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Imaging dataset from: Longitudinal tracking of acute kidney injury reveals injury propagation along the nephron

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May 23, 2023 version files 44.30 GB

Abstract

Acute kidney injury (AKI) is a risk factor for chronic kidney disease (CKD), but the cellular mechanisms leading to impaired tubular recovery and subsequent AKI-CKD-transition are not yet fully understood. In this study, we combined transgenic mice to monitor proliferation in vivo, a novel injury model of AKI, in which ischemia-reperfusion injury (IRI) was induced in half of the kidney (partial-IRI), and serial intravital 2-photon imaging via an Abdominal Imaging Window (AIW) to track tissue remodeling in post- and non-ischemic kidney regions longitudinally over 3 weeks. Our results and novel findings are presented in the associated pre-print "Longitudinal tracking of acute kidney injury reveals injury propagation along the nephron". 
In this dataset, we provide the imaging data which we acquired during the study. Overall, the dataset consists of: #1: serial intravital imaging via 2-photon microscopy and genetic identification of proliferating cells in kidneys undergoing partial IRI; #2: serial intravital imaging via 2-photon microscopy and genetic identification of proliferating cells of control, uninjured kidneys; #3: intravital imaging of kidney epithelial cells during intravenous injection of a fluorescent bolus to determine the identity of tubular segments across the nephron; #4: intravital imaging of and genetic identification of proliferating cells after selective laser injury of S1 proximal tubule cells; #5 in vivo and ex-vivo imaging of kidney and kidney slices after VCAM1 staining. The data descriptors provide detailed guidelines on how to place individual imaging files in the context of the pathophysiological states used in the study. 
Our goal is to provide an organized and accessible unique and unprocessed in vivo imaging dataset that documents in vivo tubule cell remodeling in the mouse kidney longitudinally and during physiological and pathological conditions. Sharing of this dataset ensures reproducing and expanding of our preclinical findings of tubule injury and (failed) recovery during AKI. Furthermore, this dataset may also be utilized for teaching purposes, as the combination of fluorescent imaging techniques allows the detailed visualization of kidney anatomy and physiology over multiple conditions.