Paul Kiessling
A Molecular Biologist by training, Paul received his undergraduate degrees from the Universities of Mannheim and Aachen, where he worked on therapeutic antibody discovery and the optimization of small molecular drugs. Since 2022, he has been a PhD student in the newly established Kuppe Lab at the University Hospital RWTH in Aachen. The focus of his research is on cardiovascular and kidney diseases, which he investigates using single-cell analysis, spatial transcriptomics, and CRISPR experiments. His work aims to leverage these cutting-edge techniques to uncover new insights into disease mechanisms and potential therapeutic targets.
Sessions
Heart attacks are one of the leading causes of death worldwide, affecting almost 3 million people annually. Several open questions regarding disease progression and post-infarction tissue remodeling can only be answered by analyzing intact tissues. A new generation of assays now makes it possible to measure RNA in patient material with high multiplexity and at subcellular resolution.
We have assembled one of the largest human datasets to date. The Xenium and MERFISH technologies allowed us to measure more than 50 samples from patients suffering acutely and chronically from the effects of myocardial infarction, as well as healthy control tissues. This wealth of new data at unprecedented resolution enables novel types of analysis. We approached this challenge with bespoke optimal transport algorithms that help us integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic datasets and analyze them at the cohort level.
Our approach allows us to identify tipping points in the disease trajectory that could potentially be used for therapeutic intervention. Crucially, our measurements are well-suited to record cellular communication events, identify spatial domains around the infarct border, and even decipher RNA localization patterns inside cells—areas of analysis that were closed off in more coarsely resolved assays..
We aim to illustrate what this new generation of spatial experiments can offer and share key learnings from processing these novel datasets.