What we do

Our group is especially focus on quantitative analysis of gene regulatory networks. Despite a wealth of literature, there is very little overall understanding of how genes are interconnected. This is because the system is so complex that no normal human being could ever grasp all the details. To overcome this we use advanced computational modelling. As functional genomics is progressing, more and more datasets have been acquired in manners that allow computers to compare and integrate these into a common model. To supplement these we are also developing new and efficient wet lab methods to efficiently map all the interactions.

Our lab is using a broad range of readouts to cover all aspects of the T cell:

  • RNA-seq of large and small RNAs
  • ChIP-seq, ATAC and HiC to analyze the chromatin state
  • Mass spectrometry of proteins and metabolites
  • Light microscopy and cryo-EM to capture the morphology
  • Single-cell scale protocols to multiplex the experiments and measure response heterogeneity

We have access to a range of pathogen models, in cell culture and in vivo (mouse and human). To perturb the system we are using CRISPR and/or drug/cytokines in multiwell plates.

 

Toward a quantitative model of T cell regulation

For an engineer or a physicist, biology can be made simple – It is a vector state variable, which is updated given the current state as well as input from the environment. The challenge of our group is to fit such a model, making use of existing knowledge, filling in the gaps wherever needed. But when we lack data we also need to impute whatever we can. There is little room for idealists in biology, we have to make do with what we have!

Our basic regulatory model is focused on transcription factors since we can easily limit ourselves to putative binding sites, which drastically reduces the parameters needed for the model. Onto this network we are then adding perturbed states. A key idea behind the model is that we are trying to solve the full model in one go. Because transcriptional regulation is so intertwined, the only way to beat the complexity is by brute force data collection. We deploy a hybrid explanatory machine learning model / kinetics model for our purpose,

Integrating metabolism

We have found a number of genes controlling metabolism which are different in different T cell states. We also know from the past that metabolites can affect T cell state, such vitamin A able to induce regulatory T cells (these guys are important for modulating the immune system, avoiding autoimmunity). Likewise, calcium signalling is a key part of T cell activation after TCR (T cell receptor) induction. Recently it has been shown that tumours induce steroid biosynthesis in T cells to evade immunity. All that said, there has so far been little effort to link unbiased transcriptomics with metabolism, largely due to poor overlap between these two communities. With the clear importance of metabolism, this is a gap we are trying to close (we define metabolism broadly as the turnover of any non-protein, non-RNA/DNA compounds).

Integrating circadian rhythm

T cells actually move around in your body, depending on the time of the day (and differently in mice and humans!). This is thought to help T cells find pathogens, as well as communicate with other cells in the immune system. Because many researchers have not been aware of this and ignored the effect, we believe many findings might be confounded by this factor. While it is not a major part of our research, this is a factor we are determined to resolve the circadian impact and make sure our model properly takes it into account. What are the signals, what does the T cells know, and how does this system interact with T cells during infection?

Integrating prior knowledge

A large amount of knowledge is already present. This data is however not commonly used in current gene regulatory network reconstruction models, part due to the challenge of integrating very heterogeneous data. However, adding this knowledge constrains the model such that more sensible solutions can be found despite noisy data (picture on the right). We are investing various Bayesian models in which putative interactions can be plugged in as prior distributions, and unlikely links removed entirely. This way hope to include, among other, alternative splicing, post-translational modifications, and protein-protein interactions. The resulting equations require new computational approaches for efficient solving.

Understanding T cells in different tissue contexts

Those who look carefully will see that T cells express different cells in different tissues. Again, this is confounded by the circadian rhythm, but it is clearly quite not that simple – several organs like the testicles and the brain are “immune privileged”. That is, there is a different representation of immune cells, typically less. Possibly because their action would create too much damage if not kept under sufficient control. This process can only be controlled by the surrounding tissue emitting signals telling the T cells to stay away. When this system fails, you risk ending up with diseases such as multiple sclerosis.

Understanding T cells as part of the immune system

Sometimes you forget that T cells are just one component of the immune system. But if you see a change in T cells during a pathogenic condition, was the change directly due to the pathogen or did the signal first get modulated through another cell type? Modelling the full immune system is currently beyond our scope. However, as with the interaction with different tissues, we will have a particular focus on the signal cues from other immune cells. We will do this by screening the impact of all signal molecules we can get our hands on, to build a catalogue of possible interactions.

Understanding T cells during pathogenic conditions

The ultimate goal is to understand the T cells does at work – what are they doing when they detect a parasite, a virus, or cancer, and why? By looking at how the state is perturbed, and cross-checking it with our catalogue of possible interactions, we will find the critical paths of influence (there are likely more than one!). Using this model we will be able to develop better drugs to module the response, strengthening, weakening it or making it more specific. This approach is already widely successful in cancer immunotherapy, but the drugs used are best used as sledge-hammers. How do you make sure the car doesn’t stop? Take out the breaks. How do you make sure a car doesn’t go too fast? Remove the wheels. Obviously such as an approach has strong side-effects, with immuno-therapy sometimes leading to auto-immune responses, and anti-autoimmunity drugs making patients susceptible to viruses. Our long-term goal is to take the toolbox and sharpen the blades even further.

Integrating heterogeneity

The same cell come in many flavours. As many molecules are present at low concentrations, the cells are at mercy of stochasticity. This has been observed since long, simply because not all T cells do not divide at the same rate. This makes well-controlled bulk analyses really challenging. We are resolving the heterogeneity using single-cell technology, enabling RNA-seq and targeted proteomics readouts. Microscopy is of course also a single-cell method. While this is not a complete toolbox yet, we are continuously working on new approaches to increase the resolution and the fidelity of our model.