ESR 11: Exploring new ways to study kidney disease at the single-cell level

Supervisor: Magda Grudniewska – Lawton

PhD Student: Claudio Novella Rausell

Short Summary

Polycystic Kidney Disease is progressive inherited kidney disease characterised by the formation of many fluid-filled cysts in the kidneys. The number and size of cysts increase over time, finally causing renal failure. The main focus of our research is to unravel the disease mechanism with the aim to develop therapies. To this aim mouse models, cell lines and organoids are being used with (tissue-specific) disruption of the gene involved. In the current project, we will identify and functionally characterise critical molecular pathways involved in cystic tissues, with the goal to identify key molecules involved in progressive cyst formation. You will apply molecular-biological techniques combined with (single cell) sequencing, bioinformatics, cell biology, microscopic analyses and animal studies.

Scientific strategy


Chronic kidney disease (CKD) is an increasing global health problem. Despite the significant scientific efforts, the precise molecular underpinnings of CKD are yet to be elucidated. The complexity of a human kidney is associated with a large number of specialized cell types that are organized into functionally distinct compartments. Therefore, to address the mechanistic origins of renal disease, it is critical to establish a comprehensive cellular anatomy of the organ. The aim of this project is to generate transcriptional signatures of individual cells during pathogenesis. To that end, the prospective ESR will employ the cutting-edge single cell RNA sequencing (scRNAseq) technology coupled with a sophisticated Bioinformatics (Bio-IT) tools. He/she will design a complete workflow for scRNAseq experiments and create an efficient Bio-IT platform recognizing different cell types and highlighting significant changes in expression profiles of analyzed cells. The workflow and the platform will be then validated using Polycystic Kidney Models (PKD), which will provide insights into molecular determinants of CKD progression and treatment stratification.

Expected Results:

  1. Generation of state-of-the-art bioinformatic platform for the analysis of scRNAseq CKD data;
  2. Validation of the BioIT platform using PKD models in collaboration with ESR10;
  3. Identification of molecular determinants of CKD progression and treatment stratification in collaboration with ESR9.