Last update on 2021/01/06
An R package to fish out isotopically labeled analytes from single, dual or multiple isotope labeling experiment.
Features
(1) Fully automated: Miso uses xcms object as input, and automatically processes the data by taking advantage of the phenoData from xcms object.
(2) Flexible: It enables detecting molecules labeled with various biologically relevant stable isotopes such as hydrogen (2H), carbon (13C), oxygen (18O), nitrogen (15N) and sulfur (34S). It can be applied to single, dual and multiple isotope labeling experiments without modifing any scripts. In addition, different algorithms are used to process the data with and without replicates.
(3) Efficient: fast
(4) User-friendly: Easy to use. Three different outputs are provided. (i): a full data table list, which includes a full list of isotopologues; (ii) a reduced data table list, in which only the base peaks of each isotopologue are included; (iii) the interactive plot, which allows the user to visually check the result.
Figure 1 describes single, dual and multiple stable isotope labeling experiments. Detailed explainations regarding the experiment design can be found in (Liron et al., 2009; Liron et al., 2018; Dong et al., 2019)
Figure 1. Experimental set up for single, dual and multiple stable isotope labeling experiments
The example data (Data/lcms.rda) provided in this example is from a dual labeling experiment. It consists of 5 groups: Group A (SP medium, control group), Group B (0.5 mg mL−1 unlabeled tyrosine), Group C (0.5 mg mL−1 tyrosine D4, Label I), Group D (0.5 mg mL−1 tyrosine 13C915N1, Label II), and Group E (0.5 mg mL−1 Label I + Label II).
The Groups B, C and D are shown in Figure 1. More description of this dataset can be found in (Liron et al., 2018).
Attention: This tutorial is for Miso package version 0.2.0 and above, which is slightly different from the version 0.1.5 described in the publication (Dong et al., 2019). The old versions and related turotials can be found here.
Attention: Miso has been removed from CRAN due to its dependency package mzR was removed from CRAN. "install.packages("Miso")" will therefore not work now.
devtools::install_github("YonghuiDong/Miso")library(Miso)This step selects all the possible labeled m/z peaks by comparing the MS signals among unlabeled and two differently labeled equivalent sample groups. This step could largely eliminate false positives and improve the overall data analysis time.
Set reps = FALSE if your sample groups do not contain any replicates or you think the variations among the replicates are too large. It will use a different algorithm to process the data.
Here we use Group B, C and D for our data analysis, and B is the unlabeled group.
explist <- prefilter(lcms, subgroup = c("B", "C", "D"), unlabel = "B", reps = TRUE, p = 0.05, folds = 10)Next, Miso searches for the isotopologue sets according to the defined labeling patterns.
(3.1) Group C was fed with four H2 labeled tyrosine (Figure 1). Here we are interested in detecting molecules labeled with 4, 3 or 2 H2 (n11 = 4, n12 = 2).
exp.B <- explist$B
exp.C <- explist$C
exp.D <- explist$D
iso.C <- diso(iso1 = 'H2', n11 = 4, n12 = 2, exp.base = exp.B, exp.iso = exp.C, ppm = 10, rt.dif = 6)(3.2) Group D was fed with nine C13 and one N15 labeled tyrosine (Figure 1) Here we are interested in detecting molecules labeled with 9, 8, 7 or 6 C13, and 1 or 0 N15 (n11 = 9, n12 = 6 for C13, and n21 = 1, n22 = 0 for N15).
iso.D <- diso(iso1 = 'C13', n11 = 9, n12 = 6, iso2 = 'N15', n21 = 1, n22 = 0,
exp.base = iso.C[, 1:3], exp.iso = exp.D, ppm = 10, rt.dif = 6)Three types of results are provided. A Full list, a reduced list which contains only the base peaks of all the isotopelogues and the interactive plot.
4.1) Full list
full_Result <- Fresult(iso.C, iso.D)(4.2) Reduced list
reduced_Result <- Rresult(full_Result)(4.3) Plot the result
## view the first row of Full_result
isoplot(full_Result, 1)The overall workflow takes approximately 2.2 min using a PC with 16 GB memory and a 3.1 GHz Intel Core i7 processor.
Example of the result (from reduced list) is shown in Figure 2.
Figure 2. Example of the output table.
- R memory limit error may appear during data processing especially for high resolution dataset.
Error: memory exhausted (limit reached?), Error during wrapup: memory exhausted (limit reached?)
This error is due to the following script:
## In group C, we are looking for analytes labeled with 5, 4, or 3 deuteriums (H2).
iso.C <- diso(iso1 = 'H2', n11 = 4, n12 = 2, exp.base = exp.B, exp.iso = exp.C, ppm = 10, rt.dif = 6)To solve this memory limit problem, the above script can be decomposed into 3 sub-scripts, which respectively search for analytes labled with 4, 3, and 2 deuteriums (H2).
iso.C5 <- diso(iso1 = 'H2', n11 = 4, n12 = 4, exp.base = exp.B, exp.iso = exp.C)
iso.C4 <- diso(iso1 = 'H2', n11 = 3, n12 = 3, exp.base = exp.B, exp.iso = exp.C)
iso.C3 <- diso(iso1 = 'H2', n11 = 2, n12 = 2, exp.base = exp.B, exp.iso = exp.C)
## The results are then combined as iso.C:
iso.C <- rbind(iso.C5, iso.C4, iso.C3)The decomposition step is only usually necessasy for iso.C, as the result list has been significantly reduced. we do not have to do it again for iso.D.
- Polymer precursor-derived metabolite.
There are cases that a precursor-derived metabolite could be the modification of the dimer, trimer or even polymer of this precursor.
For instance, a metabolite could be [2M+H]+, where M is the precursor. if the mass difference of between labeled and unlabeld precursor is 10 (suppose 10 13C). Then the mass difference between the labeled and unlabeled metabolite will be 20 (2 * 10).
The poly parameter in the diso() function is therefore used to fish out such metabolites. poly = 2 means to look for dimer precursor-derived metabolites; poly = 3 for trimer derived metabolites, and so on.
An example is give below:
iso.C2 <- diso(iso1 = 'H2', n11 = 4, n12 = 2, exp.base = exp.B, exp.iso = exp.C, ppm = 10, rt.dif = 6, poly =2)
iso.D2 <- diso(iso1 = 'C13', n11 = 9, n12 = 6, iso2 = 'N15', n21 = 1, n22 = 0,
exp.base = iso.C2[, 1:3], exp.iso = exp.D, ppm = 10, rt.dif = 6, poly = 2)Although Miso was originally developed for tracer-based stable isotope labeling experiments, it can also be easily adapted for global stable isotope labeling.
Here I show you one example. The data is from geoRge R package
#(1) load packages. Example data is from R package geoRge
library(Miso)
library(geoRge)
#(2) first filtering
explist <- prefilter(mtbls213,
subgroup = c("CELL_Glc12_25mM_Normo", "CELL_Glc13_25mM_Normo"),
unlabel = "CELL_Glc12_25mM_Normo",
reps = TRUE,
p = 0.05,
folds = 10)
exp.B <- explist$CELL_Glc12_25mM_Normo
exp.C <- explist$CELL_Glc13_25mM_Normo
#(3) second filtering. Searching for 13C labeled isotopes
## the script is not optimized, it takes about 6 min
df = NULL
for (i in 1: dim(exp.C)[1]) {
exp.iso <- exp.C[i,]
n11 <- floor(exp.iso$mz/13)
iso.C <- diso(iso1 = 'C13', n11 = n11, n12 = 3, exp.base = exp.B, exp.iso = exp.iso, ppm = 10, rt.dif = 10)
df <- rbind.data.frame(df, iso.C)
}
## df is the complete result.
## we can also get a reduced list by removing some duplicated results
reduced_Result <- Rresult(df)If you find Miso useful, please consider citing our work :)
[1] L Feldberg, Y Dong, U Heinig, I Rogachev, A Aharoni (2018). DLEMMA-MS-imaging for identification of spatially localized metabolites and metabolic network map reconstruction. Analytical chemistry 90 (17), 10231-10238.
[2] Y Dong, L Feldberg, A Aharoni (2019). Miso: An R Package for Multiple Isotope Labeling Assisted Metabolomics Data Analysis. Bioinformatics, btz092.