Discovery, Identification, and Screening of Lipids and Oxylipins in HPLC-MS Datasets Using LOBSTAHS

Install current production version

Users can the current production version of LOBSTAHS from Bioconductor by following the directions here (under “Installation”). The Bioconductor installation function will prompt you to install the latest versions of some other packages on which LOBSTAHS depends.

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install()

Note: At this point, you should verify that you have, in fact, installed the most current version of Bioconductor. You may be required to install the latest version of R itself before you can obtain the latest version of Bioc. If you fail to ensure you are running the latest version of Bioconductor at this point, you won’t necessarily be able to install the most up-to-date version of LOBSTAHS since the Bioc installer will only provide you will the most recent versions of packages attached to that version of Bioconductor. (If you are confused by any of this, check out this help section. Or, just install the current development version directly from GitHub using the directions, below.) Once you are satisfied that you’re running the latest version of Bioconductor, you can then install LOBSTAHS:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("LOBSTAHS")

Users are also encouraged to download the PtH2O2lipids data package, which can be used for familiarization with the software.

Install “no warranties” development version with latest features

Following these directions, you will install the latest version of the software from the files present in this GitHub repository. Some features may be unstable.

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("xcms")
BiocManager::install("CAMERA")

install.packages("devtools")

library("devtools")
install_github("vanmooylipidomics/LOBSTAHS", build_vignettes = TRUE) 

## install dataset 'PtH2O2lipids'
## see LOBSTAHS documentation for examples 

install_github("vanmooylipidomics/PtH2O2lipids")

Acquisition of HPLC-MS data suitable for LOBSTAHS

Data for LOBSTAHS should be acquired using a mass spectrometer with sufficiently high mass accuracy and resolution. Suitable MS acquisition platforms with which LOBSTAHS has been tested include Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap instruments. A quadrupole time-of-flight (Q-TOF) instrument could also be used if it possessed sufficient mass accuracy (i.e., < 5-7 ppm). While the software will accept any HPLC-MS data as input, insufficient mass accuracy and resolution will produce results with large numbers of database matches for each feature that cannot be distinguished from each other.

File conversion

After acquisition, each data file should be converted from manufacturer to open source (.mzXML) format in centroid (not profile) mode. If data were acquired on the instrument using ion mode switching, the data from each ionization mode (i.e., polarity) must also be extracted into a separate file. The msConvert tool (part of the ProteoWizard toolbox) can be used to accomplish these tasks. msConvert commands can be executed with either the provided GUI or at the command line; however, conversion of manufacuter file formats can only be accomplished using the Windows installation. An R script (Exactive_full_scan_process_ms1+.r) for batch conversion and extraction of data files acquired on a Thermo Exactive Orbitrap instrument is provided as part of the Van Mooy Lab Lipidomics Toolbox. This vignette is not intended as a comprehensive guide to mass spectrometer file conversion; users are encouraged to digest the msConvert documentation. However, assuming a hypothetical data file “Exactive_data.raw” was acquired on an Thermo Orbitrap instrument with ion mode switching, the following code could be within R used to convert and then extract positive and negative mode scans to separate files. (Code presumes you’ve already installed msConvert.)

system(paste("msconvert Exactive_data.raw --mzXML --filter \"peakPicking true 1-\" -o mzXML_ms1_two_mode -v"))

system(paste("msconvert mzXML_ms1_two_mode/Exactive_data.mzXML --mzXML --filter \"polarity positive\" -o mzXML_ms1_pos_mode -v"))
system(paste("msconvert mzXML_ms1_two_mode/Exactive_data.mzXML --mzXML --filter \"polarity negative\" -o mzXML_ms1_neg_mode -v"))

Pre-processing with xcms

After all data files in a particular dataset have been converted and extracted into files of like polarity, the R-package xcms can then be used to perform feature detection, retention time correction, and peak grouping. While the paragraphs below contain basic instructions for preparation of data, this vignette is not intended as ae manual or guide to the complex world of mass spectrometer data processing in xcms and CAMERA; users should acquaint themselves with the manuals (here and here) and very helpful vignettes (here and here) for the two packages.

First, data files should be given intuitive file names (containing, e.g., a sample ID along with information on experimental timepoint and treatment) and placed into a directory structure according to an index variable; the xcms vignette LC/MS Preprocessing and Analysis with xcms contains a detailed explanation. Feature detection, retention time correction, and peak grouping should then be performed. Values of parameters for xcms functions can be obtained from several sources:

• Package default values can be used (not recommended)
• Values may be obtained from the literature⁴
• The R-package IPO⁵ can be used to optimize the values of many of the required parameters

The R script prepOrbidata.R (part of the Van Mooy Lab Lipidomics Toolbox) contains code for complete preparation in xcms of the PtH2O2lipids (or similar) dataset. The script allows the user to apply parameter values assembled from the literature or obtain them from IPO optimization of a subset of samples. The final parameter values used in Collins et al. 2016¹ for analysis of the PtH2O2lipids dataset – obtained using HPLC-ESI-MS on an Exactive Orbitrap instrument – are given in Table S5 of the electronic supplement. The settings and values listed in the table could be used as a starting point for analysis of similar data.

The xcmsSet produced using these settings from the P. tricornutum data is stored in the PtH2O2lipids package.

Whatever settings are used in xcms, the processed data should contain high-quality features which have been aligned across samples; the quality of the processed data should be verified by individual inspection of a subset of features. At the conclusion of processing with xcms, the user should have a single xcmsSet object containing the dataset.

Final pre-processing with CAMERA

In the final pre-processing step, the R-package CAMERA should be used to (1) aggregate peak groups in the xcmsSet object into pseudospectra and (2) identify features in the data representing likely isotope peaks. Use of CAMERA to aggregate the peak groups into pseudospectra is critical because LOBSTAHS applies its various orthogonal screening criteria to only those peak groups within each pseudospectrum. CAMERA should not be used to eliminate adduct ions since the adduct ion hierarchy screening function in LOBSTAHS presumes that all adduct ions for a given analyte will be present in the dataset. We can create an xsAnnotate object from the P. tricornutum data by applying the wrapper function annotate() to the xcmsSet we created in the previous step:

library(xcms)
library(CAMERA)
library(LOBSTAHS)

# first, a necessary workaround to avoid a import error; see
# https://support.bioconductor.org/p/69414/

imports = parent.env(getNamespace("CAMERA"))
unlockBinding("groups", imports)
imports[["groups"]] = xcms::groups
lockBinding("groups", imports)

# create annotated xset using wrapper annotate(), allowing us to perform all
# CAMERA tasks at once

xsA = annotate(ptH2O2lipids$xsAnnotate@xcmsSet,
                   
               quick=FALSE,
               sample=NA, # use all samples
               nSlaves=1, # set to number of available cores or processors if 
                          # > 1
                   
               # group FWHM settings (defaults)
                   
               sigma=6,
               perfwhm=0.6,
                   
               # groupCorr settings (defaults)
                   
               cor_eic_th=0.75,
               graphMethod="hcs",
               pval=0.05,
               calcCiS=TRUE,
               calcIso=TRUE,
               calcCaS=FALSE,
                   
               # findIsotopes settings
                   
               maxcharge=4,
               maxiso=4,
               minfrac=0.5,
                   
               # adduct annotation settings
                   
               psg_list=NULL,
               rules=NULL,
               polarity="positive", # the PtH2O2lipids xcmsSet contains
                                    # positive-mode data
               multiplier=3,
               max_peaks=100,
                   
               # common to multiple tasks
                   
               intval="into",
               ppm=2.5,
               mzabs=0.0015
                   
               )
#> Start grouping after retention time.
#> Created 113 pseudospectra.
#> Generating peak matrix!
#> Run isotope peak annotation
#>  % finished: 10  20  30  40  50  60  70  80  90  100  
#> Found isotopes: 5692 
#> Start grouping after correlation.
#> Generating EIC's .. 
#> 
#> Calculating peak correlations in 113 Groups... 
#>  % finished: 10  20  30  40  50  60  70  80  90  100  
#> 
#> Calculating isotope assignments in 113 Groups... 
#>  % finished: 10  20  30  40  50  60  70  80  90  100  
#> Calculating graph cross linking in 113 Groups... 
#>  % finished: 10  20  30  40  50  60  70  80  90  100  
#> New number of ps-groups:  5080 
#> xsAnnotate has now 5080 groups, instead of 113 
#> Generating peak matrix for peak annotation!
#> 
#> Calculating possible adducts in 5080 Groups... 
#>  % finished: 10  20  30  40  50  60  70  80  90  100 

We now have an xsAnnotate object “xsA” to which we will next apply the screening and identification functions of LOBSTAHS. In the annotate() call, we set quick = FALSE because we want to run groupCorr(). This will also cause CAMERA to perform internal adduct annotation. While we will perform our own adduct annotation later with LOBSTAHS, allowing CAMERA to identify its own adducts doesn’t hurt, particularly if it leads to the creation of better pseudospectra.

A note about molecular diversity in the current databases

By virtue of the origin of LOBSTAHS — the study of lipids in marine algae and bacteria — most of the lipids currently in the database are of marine microbial origin. While algae and marine bacteria have many lipids in common with humans and other mammals, there are many classes which are not currently represented in the databases. If you find yourself adding new lipids to the default databases because the existing coverage is insufficient in your research area, consider sharing your additions with others via a pull request or by opening an issue on the package GitHub page. Some new lipid classes (e.g., sphingolipids and most non-glycerolipids) may not be amenable to in silico simulation in the same easy fashion as the existing lipid classes for which functionality already exists in the package code. In these cases, users should open a new issue request for new functionality or — in the spirit of open-source collaboration — modify the code in your own Git fork and then request incorporation of your new feature so others may also discover new lipids!

Accessing the default databases

We can access the default databases to examine their scope:

library(LOBSTAHS)
data(default.LOBdbase)

default.LOBdbase$positive # default positive mode database

## 
## A positive polarity "LOBdbase" object.
## 
## Contains entries for 133273 possible adduct ions of 25741 unique parent compounds.
## 
## Parent lipid types ( 22 ): astaxanthin, bile_salt, DNPPE, fungalGSL, hapCER, hapGSL, hGSL, IP_DAG, IP_MAG, PDMS, pigment, plastoquinone_9, plastoquinone_9OH, plastoquinone_9OH2, PUA, reduced_scytonemin, scytonemin, sGSL, sterol, TAG, ubiquinone, vGSL 
## IP-DAG classes ( 14 ): WaxEster, DGCC, DGDG, DGTS_DGTA, DAG, PA, PE, PG, PC, SQDG, MGDG, BLL, PDPT, S_DGCC 
## IP-MAG classes ( 12 ): CoprostanolEsters, CholesterolEsters, LDGCC, LDGDG, LDGTS_DGTA, MAG, LPA, LPE, LPG, LPC, LSQDG, LMGDG 
## Photosynthetic pigments ( 22 ): Chl_a, 19prime_but_fuco, 19prime_hex_fuco, Allox, Alpha_carotene, Beta_carotenes, Chl_b, Chl_c2, Chl_c3, Chlide_a, Croco, Dd_Ddc, Dt, Echin, Fuco, Lut, Neox_Nos, Peri, Pheophytin_a, Pras, Viol, Zeax 
## Adducts ( 9 ): [M+2Na-H]+, [M+2Na+Cl]+, [M+C2H3Na2O2]+, [M+C4H10N3]+, [M+H]+, [M+K]+, [M+Na]+, [M+NH4]+, [M+NH4+ACN]+ 
## 
## m/z range: 97.0647914-2016.54119204 
## 
## Ranges of chemical parameters represented in molecules with acyl moieties:
## 
## Total number of acyl carbon atoms: 6-78 
## Total number of acyl carbon-carbon double bonds: 0-18 
## Number of additional oxygen atoms: 0-4 
## 
## Memory usage: 11.4 MB

default.LOBdbase$negative # default negative mode database

## 
## A negative polarity "LOBdbase" object.
## 
## Contains entries for 97007 possible adduct ions of 21063 unique parent compounds.
## 
## Parent lipid types ( 16 ): bile_salt, DNPPE, FFA, fungalGSL, hapCER, hapGSL, hGSL, IP_DAG, IP_MAG, pigment, plastoquinone_9, PUA, scytonemin, sGSL, ubiquinone, vGSL 
## IP-DAG classes ( 13 ): DGCC, DGDG, DGTS_DGTA, DAG, PA, PE, PG, PC, SQDG, MGDG, BLL, PDPT, S_DGCC 
## IP-MAG classes ( 10 ): LDGCC, LDGDG, LDGTS_DGTA, MAG, LPA, LPE, LPG, LPC, LSQDG, LMGDG 
## Photosynthetic pigments ( 22 ): Chl_a, 19prime_but_fuco, 19prime_hex_fuco, Allox, Alpha_carotene, Beta_carotenes, Chl_b, Chl_c2, Chl_c3, Chlide_a, Croco, Dd_Ddc, Dt, Echin, Fuco, Lut, Neox_Nos, Peri, Pheophytin_a, Pras, Viol, Zeax 
## Adducts ( 11 ): [M-H]-, [M+2NaAc+Cl]-, [M+3Ac+2Na]-, [M+Cl]-, [M+HAc-H]-, [M+K-2H]-, [M+Na-2H]-, [M+Na+Cl-H]-, [M+NaAc-H]-, [M+NaAc+Cl]-, [M+NaAc+HAc-H]- 
## 
## m/z range: 95.05023848-1459.92498456 
## 
## Ranges of chemical parameters represented in molecules with acyl moieties:
## 
## Total number of acyl carbon atoms: 6-52 
## Total number of acyl carbon-carbon double bonds: 0-12 
## Number of additional oxygen atoms: 0-4 
## 
## Memory usage: 8.55 MB

Concept of operation: Using input tables

Though it might not be evident from examination of these defaults, LOBSTAHS generates databases based on values of parameters defined in simple tables. The values in these tables define the molecular properties of each individual compound or lipid class for which database entries will be created. Database generation is accomplished with the function generateLOBdbase(). Default values for the various inputs can be viewed by loading the default tables into the R workspace (directions below); the default input data are also given in Tables 1 and 2 of Collins et al. 2016.¹ Package versions ≥ 1.1.2 contain adduct hierarchy and retention time window data for lipid classes BLL, PDPT, vGSL, sGSL, hGSL, hapGSL, and hapCER, which are described in Fulton et al. 2014 and Hunter et al. 2015.⁶. By modifying the default values in each table using provided templates, users can generate custom databases that include additional lipid classes. Customization directions are provided in the following section.

Users can load the default versions of the four input tables into the R workspace (and subsequently view them) at any time:

data(default.acylRanges)
data(default.oxyRanges)
data(default.componentCompTable)
data(default.adductHierarchies)

Introduction to the four input tables

Custom databases can be created in LOBSTAHS by modifying or adding to the default values in the four tables. We can generate custom databases in three steps:

• Modify the values in one or more of the tables
  • Tables containing the default values are provided with the standard package installation in both Microsoft Excel and .csv format. These are automatically installed into the /LOBSTAHS/doc/ subdirectory of your R library path and can be used as templates for production of custom inputs. (You can use .libPaths() to find the location of your R library.) Alternatively, templates for the tables used to generate databases in the latest release of LOBSTAHS can downloaded from the package GitHub repository.
• Export the new, custom tables as .csv files (if the .xlsx files were used as a starting point), and
• Load the tables when calling the function generateLOBdbase(), which will then uses the value to generate the LOBSTAHS databases

Each of the four tables is introduced below, followed by instructions for adding new lipids and lipid classes to the LOBSTAHS databases.

We’ve also created a flow chart to help you navigate the database customization process; the flow chart is designed to complement — not replace — the detailed instructions below. Download a copy of the flow chart: as .pdf | as .svg

Modification of table data to create new database entries

What modifications we make to the table(s) will depend on our goal. For example, if we wish only to expand (or constrain) the range of molecular properties for which database entries are to be created within an existing lipid class (i.e., of DB_gen_compound_type = “DB_acyl_iteration”), we’ll only have to modify existing values in the acylRanges and/or oxyRanges tables. However, if we wish to add new molecule(s) or lipid class(es) to the database(s), we’ll first need to define the new molecule/class in both the componentCompTable ( LOBSTAHS_componentCompTable.xlsx) and adductHierarchies table ( LOBSTAHS_adductHierarchies.xlsx). This is relatively trivial; don’t be discouraged! We will consider two cases:

Saving of files

Once we’ve made our appropriate additions or modifications under one or both of the scenarios above, we should then save our modified table(s) to text file(s) in comma-separated values (.csv) format; we’ll then imported these .csv tables into R when the generateLOBdbase() function is called.

Database generation using generateLOBdbase

Once we get everything the way we want it in our input tables (described above), we can generate LOBSTAHS databases using the generateLOBdbase() function. generateLOBdbase uses an in silico simulation to create database entries defined by the parameters in the input tables. One entry is created for each possible adduct ion of a parent compound. generateLOBdbase can create databases for one or both ion modes. We specify the paths of any .csv files containing our customized input data (i.e., LOBSTAHS_componentCompTable.csv, LOBSTAHS_adductHierarchies.csv, LOBSTAHS_acylRanges.csv, or LOBSTAHS_oxyRanges.csv) during the call to generateLOBdbase. If NULL or no value is specified for any of the input tables, the defaults (see above) will be used. Finally, when calling generateLOBdbase, we can specify whether a .csv file containing the new database should be created in addition to the LOBdbase object.

To recreate the default databases and store them to an object called “LOBdb”, we would run the following:

LOBdb = generateLOBdbase(polarity = c("positive","negative"), gen.csv = FALSE,
                 component.defs = NULL, AIH.defs = NULL, acyl.ranges = NULL,
                 oxy.ranges = NULL)

Additional information about the function and use of generateLOBdbase (and all LOBSTAHS functions) is provided in the onboard package help (i.e., manual pages).

Follow-on analysis of screened data

With the set of screened compound assignments in hand, we now have several options. Users familiar with R can extract data for further analysis or screening directly from the LOBSet object. Alternatively, the function getLOBpeaklist can be used to extract a table of results from the LOBSet. Options in getLOBpeaklist allow the user to (1) include isomer and isobar cross-references (the default; recommended), (2) include data for features in the original xsAnnotate object that were not identified or were discarded during the screening process, and (3) simultaneously generate a .csv file with the results. The .csv file is exported with a unique timestamp to the R working directory.

Package updates and improvements

Users are encouraged to submit issues with LOBSTAHS (or feature request) via the package GitHub site. Users can also submit bug reports via Bioconductor.

Copyright

LOBSTAHS is copyright (c) 2015-2017, by the following members of the Van Mooy Laboratory group at Woods Hole Oceanographic Institution: James R. Collins, Bethanie R. Edwards, Helen F. Fredricks, and Benjamin A.S. Van Mooy. All accompanying written materials, including this vignette, are copyright (c) 2015-2017, James R. Collins. LOBSTAHS is provided under the GNU Public License and subject to terms of reuse as specified therein.

References

Benton, H.P., Want, E.J., and Ebbels, T.M.D. 2010. Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data. Bioinformatics 26: 2488-2489

Collins, J.R., Edwards, B.R., Fredricks, H.F., and Van Mooy, B.A.S. 2016. LOBSTAHS: An adduct-based lipidomics strategy for discovery and identification of oxidative stress biomarkers. Anal. Chem. 88: 7154-7162; doi:10.1021/acs.analchem.6b01260

Fulton, J.M., Fredricks, H.F., Bidle, K.D., Vardi, A., Kendrick, B.J., DiTullio, G.R., and Van Mooy, B.A.S. 2014. Novel molecular determinants of viral susceptibility and resistance in the lipidome of Emiliania huxleyi. Environmental Microbiology 16(4): 1137-1149; doi:10.1111/1462-2920.12358

Hummel, J., Segu, S., Li, Y., Irgang, S., Jueppner, J., and Giavalisco, P. 2011. Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipids. Front. Plant. Sci. 2

Hunter, J.E., Frada, M.J., Fredricks, H.F., Vardi, A., and Van Mooy, B.A.S. 2015. Targeted and untargeted lipidomics of Emiliania huxleyi viral infection and life cycle phases highlights molecular biomarkers of infection, susceptibility, and ploidy. Front. Mar. Sci. 2: 81; doi: 10.3389/fmars.2015.00081

Kuhl, C., Tautenhahn, R., Bottcher, C., Larson, T. R., and Neumann, S. 2012. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84: 283-289

Libiseller, G., Dvorzak, M., Kleb, U., Gander, E., Eisenberg, T., Madeo, F., Neumann, S., Trausinger, G., Sinner, F., Pieber, T., and Magnes, C. 2015. IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 16: 118

Patti, G.J., Tautenhahn, R., and Siuzdak, G. 2012. Meta-analysis of untargeted metabolomic data from multiple profiling experiments. Nat. Protocols 7: 508-516

Pearson, A. 2014. Lipidomics for geochemistry. In Treatise on Geochemistry, Holland, H. D., and Turekian, K. K. eds., 2nd Ed., pp. 291-336. Elsevier: Oxford.

Smith, C.A., Want, E.J., O’Maille, G., Abagyan, R., and Siuzdak, G. 2006. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78: 779–787

Tautenhahn, R., Boettcher, C., and Neumann, S. 2008. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9: 504

Notes

1. Collins, J.R., Edwards, B.R., Fredricks, H.F., and Van Mooy, B.A.S., 2016, “LOBSTAHS: An adduct-based lipidomics strategy for discovery and identification of oxidative stress biomarkers,” Anal. Chem. 88: 7154-7162; doi:10.1021/acs.analchem.6b01260

2. Smith et al., 2006, “XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification,” Anal. Chem. 78: 779–787; Tautenhahn et al., 2008, “Highly sensitive feature detection for high resolution LC/MS,” BMC Bioinformatics 9: 504; Benton et al., 2010, “Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data,” Bioinformatics 26: 2488-2489

3. Kuhl et al., 2012, “CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets,” Anal. Chem. 84: 283-289

4. See, e.g., Patti et al., 2012, “Meta-analysis of untargeted metabolomic data from multiple profiling experiment,” Nat. Protocols 7: 508-516

5. Libiseller et al., 2015, “IPO: a tool for automated optimization of XCMS parameters,” BMC Bioinformatics 16: 118

6. Fulton et al., 2014, “Novel molecular determinants of viral susceptibility and resistance in the lipidome of Emiliania huxleyi,” Environ. Microbiol. 16(4):1137-1149; Hunter et al., 2015, “Targeted and untargeted lipidomics of Emiliania huxleyi viral infection and life cycle phases highlights molecular biomarkers of infection, susceptibility, and ploidy,” Front. Mar. Sci. 2: 81

7. We used a modified version of the chromatography presented in Hummel et al., 2011, “Ultra performance liquid chromatography and high resolution mass spectrometry for the analysis of plant lipids,” Front. Plant. Sci. 2; see electronic supplement to Collins et al. 2016 for full specification.

8. See A. Pearson, 2014, “Lipidomics for geochemistry” in Treatise on Geochemistry (Holland, H. D., and Turekian, K. K. eds.), 2nd Ed., Elsevier, Oxford, pp. 291-336.