CorMID

CorMID is an R-package providing functions to solve problems during metabolic flux analysis using HR-APCI-MS.

In metabolic flux experiments tracer molecules (often glucose containing labelled carbon) are incorporated in compounds measured using mass spectrometry. The mass isotopologue distributions (MIDs) of these compounds needs to be corrected for natural abundance of labelled carbon and other effects, which are specific on the compound and ionization technique applied. This package provides functions to correct such effects in high resolution gas chromatography atmospheric pressure chemical ionization mass spectrometry (GC-HR-APCI-MS) analyses.

Installation

You can install the development version of CorMID from GitHub with:

# install.packages("devtools")
devtools::install_github("janlisec/CorMID")

or install the version from CRAN instead.

Quick Example

CorMID is supposed to disentangle a complex MID. Complex means that the ion intensities of the isotopes are influenced by natural abundance, artificial labeling (e.g. by a ¹³C-Glucose tracer) and mass spectrometry artifacts (i.e. several potential adducts).

You can create and visualize such a complex mass spectrum by providing a chemical formula, the true MID and an adduct distribution like follows:

# a chemical formula, here: Lactic acid 2 TMS
fml <- "C9H22O3Si2"

# the true mass isotopologue distribution, here: 10% U13C enriched
mid <- c(0.9, 0, 0, 0.1)

# adduct distribution, here: 3 different APCI adducts formed
r <- list("M+H" = 0.8, "M-H" = 0.1, "M+H2O-CH4" = 0.1)

# reconstruct the measured intensity vector
rMID <- CorMID::recMID(mid = mid, r = r, fml = fml)
round(rMID, 3)
#>   M-2   M-1   M+0   M+1   M+2   M+3   M+4   M+5 
#> 0.069 0.014 0.558 0.121 0.121 0.085 0.018 0.014 
#> attr(,"class")
#> [1] "recMID"

CorMID provides a class specific plotting function for such a reconstructed MID:

plot(rMID, ylim=c(0,0.6))
mtext(text = "Reconstructed MID", side = 3, line = -1.25, adj = 0.98, outer = T)
text(x = 3, y = 0.4, labels = "[M+H]+", pos=2)

Assuming that you have measured these intensities in your experiment, the main function of CorMID can estimate the underlying MID and r for you:

# disentangle the adduct ratios and true isotopologue distribution (enrichment) from the above test data
out <- CorMID::CorMID(int = rMID, fml = fml)
print(out)
#> [class] 'CorMID'
#> MID [%] (estimated)
#>     M0    M1    M2    M3
#>  89.06 00.00 00.78 10.16
#> [attr] 'r' (estimated)
#>   M+H   M+   M-H   M+H2O-CH4
#>  0.81 0.00  0.10        0.09
#> [attr] 'err'
#> 0.003167

Please note: no information regarding the true labeling status and the adduct distribution was provided in the above function call. CorMID is able to guess the most likely combination.

This allows you to perform the correction for natural abundance and technical artifacts in a single step and extract the relevant labeling status for flux analysis or other statistical evaluations.

Detailed documentation

You might either read the Vignette describing the package functions in detail or read the publication which shows a evaluation of the performance of CorMID on real data sets.