Cooperation between Digital Life projects: Lipidomics bringing dCod 1.0 and AurOmega together
From a biology/toxicology PhD student´s point of view:
In an era where big data in science is exploding in popularity, much attention is needed to keep up with the rapid development and options. For a PhD student, the big data and omics approaches can be quite overwhelming, both in terms of understanding their principles as well as knowing how to implement and understand the use of omics in our research. In the Digital Life project dCod 1.0, we use Atlantic cod (Gadus morhua) as a model species to understand how environmental pollutants can affect marine organisms. The use of omics is increasingly common also within our project. As a Phd student with a master's degree in molecular biology, the terms “transcriptomics”, “proteomics” and “metabolomics” are fairly familiar. However, starting my PhD in the dCod project soon introduced me to a new omics term: “lipidomics”. Based on already acquainted knowledge from my degree, I understood that this had to involve the lipid profile within a species, however more details remained mysterious to me. In our research group in Bergen, previous work had shown that genes involved in the lipid metabolism in cod have been affected by exposure to various environmental pollutants. In a world where the majority of pollutant effects have been studied at gene and protein levels, effects on lipids have gotten limited attention. Following our discoveries, we started to search for methods that could help us to determine lipid profile changes in the cod. Luckily, a meeting arranged by Digital Life brought together the project leaders of the dCod 1.0 and AurOmega projects: Anders Goksøyr and Per Bruheim, respectively. Contacts were made and later followed up by skype discussions and planning.
Suddenly, I found myself on a plane to Trondheim, with 100 cod samples in the luggage, a bit unsure but excited for what awaited at NTNU in Trondheim. I was warmly welcomed by Zdenka and quickly updated on our plans for the lab work. I travelled to Trondheim mainly to contribute to the sample preparation, a process which is quite cumbersome when involving many samples. Zdenka taught me well and together we prepared all samples in a few days. In the final day of my stay, we could start the first analyses. I was overwhelmed by the amount and size of instruments needed to process and study lipidomics, and felt quite relieved that I could leave the analyses and big data handling to Zdenka. I left Trondheim with a sense of increased understanding of lipidomics, its processes and the amount of resources necessary to conduct this type of research. Later on, we received the exciting results from Trondheim, and the mathematicians and bioinformaticians of the dCod project are currently digging into the details of these results.
From the analytical chemist´s point of view:
Lipid analysis has gained growing interest in recent decades as lipids serve multiple functions across species, such as energy storage, cell signaling, protection and other important biological roles. Lipids are large and complex group of compounds with different structures and polarities. Therefore, their analysis and characterization is a challenging task. Recently, we have established a high-throughput methodology for lipid profiling in our lab. Briefly, homogenized samples are extracted using a mixture of chloroform and methanol. For lipid analysis we utilize supercritical fluid chromatography coupled to a high-resolution mass spectrometer. Identification of lipid compound is based on three main characteristics - retention time, accurate mass of a molecular ion and fragmentation pattern. LipidBlast and Lipid Maps databases are used for compound identification.
However, to develop a robust and reliable methodology one need to collect enough knowledge and skills. Based on discussions with collaborators, such as Karina, I have found out what information they need, how they intend to use them and finally, how I can help them. I early realized that multivariate statistics play a key role in evaluation of non-targeted mass spectrometric data. The methodology we use now is very well established and verified by a few various samples sets. Karina brought nearly 100 cod plasma and liver samples for lipid analysis and so far I have characterized lipids in salmon tissue (liver, muscle), rat (plasma, liver), goat neural tissue or microalgae. Applying our method, we are not only able to characterize the lipidome but also able to reveal e.g. diet changes or lipid metabolism disorder.
I was glad when I found out that Karina had decided to arrive to Trondheim as it was a great opportunity to get overview of her project and expectations, as well as to present our lipidomics workflow to her. She adapted very quickly to the lab and we accomplished sample extraction in two days only. Once we had prepared her samples, we made an excursion to our mass spectrometry lab to introduce instruments and software tools we use for lipid analysis and data processing. Mass spectrometers produce overwhelming volume of data, so I have advised Karina which tools she might find useful for data mining.
We thank Digital Life Norway for funding our cross-project activity, which was highly appreciated!