This summer, Dr. Upmacis and I have sought to understand the antioxidant potential of docosahexaenoic acid (DHA) by examining its interaction with oxygen. Since a structurally similar polyunsaturated fatty acid known as eicosapentaenoic acid (EPA) has been shown to progressively oxidize in the presence of air, we expected DHA to behave in much the same way when subjected to the same circumstances. In my last blog post, I described our use of mass spectrometry to test this hypothesis. Dr. Upmacis and I dissolved DHA separately in water and ethanol, then left both samples exposed to air for a period of five days. Each day, we processed both samples in the mass spectrometer several times in order to glean a consistent picture of each sample’s chemical constituents. Our first three daily trials collected intensity data at 0.1 mass-to-charge (m/z) increments ranging from 50 m/z to 650 m/z. We also conducted two additional daily trials, expanding our scope of measurement beyond 650 m/z in order to hunt for even larger molecules. The final result was five days worth of detailed atomic mass spectra consisting of multiple trials per sample per day.
The configuration of our atomic mass spectra has raised some interesting questions and curiosities, which we have attempted to rationalize. In our primary trials, we noted that the most prominent base peaks usually occurred at about 327.4 m/z and 328.4 m/z; we think these peaks correspond to the molar mass of the deprotonated and protonated forms of DHA respectively. Similarly, we believe a hydrogen ion (proton) is responsible for single unit gaps between twin peaks that occur elsewhere in our spectra. We also noted a rhythm of peaks occurring at regular intervals of 16 m/z beyond the main peak. As the sample aged, the main peak declined, while the series peaks that followed grew in intensity. This finding appears to be consistent with our hypothesis that DHA takes on one or more oxygen – an atom weighing 16 amu – when progressively exposed to air. Our additional trials on extended m/z ranges revealed another fascinating discovery: the emergence of an additional prominent peak at 654.8 m/z. We noted this peak to be precisely double the m/z of the main base peak but roughly half the intensity. We suspect this phenomenon provides evidence for the dimerization of the initial DHA compound. A rhythmic succession of peaks also followed the 654.8 m/z peak at regular intervals of 16 m/z (corresponding to the size of an oxygen atom) and at 32 m/z (corresponding to the size of a pair of oxygen atoms, which might join to the possible dimer). Finally, we turned our attention back to earlier ranges of the spectra, where we found another rhythmic succession of peaks. Curiously, this series actually preceded the 327.4 m/z main base peak, which initially puzzled us. However, we now postulate that these peaks actually represent an “echo” of larger molecules that have become doubly charged. The double charge sets the m/z denominator (z) = 2, rendering an m/z value corresponding to half the given molecule’s true size.
The majority of time since my last blog post has been spent analyzing and making sense of our empirical data. Because we conducted five trials for each sample over five days, we amassed an enormous cache of data. Furthermore, each trial reported m/z values in 0.1 increments, and in some trials, values ranged upwards of 1800 m/z. This culminated in a grand yield of a quarter-million distinct data-points across the entire project! To complicate matters, it was necessary to align the data so that m/z values from one day could be meaningfully compared with those of the next day. Initially, I attempted this task manually, by placing tabular data sets side-by-side in Microsoft Excel and moving misaligned records into position. Ultimately, I found this method impractical and tedious, and so I abandoned it in favor of an automated approach. I therefore attempted to construct an Excel macro that would align the data automatically, consulting an external Excel expert to assist me with portions of Visual Basic software coding, programming syntax, and debugging. After weeks of continued development and thorough testing, our macro evolved from a simple data-aligning routine to an intelligent, sophisticated program capable of retrieving whole datasets from multiple source files, initiating user-driven comparisons, and assembling graphical overlays so as to highlight changes occurring in our samples from one day to the next.
In order to more fully understand the detailed patterns and relationships inherent in the data, I am currently zooming into local areas of each trial’s graph and labeling each peak. I am noticing that some day-to-day peaks grow, some decline, and others do not seem to conform to any recognizable pattern at all. Nevertheless, I anticipate this careful examination of peaks under magnification will uncover even more numerical patterns within our atomic mass spectra. We hope an understanding of these patterns will help unlock the mysteries behind the chemistry of DHA, particularly with respect to our original hypotheses.
Next week, Dr. Upmacis and I plan to simulate DHA’s antioxidant mechanisms as they realistically occur within the body. We will observe the reactions between DHA and several nitric oxide (NO)-releasing compounds, including S-Nitroso-N-acetylpenicillamine (SNAP) and S-Nitrosoglutathione (GSNO). NO is a radical species important in many physiological and pathological processes. We expect DHA to scavenge NO and couple with NO’s unpaired electron. If these tests are successful, we will be one step closer to painting a more complete picture of the antioxidant potential inherent in DHA.
This project has certainly presented its own set of challenges, but I felt rewarded by the satisfaction earned by overcoming them. My first obstacle lay in the alignment of my datasets. I felt overwhelmed by the prospect of tediously combing through tens of thousands of data records, one by one. Moreover, I realized such a manual approach was vulnerable to human error. I knew there had to be a more efficient method, so I began to envision a program that would automate the task. This gave rise to my next challenge: designing and testing the Excel macro. Many steps were taken to ensure the macro sorted the data correctly. A new round of thorough testing accompanied each additional feature or level of complexity. This process required a significant investment of time. However, the result was well worth the effort because it yielded a flexible, self-sufficient program capable of sorting and analyzing data under varying conditions. I am hopeful that this macro will also improve the repeatability of our experiment, should a future study necessitate a similar data analysis. In this way, the investment of time and effort here will continue to pay off in the future. Unfortunately, the amount of time spent in research and analysis initially left me feeling rushed to produce more results by summer’s end. This brings me to my final challenge – the time constraints of this project. Many studies often take many months or years to come to fruition, so I initially felt rushed to complete my work before the end of the initiative. I’m now learning that the research and analysis process requires the time that proper diligence and care demand. Therefore, it may be unrealistic to expect that ambitious research undertakings will be completed within a relatively short time frame.
That said, Dr. Upmacis and I will be starting a new project in the fall, but we will continue our work with the current one as well. Once the numerical patterns behind our mass spectra peaks are more fully elucidated, we will assemble a cohesive narrative to explain the chemistry of DHA in terms of the changing relationships in our data set. This story will form the backbone of our manuscript, which we may submit for publication.
This project has taught me much about the research process. I’m thankful for the chance to work with Dr. Upmacis, a phenomenal mentor, who has guided me through the journey of the scientific method. I also feel empowered by the knowledge and technique that I’ve acquired in the lab. I learned how to use the mass spectrometer, an important instrument in analytical chemistry. Moreover, I gained valuable skills in critically evaluating data and drawing meaningful conclusions. Helping this study come together has been a rewarding experience and will continue to be one as we approach our goal.
In addition to learning the ropes of research, this project has also enhanced my sense of patience and resolve. There is rarely immediate gratification in the worthwhile endeavors of life; one has to be in it for the long haul. It’s easy to become discouraged or frustrated when long hours produce slow results. The key to success, however, is to stay engaged in the process, to pace oneself, and to never give up. Give your best effort with the tools available to you at present, stay enthusiastic, and success will take care of itself. This is a lesson that I will carry on through my life’s ventures and professional pursuits.
 Assuming the mass spectrometer singularly charges component molecules, then z = 1 and “m/z” directly represents the atomic mass units (amu) of the molecule. Some molecules, however, inevitably become doubly charged; in this case, z = 2 and “m/z” represents half the amu of the molecule. Thus, for accuracy, we report our data in terms of m/z units, rather than amu.