Sex Differences in Glomerular Lesions, in Atherosclerosis Progression, and in the Response to Angiotensin-Converting Enzyme Inhibitors in the ApoE−/− Mice Model

Figures

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  Figure 1. Treatment with enalapril does not regress atherosclerotic lesions in ApoE−/− mice. (A) ORO staining of a representative aortic sinus of 22-week-old ApoE−/− mice, female (left) or male (right), after 4 weeks of enalapril, 5 mg/kg/day, or PBS. Scale bar = 500 μm. Black arrows indicate ATH. (B) Lesion quantification was performed with ImageJ2 v 2.9.0/1.53t using the sum of all the individual areas measured throughout the valve (6–8 sections). Data are represented as mean ± SEM. (C) Representative en face aortas stained with ORO. Scale bar = 5000 μm. Red stains show ATH plaques. (D) Quantification of the ORO-positive areas of the en face images. Enalapril reduced the number of plaques in ApoE−/− males compared with the PBS group. (E) Representative aortic sinus stained with Masson’s trichrome for quantification of collagen patches (in blue). Scale bar = 500 μm. (F) Quantification of Collagen deposition in aortic roots was similar in all groups. Kruskall Wallis test; ** p < 0.01. Abbreviations: ORO: Oil Red O; M: Male; F: Female; PBS: phosphate-buffered saline; ns = nonsignificant; SEM: Standard error of the mean.

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  Figure 2. Glomerular injury was increased in male ApoE−/− mice. (A) Representative glomerulus (G) from females (left) or males (right). Green arrows indicate urinary space (upper), while foam cells, and parietal epithelial cells are shown by letters (FC and PECS, respectively). The window in the right-upper panel shows activated PECs. (B) Quantification of the urinary space in 15 glomeruli from each mouse with ImageJ2 v 2.9.0/1.53t (n = 6 male; n = 6 female) showing its reduction in males. Scale bar = 100 μm. (C) Quantification of mean mesangial cellularity showed no sex-associated differences or according to the enalapril treatment. (D) Quantification of activated PECs in glomeruli showed their increase in males. (E) Representative glomerulus showing macrophages as F4/80+ cells (black arrows). (F) Quantification of total macrophage (F4/80+ cells) infiltration. (G) Representative glomerulus showing NF-κB+ cells (black arrows). (H) Quantification of NF-κB+ cells. Kruskal-Wallis was performed for statistical analyses; * p < 0.01; ** p < 0.0001. Abbreviations: G: Glomerulus; PECs: parietal epithelial cells; FC: foam cells, M: Male; F: female; PBS: phosphate-buffered saline; ns = nonsignificant.

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  Figure 3. Examples of deep learning quantification of glomerular lesions. (A) Representative whole slide image (WSI) used for glomerular selection in QuPath (QUP) software. Scale bar = 400 μm). (B) A total number of 141 glomeruli were selected in QuPath (ORIGINAL) and 4 categories defined (urinary space, glomerulus, tuff area, and exterior) to extract a mask using a QUP script (MASK). (C) Representative loss function plot of trained DeepMIB2 software. Blue lines in the plot showed that the system was trained and stabilized around itineration 100 (x axis) with 97.72% of accuracy while the validation phase (red lines) was stabilized around 50 itinerations with 94.88% of accuracy. Information about the number of itinerations and training progress is also shown. (D) A representative glomerulus (ORIGINAL) loaded into the DeepMIB2 trained and validated network as a test-set category for class prediction for PBS (upper images) and enalapril-treated (lower images) groups; Scale bar = 100 μm. The DeepMIB2 software predicted the glomerular (brown area in the PREDICTED images) and urinary space areas (green area from PREDICTED images); Scale bar =100 μm. (E) Quantification of normalized urinary space…

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  Figure 4. Diagram of the experimental study design, presenting the mice groups, chronogram in weeks, interventions, and sample collection.