#439 Nephron number determination in whole slide mice kidney specimens using a deep learning assisted disector/fractionator approach
Authors: Klaus Martin , Abdullah Rosch Ronny , LI Qiubo , Walz Christoph , Anders Hans Joachim , Steiger Stefanie 
Published: May 2024

Background and Aims

Nephron number highly varies between different species and within species, ranging in humans from 200 000 to 2 000 000 nephrons per kidney. Low nephron numbers can promote early onset of end-stage kidney disease. Nephron assessment is highly needed for better diagnosis and more targeted treatment. However, its assessment in vivo is not established in clinical practice yet. Pioneer studies suggest nephron number estimation in vivo through a combination of glomerular densities in kidney biopsies together with medical imaging. In contrast, post-mortem nephron assessment either using optical clearing or by exhaustive kidney sectioning is well studied but remains laborious. Here, we propose a new semi-automated nephron counting approach using serial PAS-stained whole slide kidney sections, deep learning segmentation and slide registration.

Method

Mice were sacrificed, kidneys harvested, measured, halved in the coronary plane and then opposingly embedded in paraffin. Subsequently, the entire kidney was sectioned with 10 µm thickness per slide. Tissue was stained with PAS and slides were digitalized using Aperio GT 450 DX scanner (Leica, Wetzlar, Germany) (Fig. a). Deep learning segmentation of glomeruli was performed in the first and every 10th (reference) section and subsequent (look-up) section using Tensorflow. Obtained segmentation results were manually quality controlled and adjusted, if necessary, in QuPath (Fig. b). Final segmentations of the look-up slides were registered to the reference slides in ImageJ using manual landmarks with the ImageJ BigWarp plugin (Fig. c). Glomeruli being present either in the look-up slide, or in the reference slide were counted as disector particles (Q-). Kidney areas on every examined slide were measured. Total number of glomeruli and total kidney area were determined as previously described (Fig. d).

Results

A kidney from a 12-week-old mouse suffering chronic kidney disease (mouse line Alb-creERT2 (ki/ki); Glut9 (fl/fl)) was used. Kidney weight was 162 mg, length 11 mm, width 6 mm, depth 4 mm and kidney volume 260 mm³. In total the kidney was sectioned in 95 slices. 20 reference sections and its respective look-up sections were stained and digitalized. In total 5981 glomeruli were segmented in 40 kidney slices using the deep learning algorithm. Glomerular segmentation quality was verified by two independent observers (mean dice score 0.79). Inaccuracies, if any, where manually corrected. For image registration in average 18.1 landmarks per slide were created within the ImageJ BigWarp plugin. Registration took in average 5 minutes per slide. Glomeruli without match (disector particles, Q-) were automatically counted. Number of disector particles (Q-) ranged from 32 to 244 per slide. Total nephron number was 11.085 and kidney area 210 mm³. The differences to the manual measured volume (260 mm³) might be explained by discard of small kidney fractions for the used sectioning technique.

Conclusion

We report, to our knowledge, the first deep learning assisted disector/fractionator approach to determine nephron number in whole slide kidney specimens. This method requires no physical disector setup, is scalable, cheap, time efficient, and unbiased. The method was so far validated in a mouse model of chronic kidney disease. The method will further be validated on different animal models. Additionally, occurrence of atubular glomeruli will be considered in nephron number determination. Furthermore, the integration of GFR measurements might allow the calculation of mean single nephron GFR and might together with automated glomerular area measurements unravel pathophysiologic processes in kidney ageing and disease.