Computer-assisted imaging algorithms facilitate histomorphometric quantification of kidney damage in rodent renal...

Area of Science:

• Nephrology
• Pathology
• Digital Pathology

Background:

• Surgical 5/6 nephrectomy and adenine-induced kidney failure are common rat models for studying progressive renal failure.
• These models exhibit significant kidney morphological changes, making them suitable for evaluating interventions.
• Quantification of these changes is crucial for assessing the efficacy of prophylactic or therapeutic strategies.

Purpose of the Study:

• To evaluate the utility of Aperio Genie Pattern Recognition technology, including Positive Pixel Count, Nuclear, and Rare Event algorithms, for quantifying histological changes in rat renal failure models.
• To assess the reliability and reproducibility of these algorithms in characterizing renal injury.

Main Methods:

• Digitized whole kidney sagittal sections from rat models of renal failure were analyzed.
• Stains included hematoxylin and eosin, and immunohistochemistry for nestin (glomeruli, regenerating tubules, myofibroblasts) and polymorphonuclear neutrophils (PMNs).
• Aperio Genie Pattern Recognition technology with specific algorithms was employed for quantitative analysis.

Main Results:

• Image analysis enabled rapid and accurate quantification of histopathologic changes, including glomerular cellularity/expansion, tubular dilatation/degeneration, inflammation, and mineral aggregation.
• The algorithms demonstrated reliable and consistent results across control and experimental groups.
• A quantifiable degree of damage associated with each renal failure model was established.

Conclusions:

• The employed algorithms are effective tools for the uniform and reproducible characterization of renal injury in rat models.
• This quantitative approach aids in the assessment of disease progression and therapeutic interventions in kidney research.