 Pathology-oriented multiplexing enables integrative disease mappingMalte Kuehl (),
Yusuke Okabayashi (),
Milagros N. Wong,
Lukas Gernhold,
Gabriele Gut,
Nico Kaiser,
Maria Schwerk,
Stefanie K. Gräfe,
Frank Y. Ma,
Jovan Tanevski,
Philipp S. L. Schäfer,
Sam Mezher,
Jacobo Sarabia del Castillo,
Thiago Goldbeck-Strieder,
Olga Zolotareva,
Michael Hartung,
Fernando M. Delgado Chaves,
Lukas Klinkert,
Ann-Christin Gnirck,
Marc Spehr,
David Fleck,
Mehdi Joodaki,
Victor Parra,
Mina Shaigan,
Martin Diebold,
Marco Prinz,
Jennifer Kranz,
Johan M. Kux,
Fabian Braun,
Oliver Kretz,
Hui Wu,
Florian Grahammer,
Sven Heins,
Marina Zimmermann,
Fabian Haas,
Dominik Kylies,
Nicola Wanner,
Jan Czogalla,
Bernhard Dumoulin,
Nikolay Zolotarev,
Maja Lindenmeyer,
Pall Karlson,
Jens R. Nyengaard,
Marcial Sebode,
Sören Weidemann,
Thorsten Wiech,
Hermann-Josef Groene,
Nicola M. Tomas,
Catherine Meyer-Schwesinger,
Christoph Kuppe,
Rafael Kramann,
Alexandre Karras,
Patrick Bruneval,
Pierre-Louis Tharaux,
Diego Pastene,
Benito Yard,
Jennifer A. Schaub,
Phillip J. McCown,
Laura Pyle,
Ye Ji Choi,
Takashi Yokoo,
Jan Baumbach,
Pablo J. Sáez,
Ivan Costa,
Jan-Eric Turner,
Jeffrey B. Hodgin,
Julio Saez-Rodriguez,
Tobias B. Huber,
Petter Bjornstad,
Matthias Kretzler,
Olivia Lenoir,
David J. Nikolic-Paterson,
Lucas Pelkmans,
Stefan Bonn and
Victor G. Puelles ()
Nature, 2025, vol. 644, issue 8076, 516-526 Abstract: Abstract The expression and location of proteins in tissues represent key determinants of health and disease. Although recent advances in multiplexed imaging have expanded the number of spatially accessible proteins1–3, the integration of biological layers (that is, cell structure, subcellular domains and signalling activity) remains challenging. This is due to limitations in the compositions of antibody panels and image resolution, which together restrict the scope of image analysis. Here we present pathology-oriented multiplexing (PathoPlex), a scalable, quality-controlled and interpretable framework. It combines highly multiplexed imaging at subcellular resolution with a software package to extract and interpret protein co-expression patterns (clusters) across biological layers. PathoPlex was optimized to map more than 140 commercial antibodies at 80 nm per pixel across 95 iterative imaging cycles and provides pragmatic solutions to enable the simultaneous processing of at least 40 archival biopsy specimens. In a proof-of-concept experiment, we identified epithelial JUN activity as a key switch in immune-mediated kidney disease, thereby demonstrating that clusters can capture relevant pathological features. PathoPlex was then used to analyse human diabetic kidney disease. The framework linked patient-level clusters to organ disfunction and identified disease traits with therapeutic potential (that is, calcium-mediated tubular stress). Finally, PathoPlex was used to reveal renal stress-related clusters in individuals with type 2 diabetes without histological kidney disease. Moreover, tissue-based readouts were generated to assess responses to inhibitors of the glucose cotransporter SGLT2. In summary, PathoPlex paves the way towards democratizing multiplexed imaging and establishing integrative image analysis tools in complex tissues to support the development of next-generation pathology atlases. Date: 2025 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:644:y:2025:i:8076:d:10.1038_s41586-025-09225-2 Ordering information: This journal article can be ordered from DOI: 10.1038/s41586-025-09225-2 Access Statistics for this article Nature is currently edited by Magdalena Skipper More articles in Nature from Nature |
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