Engineering Strategies to Move from Understanding to Steering Renal Tubulogenesis

Rebuilding the kidney in the context of tissue engineering offers a major challenge as the organ is structurally complex and has a high variety of specific functions. Recreation of kidney function is inherently connected to the formation of tubules since the functional subunit of the kidney, the nephron, is based on tubular structures. In vivo, tubulogenesis culminates in a perfectly shaped, patterned, and functional renal tubule via different morphogenic processes that depend on delicately orchestrated chemical, physical, and mechanical interactions between cells and between cells and their microenvironment. This review summarizes the current understanding of the role of the microenvironment in the morphogenic processes involved in in vivo renal tubulogenesis. We highlight the current state-of-the-art of renal tubular engineering and provide a view on the design elements that can be extracted from these studies. Next, we discuss how computational modeling can aid in specifying and identifying design parameters and provide directions on how these design parameters can be incorporated in biomaterials for the purpose of engineering renal tubulogenesis. Finally, we propose that a step-by-step reciprocal interaction between understanding and engineering is necessary to effectively guide renal tubulogenesis. Impact statement Tubular tissue engineering lies at the foundation of regenerating kidney tissue function, as the functional subunit of the kidney, the nephron, is based on tubular structures. Guiding renal tubulogenesis toward functional renal tubules requires in-depth knowledge of the developmental processes that lead to the formation of native tubules as well as engineering approaches to steer these processes. In this study, we review the role of the microenvironment in the developmental processes that lead to functional renal tubules and give directions how this knowledge can be harnessed for biomaterial-based tubular engineering using computational models.