Single-cell Transcriptomics of the Pancreas: Experimental and Analytical Tools to Study Organ Development and Composition

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.