We have covered several subjects in this thesis that can be classified into two main research efforts: In the first, we develop experimental and analytical techniques to automate single-cell mRNA sequencing and to subsequently analyze the data generated with this technique. The technical part of automating single-cell transcriptomics is described in the Figure 1 of chapter 3, while the first and last chapter are dedicated to algorithms that deal with single-cell data. The second research effort is the application of these techniques to pancreatic biology in an attempt to address some of the open questions in the field. The work described here formed part of the progress that was made in several labs across the world in the “second wave” of single-cell transcriptomics (see introduction). In these last five years our lab moved from manually processing dozens to hundreds of cells per week to routinely sequencing thousands of cells from primary tissue in a single day. On the computational side, we took part in the development of a set of algorithms that allow the user to cluster single-cell transcriptomics data, infer lineages between cell types and predict FACS gates that can be used to purify cell types without the need for fluorescent reporters or antibodies. We applied these methods to the developing mouse and the adult human pancreas, which yielded two resources that can be used to both mine for cell type-specific expression of a gene of choice in the adult pancreas and to see if the expression of this gene changes during pancreatic development. We have validated some of the genes found in these chapters, but more work is required to understand the function of these genes in pancreas biology. For now, I hope others find the progress we made in single-cell sequencing to shine light on pancreas biology, useful. I for one wholeheartedly enjoyed working on it.
|Award date||18 Jan 2018|
|Place of Publication||Utrecht|
|Publication status||Published - 18 Jan 2018|