                      NA                     NA
## CP03                      NA                     NA
## CP04                      NA                     NA
## CP05                      NA                     NA
## ...                      ...                    ...
## CP364                  388.8                  814.9
## CP365                  388.8                  814.9
## CP366                  388.8                  814.9
## CP367                  388.8                  814.9
## CP368                  388.8                  814.9

Below we count the number of chromatographic peaks identified within each isolation window (the number of chromatographic peaks identified in MS1 is 62).

## 163.75 208.95 244.05 270.85  299.1  329.8 367.35 601.85 
##      2     38     32     14    105     23     61     31

We thus successfully identified chromatographic peaks in the different MS levels and isolation windows, but don’t have any actual MS2 spectra yet. These have to be reconstructed from the available chromatographic peak data which we will done in the next section.

3.2 Reconstruction of MS2 spectra

Identifying the signal of the fragment ions for the precursor measured by each MS1 chromatographic peak is a non-trivial task. The MS2 spectrum of the fragment ion for each MS1 chromatographic peak has to be reconstructed from the available MS2 signal (i.e. the chromatographic peaks identified in MS level 2). For SWATH data, fragment ion signal should be present in the isolation window that contains the m/z of the precursor ion and the chromatographic peak shape of the MS2 chromatographic peaks of fragment ions of a specific precursor should have a similar retention time and peak shape than the precursor’s MS1 chromatographic peak.

After detection of MS1 and MS2 chromatographic peaks has been performed, we can reconstruct the MS2 spectra using the reconstructChromPeakSpectra function. This function defines an MS2 spectrum for each MS1 chromatographic peak based on the following approach:

  • Identify MS2 chromatographic peaks in the isolation window containing the m/z of the ion (the MS1 chromatographic peak) that have approximately the same retention time than the MS1 chromatographic peak (the accepted difference in retention time can be defined with the diffRt parameter).
  • Extract the MS1 chromatographic peak and all MS2 chromatographic peaks identified by the previous step and correlate the peak shapes of the candidate MS2 chromatographic peaks with the shape of the MS1 peak. MS2 chromatographic peaks with a correlation coefficient larger than minCor are retained.
  • Reconstruct the MS2 spectrum using the m/z of all above selected MS2 chromatographic peaks and their intensity; each MS2 chromatographic peak selected for an MS1 peak will thus represent one mass peak in the reconstructed spectrum.

To illustrate this process we perform the individual steps on the example of Fenamiphos (exact mass 303.105800777 and m/z of [M+H]+ adduct 304.113077). As a first step we extract the chromatographic peak for this ion.

fenamiphos_mz <- 304.113077
fenamiphos_ms1_peak <- chromPeaks(swath_data, mz = fenamiphos_mz, ppm = 2)
##            mz    mzmin    mzmax      rt   rtmin   rtmax     into     intb
## CP34 304.1124 304.1121 304.1126 423.945 419.445 428.444 10697.34 10688.34
##          maxo  sn sample
## CP34 2401.849 618      1

Next we identify all MS2 chromatographic peaks that were identified in the isolation window containing the m/z of Fenamiphos. The information on the isolation window in which a chromatographic peak was identified is available in the chromPeakData (which contains arbitrary additional annotations to each individual chromatographic peak).

keep <- chromPeakData(swath_data)$isolationWindowLowerMz < fenamiphos_mz &
        chromPeakData(swath_data)$isolationWindowUpperMz > fenamiphos_mz

We also require the retention time of the MS2 chromatographic peaks to be similar to the retention time of the MS1 peak and extract the corresponding peak information.

keep <- keep &
    chromPeaks(swath_data)[, "rtmin"] < fenamiphos_ms1_peak[, "rt"] &
    chromPeaks(swath_data)[, "rtmax"] > fenamiphos_ms1_peak[, "rt"]

fenamiphos_ms2_peak <- chromPeaks(swath_data)[which(keep), ]

In total 24 MS2 chromatographic peaks match all the above condition. Next we extract their corresponding ion chromatograms, as well as the ion chromatogram of the MS1 peak. In addition we have to filter the object first by isolation window, keeping only spectra that were measured in that specific window and to specify to extract the chromatographic data from MS2 spectra (with msLevel = 2L).

rtr <- fenamiphos_ms1_peak[, c("rtmin", "rtmax")]
mzr <- fenamiphos_ms1_peak[, c("mzmin", "mzmax")]
fenamiphos_ms1_chr <- chromatogram(swath_data, rt = rtr, mz = mzr)

rtr <- fenamiphos_ms2_peak[, c("rtmin", "rtmax")]
mzr <- fenamiphos_ms2_peak[, c("mzmin", "mzmax")]
fenamiphos_ms2_chr <- chromatogram(
    filterIsolationWindow(swath_data, mz = fenamiphos_mz),
    rt = rtr, mz = mzr, msLevel = 2L)

We can now plot the extracted ion chromatogram of the MS1 and the extracted MS2 data.

plot(rtime(fenamiphos_ms1_chr[1, 1]),
     intensity(fenamiphos_ms1_chr[1, 1]),
     xlab = "retention time [s]", ylab = "intensity", pch = 16,
     ylim = c(0, 5000), col = "blue", type = "b", lwd = 2)
#' Add data from all MS2 peaks
tmp <- lapply(fenamiphos_ms2_chr@.Data,
              function(z) points(rtime(z), intensity(z),
                                 col = "#00000080",
                                 type = "b", pch = 16))