Abstract
During the development of the brain, different types of neuronal and non-neuronal cell types are formed. These cells migrate through the developing brain to arrive at their final location. In addition, they send out axons in order to establish connections with other brain areas. These processes are controlled and guided
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by axon guidance proteins. This ultimately forms a fully functioning brain that performs complex functions. Studying the RNA profile of individual cells provides insight into the heterogeneity, and therefore complexity, of the brain. This can be done with the aid of 'single-cell RNA sequencing'. With this technique, the RNA profile of individual cells is mapped. With this information, the properties of subgroups of cells can be investigated.
An example of a heterogeneous brain region is the habenula. This structure is present in both humans and mice. Research into the function of the habenula has distinguished between two subgroups within the habenula: the medial and lateral habenula. Recently, however, it has been shown that there are more than two subgroups within the habenula. How these subgroups arise during brain development, however, remained unknown.
In chapter 2, we characterize mouse habenula cell types one day before birth, on embryonic day 18, using single-cell RNA sequencing. We find that at the end of embryogenesis multiple subgroups of habenula neurons are already present. In addition, our experiments show that on day 18 actively dividing cells are still present in the habenula. We also describe in detail where different cell types are located in the habenula.
In chapter 3 we investigate the development of habenula neurons during embryogenesis and just after birth in mice. We analyze the RNA profile of developing habenula neurons at five embryonic timepoints and three postnatal timepoints. This allowed us to map how habenula neurons develop and mature during development.
In Chapter 4 we discussed the technical aspects of analyzing data obtained in a single-cell RNA sequencing experiment. Given the high dimensionality of the data, the analysis can be complex. The complexity is increased when one wants to merge data obtained in different labs or from different experiments. However, such a comparison can be of enormous added value as it allows putting newly acquired data into context of previously acquired data. We looked specifically at how to deal with these challenges. A large part of the solution lies in removing batch effects. We tested the effectiveness of different methods that correct for batch effects.
In chapter 5, we investigated the dynamics between axon guidance receptor Neogenin and two of its ligands: the signal proteins RGM and Netrin-1. These three molecules regulate important processes during development and in disease. We found that instead of competing for binding, the two ligands can simultaneously bind the receptor. This has important biological implications because during development and during disease these three molecules are present in overlapping locations in the brain. Our experiments show that the supercomplex suppresses the normal effects that binding of RGM alone or Netrin1 to the receptor Neogenin brings about.
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