How to use with mzMine for LC-MS analysis

Margot Bligh

2025-01-20

This is a quick tutorial for how to use GlycoAnnotateR in conjuction with mzMine, a popular GUI-based tool for MS data analysis. In this tutorial I am using mzMine version 4.4.3 to analyse the same LC-MS/MS data that is used in the simple GlycoAnnotateR tutorial. An enzyme digest of fucoidan, a sulfated fucan from brown algae, was separated by HILIC and detected with an Orbitrap. The data can be downloaded from the MassIVE server (f.MSV000095410/peak/MS31_20240618_newMpyrifera_digest_SIMddMS2_2.5uL_85%EtOH_11.mzML).

Detect features with mzMine

First, the data needs to be processed in mzMine. Since the mzMine documentation is excellent I will only briefly describe the steps here. I always used the default parameters unless specified.

1. Import the data to mzMine (Raw data methods –> Import MS data).

2. Detect masses (Raw data methods –> Spectra processing –> Mass detection)

3. Detect LC-MS peaks (Feature detection –> LC-MS –> Chromatogram builder)

4. Export feature list (Feature list methods –> Export feature list –> CSV)

Annotate feature list in R

Just a few lines of code are needed to annotate the feature list in R. For a detailed explanation of the annotation parameters see here

#load GlycoAnnotateR package and tidyverse
library(GlycoAnnotateR)
library(tidyverse)
#import exported feature list. make sure you are in the correct directory!
mzmine_export <- read_csv('export_mzmine.csv', show_col_types = F)
#set parameters for annotation
#change as needed for your data
param <- glycoPredictParam(
  #degree of polymerisation data
  dp = c(1, 10), 
  #modifications
  modifications = c('sulfate', 'deoxy'),
  #is double sulfation possible?
  double_sulfate = T,
  #maximum number of monomers per modification
  nmod_max = 3,
  #adducts
  adducts = 'H',
  #polarity
  polarity = 'neg',
  #ionisation type 
  ion_type = 'ESI',
  #mass range
  scan_range = c(145, 1500))
#annotate exported feature list
mzmine_export_annot <- glycoAnnotate(mzmine_export,
                                     #name of column with mz value to annotate
                                     mz_column = 'mz', 
                                     #parameters for composition prediction
                                     param = param,
                                     #error tolerance for annotation
                                     error = 3.5, error_units = 'ppm')
#filter annotated feature list to only contain annotated features
#and format for import into mzmine
mzmine_export_annotonly <- mzmine_export_annot %>%
  #filter for only annotated features
  drop_na(`IUPAC name`) %>% 
  #rename columns to match mzmine requirements
  rename(`neutral mass` = mass,
         name = `IUPAC name`, #this will be the composition description
         adduct = ion) %>% 
  #select columns
  select(mz, rt, `neutral mass`, name, adduct, formula)
#write formatted and filtered table to file
write_csv(mzmine_export_annotonly, 'export_glycoannotateR.csv')
#print for inspection
print(mzmine_export_annotonly)

##          mz      rt neutral mass                     name     adduct
## 1  243.1237  0.6425    3173.6954 Hex6 DeoxyHex4 Sulfate20 [M-13H]-13
## 2  243.0178  0.9809     244.0253       DeoxyHex1 Sulfate1     [M-H]-
## 3  234.0123  1.3713     470.0400       DeoxyHex2 Sulfate2   [M-2H]-2
## 4  313.9686  6.7659     629.9536       DeoxyHex2 Sulfate4   [M-2H]-2
## 5  386.9985  7.8018     776.0116       DeoxyHex3 Sulfate4   [M-2H]-2
## 6  426.9768  9.8313     855.9684       DeoxyHex3 Sulfate5   [M-2H]-2
## 7  500.0064 11.3063    1002.0263       DeoxyHex4 Sulfate5   [M-2H]-2
## 8  539.9851 13.3453    1081.9831       DeoxyHex4 Sulfate6   [M-2H]-2
## 9  466.9552 13.9708     935.9252       DeoxyHex3 Sulfate6   [M-2H]-2
## 10 579.9630 15.3721    1161.9399       DeoxyHex4 Sulfate7   [M-2H]-2
## 11 692.9713 17.5747    1387.9546       DeoxyHex5 Sulfate8   [M-2H]-2
## 12 766.0006 18.4773    1534.0125       DeoxyHex6 Sulfate8   [M-2H]-2
##           formula
## 1  C60H102O107S20
## 2        C6H12O8S
## 3     C12H22O15S2
## 4     C12H22O21S4
## 5     C18H32O25S4
## 6     C18H32O28S5
## 7     C24H42O32S5
## 8     C24H42O35S6
## 9     C18H32O31S6
## 10    C24H42O38S7
## 11    C30H52O45S8
## 12    C36H62O49S8

Filter features in mzMine to annotations

The exported csv can then be used as a ‘database’ for targeted feature detection in mzMine. This can be done by selecting ‘Feature detection –> LC-MS –> Targeted feature detection’ in mzMine. The file exported from R should be selected as the ‘Database file’. The following columns should be checked: neutral mass, mz, rt, formula, adduct, and name (and then press OK). This will create a new feature list in mzMine with only the annotated features, and will show the annotated compositions under ‘Compound DB’.

MS/MS spectra can be associated (Feature list methods –> Processing –> Assign MS2 features) to features, exported, and annotated in a similar way as the MS1 features.