User-friendly microfluidic system reveals native-like morphological and transcriptomic phenotypes induced by shear stress in proximal tubule epithelium

Creators: Khalil, Natalie N.¹; Petersen, Andrew P.¹; Song, Cheng J.^{1, 2}; Chen, Yibu¹; Takamoto, Kaelyn¹; Kellogg, Austin C.¹; Chen, Elaine Zhelan¹; McMahon, Andrew P.¹; McCain, Megan L.¹

1. University of Southern California
2. Amgen Research, Cardiometabolic Disorders, San Francisco, California 94080, USA

Abstract

Drug-induced nephrotoxicity is a leading cause of drug attrition, partly due to the limited relevance of pre-clinical models of the proximal tubule. Culturing proximal tubule epithelial cells (PTECs) under fluid flow to mimic physiological shear stress has been shown to improve select phenotypes, but existing flow systems are expensive and difficult to implement by non-experts in microfluidics. Here, we designed and fabricated an accessible and modular flow system for culturing PTECs under physiological shear stress, which induced native-like cuboidal morphology, downregulated pathways associated with hypoxia, stress, and injury, and upregulated xenobiotic metabolism pathways. We also compared the expression profiles of shear-dependent genes in our in vitro PTEC tissues to that of ex vivo proximal tubules and observed stronger clustering between ex vivo proximal tubules and PTECs under physiological shear stress relative to PTECs under negligible shear stress. Together, these data illustrate the utility of our user-friendly flow system and highlight the role of shear stress in promoting native-like morphological and transcriptomic phenotypes in PTECs in vitro, which is critical for developing more relevant pre-clinical models of the proximal tubule for drug screening or disease modeling.

Copyright and License

C 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)

Data Availability

The data that support the findings of this study are openly available in NCBI’s Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE221871, Ref. 51.

Acknowledgement

This work was funded by the National Institute of Biomedical Imaging and Bioengineering (R21EB025534 to M.L.M. and A.P.M.), USC Graduate School Rose Hills Fellowship (to A.P.P.), NSF Graduate Research Fellowship Program Grant No. DGE-1842487 (to N.N.K.), the USC Graduate School Provost Fellowship (to N.N.K.), and the Borchard Foundation. We also thank the USC Libraries Bioinformatics Service for extensive guidance for analysis of transcriptomic data and for funding the bioinformatics software and computing resources.

Contributions

Natalie N. Khalil and Andrew P. Petersen contributed equally to this work.

Natalie N. Khalil: Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Software (equal); Writing – original draft (equal); Writing – review & editing (equal). Andrew P. Petersen: Investigation (equal); Methodology (equal); Writing – review & editing (equal). Cheng J. Song: Investigation (equal); Methodology (equal); Writing – review & editing (equal). Yibu Chen: Formal analysis (equal); Writing – review & editing (equal). Kaelyn Takamoto: Investigation (equal); Writing – review & editing (equal). Austin C. Kellogg: Investigation (equal); Writing – review & editing (equal). Elaine Zhelan Chen: Investigation (equal); Writing – review & editing (equal). Andrew P. McMahon: Conceptualization (equal); Funding acquisition (equal); Writing – review & editing (equal). Megan Laura McCain: Conceptualization (equal); Funding acquisition (equal); Writing – original draft (equal); Writing – review & editing (equal).

Supplemental Material

The supplementary material contains in-depth assembly instructions, image analysis demonstrations, and gene expression fold-change data.

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