Distinct developmental reprogramming footprint of macrophages during acute kidney injury across species.

Acute kidney injury (AKI) is a common finding in hospitalized patients, particularly those who are critically ill. The development of AKI is associated with several adverse outcomes including mortality, morbidity, progression to chronic kidney disease, and an increase in healthcare expenditure. Despite the well-established negative impact of AKI and rigorous efforts to better define, identify, and implement targeted therapies, the overall approach to the treatment of AKI continues to principally encompass supportive measures. This enduring challenge is primarily due to the heterogeneous nature of insults that activate many independent and overlapping molecular pathways. Consequently, it is evident that the identification of common mechanisms that mediate the pathogenesis of AKI, independent of etiology and engaged pathophysiological pathways, is of paramount importance and could lead to the identification of novel therapeutic targets. To better distinguish the commonly modulated mechanisms of AKI, we explored the transcriptional characteristics of human kidney biopsies from patients with acute tubular necrosis (ATN), and acute interstitial nephritis (AIN) using a NanoString inflammation panel. Subsequently, we used publicly available single-cell transcriptional resources to better interpret the generated transcriptional findings. Our findings identify robust acute kidney injury (AKI-induced) developmental reprogramming of macrophages (MΦ) with the expansion of C1Q+, CD163+ MΦ that is independent of the etiology of AKI and conserved across mouse and human species. These results would expand the current understanding of the pathophysiology of AKI and potentially offer novel targets for additional studies to enhance the translational transition of AKI research.NEW & NOTEWORTHY Our findings identify robust acute kidney injury (AKI)-induced developmental reprogramming of macrophages (MΦ) with the expansion of C1Q+, CD163+ MΦ that is independent of the etiology of AKI and conserved across mouse and human species.

Accuracy and processing time of kidney volume measurement methods in rodents polycystic kidney disease models: superiority of semiautomated kidney segmentation.

Measurement of total kidney volume (TKV) using magnetic resonance imaging (MRI) is a valuable approach for monitoring disease progression in autosomal dominant polycystic kidney disease (PKD) and is becoming more common in preclinical studies using animal models. Manual contouring of kidney MRI areas [i.e., manual method (MM)] is a conventional, but time-consuming, way to determine TKV. We developed a template-based semiautomatic image segmentation method (SAM) and validated it in three commonly used PKD models: Cys1cpk/cpk mice, Pkd1RC/RC mice, and Pkhd1pck/pck rats (n = 10 per model). We compared SAM-based TKV with that obtained by clinical alternatives including the ellipsoid formula-based method (EM) using three kidney dimensions, the longest kidney length method (LM), and MM, which is considered the gold standard. Both SAM and EM presented high accuracy in TKV assessment in Cys1cpk/cpk mice [interclass correlation coefficient (ICC) ≥ 0.94]. SAM was superior to EM and LM in Pkd1RC/RC mice (ICC = 0.87, 0.74, and <0.10 for SAM, EM, and LM, respectively) and Pkhd1pck/pck rats (ICC = 0.59, <0.10, and <0.10, respectively). Also, SAM outperformed EM in processing time in Cys1cpk/cpk mice (3.6 ± 0.6 vs. 4.4 ± 0.7 min/kidney) and Pkd1RC/RC mice (3.1 ± 0.4 vs. 7.1 ± 2.6 min/kidney, both P < 0.001) but not in Pkhd1PCK/PCK rats (3.7 ± 0.8 vs. 3.2 ± 0.5 min/kidney). LM was the fastest (∼1 min) but correlated most poorly with MM-based TKV in all studied models. Processing times by MM were longer for Cys1cpk/cpk mice, Pkd1RC/RC mice, and Pkhd1pck.pck rats (66.1 ± 7.3, 38.3 ± 7.5, and 29.2 ± 3.5 min). In summary, SAM is a fast and accurate method to determine TKV in mouse and rat PKD models.NEW & NOTEWORTHY Total kidney volume (TKV) is a valuable readout in preclinical studies for autosomal dominant and autosomal recessive polycystic kidney diseases (ADPKD and ARPKD). Since conventional TKV assessment by manual contouring of kidney areas in all images is time-consuming, we developed a template-based semiautomatic image segmentation method (SAM) and validated it in three commonly used ADPKD and ARPKD models. SAM-based TKV measurements were fast, highly reproducible, and accurate across mouse and rat ARPKD and ADPKD models.