Data Processing

Once all files have been obtained, peak picking have to be performed using the dataProcessing function from LipidMS. For further details use help(dataProcessing, package = "LipidMS").

# load LipidMS library
library(LipidMS)

# Example data files can be downloaded from:
# <https://drive.google.com/drive/folders/1hSYrQBkh-rAA-oiaKqGkrNL7uWQraV75?usp=sharing>

# get mzXML files from your working directory
files <- dir()[grepl(".mzXML", dir())]

# set the processing parameters
acquisitionmode <- c("DIA", "DDA", "MS", "MS", "MS", "DIA")
polarity <- "negative"
dmzagglom <- 15
drtagglom <- 500
drtclust <- c(100, 200)
minpeak <- c(5, 4)
drtgap <- 5
drtminpeak <- 15
drtmaxpeak <- c(100, 200)
recurs <- c(5, 10)
sb <- c(3, 2)
sn <- c(3, 2)
minint <- c(500, 100)
weight <- c(2, 3)
dmzIso <- 5
drtIso <- 5

# for single file processing, only samples acquired in DIA or DDA will be processed
files <- files[acquisitionmode %in% c("DIA", "DDA")]
acquisitionmode <- acquisitionmode[acquisitionmode %in% c("DIA", "DDA")]

# run the dataProcessing function to obtain the requires msobjects
msobjects <- list()

for (f in 1:length(files)){
  msobjects[[f]] <- dataProcessing(file = files[f],
                                   polarity = polarity,
                                   dmzagglom = dmzagglom,
                                   drtagglom = drtagglom,
                                   drtclust = drtclust,
                                   minpeak = minpeak,
                                   drtgap = drtgap,
                                   drtminpeak = drtminpeak,
                                   drtmaxpeak = drtmaxpeak,
                                   recurs = recurs,
                                   sb = sb,
                                   sn = sn,
                                   minint = minint,
                                   weight = weight,
                                   dmzIso = dmzIso,
                                   drtIso = drtIso)
}

This function will return a list of msobjects which will contain raw data and peaklists for each file.

Lipid Annotation

LipidMS contains a total of 46 functions aimed to annotate lipid species: 43 class and polarity-specific functions (i.e. idPGneg) and tree general functions (idPOS, idNEG and annotatemsbatch) for ESI+, ESI+ and batch processing, respectively. Class-specific functions allow to customize fragmentation rules, while general identification functions execute all functions for a given polarity sequentially using the predefined rules.

# set annotation parameters
dmzprecursor <- 5
dmzproducts <- 10
rttol <- 5
coelcutoff <- 0.8

# Annotate lipids
## If polarity is positive
if (polarity == "positive"){
  for (m in 1:length(msobjects)){
    msobjects[[m]] <- idPOS(msobjects[[m]],
                            ppm_precursor = dmzprecursor,
                            ppm_products = dmzproducts,
                            rttol = rttol,
                            coelCutoff = coelcutoff)
  }
}

## If polarity is negative
if (polarity == "negative"){
  for (m in 1:length(msobjects)){
    msobjects[[m]] <- idNEG(msobjects[[m]],
                            ppm_precursor = dmzprecursor,
                            ppm_products = dmzproducts,
                            rttol = rttol,
                            coelCutoff = coelcutoff)
  }
}

# example code for idPEpos function
pe <- idPEpos(msobject, 
          ppm_precursor = ppm_precursor, 
          ppm_products = ppm_products, rttol = 6, 
          chainfrags_sn1 = c("mg_M+H-H2O", "lysope_M+H-H2O"), 
          chainfrags_sn2 = c("fa_M+H-H2O", "mg_M+H-H2O"),
          intrules = c("mg_sn1/mg_sn2", "lysope_sn1/lysope_sn2"), 
          rates = c("3/1", "3/1"), intrequired = c(T),
          dbs = dbs, coelCutoff = 0.8)

# additional information about how to change rules is given in the documentation 
# of the following functions: chainFrags , checkClass, checkIntensityRules, 
# coelutingFrags, ddaFrags, combineChains and organizeResults. These functions 
# could be also empoyed to build customized identification functions.

Plot your results

Once you have obtained your results, you can visualize the results with the plotLipids function. It will plot informative peaks for each lipid annotated using idPOS and idNEG (or similar functions). Plots on the left side represent raw values while plots on the left side are smoothed or clean scans (MS2 in DDA).

msobject <- idPOS(msobject)
msobject <- plotLipids(msobject)

# display the first plot
msobject$plots[[1]]
msobject$plots[["yourpeakIDofinterest"]]

# save all plot to a pdf file
pdf("plotresults.pdf")
for (p in 1:length(msobject$plots)){
    print(msobject$plots[[p]])
  }
dev.off()

Data bases

By default, LipidMS data bases (for each lipid class), are based on the combination of the following chain building blocks: 30 fatty acyl chains and 4 sphingoid bases, which were selected based on their biological relevance. If you want to add or remove any of the building blocks, createLipidDB() function can be employed to rebuild the data bases of interest.

fas <- c("8:0", "10:0", "12:0", "14:0", "14:1", "15:0", "16:0", "16:1",
"17:0", "18:0", "18:1", "18:2", "18:3", "18:4", "20:0", "20:1", "20:2",
"20:3", "20:4", "20:5", "22:0", "22:1", "22:2", "22:3", "22:4", "22:5",
"22:6", "24:0", "24:1", "26:0")
sph <- c("16:0", "16:1", "18:0", "18:1")
dbs <- createLipidDB(lipid = "all", chains = fas, chains2 = sph)

# to use for identification function two additional data frames need to be added
dbs$adductsTable <- LipidMS::adductsTable
dbs$nlsphdb <- LipidMS::nlsphdb

If just some DB need to be modified, you can use the following code:

fas <- c("8:0", "10:0", "12:0", "14:0", "14:1", "15:0", "16:0", "16:1",
         "17:0", "18:0", "18:1", "18:2", "18:3", "18:4", "19:0", "20:0", "20:1",
         "20:2", "20:3", "20:4", "20:5", "22:0", "22:1", "22:2", "22:3", "22:4",
         "22:5", "22:6", "24:0", "24:1", "26:0")
newfadb <- createLipidDB(lipid = "FA", chains = fas)
dbs <- assignDB() # This function loads all DBs required
dbs$fadb <- newfadb$fadb # Then, you can modify some of these DBs

Adducts

LipidMS uses specific adducts for each lipid class and polarity. All the adducts searched must be included in the adductsTable data frame that is within the package data. In case you want to use an adduct that is not included, you will need to add it:

adductsTable <- LipidMS::adductsTable
adductsTable <- data.frame(adduct = c(adductsTable$adduct, "M+X"), 
                          mdiff = c(adductsTable$mdiff, 52.65), 
                          charge = c(adductsTable$charge, 1), 
                          n = c(adductsTable$n, 1),
                          stringsAsFactors = F)

Once included, this adduct can be used when calling the identification function:

# The new adductsTable has to be also uploaded in the dbs list.
dbs <- assignDB()
dbs$adductsTable <- adductsTable

idPCpos(msobject = LipidMSdata2::msobjectDIApos, 
        adducts = c("M+H", "M+Na", "M+X"), dbs = dbs)

Fragmentation and intensity rules

For a higher customization of LipidMS rules, different arguments of the identification functions can be modified:

• clfrags, clrequired and ftype arguments are employed by checkClass() function to confirm a given lipid class. This function allows three different types of fragments: fragments with a specific m/z as for example 227.0326 for PG in negative mode, which needs to be defined as clfrags = c(227.0326) and ftype = c(“F”); neutral losses such as the head group of some PL (i.e. NL of 74.0359 in PG in negative mode), which will be defined as clfrags = c(74.0359) and ftype = c(“NL”); or building blocks resulting from the loss of some groups, as for example, PA as M-H resulting from the loss of the head group (glycerol) in PG in ESI-, which will be defined as clfrags = c(“pa_M-H”) and ftype = c(“BB”). The last two options could define the same fragments. In this case just one of them would be necessary. When using the third type of fragment (“BB”), the building block will be specified in lower case (i.e. pa, dg, lysopa, mg, etc.) and the adduct will be given as it appears in the adductsTable, both separated by “_“. Names for the building blocks are the ones used for the LipidMS databases without the”db” at the end. In case the presence of a fragment indicates that the candidate does not belong to the lipid class (i.e. loss of CH3 in PE, which corresponds to a PC actually), this will be specified by using clrequisites = c(“excluding”).

• chainfrags argument are employed by chainFrags() function to look for specific fragments that inform about the chains of a given class. For example, in the case of PG subclass, the chain in sn1 position is identified by the LPG as M-H resulting from the loss of the FA chain of sn2; and the chain in sn2 position is identified as the free FA chain as M-H. These two fragments need to be searched in two different steps: in the fist step we will look for LPG coeluting with the precursor using chainfrags = c(“lysopg_M-H”); then, we will look for FA chains using chainfrags = c(“fa_M-H”). This information can be combined later using combineChains function. To indicate the fragments to be searched, the class of lipid is writen using the same names as the LipidMS databases without the “db” at the end (i.e. pa, dg, lysopa, mg, CE, etc.), and the adduct has to be indicated as it appears in the adductsTable, both parts separated by “_“. In case some chain needs to be searched based on a neutral loss, this can be defined using”NL-” prefix, followed by the database and the adduct. If this neutral loss is employed to find the remaining chain, “cbdiff-” prefix allows to calculate the difference in carbons and doubles bounds between the precursor and the building block found. For example, “cbdiff-dg_M+H-H2O” will look for DG as M+H-H2O and then, it will return the difference between their number of carbons and double bounds and the ones from the precursor. On the other hand, “NL-mg_M+H-H2O” will look for fragments coming from the loss of MGs. In case these fragments identified as losses from the precursors are going to be employed for the intensity rules, this same prefix has to be added. If a chain is calculated based on the difference of total number of carbons and double bounds between the precursor and a previously searched chain, chainfrags argument must be must be a character vector c(““) and the candidates data frame and the chain fragments list must be provided.

• intrules, rates and intrequired arguments are used by checkIntensityRules() function to confirm chain structure and position. Taking PG subclass as an example, intensities of lysoPG fragments (informative for sn1) can be employed to confirm the chains structure (intrules = c(“lysopg_sn1/lysopg_sn2”)). In this case, the intensity of the lysoPG resulting from the loss of the FA chain in sn2 is at least 3 times greater (rates = c(“3/1”)) than the lysoPG resulting from the loss of the FA chain in sn1. For the intrules argument, “/” will be use to separate the fragments related to each chain (i.e. 16:0/18:1), and “_” will be use to indicate the list in which they (sn1 or sn2) will be searched. This will depend on the chain fragments rules defined previously. If you only search for LPG in chainfrags_sn1 but you want to compare in both positions, you need to use “_sn1” in both cases. For classes with more than one FA chain, if some intensity rule should be employed to identify their position but they are no defined yet, use “Unknown”. If it is not necessary because the fragmentation rules are informative enough to define the position (i.e. sphingolipid species), just leave an empty vector.

If you have any further questions, please do not hesitate to contact us at: maribel_alcoriza@iislafe.es or maribel_alcoriza@hotmail.com