Abstract

The kidney’s inherent complexity has made identifying cell-specific pathways challenging, particularly when temporally associating them with the dynamic pathophysiology of acute kidney injury (AKI). Here, we combine renal cell-specific luciferase reporter mice using a chemoselective luciferin to guide the acquisition of cell-specific transcriptional changes in C57BL/6 background mice. Hydrogen peroxide generation, a common mechanism of tissue damage, was tracked using a peroxy-caged-luciferin to identify optimum time points for immunoprecipitation of labeled ribosomes for RNA-sequencing. Together, these tools revealed a profound impact of AKI on mitochondrial pathways in the collecting duct. In fact, targeting the mitochondria with an antioxidant, ameliorated not only hydrogen peroxide generation, but also significantly reduced oxidative stress and the expression of the AKI biomarker, LCN2. This integrative approach of coupling physiological imaging with transcriptomics and drug testing revealed how the collecting duct responds to AKI and opens new venues for cell-specific predictive monitoring and treatment.

Miyazaki, Gharib, Hsu et al. use a combined cell-specific luciferase reporter system and chemo-selective substrate to identify mouse tissues at risk of renal injury. The platform can be used to identify cell-specific pathophysiological events and transcriptional changes, finding potential therapeutic targets.

Details

Title
Cell-specific image-guided transcriptomics identifies complex injuries caused by ischemic acute kidney injury in mice
Author
Miyazaki Tomoaki 1   VIAFID ORCID Logo  ; Gharib, Sina A 2 ; Hsu, Yun-Wei A 3   VIAFID ORCID Logo  ; Xu, Katherine 4   VIAFID ORCID Logo  ; Khodakivskyi Pavlo 5   VIAFID ORCID Logo  ; Kobayashi Akio 3 ; Paragas Jason 6 ; Klose, Alexander D 7 ; Francis, Kevin P 8 ; Dubikovskaya Elena 5 ; Page-McCaw, Patrick S 9   VIAFID ORCID Logo  ; Barasch, Jonathan 4 ; Paragas Neal 3   VIAFID ORCID Logo 

 University of Washington, Division of Nephrology, Department of Medicine, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657); Showa University, Division of Nephrology, Department of Medicine, Yokohama, Japan (GRID:grid.410714.7) (ISNI:0000 0000 8864 3422) 
 University of Washington, Computational Medicine Core, Center for Lung Biology, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) 
 University of Washington, Division of Nephrology, Department of Medicine, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657) 
 Columbia University, Renal Division, Department of Medicine, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729) 
 Institute of Chemical Sciences and Engineering, Swiss Federal Institute of Technology of Lausanne (EPFL), Lausanne, Switzerland (GRID:grid.5333.6) (ISNI:0000000121839049) 
 Lawrence Livermore Labs, Livermore, USA (GRID:grid.34477.33) 
 InVivo Analytics, Inc., New York, USA (GRID:grid.504638.c) 
 PerkinElmer, Inc., Hopkinton, USA (GRID:grid.419236.b) (ISNI:0000 0001 2176 1341) 
 Vanderbilt University Medical Center, Division of Nephrology, Department of Medicine, Nashville, USA (GRID:grid.412807.8) (ISNI:0000 0004 1936 9916) 
Publication year
2019
Publication date
2019
Publisher
Nature Publishing Group
e-ISSN
23993642
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2389679560
Copyright
© The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.