Deep learning-based molecular morphometrics for kidney biopsies

Marina Zimmermann, Martin Klaus, Milagros N. Wong, Ann Katrin Thebille, Lukas Gernhold, Christoph Kuppe, Maurice Halder, Jennifer Kranz, Nicola Wanner, Fabian Braun, Sonia Wulf, Thorsten Wiech, Ulf Panzer, Christian F. Krebs, Elion Hoxha, Rafael Kramann, Tobias B. Huber*, Stefan Bonn, Victor G. Puelles

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
3 Downloads (Pure)

Abstract

Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific cellular morphometrics in human kidney biopsies, which combines indirect immunofluorescence imaging with U-Net- based architectures for image-to-image translation and dual segmentation tasks, achieving human-level accuracy. In the kidney, podocyte loss represents a hallmark of glomerular injury and can be estimated in diagnostic biopsies. Thus, we profiled over 27,000 podocytes from 110 human samples, including patients with antineutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA-GN), an immune-mediated disease with aggressive glomerular damage and irreversible loss of kidney function. We identified previously unknown morphometric signatures of podocyte depletion in patients with ANCA-GN, which allowed patient classification and, in combination with routine clinical tools, showed potential for risk stratification. Our approach enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology.

Original languageEnglish
Article numbere144779
JournalJCI insight
Volume6
Issue number7
DOIs
Publication statusPublished - 8 Apr 2021

Bibliographical note

Funding Information:
Deutsche Forschungsgemeinschaft (DFG; CRC1192 to NW, TW, UP, CFK, EH, TBH, SB, and VGP), Deutsche Gesellschaft für Nephrologie to CFK and VGP, and eMed Consortia “Fibromap” from the Bundesminis-terium für Bildung und Forschung (BMBF) to RK and VGP. EH was also supported by the DFG (Heisenberg Programme). TBH was also supported by the DFG (HU 1016/8-2, HU 1016/11-1, HU 1016/12-1), by the BMBF (STOP-FSGS-01GM1901C and NephrESA-031L0191E), by the Else-Kröner Fresenius Foundation (Else Kröner-Promotionskolleg – iPRIME), by the European Research Council-ERC (616891), and by the H2020-IMI2 consortium BEAt-DKD (115974); this joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program, and EFPIA and JDRF. VGP received additional funding from National Health and Medical research Council of Australia and the Humboldt Foundation. RK received additional funding from the DFG (KR-4073/3-1, SCHN1188/5-1, SFB/TRR57). MK was part of the iPRIME program supported by the Else Kröner-Fresenius-Stiftung.

Publisher Copyright: © 2021, Zimmermann et al.

Fingerprint

Dive into the research topics of 'Deep learning-based molecular morphometrics for kidney biopsies'. Together they form a unique fingerprint.

Cite this