Great things come in small packages

In the post genomic, proteomic world there is one more ‘omic’ that could bolster the wealth of information generated – the study of small metabolites. Darwin Asa shows us how metabolomics and electrochemical array detection can be a powerful partnership

The use of redox metabolic profiling, using liquid chromatography (LC) with electrochemical (EC) detection either alone, or in conjunction with techniques such as nuclear magnetic resonance (NMR) or LC-mass spectrometry (LC-MS), can be extremely valuable in studying disease and the effects of chemical compounds on and in biological systems. The use of EC-Array detection technology enables researchers to identify potential key biomarkers quickly and efficiently analyse a wide variety of compounds in a single analysis, with greater sensitivity and selectivity than can be achieved with LC-MS or NMR alone. Here, the use of EC detection in conjunction with mass spec to efficiently identify biomarkers is demonstrated in a diabetic rat model. The application of electrochemical detection greatly expands the applications and efficiency of bioanalytical techniques (mass spec and NMR) and their use in the field of metabolomics and general compound metabolic research.

Great strides in both genomic and proteomic research have given rise to the expectation that these disciplines would lead to a host of new drug therapies and diagnostic tests. However, while the study of proteins and nucleic acids has provided new and highly valuable insights into basic biological processes and has resulted in the identification of many potential new drug targets, it has not provided all of the information necessary to quickly and effectively develop new therapeutics. To supplement and extend the value of ongoing genomic and proteomic initiatives, a long tradition of studying a variety of small molecules and metabolites has re-emerged under the designation “metabolomics.”

Metabolomics brings a bioanalytical approach to the study of biological processes. Metabolomics looks at a comprehensive set of small molecules, which together with macromolecules (proteins, nucleic acids, etc.) are integral to the establishment and maintenance of normal cellular metabolism and behavior. The patterns of molecules are the result of physiological activity within the cell and can provide powerful indicators or reporters of normal or irregular cellular function. By examining different types of compounds and molecular interactions, metabolomics can offer new insights into cellular behavior that can be of enormous value to researchers in a pharmaceutical environment.

Figure 1 Representative chromatograms obtained from a rat urine sample. A) Negative ion EMS scan 100-800 m/z (APCI). B) EC-Array 16 channel 100-1150mV (increment 70mV).

Genomics and proteomics are “systems” approaches to the study of living organisms. Likewise, metabolomics is a systems approach to the study of low molecular weight metabolites (typically <1000 Da) and has been applied to many organisms including bacteria, yeast, plants and animals. Although each of these “omics” approaches has its advantages, metabolomics provides an integrated and dynamic view of complex molecular systems through measurement of small organic molecules, which are important in many cellular physiological functions.

Because metabolomics looks at compounds such as nutrients, hormones, neurotransmitters, vitamins and end products that are integral to processes like cellular signaling and physiological control, this approach can provide valuable insights into the proper function of biological systems. Metabolomics can be critically important, for example, when researching disease processes and or when looking for methods to measure or treat those processes. Examination of the dynamic changes in small molecule profiles between normal and diseased systems can afford unique insight into specific changes associated with disease processes.

Metabolic profiling typically involves the generation of patterns of analytes, containing both known and unknown compounds, in order to differentiate one sample group from another by the use of statistical analysis and pattern recognition software.

LC-MS and NMR are two widely used techniques for analysis of unlabeled low molecular weight organic compounds. These systems are useful not only to quantify compounds, but also to provide information on their chemical identity. The high-throughput data generation capabilities of these analytical instruments allow for the tracking and detection of dynamic changes in a wide variety of compounds.

NMR traditionally has been applicable to the measurement of high level, low molecular weight compounds. LC-MS augments these capabilities with applicability to the study of lower level metabolites but only for those that are eluted and ionised under a given set of conditions.

Although it provides good information on chemical identity, has rapid throughput, and measures many compounds at once, NMR has significant limits on sensitivity. This technique, therefore, only applies to very high level metabolites and does not detect the lower level metabolites which are often the compounds with the most significant information for a given metabolomic experiment.

Figure 2: PCA analyses of Coularray data. A) Scores plot from mean centered PCA analyses showing clusters representing different phenotype and genotype. B) Variables outside the user defined region of similarity represent the regions of maximal variance.

In comparison to NMR, LC-MS can provide information on quantity and chemical structure for lower level metabolites. LC-MS is, however, not always as comprehensive as a researcher would want: compounds have to be efficiently ionised in order to be detected. Compound ionisation is also highly affected by the conditions used, and by other components in the sample. The type of information obtained with LC-MS is also very dependent on what type of LC-MS system is being used in the research.

Electrochemical array detection technology, such as ESA’s CoulArray, used in parallel with LC-MS under gradient conditions, allows targeted and very sensitive analysis of numerous redox active compounds in complex biological samples. This capability can be easily used to significantly augment the qualitative and quantitative information obtained from an MS (and diode array) detection scheme. In a single analysis of a mixture of a large number of compounds, ESA’s CoulArray detector can also process a sample in five minutes or less, providing a high speed, high throughput, information-rich pathway for measuring a wide variety of compounds.

Utilising detection EC technology in conjunction with mass spec, for instance can lead to a quicker and easier identification of potential biomarkers than may be possible with mass spec alone. As illustrated in Figure 1, biomarkers from a series of rats (diabetic and normal) were studied by both electrochemical and mass spec detection techniques to help identify potential biomarkers of diabetes. A total of 12 rats, 6 Lean Zucker (LZ) and 6 Zucker Diabetic Fatty rat model (ZDF) were used for this study, and urine collected at week 5, 8 and 12. Simultaneous EC-Array and MS data collection, resulted in complementary data based on orthogonal detection of redox active and ionisable metabolites respectively (Figure 1). Clinical data (serum glucose and triglyceride, not shown) demonstrated that all ZDF rats in this study acquired a diabetic phenotype by 12 weeks. Figure 2 shows PCA results based on EC-Array data obtained using HPLC conditions optimised for negative ion APCI-MS. The scores plot in Fig 3A shows differentiation according to genotype and disease progression. Interestingly, rat 11 (23ZDF), the most clinically diabetic at 8 weeks, clustered close to the 12-week ZDF rats. These PCA results are qualitatively similar to those obtained previously using NMR, LC/MS and GC/MS. The corresponding loadings plot (Figure 2B) was used to identify those EC-Array peaks that contributed most to these sample groupings. This allowed targeted interrogation, at the corresponding time points, of the negative ion MS data acquired in parallel. This interrogation revealed that the 12-week ZDF rat urines had the lowest abundance of m/z 159.7, the base peak at 5.27min.  Interestingly, this ion was also low in ZDF rats 10 and 11 at both 5 and 8 weeks.  Also, further data show that the 5-week ZDF rat urines had the highest abundance of m/z 252.6, 282.6, and 444.2 corresponding to base peaks at 9.15, 10.8 and 11.5 min, respectively. Furthermore, the abundance of these 3 ions was highest overall in rat 11 at 5 weeks. Preliminary Mass Spec identification of these biomarkers by mass spec is seen in Figure 3.

Figure 3: Preliminary identification of biomarkers. IDA experiment consisting of NL, ER, EPI and MS3 A) Positive ion mode NL176. Inserts shows XIC 338.2 and ER 338.2 B) EPI of 338.2 showing predominant product of 162.1 (only m/z 338.2, 162.1 & 144.2 are specific to the glucuronide species, other ions are consistent throughout) C) MS3 spectra with. An AF2 value of 50-150 shows only one product of 162.1 being 144.1. Insert shows hydrodynamic voltammogram of the peak from EC-Array data at 6.4 min.

This study demonstrates the complementary process by which electrochemical array detection data can be used in conjunction with mass spec to identify key potential biomarkers of disease. The study is a typical example of the power of the electrochemical detector and how the technique’s very low detection limits can routinely see molecules that other analytical techniques might miss. For example, in most MS systems, when used for exploring multiple analytes, there is not enough detection sensitivity to be able to detect changes in low level metabolites. Using an electrochemical array as a parallel detector with LC-MS significantly enhances the power of the analytical system to identify biomarkers. The complementary use of an EC-Array allows for more specific settings of the mass spec to be utilised to improve both specificity and sensitivity of the bioanalytical system. EC-Array, therefore, not only provides data that is complementary to MS but also can improve the effectiveness of these techniques.

Any technique, such as electrochemical array detection, that can provide large amounts of relevant information quickly on low molecular weight metabolites, specifically redox biochemicals, is applicable and important for researchers in metabolomics.

The LC-EC-Array detection technique, which has had a long history of development at ESA dating back to the 1980s, focuses on metabolically active molecules and, used in conjunction with MS and NMR systems, can routinely and quickly look at dynamic changes in complex metabolic profiles. These capabilities are very important to metabolomics efforts in diagnostic or therapeutic areas. The powerful and novel compound detection capabilities, coupled with easy integration into bio-analytical systems and outstanding throughput capacity, makes the use of an EC-Array a very efficient method for performing metabolomics and other important metabolic studies.  The EC-Array can offer significant benefits to the metabolomics community by expanding their bio-analytical capabilities to aid in the never-ending quest to better understand the pathways and molecular relationships that make up a “normal” individual and how deviations from “normal” can be rapidly and quantitatively identified and monitored.

Dr Darwin Asa has worked at Glycomed Inc. and The Upjohn Co before joining Becton Dickinson as a product development scientist. After several years of work in product development he transitioned into a marketing role. Subsequently, he has held senior Marketing positions at Tecan Inc., and is currently the Marketing Director at ESA Biosciences.