Elementor #1364


In-Vitro 3D Glomerular System for Identifying Key Pathogenic Molecules 


Team: Kala Pham
Team members: Crosslee Titus

Project Summary:

Glomerular injury and immune cell infiltration are underlying features involved in multiple renal diseases. Unbiased screens of patient urine and serum have identified a number of potential biomarkers involved in the disease pathogenesis. However, establishing the pathogenic roles of these proteins in disease has been challenging, due to the complicated and time-consuming nature of in-vivo mouse experiments and the inability of 2D culture systems to accurately mimic the in-vivo microenvironments which exist in the renal tissue. We are developing a 3D model of the glomerulus using the renal cells (podocytes, endothelial cells, mesangial cells, and tubule cells) cultured from mouse glomeruli. When these individually cultured cells are brought together in the 3D co-culture, the cell-cell signaling pathways and feedback circuits which exist within these different cells are restored, mimicking the in-vivo environment.

What is already known in the field?

  • In traditional models of the glomerulus, in-vivo experiments are time-consuming and validation of biomarkers is difficult.
  • By using cell cultures, researchers only gain access to the 2D environment, which has a different gene profile and is unable to mimic microenvironments

What is new?

  • The creation of a 3D glomerular platform to identify key pathogenic biomolecules
  • Correlating gene expression in mixed culture with biomolecule data to identify genes involved in renal diseases

Why is this important?

  • This high-throughput 3D glomerular platform will help in identifying the key biomolecules and their relevant signaling pathways involved in disease pathogenesis of multiple renal diseases, and therapeutic interventions targeting these biomolecules could be beneficial in the treatment of these diseases

Ongoing/future steps:

  • Create transwell glomerulus
  • Expose mixed culture to stimulus (a) high glucose-diabetic model (b) TNFa (c) IFNa