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.
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
Usage data is cumulative from October 2022 through October 2023.
Usage | JCI | PMC |
---|---|---|
Text version | 620 | 243 |
103 | 71 | |
Figure | 187 | 4 |
Table | 25 | 0 |
Supplemental data | 37 | 3 |
Citation downloads | 20 | 0 |
Totals | 992 | 321 |
Total Views | 1,313 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.