Evidence map›Paper›PMID 41721135›Full record

ArticleNature metabolism2026

Single-cell spatial proteomics maps human liver zonation patterns and their vulnerability to disruption in tissue architecture.

Caroline A M Weiss, Lauryn A Brown, Lucas Miranda, Paolo Pellizzoni, Sophia Steigerwald, Kirsten Remmert, Jonathan M Hernandez, Karsten Borgwardt, David E Kleiner, Shani Ben-Moshe and 3 more

Abstract read
In one paragraph

Article in Nature metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Review
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  5. Article
  6. Article
  7. Review
  8. An update on spatial proteomics.Nature methods · 2026
    Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Caroline A M WeissProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID http://orcid.org/0009-0003-7080-619X
Lauryn A BrownCell Biology and Imaging Sections, Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Lucas MirandaMachine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.
Paolo PellizzoniMachine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.
Sophia SteigerwaldProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID http://orcid.org/0000-0002-7513-1131
Kirsten RemmertSurgical Oncology Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0009-0004-0668-4164
Jonathan M HernandezSurgical Oncology Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Karsten BorgwardtMachine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.
David E KleinerLaboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Shani Ben-MosheProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
Florian A RosenbergerProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany. rosenberger@biochem.mpg.de.ORCID http://orcid.org/0000-0003-4604-6170
Natalie Porat-ShliomCell Biology and Imaging Sections, Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. natalie.porat-shliom@nih.gov.ORCID http://orcid.org/0000-0003-2676-8483
Matthias MannProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany. mmann@biochem.mpg.de.ORCID http://orcid.org/0000-0003-1292-4799

Funding

European Molecular Biology Organization (EMBO) ALTF 399-2021U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) 1ZIABC011828
6 · The paper itself

Abstract

Understanding protein distribution patterns across tissue architecture is crucial for deciphering organ function in health and disease. Here we show the application of single-cell Deep Visual Proteomics to perform spatially resolved proteome analysis of individual cells in native liver tissue. We built a robust framework comprising strategic cell selection and continuous protein gradient mapping, allowing the investigation of larger clinical cohorts. We generated a comprehensive spatial map of the human hepatic proteome by analysing hundreds of isolated hepatocytes from 18 individuals. Among the 2,500 proteins identified per cell, about half exhibited zonated expression patterns. Cross-species comparison with male mice revealed conserved metabolic functions and human-specific features of liver zonation. Analysis of samples with disrupted liver architecture demonstrated widespread loss of protein zonation, with pericentral proteins being particularly susceptible. Our study provides a comprehensive and open-access resource of human liver organization while establishing a broadly applicable framework for spatial proteomics analyses along tissue gradients.

Indexed as

LiverProteomeProteomicsSingle-Cell AnalysisAnimalsHepatocytesHumansMaleMiceProteome

Identifiers

PMID41721135
PMCPMC13031132

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.