Lipid quantification & data visualization

In this episode, Alice and Robert Ahrends talk about the importance of standardization and quantification in lipidomics, how to analyze and interpret lipidomic datasets, and how lipids are much more that structural or energy storage molecules.

Robert Ahrends

Robert Ahrends @UniVie

Favorite lipid

Discussed paper by Peng et al.:
Identification of key lipids critical for platelet activation by comprehensive analysis of the platelet lipidome

Lipidomics pioneers mentioned in the interview
shorthand nomenclature developed by Gerhard Liebisch

With the corresponding text from the interview:
There was great work done on lipids in the 1980’s and 1990’s already. People like Britta Brüger, Andrej Shevchenko, and Wolf Dieter Lehmann were pioneers of lipidomics and translated nanospray methods for the analysis of lipids. In the early 2000’s, Britta Brüger published an application of nanospray for direct infusion instrument, which made it more interesting for membrane biologists such as Kai Simons or Felix Wieland who also works in Heidelberg. They then adapted this for their own research on lipid transporters. And since then, it became clearer and clearer that mass spectrometry is the tool to go with for lipid analysis: you can analyze a lot of things simultaneously and do it in a quantitative context.

International lipidomics society – ILS

Episode Transcript

Alice: Today on the podcast, I’m joined by Robert Ahrends.
Hello Robert, you studied biochemistry at the University of Giessen in Germany and got your PhD from the Humbold University in Berlin. After that, you went on to work for a year at Agilent, developing analytical methods, and then went on to Stanford for a postdoc in chemical and systems biology. You returned to Germany in 2013 to be a group leader at the University of Dortmund.

Since 2020 you are at the University of Vienna in Austria where you’re an Associate Professor at the faculty of chemistry. Your work combines mass spectrometry and data analysis to decipher the metabolome, especially in contexts such as cardiovascular disease and signal transaction but you do have other activities, like community building activities in the world of lipidomics.
What would you add to this by you?

Robert: So, was the Institute for Analytic Sciences (ISAS) in Dortmund, not the Technical University.
In general we are interested in lipids in a very holistic way.

We are interested in the lipid itself, but we are also interested in the metabolism around these lipids – So what enzymes are shaping membrane changes, signal transaction, … And for us it’s very important to always have a functional angle on this. What does it help us if we’re studying the lipidome or individual lipids if we do not find the root of the changes and put it in context.

Alice: Yes, and this is something we will, we will discuss today together. We will talk about one of your publications from 2018 about platelet activation and the role of the Lipidome in that context.

Before that, maybe more general notions about lipids, as we all know, if we work in biology, lipids have been poorly considered at the beginning of biochemistry. They’re one of the last classes of molecules to be investigated with omics, for example, What do you think that is? Even though we have a lot of lipids in our membranes and lipids are a huge part of our brain. So I you would expected people paid attention a bit earlier. Do you think there’s a specific reason why lipidomics took a bit longer?

Robert: No, I think there was great work done on lipids in the eighties and nineties. However at the time, mass spectrometry was not a key parameter of many molecular biology labs. With Britta Brügger, Andre Shevshenko, Wolf-Dieter Lehmann, Kim Ekroos who were pioneers of lipidomics and translated actually nano spray to the analysis of lipids.

There was a very interesting publication around the year 2000 from Britta Brügger who applied electrospray for direct infusion experiment and this then became more interesting for membrane biologists, such as Kai Siekmann and or Felix Wielan, which are also working in Heidelberg.

They adopted this for their own research on lipid transporters and things like this. And since then, it’s becoming more and more clear that mass spectrometry is the tool to go for lipid analysis – because you can analyze a lot of things simultaneously and you can put this in a quantitative context.

Compared to metabolomics and proteomics [in lipidomics] you have to do quantification because lipids are acting in bulk and not as single entities. For proteins it is easier to put in context because you have, for example, a nuclear localization sequence on the end of the protein and you know that this protein goes to the nucleus.

For lipids, you don’t have something similar. And lipids are most often sticking in the membrane together. So if you analyze changes without standard, you do not know what’s the impact of this certain lipid on the membrane fluidity, stiffness of the membrane, and other things. So reporting in concentration is key. And therefore, it took a little bit longer because at the beginning not much standards were available. Now it’s becoming easier because we have big companies producing the standards and we stepping forward.

Alice: You’re mentioning the importance the composition of the lipids, for example, for the fluidity of the membrane. This is something also where we needed this level of detail in the analytics to be able to know how long the chain lengths were and how many unsaturation we had in certain complex lipids to have a way to interpret also the effects that it has.

If you talk about membrane lipids, if you know that there’s an increase in the number of unsaturation or a change in the whole family of complex lipids with a certain type of fatty acid composition, then you can start interpreting the effects they would have on the qualities of the membrane itself, right?

Robert: The more information you can get, the better. There is a shorter nomenclature, which was developed by Gerhard Liebisch and colleagues where you go from lipid category level down to the isomer level of lipid – At the moment we are not at the isomeric resolution, we are somewhere in between where it’s possible to do MS2 experiments easily and get the fatty acyls with the number of double bonds and chain lengths.

This brings us already a big step forward because with this information you get an idea of what lipids are connected to the backbone. This information makes it easier to interpret membrane physics but also helps you to pin down if a certain lipid acts as a precursor for a signaling molecule. From there, you can also go deeper down to pinpoint the location of the double-bond. Though there are instruments which can deal with this, however, this is not broadly applied in lipidomics.

Alice: At the moment, there’s still a lot we can do with the methods we have. Your paper that we will discuss is a good example of this, I think. So you really start from the global lipidome and then progressively focus on the specific species of lipid that is going to be the beginning of a whole pathway. That we will discuss in a minute. Before we get into that detail, I wanted to ask you: What is a lipid for you?

Robert: As I started, there was a definition of a lipid as a hydrophobic biomolecule but this is a problem now because we are dealing with all the water soluble and water insoluble entities at once. If you thinking about a fatty acid, which has some hydroxyl group, this will be definitely water soluble but if you goes into a cholesterylester; this will be water insoluble.

You have to cross some 25 log orders of magnitude in water solubility. So I think people more and more say lipids are organic molecules to prevent to say they are hydrophobic entities. However, if you are thinking in the classical way about it I would say: Hydrophobic biomolecule, because even if it is water soluble, it will be less water soluble compared to molecules like phosphate or an ATP or something like this.

Alice: This is important to know for sample preparation. Doesn’t it. If you prepare to do analytical chemistry to study lipidomics more than small molecules, for example, do you use different methods to prepare your samples?

Robert: This is true. There are 1-phasic extraction methods, but most of the people in the field prefer biphasic extraction protocols. This has the benefit to first precipitate all the protein. On top of this, compared to the monophasic extraction protocols, you can separate more hydrophilic molecules from hydrophobic molecules. This doesn’t mean that you have to throw the hydrophilic phase away. You can still analyze it. But you separate out things to put them in different workflows. A proteomic workflow can go there.

The lipidomic workflows there and then a metabolomic workflow. On the other side, this metabolite fraction or the more hydrophilic fraction still contains a lot of lipids such as lysolipids, highly phosphorylated lipids, sugar lipids. Most likely will find them in this fraction. But you get any cleaner detection; there’s a reduced background noise. I would recommend that.

Alice: from the background I have, I remember we had different processes for different omics, and I think the most important thing is to be aware of which method you use and where it might make you lose certain analytes.

For example, we tried to combine proteomics and metabolomics from the same samples, and we did that, but you have to be aware that when you use a special solvent or a certain method, then you might lose a certain class of metabolites or a certain part of the sample to the extraction method you have. And doesn’t mean you can’t use it for anything else, but you have to be aware of the limitations of your methodology.

Robert: Exactly. And there was a nice publication Christina Kromann did from my lab in Molecular and Cellular Proteomics and you have to care of the partitioning in between the two phases. However, if you add a standard directly at the beginning, a lipid standard or a metabolite standard, it is accounted for. It helps you just have to detect the molecule and then it’s accounted for. This is a great deal when you are doing quantitative analysis. You just calculate it back.

Alice: You mentioned these membrane forming lipids and also signal transduction-related lipids. Do you want to give examples for those people who are not too familiar with lipid classes. What would be more on the signal transaction side, or that would be less part of this structural world and more of the effective world.

Robert: In general, if you think about how signaling can work, there are two different direction, right? So one is a signaling molecule binds to a receptor. And therefore, it should in most cases be water soluble. So we are talking about molecules such as oxylipins, phosphatidyl-1-phosphate and things like this. – On the other hand, there are signaling pathways, which called the unfolded protein response, where the membrane has changed.

And the change of the membrane or the distortion of the membrane is doing something with the dimerization of proteins and then downstream is a signaling pathway triggered which not directly involves a signaling lipid, however, it’s still a signaling pathway. So if you focus on the first one, there are all the different lysolipids there. It doesn’t matter where it’s coming from. It can be the sphingolipid metabolism or from the glycerophospholipid metabolism. There you most often find transmembrane receptor protein receptors. They are doing signaling.

Alice: And the lipids can be both, the ligands to the receptors and they can also be the effectors downstream of what happens once the receptor has been activated. For example, in terms of ligands, I’m working at the moment a lot with bile acids and if you had asked someone 30 years ago what bile acids do, they would’ve just told you: They just help us to absorb fat.

Then, in the late nineties, it was discovered that there were ligands for certain nuclear receptors and that revolutionized the world of the research on bile acids and suddenly they were interesting again. So we might discover other things like this for other lipids as well. We still discover relatively recently things that we thought we knew and then find entirely new ways that they can regulate biology as well.

Robert: Maybe I forgot something. We also have also Phosphoinositols. They’re activated by a pathway and they’re shut down then becoming diacylglycerols or other metabolite entities, and then act again as signaling molecules on calcium signaling. This is somewhere in between membrane lipid and a lipid is binding to receptor.

Alice: There are lots of possibilities. So before we go to the specific paper we’ll discuss, I would like to ask you a generic question about how you handle data interpretation in general. Let’s say you have a typical lipidomic data set. Do you have a strict workflow of how you analyze the data or do you tailor this to every paper and every study and question that you want to address?

Robert: This is a complicated question. We are tailoring this most often to the model system because somehow there is always a bit of knowledge known. For example, if you’re working on synaptic junctions with the synaptic cleft, you have to consider gangliosides.

So if you are in the platelet regime you have to consider oxylipins and molecules like lysosphingolipids, which are known to do something to platelets. So it has to be a little bit tailored and there we try the following thing. So we have a direct infusion essay where we cover all the phosphor lipid triacylglycerols and cholesterolesters (the bulk lipids), and then we have special targeted workflow for sphingolipids, oxylipins, PIPs, for example.

Alice: These targeted workflows, they do special kind of combinations of lipids because they have specific structures and combining them differently because of the way they are made or what kind of specific workflows are you talking about there?

Robert: So the workflows are more or less analytical tailored. They’re about accessibility – The extraction methods are the same. But for sphingolipids you would like to get rid of the phospholipids. Okay. So you keep all the esterbonds with alkaline hydrolysis.

For PIPs you would like to extract very sour. So you reduce the pH and then things like this. So they’re a little bit different requirements on the detection. You have bulk lipids which are very abundant and then other lipids. PIPs are very low abundant and certain sphingolipids are also very low abundant.

For them you have to go into a more a targeted direction whereas, if you go for general lipidomics you can stay more untargeted, discovery like extraction by covering with standard certain lipid classes. – You always want to work quantitatively.

Alice: Do you often then go to the route where you would start with untargeted to figure out which are the most relevant classes of lipids and then maybe go more towards targeted.

Robert: Somehow we are we doing this? The bulk lipid analysis is untargeted, anyway. For the targeted things, you cannot really do untargeted because you are losing sensitivity. What we do? We open the panel using lipid creators, a software tool, which we have developed.

Then we are putting a lot of information inside and do a screen. This is not really untargeted. However you can cover in some runs a lot of different lipids with the speed of modern instruments. And then we shrink it down to a defined target list on the end of the day of lipids, which are in the systems. The key is that we can do the permutations of the target list at the beginning to figure out what is in our sample.

Alice: This is to analyze the sample and then identify the lipids. Then let’s say you have now a matrix of your data where you have all your samples or your piece that you’ve identified. What do you do from there? Do you do more data driven analysis where you do a lot of correlations between different sets of lipids or do you also work on a more knowledge base where you would use pathway analysis or chemistry driven analysis where you group things per class? Do you have a typical workflow for that?

Robert: We can create this matrix because we are quantitative. So we can get all our workflows together and we creating a big matrix. Currently, we do two things. The first is more a mathematical correlation driven approach (using Pearson correlation get different clusters and different clusters using correlation and anticorrelation then gives you a network).

So it’s still a network and you can easily visualize what lipid classes are there and what is changing there? With one look you can see the changes in the system. You don´t fully rely on this information but you also don’t lose information. This is the first approach. The other approach we call the chemical space. There’s a model developed by Dominic Schwudtke and here you can calculate distances in between different lipidomes. This helps also, if you go across species which have different lipid entities, so different lipid species in inside. There are species of animals and species of lipids.

Alice: I also feel like I have to explain which species I’m talking about very often.

Robert: Since you have this chemical space model you can start comparing the things and visualize the things. At the beginning we started, of course, with some reaction pathways and network biology-driven things. However, we came very quickly to an end because with lipids, not all the information are in reactome or in KEGGs. There are some things already are now getting more and more established.

However, there’s still a lack of knowledge because nobody doing the hard groundwork where in molecular biology to find out what a substrate specificity a certain ceramide synthesis has and what not. This knowledge is missing in biology and therefore the databases are not becoming better (The information is just not generated). This is a problem. So therefore we decided to go to more data driven approaches.

Alice: And this is something that you can afford as well when you have a relatively large data set where, I think, in the paper we’re talking about 400 lipids or something that were measured. So you have a matrix that’s big enough that something comes out of it. Of course, when you measure 12 metabolites, unless they’re very strongly related to each other because you chose them like this you have less chances to find something interesting. With larger data sets it starts to really be powerful to use this kind of correlation.

Robert: But even if it is a larger dataset. So the 11 lipids, which are highly correlated, which was a pop up in this data, said as a very dense of course goal. Of course. Yeah. And then you’ll see it straight.

Alice: Let’s talk about the paper. The paper I wanted to discuss with you is titled: “Identification of key lipids critical for platelet activation by comprehensive analysis of the platelet lipidome” you are last and corresponding author on that paper with Oliver Borscht. And this was published in blood in 2018.

I wanted to talk about this paper because there are a couple of figures I really like about it. It’s a good example of a common approach that is to take a large data set and that I’m really mostly talking about the lipidomic data set. So there’s a whole story behind it, about the biology that I will let you tell in a second.

The thing I really liked about the data approach in here is that you have a kind of

  1. Overall description of the omic data set
  2. Very tailored tools that allow us to look at it from different angles to have an idea of what are the important, features in it. What is the spread of the data set in the chemical space and all this kind of things
  3. Another couple of figures where you compare with and without activation of the platelets to see what is the impact on the lipidome and
  4. Look at one specific pathway and see by looking at the different species of the different lipids involved in the pathway, how you can actually follow the pathway when you activate the platelets.

I found it really elegant. Could you say a word about the biological background about this study? Just what were you trying to look at? And, why was lipidomics an interesting method to look at it in that system?

Robert: First, thanks that you like to paper – Nice to hear. Why we did this: Because lot of things happen in the platelet lipidome! Platelets are changing drastically the morphology upon activation. Fatty acid pathways, the PIP pathway, phosphatidylserins which switch to the outside, different phospholipases involved in calcium signaling linking back to oxylipins, autocrine feedback loops to the entire system where lipids are responsible for. So really a lot goes on.

However, dynamic changes also mean instability. So, you don’t want to have something dynamically changing where you have to keep the sinks intact, at least for a certain time. This was contradictory in our opinion and therefore we went in to make is this Quantitative platelet Atlas. We used different stimuli to look how big are the changes? What is the impact on the membrane? These were the interesting things to find out.

First of all, which percentage of lipids are changing either within five minutes. In five minutes you have clotting. (You poke yourself. How long are you bleeding? Five minutes. – Not longer than five minutes. Depends on the size of the wound. But you should not bleed very long. So it must be a quick process. Five minutes seems to be okay for it. We discovered that just 20% are changing.

And this also stimulus specific. There was another very interesting catch that just 20% lipids making up 80% of the entire lipidome. And a lot of these lipids were containing PUFA lipids. Especially here arachidonic acid. This was then the next step.

We remembered all the classes at university in biochemistry – How signaling of oxylipins is working. You need a precursor – And this precursor is arachidonic acid. So we focused subsequently on our arachidonic acid signaling (thromboxane, prostaglandins, …).

Alice: Yes. Let’s look first at the description of the dataset in general. Kind of this landscape view of what the dataset is; what does it look like? And this in the paper is figure three. So there are a couple of graphs and the one you mentioned, for example, where you see that these 15 lipids that are actually corresponding to about 70% of the lipidome in the platelets and the rest (the other 380 something that you measured).

So it’s really intense. So cholesterol being the first component. And then, as you mentioned, a lot of PUFAs. You also compare human and mice platelet lipidome and check to see if you find the same kind of species in both. – Sometimes it’s the case. Sometimes there are differences. This is also interesting when you think, okay. We might be interested in studying, plate activation in the mouse model. So we should be aware that there are things we will see there that we might not see in humans and vice versa.

This is always the main thing you have to keep in mind when you use animal models to try and better understand human biology. And then there’s this beautiful figure 3e where you break down the lipidome into different classes and beautifully visualize with the different classes and the order of magnitude of concentration that you find for each member of those subgroups of the lipidome.

That really gives a flavor of the spread of concentration that you already mentioned before and also of the variety of different types of lipids that we can look at. – Because, if like me, you know little about lipids before getting into this field; just what you learned in school and on the benches of the university – this is not at all what I remembered from my classes 20 years ago when I learnt biochemistry and molecular biology. It didn’t go into that detail. And I hope that now we’re including this into the teaching.

It’s really a beautiful new world of biology that is super interesting to investigate. In omics papers you often have this one or two figures that give us the vision of how broad the data set is, where it can take us, and what makes the main components of it. – So you also see the contribution of the different classes of lipids to the overall lipidome. The same thing is in the next figure when you compare then activated versus resting platelet lipidome that you see then which are the classes that change and in which direction does it move.

And one figure I really liked was figure 4c where you make this very simple graph where you show the lipid-lipid correlation within class and between class. This is very interesting thing to see because you have correlation between one lipid and another lipid, of course, but if that lipid is from the same class, then that would make sense (e.g. you have a whole pathway or a whole metabolic biosynthetic pathway that moves in one direction), but when it’s between classes, this can be for different reasons.

So when you have a correlation between classes it could be a typical example if they share a common fatty acid that they incorporate in different types of complex lipids and then this kind of makes them move together because we have an excess or lack of a certain component.

Robert: Exactly. That’s something coming from the outside.

Alice: It’s like one of the small building blocks is coming to all those groups and they all move in the same direction, which, you can guess, or at least you can think of looking at it when you do this kind of simple correlation matrices, looking at the, at what you can see within class and between class. I thought this was a very simple graph to make but it can be really informative.

And this is something that’s really specific to lipids. If you work in small molecules, you can make a similar kind of, graph but you wouldn’t come to the same conclusions because they usually don’t share the same building blocks. What I’ve also found interesting from at biological point of view is that when you activate the platelets with thrombin, CRP, or both, you don’t get the same impact on the lipidome. So platelet activation is not just platelet activation.

Robert: That´s true. There are different receptors; a lot of them.

Alice: It’s like it’s platelet activation, but it’s not exactly the same. And actually, in the figure where you focus more on the oxylipins and the prostaglandins, then you see that it’s only when you combine both that you actually get this really intensive impact on the arachidonic pathway.

To go back to figure 4 where you compare activated versus non-activated platelets you have one of the most efficient ways of analyzing large data sets is this correlation matrices and then finding clusters of lipids that correlate together and looking inside those clusters. And here I always focus a lot on the visualization because I liked the way you visualize this.

So you have the typical correlation matrix and then you extract each cluster and open it to see what’s inside on the class level in the next graph. And then you take these components of these clusters and put them inside cytoscape to build the networks and then see how the networks light up with the different activation strategies that you have.
My main question is it how you always do things or do you do a lot of trial and errors and a lot of exploratory analysis and trying visualizations to find most efficient ways to show it in the best way in the figures.

How does that work on your side?

Robert: This depends on the data that we are looking at. However, if you go through the data, there are certain key features which just jump into your eyes. Then it’s a long way of proving this and connecting this to the greater lipid environment.

And I think, such tool such as Pearson correlation cluster approach are just standard tools in omics but together with these networks it is quite powerful to visualize lipidomes. We don’t always do it in such a way – This is just data driven.

Say we would go, for example, to a deeper level and the human plate lipidome and mouse lipidome would be completely separated. You cannot do this anymore because the lipids are not shared. So if you cannot compare A against B you must come up with something that adds up. Therefore, for example, you could use the chemical space to identify chemical features of a lipids.

Even if the chain lengths are different and still you somehow get the same hydrophobicity, the chemical space model would better reflect what’s going on in the system. In this example, a different class of lipids is taking over the function of a given class of lipids in the comparing system.

However, if it’s totally the same set, then this is the perfect to go. If they are more distinct, I would slightly change the direction. At the end of the day, of course, you have to adapt this in the best way to bring out what you want to prove with the experiment.

Alice: Once you have the result of the correlation and the components of the cluster, you could also make a heat map for example, and you could show how the heat map lights up with the different activation methods. – Would that be the same for you or do you see a benefit of using the network? There is an inherent component of the network that shows the hotspots and that shows, which ones are heavily linked to others.

Robert: If you would use hierarchical clustering you would consider everything. For certain components you would’ve a nice cluster on a certain point, as you can see it already in the Pearson correlation and Cosine clustering on top. Then you have this cluster there and still you ask yourself “What is going on there?”, “How good are they correlated?”. And you don’t have the cutoff – With the correlation, you can define a cutoff, just go inside and look for the highly correlated or anticorrelated molecules at once.

You look at them at once. With the other visualization you cannot look to the things at once; they’re on different spots. And here you can do this at once and on top of this, you can put mappings of classes onto this network before that lets the reader instantly understand what classes are involved.. In your hierarchical clustering map you can do this, too, with extensive labeling but I think this is already information overkill for a visualization. To summarize – On the one hand we reduce information in order to get the right information to the reader at the right time.

Alice: It’s a communication game in the end. How long does it take to make an analysis like this? In terms of scale what would you say in general? I think people underestimate how much time it takes.

Robert: The most challenging thing is to get the matrix done. If you have the matrix right, then it’s fast. Then you talk about two weeks to get to something which you can present. Just to get some data to look it is just some hours because it’s all scripted. In order that you are happy with this, I would say, two weeks. – But the data analysis upfront is rather two months.

Alice: I expected something in the range of months like overall after the experiment is done and everything like it does take a lot of time to process data. Of course, it always depends on the question you want to answer in the first place, but still, even if you know what your question you want to answer there are still different ways to get to the answer.