Advancing Health and Disease Research with Efficient Analytical Methods

August 1, 2018

Biological and health research often involves the analysis of highly complex classes of similar compounds that are difficult to distinguish. Analytical methods that can distinguish between similar forms give research­ers more power, while methods that can separate related classes in a single run save time. Guowang Xu, the Director of the CAS Key Laboratory of Separation Sci­ence for Analytical Chemistry at the Dalian Institute of Chemical Physics, of the Chinese Academy of Sciences, has been developing methods that can do both, and using those approaches to advance research on a variety of compounds, such as acyl-coenzyme A, acylcarnitines, and a wide range of metabolites and lipids. He recently spoke to us about some of this work.

Biological and health research often involves the analysis of highly complex classes of similar compounds that are difficult to distinguish. Analytical methods that can distinguish between similar forms give research­ers more power, while methods that can separate related classes in a single run save time. Guowang Xu, the Director of the CAS Key Laboratory of Separation Sci­ence for Analytical Chemistry at the Dalian Institute of Chemical Physics, of the Chinese Academy of Sciences, has been developing methods that can do both, and using those approaches to advance research on a variety of compounds, such as acyl-coenzyme A, acylcarnitines, and a wide range of metabolites and lipids. He recently spoke to us about some of this work.

Q. You have developed a two-dimen­sional (2D) liquid chromatography (LC) method coupled with high-res­olution mass spectrometry (UHPLC– HRMS) for the simultaneous analysis of short-, medium-, and long-chain acyl-coenzyme A (CoAs) (1). Why are these compounds important to study?

A: Acyl-CoAs are essential substrates. These compounds are involved in many meta­bolic pathways, such as the Krebs cycle, lipid synthesis and remodeling metabo­lisms, fatty acid oxidation, and so on. The dysregulation of acyl-CoAs plays a very important role during the development of many diseases, including cancer and diabetes.

Q. Why was a better method needed for the analysis of these compounds?

A: Traditional methods covered only a limited range of acyl-CoA species in one analysis, usually either short-, medium- or long-chain acyl-CoAs. Thus, a comprehensive method was needed to simultaneously analyze short-, medium-, and long-chain acyl-CoAs. This method could help researchers to understand physiological and pathological processes better.

Q. A key challenge in developing this method was the incompatibility of the mobile phases needed for separation of each type of CoA. How did you address this challenge?

A: We introduced a precolumn and a makeup flow into the construction of a two-dimensional (2D) LC–MS system. The acyl-CoAs could be trapped on the precolumn, and meanwhile, the residual second-dimension mobile phases in the precolumn were replaced by the makeup flow, thus making the precolumn and the remaining buffer suitable for the next analysis. Thus, the incompatibility of mo­bile phases was solved.

Q. You then used the 2D LC–MS method to profile all acyl-CoAs in mouse liver tissues to investigate metabolic differences of acyl-CoAs in glioma cells. What did these initial applica­tions of your method show?

A: These applications further demonstrated the power and practicability of the 2D LC– MS method. In our study, more than 90 acyl-CoAs were identified in mouse liver tissue, much more than those from previously reported methods. With this method, we designed a metabolomics study of malignant glioma cells with an IDH1 mutation. As expected, we found some helpful clues of cell status and significant changes of enzyme activity, which influences cancer malignancy and progression.

 

Q. You have also developed a 2D LC–MS technique to conduct simultaneous metabolomics and lipidomics analy­sis (2). Why is it valuable to be able to do both types of analysis at once?

A: It is very important for an untargeted me­tabolomics method to increase metabolite coverage. First, researchers can better un­derstand related metabolic mechanisms and identify biomarkers because more information about the metabolites is obtained. Second, the analysis throughput is significantly improved by comparison with dual conventional metabolomic and lipidomic analyses. This is particularly important for large-scale metabolomics studies with small sample amounts.

Q. Prefractionation and fractionation of the samples seem to be important aspects of this technique. Can you explain how the prefractionation and fractionation were carried out, and their role in the technique?

A: Prefractionation was carried out using a precolumn. Complex metabolites in a bio­logical sample were divided into two frac­tions including metabolome and lipidome based on their different polarity. Fraction­ation was implemented respectively with a C18 column and acetonitrile–water mo­bile phases for the metabolomics analysis as well as a T3 column and isopropyl alcohol–acetonitrile–water mobile phases for the lipidomics analysis.

Q. You developed an LC–MS method for comprehensive identification of acylcarnitines using high-resolution targeted MS (3). How were you ableto use a targeted approach for such a large group of analytes?

A: We performed layer−layer progressive MS/MS spectra acquisitions. Initial full-scan MS–data-dependent MS/MS mode produced MS/MS spectra for the top 10 peaks in every cycle. Some low-concentra­tion acylcarnitines were detected in MS but without MS/MS spectra, they could not be identified. To make up this defect, each precursor ion that might be identified as an acylcarnitine was extracted in MS mode, and the time window was sched­uled for targeted high-resolution MS/MS. Based on the targeted MS/MS spectra, a large number of peaks were able to be labeled as acylcarnitines. These analytes might have been detected before, but no­body knew what they were or focused on them. We have proposed a strategy to an­notate a series of unknown compounds.

Q. You also used parallel reaction monitoring (PRM) mode, rather than selected reaction monitoring (SRM) mode for MS data acquisition. Why did you take this approach? What challenges did you face in using it?

A: Compared with SRM, which provides precursor–product ion pair unit resolution, PRM mode can provide high-resolution MS/MS spectra. In PRM mode, the relat­ed product ions as well as precursor ions were detected in the high-resolution or­bital trap mass analyzer, which was helpful for identification. Since PRM mode was limited by scanning speed, it was difficult to fulfill the need for the simultaneous MS/MS spectra acquisition for all potential acylcarnitines in a single run. We devoted efforts to solving this problem using ap­proaches such as time window scheduling and adjusting other parameters.

 

Q. What process did you use to identify the acylcarnitines in each class?

A: First, we extracted the chromatograms of precursor ions as well as characteristic product ions in PRM mode. If their chro­matographic behavior were similar, we checked the high-resolution MS/MS to in­fer acylcarnitine structure. The compounds identified as acylcarnitines had at least four characteristic product ions. Moreover, the retention times of homologue acylcar­nitines should conform to the structure– retention relationship. For example, the acylcarnitine with 16 carbons should be eluted later than the homologue acylcarni­tine with 14 carbons in a reversed-phase separation.

Q. How will this work be useful in future work, by you or other researchers, who are investigating these com­pounds?

A: In this work, we established the most comprehensive database of acylcarnitines reported to date, which includes more than 700 acylcarnitines, along with their exact mass, retention time, and high-resolution MS/MS information. Applying this database, acylcarnitines were rapidly and reliably annotated in biological samples. Some acylcarnitines were omit­ted before, but now they can be annotated and studied for related disease research on health problems such as inborn errors of metabolism, diabetes, and so on. Moreover, this large-scale identification strategy has the value to the future identification of other metabolite groups, for example, fatty acids, acyl-CoAs, and so on.

Q. What are your next steps in this work?

A: The sufficient information including ex­act mass, retention time, and MS/MS spectra, will be added to our in-house database, which can identify metabolites automatically. At the same time, we might reuse the existing raw data and focus on additional acylcarnitines to discover their biomarkers. Moreover, we hope to find acylcarnitines with new groups, such as nitro groups.

References

  1. S. Wang, Z. Wang, L. Zhou, X. Shi, and G. Xu, Anal. Chem. 89, 12902−12908 (2017). DOI: 10.1021/acs. analchem.7b03659
  2. S. Wang, L. Zhou, Z. Wang, X. Shi, and G. Xu, Anal. Chim. Acta966, 34–40 (2017). DOI: 10.1016/j. aca.2017.03.004
  3. D. Yu, L. Zhou, Q. Xuan, L. Wang, X. Zhao, X.Lu, and G. Xu, Anal. Chem.90, 5712−5718 (2018). DOI: 10.1021/acs.analchem.7b05471

Guowang Xu, PhD, is the administrative vice-director of the Biotechnology Division, the Director of the Metabonomics Research Center, and the Director of Chinese Academy of Sciences (CAS) Key Laboratory of Separation Science for Analytical Chemistry, all at the Dalian Institute of Chemical Physics, CAS, in Dalian, China. His main research fields are in chromatography-related research and mass spectrometry–based metabolomics applications in disease biomarker discovery, traditional Chinese medicines, and food safety. Prof. Xu has co-written five books, published more than 380 peer-reviewed papers, and holds more than 50 Chinese patents. He is a member of permanent scientific committee of the HPLC conference and a member of editorial boards of more than ten journals including Anal. Chim. Acta, Metabolomics, and Anal. Bioanal. Chem.