ASMS 2019

 [1] IROA Technologies LLC, Chapel Hill, NC, USA

Targeted metabolic analyses are very useful and generally more quantitative than non-targeted analyses, but often miss the appropriate compounds. Non-targeted analyses theoretically avoid this problem but their ability to correctly identify peaks and quantify them is a major challenge. The TruQuant protocol was developed as a targeted analysis
for hundreds of compounds in a biochemically-complex Internal Standard (IROA-IS) that was quantitatively enhanced by providing a mechanism for the correction of ion-suppression, and a very advanced sample-to sample normalization. This report concerns the identification of hundreds of additional authenticated peaks that can also be normalized even though they will not be associated with an internal standard.

Using IROA- based Internal Standard Normalization to minimize non-IROA metabolite variation

Chris Beecher1, Felice de Jong1
 [1] IROA Technologies LLC, Chapel Hill, NC, USA
The IROA TruQuant IQQ (TQ) protocol was designed to enhance quantitation, identification, and provide a daily QA/QC. It is based on the use of two Standards, the TQ-LTRS (Long-Term Reference Standard), and a comparable Internal Standard, the TQ-IS. We have previously demonstrated that this system can be used to correct for suppression, and generate a universal normalization factor. In this poster we extend the use of the normalization factor to compounds which do not have acomparable internal standard.


Lucas Veillon1, John N. Weinstein1, Philip L. Lorenzi1, Felice de Jong2, Chris Beecher2
[1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA, [2] IROA Technologies LLC, Chapel Hill, NC, USA
The degree of ion suppression observed for a variety of classes of metabolites over a range of aliquot concentrations was determined and suppression-corrected values were calculated using the IROA TQ-IS and its associated LTRS.
• MS signal suppression was investigated using HILIC and RPC chromatographic methods in both positive and negative ioniziation modes.
• Suppression-corrected values were calculated for both the C12 and C13-enriched peaks for every standard compound detected; generally 500 compounds were observed for HILIC and RPC.
• Almost all compounds exhibit some level of suppression and compounds are suppressed to different extents between HILIC and RPC-MS.


Fei Tang1, Felice de Jong2, Chris Beecher2. Markos Leggas1
[1] Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, Lexington, KY, USA, [2] IROA Technologies LLC, Chapel Hill, NC, USA
Experimental LC-MS runs, which extend for several days, may have significant source variance coming from sensitivity, in-source fragmentation, transport efficiency, etc. This variation makes it difficult to analyze and quantitate data from very large experiments. To address this issue, several strategies have been developed to “normalize” the datasets as a function of time, but there is no universally accepted method.
In this presentation we will discuss a novel scheme that allows for data normalization even after significant source changes. The system relies on the use of a biochemically complex IROA Internal Standard (IS), and a closely related IROA Long-Term Reference Sample (LTRS).


IROA approach enabling detection of metabolites whose production is initiated or ceased in response to treatment

Amy L. Lane1, Felice de Jong2, Chris Beecher2

[1] University of North Florida, Jacksonville, FL, USA, [2] IROA Technologies LLC, Bolton, MA, USA
In previous IROA metabolomics protocols, treatment and control organisms were grown in medias containing uniformly-labeled carbon sources with different 12C:13C ratios (95:5 vs. 5:95), pooled prior to chemical extraction and LC/MS. Analytes below the limit of detection for treatment or control group evaded recognition, since they did not form a pair of corresponding 12C- and 13C-enriched MS peaks. This limits the utility of established IROA protocols for natural product studies, where molecules and their biosynthetic pathways are commonly identified by comparing metabolite profiles between natural product producing and non-producing organisms (e.g. wild type vs. gene elimination mutant organisms). Here we devised an IROA protocol enabling the detection of metabolites whose production is initiated or halted in response to experimental conditions.

Metabolomics Society 2018

Correction of Ion Suppression and Normalization for Improved Quantitative Rigor and Reproducibility using IROA

Philip L. Lorenzi1, Marc Warmoes1, Lucas Veillon1, John N. Weinstein1, Felice de Jong2, and Chris Beecher2
[1] MD Anderson Cancer Center, Houston, TX, USA, [2] IROA Technologies LLC, Bolton, MA, USA
Ion suppression and ion enhancement are well-known phenomena in mass spectrometry, and spiking stable isotope-labeled internal standards into a sample at known concentrations is generally thought to represent the only method for dealing with the problem. Here we show that a chemically complex Internal Standard (IROA-IS) containing hundreds of stable isotope-labeled metabolites can correct for loss or gain of corresponding targeted metabolite signals during sample preparation and data acquisition, yielding more accurate measurements. Notably, we demonstrate that the strategy is effective over a wide range of IS:analyte ratios. The resulting, suppression-corrected measurements, in most cases, correlated strongly (r2 > 0.98) with the concentration of each metabolite. Furthermore, from the ratio both suppression-corrected values and sample normalization may be calculated, correcting for variation introduced by sample preparation and analytical sources of error. In summary, incorporation of a chemically complex IS into targeted and non-targeted metabolomic workflows provides a viable strategy for increasing quantitative rigor and reproducibility.

American Society for Mass Spectrometry (ASMS) Annual Conference 2018

Collin Wetzel1; Kelly N. Ennis2; Brian Johnson2; Nicholas J. Talbot2; David R. Plas2; Chris Beecher3; Felice A. de Jong3
[1] University of Cincinnati, Cincinnati, OH; [2] University of Cincinnati, Department of Cancer Biology, Cincinnati, OH; [3] IROA Technologies, Bolton, MA
LC-MS based metabolomics is the current state technology revolutionizing the field of cancer metabolism. However, data analysis is a significant bottle neck in most metabolomics work-flows where interpretation of features is confounded by instrumental disturbances, background ions, sample prep variation and chromatographic anomalies.  These challenges can be even more pronounced in samples derived from humans due to increased biological variability.  Isotope Ratio Outlier Analysis (IROA) has the potential to overcome these technical limitations using both isotope labeled internal standards and software.  Here, we develop growth conditions suitable for extending the IROA methodology to the analysis of solid tissues.


Vanessa Y. Rubio1 , Jaime Guavara 2 , Clive H. Wasserfall 3 , Chris Beecher 4 , Richard A. Yost 1 , Timothy J. Garrett 3
1Department of Chemistry, University of Florida, Gainesville, FL 32611 2Universidad San Francisco de Quito, Quito, Ecuador
3Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL, 32611
Traditional LC MS metabolomic workflows for biomarker detection typically rely on untargeted global analysis of metabolites with secondary validation of significant biomarkers using a separate targeted assay to quantify and improve measurement reliability. Under this workflow, additional targeted analyses are often limited to a few metabolites due to the restricted availability of standards and the use of additional sample material. Here, we describe the generation of isotopically labeled
yeast extracts for the identification and quantitation of thirty small metabolites in serum/plasma. The 5 or 95 U 13 C extracts were used to generate calibration curves and complex internal standards (respectively, to locate, validate, and quantify biochemical equivalents in plasma samples of subjects with the CDKN 1 C gain of function mutation expressing insulin inefficiency.
Jan Hazebroek1, Chris Vlahakis1, Felice de Jong2, Chris Beecher2
[1] DowDuPont,  Johnston, Iowa; [2] IROA Technologies LLC, Ann Arbor, MI
We evaluated Isotope Ratio Outlier Analysis (IROA) as a metabolome-wide internal standard approach to improve the quality of LC/MS data.  A large-scale greenhouse experiment was designed to metric the ability of metabolomics to model quantitatively nitrogen treatments. We compared IROA processed data with that generated without the benefit of metabolome-wide internal standards using our current tool, Genedata Expressionist, from the same raw LC/MS data files. Corn plants were treated from germination on with varying concentrations of nutrient nitrate. Metabolomics analysis of leaves was performed by LC/MS positive and negative electrospray ionization modes, and raw data were processed with both our routine Genedata software and IROA protocols.  Genedata analysis without IROA yielded 281 metabolites in positive ionization mode and 172 in negative ionization mode.  IROA data analysis detected 184 metabolites in each ionization mode. We demonstrate that the IROA protocol improve predictive modeling of nitrogen treatment.  In addition, IROA corrected for detector saturation for several high abundant peaks.
Reduction of interferences for IROA pattern detection by use of SelexION ion mobility at unit dalton resolution

Yunping Qiu1, Felice de Jong2, Chris Beecher2, Irwin Kurland1

[1] Department of Medicine, Albert Einstein College of Medicine, New York City, New York, USA, [2] IROA Technologies LLC, Bolton, Massachusetts, USA
Differential mobility spectrometry (DMS) is a valuable tool in compound identification and quantitation.  In DMS, quantification may be done at either the MS or DMS level.  However, it has never been clear that in DMS the isotopomers of compounds will have the same CCS and therefore would always locatable.  In this study, we used Sciex QTRAP 6500+ SelexION DMS to analyze a mixture of amine-containing compounds and, separately, an IROA Matrix sample (95% and 5% 13C-labeled yeast extract) derivatized with either labeled (13C6, 99%) or unlabeled PITC.   SelexION plus IROA proved effective for reducing metabolite interferences and isotope overlap seen in a complex metabolite sample enabling more effective detection and quantitation.


Marjorie Jones*1, Haley Albright*1, Michael Fitch1, Felice de Jong#2, Tim Garrett3, Chris Beecher2,3, John L Hartman IV1
* equal contribution; # presenting author, [1] University of Alabama at Birmingham, Birmingham, Alabama, USA 35294, [2] IROA Technologies LLC, Bolton, Massachusetts, USA, [3] University of Florida, Gainesville, Florida, USA
S. cerevisiae is a genetic model for eukaryotic cellular aging. Yeast chronological lifespan(CLS) is measured as stationary phase survival. Genes, nutrients and environmental factors are all known to influence CLS in yeast and animal models. Succinate dehydrogenase (SDH) is an enzyme complex, encoded by four genes (SDH1-4), which functions in the TCA cycle and electron transport chain. We observed loss of SDH activity to be associated with aging, but to date biochemical mechanisms linking SDH energy metabolism and mitochondrial aging are unclear.  To assess the functional metabolic effects of SDH on chronological aging, herein we applied isotope ratio outlier analysis (IROA) LC-MS metabolomics to compare the biochemical profiles of individual SDH subunit knockout strains in S. cerevisiae.

American Society for Mass Spectrometry (ASMS) Annual Conference 2017

Chris Beecher1,2 & Felice A. de Jong2 and Baljit K. Ubhi3
[1] IROA Technologies, Boston, MA, USA, [2] University Florida, Gainesville, FL, USA and [3] SCIEX, Redwood City, CA, USA
Metabolomics focuses on the chemical processes central to cellular metabolism. Mass spectrometry and specifically data dependent workflows tend to be the choice for the measurement of these metabolites. Data independent techniques such as SWATH® Acquisition are different in that they allow for unbiased data collection and MSMS of every single mass precursor can be collected allowing for information rich datasets. However, unambiguous metabolite identification can be increasingly challenging due to the lack of databases, chemical noise and isobaric compounds.
The SWATH analysis of the Isotope Ratio Outlier Analysis (IROA) labeled Internal Standard (IS) provides the first mechanism for simultaneous and unambiguous compound identification and quantitation for unbiased metabolomics analysis.


Just how many unknowns are there in a metabolomics data set? Really?

Chris Beecher1, Alexander Raskind1, Casey Chamberlain2, Joy Guinguab2, Felice de Jong1, Tim Garrett2
[1] IROA Technologies, Ann Arbor, MI, [2] University of Florida, Gainesville, FL
The question of unknowns has become probably the single most common question in metabolomics.  It is generally suggested that there are hundreds or even thousands of unknowns in most datasets and that the number of unknowns is larger than the number of known (named) compounds.  We have constructed a very specific dataset using IROA materials, and performed an in-depth LC-MS analysis of the peaks in these samples.  Because of the IROA patterning we can easily separate peaks of biological origin from artifacts, and using a specially written program we are annotating all of the biological peaks.  The majority of unknown peaks of biological origins are fragment ions, adduct ions, or in-source polymeric ions. There appear to be few true unknowns.


Amy L. Lane1. Elle D. James1, Felice de Jong2, Chris Beecher2
[1] University of North Florida, Jacksonville, FL, [2] IROA Technologies, Bolton, MA
The Nocardiopsis species actinomycete bacteria harbor biosynthetic pathways for bioactive compounds including toxins, antimicrobial agents, and anticancer substances.  Expression of these pathways is dependent upon the diverse conditions in which actinomycetes prevail.   It was hypothesized that iron limitation may induce Nocardiopsis to upregulate the production of siderophore secondary metabolites (e.g. desferrioxamine or other previously unidentified siderophores), since these types of molecules are known to aid bacteria in sequestering iron.


Improved Metabolomics Analysis using Isotopic Ratio Outlier Analysis (IROA) with Ion Mobility – Mass Spectrometry 

Robin H.J. Kemperman1, Chris W.W. Beecher1,2, Richard A. Yost1

[1] University of Florida, Gainesville, FL, [2] IROA Technologies, Ann Arbor, MI
Isotopic ratio outlier analysis (IROA) is a novel technique for untargeted metabolomics profiling studies using a characteristic isotopic labeling methodology.1 5% and 95% 13C-labeled media are utilized to grow cells and enrich the natural abundance of 13C of all biologically formed metabolites. Characteristic isotopologues are obtained by mass spectrometry for all IROA-labeled biochemical compounds and are easily distinguishable from artifactual noise. IROA is advantageous for metabolite identification, here we demonstrate the use of IMS as an additional separation dimension for improved peak deconvolution and confidence in metabolite identification.


Global Metabolomic Investigation of Tissues from Melanoma Patients with HRMS Using a Yeast Standard for Isotopic Ratio Outlier Analysis (IROA)Taylor Domenick1, Christopher Beecher1, Peter A. Kanetsky2, Richard A. Yost1, Nicholas Taylor3, John Koomen2, Timothy J. Garrett1
[1] Departments of Chemistry and Pathology, University of Florida, Gainesville, FL, [2] H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, [ 3] Texas A&M University, College Station, TX

Untargeted metabolomic and lipidomic analyses of tissue offer insight into the biochemistry of disease and help to potentially discover candidates for development as clinical biomarkers.  Identification of metabolites and lipids that differentiate diseased tissue from adjacent tissue representing the tumor microenvironment can be a major challenge. Isotopic ratio outlier analysis (IROA) using 13C labeling offers a complementary approach to traditional strategies for these studies.  These approaches have been applied to frozen tissue samples from melanoma patients, including primary tumors, metastatic tumors, and adjacent skin, to help identify and evaluate metabolic signatures of human melanoma.  These methods offer new strategies for studying the mechanisms of disease onset and progression in melanoma, the most aggressive form of skin cancer causing over 10,000 deaths per year in the U.S. alone.

Biomarkers for Human Plasma Sample Quality using an IROA-Labeled Universal Internal Standard
Casey A. Chamberlain1, Chris Beecher2, Timothy J. Garrett2
[1] University of Florida, Gainesville, FL, [2] IROA Technologies, Ann Arbor, MI,
The degradation of plasma metabolites is highly-important from both a clinical and research perspective to ensure accurate and thorough analysis of diagnostic and experimental assays; however, little work has been done to fully understand it.  Identifying the degradation products of plasma and other biologically relevant samples is critical as these compounds could serve as biomarkers for sample age and quality. This study seeks to identify changes in the metabolic profile of human plasma resulting from exposure to various laboratory conditions: storage at 4°C and room temperature for 0-24 hours, as well as repeated freeze-thaw cycles at -80°C. This work employs Isotopic Ratio Outlier Analysis (IROA) using a 95% 13C-labeled yeast extract for universal internal standardization, unknown identification, and data simplification.


Daniel Mikel1, Simon J Prosser1, Chris Beecher2
Cost effective, multidimensional GRAS (generally recognized as safe) counterfeit protection of drugs based on uniformily 13C labeled amino acids with compact mass spectrometry detection.

Metabolomics Society 2016

Felice de Jong1, Chris Beecher1,2
[1] IROA Technologies, Ann Arbor, MI, [2] University of Florida, Gainesville, FL
The standard workflow for unbiased metabolomics matches peaks against a library for identification and finds a large number of unknowns since fragments are rarely identified as such. Source variations yield fragmentation variation; therefore, spectral databases are of little use in the face of instrumentation variation, and data impurities stemming from artifacts, noise and ion-suppression. We have developed a general method for associating all fragments, adducts, dimers, etc., that also makes the structure elucidation of unknowns easier. The IROA protocol incorporates stable-isotopes into metabolites, creating stable-isotopic Internal Standards (IS) for each and every metabolite measured so that specific alterations can be accurately measured and quantitated. Biochemically-complex IS are generated using growth media wherein all natural abundance 12C compounds (amino acids, sugars etc.) are replaced with randomly and universally 95% 13C-labelled compounds (95% 13C media) so when populations of cells are incubated in such media, all biological components in the cells, including metabolites, carry unique 13C signatures. When an IS, so created, is added to natural abundance samples and analyzed by MS, each metabolite peak carries a ready identifier of its origin, an enhanced M-1, M-2 etc. for the 95%13C IS and natural abundance M+1 for the 12C (Experimental) sample. Because of the presence of the IS all adducts and fragments may now be correctly identified even within areas of high co-elution, by the characteristics of the IROA peaks without the need of a fragmentation library. ClusterFinder software identifies IS parent ions and their collective fragments, adducts, and provides structural confirmation of metabolites since the masses and ratios between the IS and it’s NA analogues will be a unique determinant for each such cluster. Furthermore, for each fragment the number of carbons present and their monoisotopic masses provide accurate formulae which supports structure elucidation of the parent compound in an unknown.


Chris Beecher1,2 , Tim Garrett2, Rick Yost2, Felice de Jong1,
[1] IROA Technologies, Ann Arbor, MI, [2] University of Florida, Gainesville, FL
All IROA protocols incorporate stable isotopes  into metabolites, creating unique isotopic patterns in all metabolites. The IROA Fluxomic protocol is used to first label every metabolic pool with a 5% 13C isotopic pattern, and then introduces a specific precursor whose flux is to be determined asa 95% to 99% 13C-labeled compound. Since every metabolic pool is labeled with a 5% 13C isotopic pattern the ClusterFinder software can easily identify all metabolic pools, differentiate them from artifacts and noise, and since their exact pattern is known, i.e. it is a 5% 13C isotopic pattern, it can automatically seek any perturbations in the expected 13C isotopic pattern that would indicate flux into that metabolic pool. Unlike other fluxomic approaches this process is completely automated, and examines every metabolic pool without the bias or the need to predetermine it as a target for investigation. We present a series of experiments in which the flux of glucose and glutamine are separately examined as the flux agent, in a HepG2 cell in a time-course experimental system. Our results are compared with similar experiments done using a more traditional approach. In the traditional approach, specific metabolic pools are queried and the label may be targeted to obtain highly specific information often detailing which carbon is transferred (i.e. positionally). In the IROA Fluxomic approach, the total number of carbons derived from the flux agent and transferred into every metabolic pool is easily determined; however positional effects are not available. These two techniques are therefore very complimentary, and increase the ability to understand flux as a function of physiological change. The IROA ClusterFinder software was specifically modified to complete the entire unbiased analysis with no preconceptions. It affords a new and unique fluxomic point of view.

ASMS 2016

Chris Beecher1,3 , Timothy J. Garrett2 , Elizabeth S. Dhummakupt1, Vanessa Y. Rubio1
1 University of Florida, Gainesville, FL; 2 Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, 3 IROA Technologies, Ann Arbor, MI
Mixtures are inevitable. In fact, most metabolomic samples are mixtures of one kind
or another, e.g. most tissue is a collection of different cell-types, most biopsies include both “normal” and disease, blood and plasma are mixtures of bodily functions, a cell population may include cells at different growth states, and even a single cell may represent a mixture of physiological states. Thus, the data we generate, rather than representing a single state, almost always represents the sum of data for all of the underlying tissue or cell types. Most of the time we attempt to solve this problem by trying to acquire tissues as pure as possible, and do not deal with the inherent mixed nature, assuming it will “all come out in the wash”. This study attempts to take a direct approach, namely to factorize the experimental dataset in an attempt to determine what the original mixtures were.
Non-negative Matrix Factorization (NMF) is a factorization technique that analyzes the correlation and partial correlation structure of the entire dataset to determine these underlying factors. For NMF to be successful it is necessary to have a number of samples; each one of which represents a slightly different mixture. It does not need to know the percentages of the mixed ingredients, nor does it have to have a pure species, i.e. any samples that are “unmixed”. This technique has received a great deal of use in image analysis, and text analysis, but has been rarely used in biology. In 2006, Golub pioneered the use of a NMF-based approach to create “Connectivity Maps” of gene expression data to great effect, but it has not yet received much use in metabolomics. In an effort to demonstrate the utility of NMF in metabolomics, this study created a number of artificial, and unknown to the researchers, mixtures of two meats, beef and pork. The question was “could NMF sort the resulting dataset and determine what a metabolomics signature for pure pork or pure beef would look like?”. If it can do this, then when presented with a collection of liver biopsies it can just as easily determine what a pure tumor metabolomics profile looks like even though the biopsies all represent mixtures of tumorous and normal cells. IROA[4] is a collection of protocols that dramatically strengthens the identification and quantification of metabolites. The IROA-based workflow (see below) inserts a mixture of 400+ internal standard compounds into a sample in order to measure 400+ compounds more accurately and reproducibly than otherwise possible. The IROA-based workflow is essentially noise-free, and therefore could generate a dataset of sufficient quality to be sorted by NMF. Here we introduce a novel approach based on the use of IROA to generate very clean complex targeted dataset devoid of artifacts and noise, and NMF to deconvolute mixtures of any kind into their pure component mixture. This is a general algorithm that may be successfully applied in most cases of such problems.


Isotopic Ratio Outlier Analysis (IROA) global metabolome interrogation of an actinomycete bacterium following introduction of a novel pathway

Felice de Jong2, Taylor A. Lundy1, Chris Beecher2,3, Amy L. Lane1
1 University of North Florida, Jacksonville, FL, USA, 2 IROA Technologies LLC, Bolton, MA, USA, 3 University of Florida, Gainsville, FL, USA
Microbial genomes harbor biosynthetic pathways for unknown natural products that are
potentially a rich source for medicinally important molecules. The key is to apply genetic and biochemical approaches to best activate and analyze these bioactive pathways and their products. The goal here was to evaluate global metabolic changes for a model actinomycete bacterium in response to the introduction of the ~55 kb cyanosporaside biosynthetic gene cluster (cyn) into the genome of Streptomyces lividans TK24, an actinomycete widely used as a host for producing small molecules. The addition of the cyanosporaside natural product biosynthetic pathway gives us the ability to interrogate metabolic response and track novel compounds introduced by this biosynthetic pathway. Comparative LC-hrMS profiling and IROA were used to quantify small-molecule readouts in response to the addition of the cyanosporaside pathway.


Metabolomic Analysis of Type 1 Diabetic Primary Cells using Isotopic Ratio Outlier Analysis (IROA) by LC-HRMS

Candice Z. Ulmer 1, Christopher Beecher 2, Timothy J. Garrett 3, Jing Chen 3, Clayton Matthews 3, Richard A. Yost 1,3
1Department of Chemistry, University of Florida, Gainesville, FL; 2IROA Technologies, Ann Arbor, MI; 3Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL

Type 1 Diabetes (T1D) is an incurable, auto-immune disease that results from the destruction of insulin-producing pancreatic beta cells by pathogenic T lymphocytes. These defective T cells can differentiate into CD4+ T cells that correlate with T1D progression. Of the few experimental designs targeted to identifying the metabolic profile of solely T1D, many incorporate animal models that fail to account for pathophysiological differences in humans. There is a need to better understand the metabolic and lipidomic signature of this disease using human samples. This work employs isotopic labeling LC-HRMS methodologies to identify the metabolic and lipidomic trends of immune dysregulation using primary T cells obtained from T1D patients compared to 1st degree relatives and healthy controls.

Structural Elucidation of the Metabolome using Isotopic Ratio Outlier Analysis (IROA) in combination with UHPLC-QTOF and Data-Independent Acquisition

Chris Beecher1; Felice de Jong1; Amrita Cheema2; Tyrone Dowdy2; Giuseppe Astarita3
1IROA Technologies LLC, Ann Arbor, MI, 2Georgetown University, Washington, DC, 3Waters Corporation, Milford, MA
Metabolite identification represents the bottleneck of most metabolomics studies. This is aggravated by the presence of noise signals, impurities due to sample collection and extraction procedures and other non-biological relevant information. Isotopic Ratio Outlier Analysis (IROA)1,2 protocol mitigates several of these commonly encountered sources of variance by using specific isotopic signature. Once the biological relevant analytes have been identified, the characterization of their structure often relies only on accurate mass and isotopic pattern. Here, we propose a metabolomics approach using IROA in combination with UHPLC-QTOF in data-independent acquisition (DIA) mode for a rapid screening of the metabolome and the simultaneously collection of both qualitative and quantitative information of known and unknown metabolites.


Metabolic effect of drought stress during the grain filling growth stage in wheat measured by Isotopic Ratio Outlier Analysis (IROA)

Felice de Jong1, Chris Beecher2, Masum Akond3, John Ericson3, Md Ali Babar3
1IROA Technologies LLC, Bolton, MA, 2University of Florida, Gainsville, FL, Dept of Chemistry, 3University of Florida, Gainsville, FL, Dept of Agronomy

Metabolomic approaches have been documented to have great value in phenotyping and diagnostic analyses in plants1. The IROA® protocol2,3 was applied to determine the biochemical response of wheat metabolomes to water-stress during the grain filling growth stage. SS8641, a high-yield soft-red winter wheat, was grown under well-watered and drought conditions. In this IROA phenotypic analysis, controlled greenhouse-grown leaves containing carbon at natural abundance were compared to Standard wheat leaves that were grown to contain universally-distributed ~97% 13C; namely, a targeted analysis using a biologically-relevant Internal Standard. The IROA patterns allowed the identification of the isotopically labeled peaks and their 12C isotopomers, and the removal of artifacts, noise and extraneous peaks. By pooling experimental and Standard samples, variances introduced during sample-preparation and analysis were controlled.

Untargeted metabolomic analysis of the yeast lipin phophatidate phosphatase deletion using IROA and LC-HRMS

Yu-Hsuan Tsai;1 Timothy J. Garrett;2 Yunping Qiu;3 Robyn Moir;5 Ian Willis;5 Chris Beecher;4 Richard A. Yost;1,2 Irwin Kurland3
1Department of Chemistry, University of Florida, Gainesville, FL; 2Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL; 3 Department of Medicine, Albert Einstein College of  Medicine, Bronx, NY; 4 IROA Technologies, Ann Arbor, Michigan; 5 Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY
Lipins are phosphatidate phosphatases that generate diacylglycerol (DAG) from phosphatidic acid (PA), regulating a pathway key for production of triglycerides (PA->DAG->TAG). Absence of the mammalian lipin results in lipodystrophy, and yeast lipin (Pah1p)controls the formation of cytosolic lipid droplets. Depletion of PAH1 (pah1Δ) has been shown to result in a dramatic decrease in lipid droplet number. Metabolic pathways that might mediate this effect, and possibly have relevance to mammalian lipin-dependent lipodystrophy, were examined using Isotopic Ratio Outlier Analysis (IROA). IROA is a mass spectrometry based metabolomic profiling method using 13C labeling to eliminate sample-to-sample variance, discriminate against noise and artifacts, and improve compound identification. This work utilized IROA with LC-HRMS and investigated the metabolomic profiles from WT yeast vs pah1Δ.


Metabolomics of Hermaphroditic C. elegans via Isotopic Ratio Outlier Analysis using High-Resolution Accurate Mass LC/MS/MS

Caenorhabditis elegans is one of the best-studied animals in science. Despite this, metabolomic studies in C. elegans have only recently become active areas of research. The Isotopic Ratio Outlier Analysis (IROA) protocol uses 13C-isotopic signatures to identify and to quantitate metabolites. It reduces error introduced during sample preparation and analysis, including ionization suppression by the use of IROA standards. The marriage of IROA and high-resolution accurate mass (HRAM) LC/MS/MS with C. elegans metabolomics allows experiments which assess the biological response to stresses or stimuli. These experiments would conventionally be difficult due to interferences by metabolites of unlabeled organisms. With IROA labeling and HRAM detection, metabolites can be distinguished in an untargeted manner, quantitated and unambiguously identified to their chemical formulas.

Isotopic Ratio Outlier Analysis (IROA) of Myxobacteria using ultra high resolution mass spectrometry

Myxobacteria represent an important source of novel natural products exhibiting a wide range of biological activities. Some of these so-called secondary metabolites are investigated as potential leads for novel drugs. Traditional approaches to discovering natural products mainly employ bioassays and activity-guided isolation, but genomics-based strategies and “metabolome-mining” approaches become increasingly successful to reveal additional compounds. These newer methods hold great promise for uncovering novel secondary metabolites from myxobacterial strains, as the number of known compounds identified to date is often significantly lower than expected from genome sequence information. Analytical challenges for comprehensive MS-based profiling of myxobacteria include the need to reliably detect the significant differences between secondary metabolomes, e.g. as a consequence of gene knock-outs or regulatory effects, as well as the robust quantitation of known and unknown target compounds and their identification. The IROA protocol was applied to the analysis of myxobacterial secondary metabolomes.

Differential Metabolomic Profiling of Maize Genotypes under Drought-Stressed Conditions using IROA (Isotopic Ratio Outlier Analysis)

The IROA protocol has been applied in a phenotypic analysis of field grown maize (Zea mays) to understand the biochemical differences across selected genotypes when exposed to drought conditions. In this IROA phenotypic analysis, field-grown leaves containing carbon at natural abundance were compared to a standard maize leaf that was grown to contain universally-distributed ~97% 13C; becoming a targeted analysis using a biologically-relevant internal standard. At 97% 13C the IROA patterns were sufficient to find isotopically labeled peaks, identify their 12C isotopomers, and remove artifacts, noise and extraneous peaks. With accurate mass and IROA, the identification of observed component peaks to chemical formula is unambiguous. The benefit of IROA is it takes into account variances introduced during sample-preparation and analysis, including ion suppression.

Characterization and identification of unknown metabolites using Isotopic Ratio Outlier Analysis (IROA)

The identification of unknown metabolites is one of the biggest bottlenecks of metabolomics. The IROA protocol utilizes isotopically-defined media (in which all nutrients are labeled with either 5%13C, “C12 IROA media” (experimental), or 95%13C, “C13 IROA media” (control), to label all biological compounds with differing masses. Therefore, control and experimental samples can be analyzed as a single sample by LC-MS with all biological peaks uniquely paired. For any compound, the peak from the C12-media is mirrored by a second peak from the C13-media. The distance between these peaks is the number of carbons in the compound. The formula of the compound can be readily determined if the high-resolution mass and number of carbons is known.

Differential Metabolomic Profiling of Wheat Cultivars by IROA (Isotopic Ratio Outlier Analysis)

The interest in metabolomics to understand fundamental biology and applied biotechnology, especially in the field of plant science, has driven technology development. This study describes the use of a combined analytical and bioinformatic metabolomics technology applied to the understanding of plant metabolism. The diurnal metabolome changes exhibited in a cultivar of wheat, TX8544, were determined using the IROA protocol. Metabolomics plays an important role in how an organism adapts to change, in this case the diurnal pattern of heat and light. Here an isotopically-defined standard wheat sample is added to the experimental sample and is analyzed as a single sample, reducing suppression, and sample-to-sample variance, including variance introduced during preparation and analysis.