VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations
René Hägerling, Dominik Drees, Aaron Scherzinger, Cathrin Dierkes, Silvia Martin-Almedina, Stefan Butz, Kristiana Gordon, Michael Schäfers, Klaus Hinrichs, Pia Ostergaard, Dietmar Vestweber, Tobias Goerge, Sahar Mansour, Xiaoyi Jiang, Peter S. Mortimer, Friedemann Kiefer
René Hägerling, Dominik Drees, Aaron Scherzinger, Cathrin Dierkes, Silvia Martin-Almedina, Stefan Butz, Kristiana Gordon, Michael Schäfers, Klaus Hinrichs, Pia Ostergaard, Dietmar Vestweber, Tobias Goerge, Sahar Mansour, Xiaoyi Jiang, Peter S. Mortimer, Friedemann Kiefer
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Clinical Research and Public Health Dermatology Vascular biology

VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations

Abstract

BACKGROUND. Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections. METHODS. VIPAR (volume information-based histopathological analysis by 3D reconstruction and data extraction), a light-sheet microscopy–based approach for the analysis of tissue biopsies, is based on digital reconstruction and visualization of microscopic image stacks. VIPAR allows semiautomated segmentation of the vasculature and subsequent nonbiased extraction of characteristic vessel shape and connectivity parameters. We applied VIPAR to analyze biopsies from healthy lymphedematous and lymphangiomatous skin. RESULTS. Digital 3D reconstruction provided a directly visually interpretable, comprehensive representation of the lymphatic and blood vessels in the analyzed tissue volumes. The most conspicuous features were disrupted lymphatic vessels in lymphedematous skin and a hyperplasia (4.36-fold lymphatic vessel volume increase) in the lymphangiomatous skin. Both abnormalities were detected by the connectivity analysis based on extracted vessel shape and structure data. The quantitative evaluation of extracted data revealed a significant reduction of lymphatic segment length (51.3% and 54.2%) and straightness (89.2% and 83.7%) for lymphedematous and lymphangiomatous skin, respectively. Blood vessel length was significantly increased in the lymphangiomatous sample (239.3%). CONCLUSION. VIPAR is a volume-based tissue reconstruction data extraction and analysis approach that successfully distinguished healthy from lymphedematous and lymphangiomatous skin. Its application is not limited to the vascular systems or skin. FUNDING. Max Planck Society, DFG (SFB 656), and Cells-in-Motion Cluster of Excellence EXC 1003.

Authors

René Hägerling, Dominik Drees, Aaron Scherzinger, Cathrin Dierkes, Silvia Martin-Almedina, Stefan Butz, Kristiana Gordon, Michael Schäfers, Klaus Hinrichs, Pia Ostergaard, Dietmar Vestweber, Tobias Goerge, Sahar Mansour, Xiaoyi Jiang, Peter S. Mortimer, Friedemann Kiefer

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Figure 5

Automated segmentation of the blood vasculature in digital 3D volume reconstructions of control and diseased skin biopsies.

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Automated segmentation of the blood vasculature in digital 3D volume rec...
(A–C) Digitally generated spatial reconstructions of image stacks obtained by light-sheet–based microscopy (UltraMicroscope II) of skin biopsies, which were whole-mount immunostained for ESAM1. Biopsies were either from the lower leg of a healthy control (A, D, and G) or lymphedematous leg skin (B, E, and H) or a lymphangiomatous skin biopsy (C, F, and I). Scale bars: 100 μm. Computer reconstruction and rendering was performed using the volume visualization framework Voreen. (D–F) Identified by ESAM1 immunostaining, the blood vascular network was segmented in a semiautomated fashion and is represented by digital volume rendering. (G–I) Following skeletonization, an automated determination of vessel branching points the vascular network was performed allowing a classifying node analysis. Note the exceptional level of complexity within the healthy control tissue sample that was still successfully segmented, skeletonized, and analyzed for automated data extraction. Areas of high background signal intensity (B, E, and H, red broken circle) can result in false-positive signals, while a low foreground signal (C, F, and I, red broken ellipse) may result in false-negative segmentation results.