Oliver Fiehn, Ph.D., a faculty member of the University of California, Davis Genome Center, discussed his group’s efforts in developing quality control in GC/MS-based metabolite profiling. Dr. Fiehn described how in gas chromatography metabolites may need to be modified in order to increase their volatility. Thus prepared, liquid samples are injected into a capillary column where they are separated before they are ionized and then introduced into the time-of-flight mass spectrometer. Their passage through the flight tube is measured, with small ions reaching the detector before large ones.
According to Dr. Fiehn, the major problem in mass spec-based metabolomics is that the instrument gets into physical contact with the sample that is contaminated and highly heterogeneous. “The sample is ‘dirty’ because metabolomics gears up to detect everything with little or no clean-up steps, and this ‘dirt’ or ‘matrix’ causes major quality problems.”
Dr. Fiehn believes that the hot injection system is the most problematic part in the mechanics of gas chromatography-coupled mass spec. As the samples are introduced into the system through a hot injection, a phenomenon referred to as the Leidenfrost effect occurs—a liquid, in near contact with a mass significantly hotter than its boiling point, produces an insulating vapor layer which keeps that liquid from boiling rapidly.
“The droplet became flat as a small disk and hovers a fraction of a millimeter above the plate. It may have moved nervously, jumping around the hot griddle. Nebulization does not occur with fast injection auto-samplers,” stated Konrad Grob, chief of the GC Department at the Kantonales Laboratory in Zürich, Switzerland.
The Fiehn team dealt with these issues through a number of strategies. Among these were cleaning or exchanging the syringe needles, replacing the injector, and changing the gold plate on which the samples are sprayed.
“We believe that with appropriate quality control, GC/MS is the method of choice for primary metabolism; indeed, even amino acids can be quantified using daily QC calibrations,” Dr. Fiehn concluded.
Ron Bonner, Ph.D., and his analytical team at Applied Biosystems/MDS Analytical Technologies have been grappling with artifacts introduced into LC/MS-based analysis. He introduced his presentation by stating, “There is one source of expected (and useful) variance, but many sources of unexpected, confounding variance. While they may appear to be real, they may be marauders that mask the authentic distinguishing features of the data set.”
Variation may be introduced into samples by the collection process, by improper handling and storage, or by the limited long-term stability of the experimental material. Another source of variation is that introduced by instrumentation failure. Sensitivity drift, carryover, contamination (build up or clearout), and retention time changes may all be among the problems.
When the time for data analysis arrives, peak determination and failure to align the data properly may contribute to unwanted variability in the outcome. Finally, biological variation including presence of xenobiotics along with unaccounted differences in gender, age, diurnality, and individual variability all may introduce confounding variability into the assessment process.
To deal with this myriad of challenges, Dr. Bonner and his associates have developed a software program, Marker View™, designed to handle many of these problems. The general features include ability to go from data to PCA quickly and in one program, feature extraction and selection, retention time, alignment, normalization, and scaling. The software also provides a variety of other features for crunching and managing the data.
Dr. Bonner also presented another software tool for analyzing and eliminating fraudulent variance from mass spec data sets. The Principal Component Variable Grouping is a tool for data interpretation and visualization. It uses samples to find correlated variables, and identifies peaks from the same compound and with similar expression profiles. Correlated variables are assigned to a group, and the same symbols are used for group members in the loadings and profile plots.
Dr. Bonner provided examples of how the application of his tools can clean up messy data sets and transform them into manageable, meaningful collections of information from which solid scientific conclusions may be drawn. These included mass-spec analysis of individual samples of 14 different types of fruit and a study of the metabolic distribution of the drug Vinpocetin in rats.
The fact that so much of the symposium was given over to a discussion of the many ways in which investigators can go astray suggests apprehension within the field over the quality standards of mass spec as applied to metabolomics. If these admonitions concerning quality control and rigorous adherence to analytical standards are addresed, this could go a long way toward accounting for the failure of metabolomics programs to come up with viable drug candidates and workable therapies.