MSACL 2018 US Abstract

Topic: Metabolomics

Multiple Reaction Monitoring (MRM) Profiling: Metabolomics Discovery Methodology Demonstrated with a Human Plasma Coronary Artery Disease Study

Karen Yannell (Presenter)
Purdue University

Bio: Karen E. Yannell is a graduate student studying analytical chemistry at Purdue University under the advisement of R. Graham Cooks. Her research is focused on biofluid analysis by mass spectrometry. Previously she worked on quantitiative analysis of drugs for point of care methods using paper spray ionization. Now, her research is developing new metabolomics workflows for biomarker discovery.

Authorship: Karen E. Yannell (1), Shane E. Tichy (2), and R. Graham Cooks (1)
(1) Purdue University, Department of Chemistry, West Lafayette, IN (2) Agilent Technologies, Santa Clara, CA

Short Abstract

Multiple reaction monitoring (MRM)-profiling is demonstrated with a human plasma coronary artery disease (CAD) study. This semi-targeted first pass metabolomics methodology explores a sample for biological functional groups with precursor and neutral loss scans. Peaks from those scans are converted to transitions in a MRM method for rapid screening of samples. Univariate and multivariate statistics are used to determine which transitions are potential biomarkers. This CAD study (N>1000) found 177 transitions separating CAD from control samples with an agreement of 91% for males and 92% for females which shows the effectiveness of this methodology. Identification of these transitions is ongoing.

Long Abstract


Investigation of the metabolome is needed for finding new diagnostic and prognostic biomarkers. Traditional methods are time and sample consuming and often do not measure a whole or intact sample so information is lost. Multiple reaction monitoring (MRM)-profiling is a new semi-targeted methodology for metabolite and biomarker discovery (1). It utilizes flow injection or direct injection of a small volume of sample into a triple quadrupole mass spectrometer (MS) where it is interrogated with neutral loss (NL) and precursor (Prec) ion scans. These scans are for biological functional groups and therefore explore the sample with a biological rational. Regardless of identification, all the peaks from the NL and Prec scans along with the scan parameters are converted into ion transitions and placed into a MRM method. By using a triple quadrupole MS in MRM mode we can screen hundreds of samples for many ion signals very quickly. After screening a whole set of samples, traditional analysis workflows including univariate and multivariate statistics are used for determining which transitions are important, building models, and classifying unknowns. If needed, the discriminating transitions can then be investigated by alternative analytical tools to confirm their identity.

Unlike many metabolomics methodologies, this approach is high throughput, easy to develop and execute the experiments, and utilizes a robust instrument that has easily managed data. The process also searches the sample with biological reasoning, does not require a column, standard solutions, internal standards, or a large volume of sample. It is therefore an excellent first pass metabolomics methodology for quickly identifying biomarkers in a sample set.

In this study, over 1000 human plasma controls and coronary artery disease (CAD) samples were studied for existing and new biomarkers. CAD is a common disease but a diagnosis often requires invasive testing of the patient (2). New molecular signatures are needed to make a diagnosis easier and to develop tests for prognosis and therapy evaluation. MRM-profiling is used here to demonstrate the utility of this methodology and to evaluate human plasma for CAD biomarkers.


In this study an Agilent 1290 Infinity series pump was used for flow injection of a sample into an Agilent 6470 triple quadrupole MS with an Agilent Jet Stream source. Over 250 Prec and NL scans were performed on three pooled samples. Each sample was prepared using only 40 ┬ÁL of plasma and using an augmented Bligh Dyer method for both lipid and small metabolite discovery. The discovery phase led to 1266 transitions which were reduced to 485 transitions with one discrimination experiment that contained over 200 individual samples. The final method was under 5 minutes injection to injection. With a testing set of samples, univariate statistics, including t-test (p<0.05) and fold change, were used to reduce the transitions set to only those that discriminate the sample sets. Then a multivariate model was built and evaluated by classifying a validation set of samples. For identity confirmation of the discriminatory transitions, a reverse phase (C18) liquid chromatography method coupled to an Agilent 6545 QTOF was used to gain high resolution MS/MS data. These exact masses and spectra were compared to metabolite databases for identification.


This study achieved separation of the CAD and control samples with an agreement of 91% for males and 92% for females. A total of 177 unique transitions was found to be contributing to the separation. Many of these are lipid metabolites. Data analysis is ongoing but early results suggest lysophospholipids are up regulated in CAD samples.

Conclusions & Discussion

MRM-profiling is a rapid and reliable way to screen a sample set for potential biomarkers. In this talk, this methodology will be described and demonstrated with a CAD study. The results show that this method can find metabolites that separate the CAD and control samples and the identification of these biomarkers will be discussed.

References & Acknowledgements:

[1] C.R Ferreira, K.E Yannell, B. Mollenhauer, R.D Espy, F.B Cordeiro, Z. Ouyang, and R.G Cooks; Chemical profiling of cerebrospinal fluid by multiple reaction monitoring mass spectrometry. Analyst 2016; 141 (18): 5252-5255. doi: 10.1039/c6an01618a

[2] K.F Fox, M.R Cowie, D.A Wood, A.J.S Coats, J.S.R Gibbs, S.R Underwood, R.M Turner, P.A Poole-Wilson, S.W Davies, G.C Sutton; Coronary artery disease as the cause of incident heart failure in the population. Eur Heart J 2001; 22 (3): 228-236. doi: 10.1053/euhj.2000.2289

The authors would like to acknowledge the Fairbanks Institute for Healthy Communities for the human plasma samples and Agilent Technologies for financial and instrumental support for this study.

Financial Disclosure

SalaryyesAgilent Technologies
Board Memberno
ExpensesyesAgilent Technologies

IP Royalty: no

Planning to mention or discuss specific products or technology of the company(ies) listed above: