Evidence map›Paper›PMID 40291651›Full record

ArticlebioRxiv : the preprint server for biology2025

Single cell spatial proteomics maps human liver zonation patterns and their vulnerability to fibrosis.

Caroline A M Weiss, Lauryn A Brown, Lucas Miranda, Paolo Pellizzoni, Shani Ben-Moshe, Sophia Steigerwald, Kirsten Remmert, Jonathan Hernandez, Karsten Borgwardt, Florian A Rosenberger and 2 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Caroline A M WeissProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID 0009-0006-0847-718X
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.
Shani Ben-MosheProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID 0000-0002-7046-5423
Sophia SteigerwaldProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID 0000-0002-7513-1131
Kirsten RemmertSurgical Oncology Program, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, USA.
Jonathan HernandezSurgical Oncology Program, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, USA.
Karsten BorgwardtMachine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.
Florian A RosenbergerProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID 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.ORCID 0000-0003-2676-8483
Matthias MannProteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID 0000-0003-1292-4799

Funding

Mitochondrial metabolism in normal and transformed cellsZIABC011828 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI PORAT SHLIOM, NATALIE · 2018 to 2025
$9.4M
Intramural NIH HHS ZIA BC011828
6 · The paper itself

Abstract

Understanding protein distribution patterns across tissue architecture is crucial for deciphering organ function in health and disease. Here, we applied single-cell Deep Visual Proteomics to perform spatially-resolved proteome analysis of individual cells in native tissue. We combined this with a novel strategic cell selection pipeline and a continuous protein gradient mapping framework to investigate larger clinical cohorts. We generated a comprehensive spatial map of the human hepatic proteome by analyzing hundreds of individual hepatocytes from 18 individuals. Among more than 2,500 proteins per cell about half exhibited zonated expression patterns. Cross-species comparison with mouse data revealed conserved metabolic functions and human-specific features of liver zonation. Analysis of fibrotic samples demonstrated widespread disruption of protein zonation, with pericentral proteins being particularly susceptible. Our study provides a comprehensive resource of human liver organization while establishing a broadly applicable framework for spatial proteomics analyses along tissue gradients.

Identifiers

PMID40291651
PMCPMC12027366

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LicenceCC BY-NC-ND
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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.