Isotopic ratio outlier analysis

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Isotopic ratio outlier analysis (IROA) is a simple and direct LC-MS metabolic profiling technique that uses 95% and 5% 13C to label biochemical metabolites resulting in unique signatures (see figure below). LC-MS and software algorithms are then used find, identify and quantitate these specific signatures.  IROA is used to detect metabolites, even at low concentrations in any sample and can be applied to many life science applications (toxicology, diagnostics, drug development, bioprocess, etc.).

Why use 95% and 5% 13C labeled metabolites?  [edit]

Traditionally, stable isotope labeling analyses use molecules labeled at 99% 13C. IROA utilizes both 5% 13C and 95%13C and compounds are randomly and universally labeled to achieve this effect. The relative abundances of the isotopes of carbon are altered; i.e. enriched in one particular isotope and depleted in its other isotopic form. For example, 95% U-13C6-Glucose is not actually 95% 13C6 and 5% 12C6. In this case, each carbon atom position (1,2,3,4,5,6) has a 95% chance of being 13C labeled and a 5% chance of being 12C.

Benefits of 95% and 5% 13C labeling[edit]

Cost effective simultaneous metabolite identification, quantitation and platform QC.

Unique IROA patterns discriminate peaks of biological origin from artefactual peaks allowing the removal of natural abundance non-biological artefacts and noise (false data).

Facilitates detections of low intensity features - at natural abundance more than one isotopic peak is often not detectable and excluded as noise.

Removes variances - as a composite sample, sample-to-sample and analytical variance is removed and during MS analysis the identical compounds (unlabeled or labeled with either U-95% 13C or U-5% 13C) experience the same ionization efficiency and suppression.

Triply redundant identification – the number of carbons for each metabolite is determined by both the height of the M+1 and M-1 peaks and the distance between the monoisotopic peaks; carbon number and mass together greatly restrict the number of possible molecular formulae.

Accurate compound formula ID for MS alone; complete ID with addition of SWATH, or IM, even at low concentrations.

Mathematically calculable - enables computational analysis; ClusterFinder software solution builds libraries, IDs/quantitates compounds and normalizes data.

All IROA-based fragments have the IROA ratio pattern of their parent peaks and can be identified as such using the “peak correlation” ClusterFinder module.


Because all molecules (biochemicals, proteins, RNA and DNA) are labeled by the IROA protocol it can be used in all 'omic sciences, however biochemical profiling or metabolomics is an especially useful case. The natural abundance of carbon is approximately 98.9% 12
and 1.1% 13
. Because of this, during mass spectrometric analysis carbon-based molecules have both a monoisotopic peak and a second peak, the "M+1" peak, that is caused by the presence of the isotopes of not only 13
, but also 17
, 15
, 2
and others. The key to IROA is that analysis is done using a specific mixture of 12
and 13
. One isotope is present at approximately 95% and the other at 5%. This the concentrated isotope's corresponding peak dominates the dilute isotope.[1]


A homogeneous cell population is divided into equal-sized "experimental" and "control" groups. The biological compounds in these groups are labeled using an isotopically defined growth media in which all of the carbon components in the experimental and control samples are replaced with randomly and universally enriched 5% or 95%13
, respectively. After at least 5 subsequent cell divisions, the experiment group is exposed to a stressor (chemical, genetic, environmental, etc.) When the experiment has concluded, the experimental sample is mixed with the control sample and analyzed using mass spectrometry. The admixing of samples increases data quality as sample-to-sample variance is reduced and the identification of all biological compounds is enhanced.

ClusterFinder software fully automates the processing of IROA-based data files to identify and quantify all compounds of biological origin and to remove artifacts.

Basic mode[edit]

Basic IROA is fundamentally an unbiased analysis. This leads to clean, high resolution data sets that clearly define the biological response of a biological system. This workflow utilizes both 95%- and 5%-C13 signatures. Like Stable Isotope Labeling by/with Amino acids in Cell culture (SILAC), cells are differentially labeled by growing them in heavy medium (medium in which all carbon sources are labeled with 95% C13) or light medium (medium in which all carbon sources are labeled with 5% C13). When the cells are growing in this medium, they incorporate the heavy or light carbon into all of their metabolites. The Control and Experimental sample media are chemically identical but isotopically different, and importantly, the isotopes are universally and randomly incorporated into all carbon positions. The Control (heavy medium) and the Experimental (light medium) cell samples are grown in the IROA isotopically-defined media for a sufficient time to replace their original natural abundance carbon so that all of their contents will demonstrate distinctive isotopic patterns. Once labeled, the Experimental sample is treated with a stressing regimen, and the Control sample is treated with only vehicle (e.g. if drug & DMSO vehicle is added to the Experimental sample, DMSO should be added to the Control sample for the same time period). The stressing agent may be chemical (e.g. toxin), genetic (e.g. mutant), environmental (e.g. UV exposure), or any element or combination of elements that induce physiological alteration. Experimental samples are individually pooled with Control samples, and the resulting composite samples analyzed using LC-MS. This has the effect of reducing sample-to-sample analytical variance, and increasing data quality. In the Basic IROA protocol, Control sample metabolic pools are fully labeled with U-95%13C, and Experimental sample metabolic pools labeled with U-5%13C. Control and Experimental metabolite peaks are easily identified according to the presence of characteristic M-1 and M-1 IROA peak patterns, respectively. When pooled cells are processed, the signals for the compounds from both the Control and Experimental cells may be distinguished and differences between the ratio of their areas are directly indicative of the ratio of the respective sizes of their metabolic pools. Outliers to the normalized ratios are metabolic pools that are impacted by the experimental treatment.

Phenotypic mode[edit]

The IROA Phenotypic workflow utilizes only 95%- C13 signatures. There are many cases where it is not practical or possible to label the Experimental sample, for example, when working with tissue samples, performing large fermentation runs, or propagating field-grown plants. In these instances, the IROA “Phenotypic” experiment may be applied. In the Phenotypic Protocol the Control or complex "Internal Standard" is isotopically labeled with IROA 95% U-13C media (media in which all the carbon sources are randomly and universally composed of approximately 95%12C with 5%13C) and used to spike into Experimental samples.

The unique IROA labeling pattern again ensures that the monoisotopic peaks and the carbon envelope of the associated isotopic peaks (M-1 etc.) can be detected during LC-MS. The carbon envelope differentiates the IROA-IS from natural abundance peaks (see figure below) and is used to identify compounds of interest and exclude artifacts the may look otherwise similar.

The IROA Phenotypic Experiment. (a) The material to be phenotyped i.e. biopsy tissue, plant material, cells, etc. is mixed with the labeled 13C IROA- Internal Standard. An Ideal Standard is one that represents the entire metabolome of the fluid or tissue under study. For example, if interrogating the metabolome in pancreatic tissue, quantification of metabolites can be achieved using an IROA-grown/labeled pancreatic cell line as a complex Internal Standard. The IROA ClusterFinder software will find all IROA-labeled compounds and use these peaks to locate and identify the associated paired natural abundance peaks in the Experimental samples. This allows for a complex targeted analysis. (b) Using an IROA-labeled Standard, all of peaks may be easily identified according to presence of their characteristic M-1 peak. The natural abundance peaks corresponding to each Standard peak may be readily identified because even though they do not carry any isotopic labeling, their exact mass and position are established relative to the Standard. (c) If the pooled Standard is well characterized and the compounds that are present in it have already been identified, then it will be able to be used as a point of comparison for all of its contained compounds. Artifacts will have no match in the Standard and are eliminated by ClusterFinder in a final dataset. In a typical experimental IROA dataset the ratio of the peak areas represent the relative deviation of the metabolic pool sizes brought about by the experimental condition. In a Phenotypic experiment, all experimental samples are measured relative to the Standard; therefore, the phenotype is defined by the deviation from the Standard. (d) The Phenotypic IROA signal is made up of two halves. The C13 side tells the ClusterFinder software where to find the corresponding 12C peak. The C12 side is unlabeled and has only natural abundance carbon. The pairing makes the identity of all peaks measured unambiguous.


The basic IROA protocol relies on the creation of isotopic patterns by growing cells on media wherein all the carbon sources contain defined isotopic balances. With reasonable resolution, the mass and the number of carbons will unambiguously indicate a formula for monoisotopic peak. Since this is true for both the 12
and 13
peak sets, then either peak carries all of the information needed to find the other (and the formula), the pair of peaks provides a triply redundant mechanism to identify all compounds of biological origin. Since non-biologically derived compounds will never have IROA patterns (like H-NA), all artifactual peaks may be identified and removed from consideration. These characteristics greatly simplify and strengthen the quality for the interpretation of a mass spectrum of a biological sample.[2]

ClusterFinder software[edit]

IROA software algorithms were created based on the fact that IROA peaks are mathematically calculable. Each set of carbon isotopomers (12
and 13
) reliably and accurately account for the other set, providing a redundant quality control check. The ClusterFinder software achieves a data reduction of complex raw data to concise, high value information by performing these steps: characterization of peaks according to source, artifactual-no label, 12
experimental, or 13
control, removal of artifacts, alignment and pairing of remaining peaks across scans, pair normalization and identification, and determination of the relative 12
ratios of analytes in each sample.


The IROA approach is applicable to measuring an organism's biological response to any stressor, such as disease, environment, drugs or toxins. As an example IROA was used to label biosynthetic pathways, specifically outlining the glutathione pathway, during the fermentation process of Saccharomyces cerevisiae (S. cerevisiae). S. cerevisiae was profiled and measured using liquid chromatography/mass spectrometry.[3]


  1. "Isotopic Ratio Outlier Analysis (IROA) coupled with the Bruker maXis 4G QTOF to investigate changes in the secondary metabolite profiles of Myxobacteria". Bruker Daltonics. Retrieved 2 April 2014.
  2. de Jong, Felice A; Beecher, Chris (2012). "Addressing the current bottlenecks of metabolomics: Isotopic Ratio Outlier Analysis, an isotopic-labeling technique for accurate biochemical profiling". Bioanalysis. 4 (18): 2303–2314. doi:10.4155/bio.12.202. ISSN 1757-6180. PMC 3696345. PMID 23046270.
  3. de Jong, Felice A (2012). "Addressing the current bottlenecks of metabolomics: Isotopic Ratio Outlier Analysis™, an isotopic-labeling technique for accurate biochemical profiling". Bioanalysis. 4 (18): 2303–2314. doi:10.4155/BIO.12.202. PMC 3696345. PMID 23046270.

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