Multiplexed deep visual proteomics resolves spatial heterogeneity and rare endocrine states in human pancreatic islets
9 affiliations
- 1Department of Bioengineering, Stanford University, Stanford, CA USA
- 2Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
- 3Synthetic and Systems Biology Unit, Biological Research Centre, HUN-REN, Szeged, Hungary
- 4Single-Cell Technologies Ltd, Szeged, Hungary
- 5Department of Information Technology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- 6Department of Pharmacology and Alberta Diabetes Institute, University of Alberta, Edmonton, AB Canada
- 7Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- 8Institute of AI for Health, Helmholtz Zentrum München, Neuherberg, Germany
- 9Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden
Abstract
Understanding tissue function requires connecting spatial cellular context with molecular depth, yet such integration remains challenging in complex organs like the pancreas. We develop multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry. Powered by PIPΣX, an open-source image analysis framework enabling whole-slide membrane-aware segmentation, annotation, and laser microdissection export, mxDVP achieves >6000 protein identifications from as few as 100 small islet cells. Applied to human pancreatic islets, mxDVP segments over 860,000 cells and resolves twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations that exhibit spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures. These findings reveal a spectrum of endocrine heterogeneity and spatial organization. By integrating imaging and deep proteomics in an accessible framework, mxDVP enables discovery of rare cell states whose biological roles depend on tissue architecture.
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BibTeX
@article{mardamshina2026multiplexed,
title = {{Multiplexed deep visual proteomics resolves spatial heterogeneity and rare endocrine states in human pancreatic islets}},
author = {Mariya Mardamshina and Nicolai Dorka and Marvin Thielert and Frida Björklund and Frederic Ballllosera Navarro and Anna Martinez Casals and Ferenc Kovacs and Christophe Avenel and Jocelyn E. Manning Fox and Peter Horvath and Carolina Wählby and Patrick E. MacDonald and Matthias Mann and Emma Lundberg},
journal = {Nature Communications},
year = {2026},
month = sep,
doi = {10.1038/s41467-026-77890-6},
url = {https://doi.org/10.1038/s41467-026-77890-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multiplexed deep visual proteomics resolves spatial heterogeneity and rare endocrine states in human pancreatic islets
AU - Mariya Mardamshina
AU - Nicolai Dorka
AU - Marvin Thielert
AU - Frida Björklund
AU - Frederic Ballllosera Navarro
AU - Anna Martinez Casals
AU - Ferenc Kovacs
AU - Christophe Avenel
AU - Jocelyn E. Manning Fox
AU - Peter Horvath
AU - Carolina Wählby
AU - Patrick E. MacDonald
AU - Matthias Mann
AU - Emma Lundberg
PY - 2026
DA - 2026/09/25
JO - Nature Communications
DO - 10.1038/s41467-026-77890-6
UR - https://doi.org/10.1038/s41467-026-77890-6
AB - Understanding tissue function requires connecting spatial cellular context with molecular depth, yet such integration remains challenging in complex organs like the pancreas. We develop multiplexed Deep Visual Proteomics (mxDVP), an end-to-end workflow combining high-plex imaging, automated computational analysis, and spatially guided ultra-sensitive mass spectrometry. Powered by PIPΣX, an open-source image analysis framework enabling whole-slide membrane-aware segmentation, annotation, and laser microdissection export, mxDVP achieves >6000 protein identifications from as few as 100 small islet cells. Applied to human pancreatic islets, mxDVP segments over 860,000 cells and resolves twelve endocrine subtypes, including rare polyhormonal and intermediate-state populations that exhibit spatial organization patterns, co-expression of INSM1 and SCG3, and hybrid α/β/δ signatures. These findings reveal a spectrum of endocrine heterogeneity and spatial organization. By integrating imaging and deep proteomics in an accessible framework, mxDVP enables discovery of rare cell states whose biological roles depend on tissue architecture.
SN - 2041-1723
ER -