ElemCor is a software tool to correct LC-MS data in isotope labeling experiments. Due to the contribution of naturally occurring isotopes and isotopic impurity from the tracer, the fractional abundances of measured isotopologues (FAM) collected from the instrument must be corrected to obtain mass distribution vectors (MDV) that correspond to the contribution from isotopic labeling. ElemCor is based on the correction matrix (Ref. 1) and uses two different approaches to account for resolution effect, mass different theory (Ref. 2), and unlabeled samples (Ref. 3). ElemCor corrects and improves the numerical schemes of both methods (Ref. 4).
ElemCor is built using MATLAB Compiler. Note that building the software is not required as compiled files are also included in the package. It is recommended to directly install the software using compiled files (Section 3) unless changes need to be made to the source code. Before building the software, one needs to be sure MATLAB Compiler is properly installed. This can be done by typing “ver” at the MATLAB prompt. To build the software, simply click on the project file “elemcor.prj”. All other files required to run the software are automatically identified if they are placed under the same directory as the project file (Fig. 1). After building the software, one should be able to obtain two files "MyAppInstaller_web.exe" and "ElemCor.exe". The former is the installation file that installs MATLAB Compiler Runtime, and the latter is the binary file to run the software.
To run the software, one needs to install MATLAB Compiler Runtime, which is similar to Java Runtime and included in the package. Double-clicking "MyAppInstaller_web.exe" will initialize the installation. After it is complete, one can use ElemCor as an executable by double-clicking "ElemCor.exe" from the download folder (not the installation directory). The installation requires network connection, but after that the software tool can be used offline. MATLAB installation is not required to run the software.
The package includes example data files from labeled samples "test_labelN_sim.xlsx" and "test_labelS_sim.xlsx", and the associated files from unlabeled samples are "test_unlabelN_sim.xlsx" and "test_unlabelS_sim.xlsx". They are simulated from XCalibur with N and S as trace elements, and at nominal instrument resolutions of 140,000 and 280,000, respectively. Both are from Orbitrap analyzer. The files show standard format of input files. Each column vector correspond to FAM of all compounds in each sample.
The software tool takes data in a spreadsheet (XLSX) format. In Steps 1 and 2, labeled and unlabeled data are loaded. Step 2 is optional, and when it is not performed, ElemCor runs based on mass difference theory only. In Step 3, isotopic purity of nutrient and nominal instrument resolution are specified. In Step 4, the tracer element is selected. In addition to 13C, 2H, and 15N, ElemCor allows 18O and 34S as the tracer element for correction. PLEASE MAKE SURE THE CORRECT TRACER IS SELECTED! In Step 5, the mass analyzer is selected. Then the loaded data are analyzed and isotopic enrichment is calculated for each compound in Step 6. After correction, the MDVs of corresponding samples are shown in the figure window in ElemCor and also stored in a different sheet in the same input file. Users can select a cell in the data table, and FAM and MDV for the corresponding compound and sample before and after correction are shown in the figure above.
- Where are the final results?
The final results are previewed in the graphic interface. They are automatically saved into additional sheets in the original XLSX file. The final results include, MDV, enrichment, and fold change of pool size for different compounds and samples.
- Can the FAM have a different length than one plus the number of tracer atoms?
FAM typically has a length of Nt + 1 to cover M+0, M+1, ..., M+Nt isotopologues. Here Nt stands for the number of tracer atoms. But ElemCor is able to handle FAM that has a length other than Nt + 1, and the answer is Yes.
- What is the advantage of ElemCor over IsoCor?
IsoCor performs correction based on combinatorics without considering ressolution effect. When instrument resolution is extremely low, ElemCor is identical to IsoCor. When instrument resolution is extremely high, ElemCor performs no correction at all. Therefore, the amount of correction perform by ElemCor is monotonically dependent on instrument resolution.
- What is the expected processing time?
The example files shown in Fig. 2 can be processed within 10 seconds on a 2.6GHz Core i7 processor, and 20 seconds on a 1.5GHz Core i5 processor. If the results do not come out within a minute, please check whether the correct tracer is selected, as the warning message suggests. If the 15N data are loaded, but C or S is selected as tracer, then the software tool would not run properly. This is because some compounds may not have C or S in their formula, and the algorithm will report error if the tracer element is not present in the compound.
The current version only prints a general error message. The follow steps usually help.
- Check if correct tracer element is selected.
- Check if the xlsx file is within the same directory as the executable.
- Check if the labeled and unlabeled files have the same analytes and the same number of rows.
ElemCor was developed in MATLAB, and packaged with graphic interface under MATLAB for distribution. If you are interested in incorporating source code into your current metabolic flux analysis pipeline, we will be glad to collaborate and share our MATLAB functions.
- Millard, P. et al. IsoCor: correcting MS data in isotope labeling experiments. Bioinformatics 2012;28:1294-1296
- Su, X. et al. Metabolite Spectral Accuracy on Orbitraps. Analytical Chemistry 2017; 89:5940–5948
- Trefely, S. et al. FluxFix: automatic isotopologue normalization for metabolic tracer analysis. BMC Bioinformatics 2016; 17:485
- Du, D. et al. ElemCor: accurate data analysis and enrichment calculation for high-resolution LC-MS stable isotope labeling experiments. BMC Bioinformatics, 2019; 20:89
Di Du dudthu06@gmail.com
Xiaoyang Su xs137@rwjms.rutgers.edu