Profiling of extracellular vesicles from primary hepatocytes, organoids, and mash patients identifies cell injury-specific signatures

A Aleksandra Leszczynska B Benedikt Kaufmann H Hana Sung C Christian Stoess A Agustina Reca A Andrea Kim Y Yeon-Kyung Choi C Chelsea Tran S Sung-Eun Kim D Davide Povero B Bruce Wolfe (Oregon Health and Science University) T Trevor Crafts A Akiko Eguchi A Ariel E. Feldstein

Abstract

Abstract Metabolic Dysfunction-Associated Steatohepatitis (MASH) is a severe form of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), traditionally diagnosed via invasive biopsy, underscoring the need for non-invasive alternatives. This study identifies biologically relevant extracellular vesicle (EV) protein signatures associated with MASH using patient serum, primary human hepatocytes (PHH), and human liver organoids (HLO). These complementary models capture distinct aspects of disease progression-circulating EV profiles in patients, hepatic cellular responses in PHH, and multicellular interactions in HLO-providing a comprehensive view of MASH pathophysiology. Using aptamer-based technology, we assayed 6596 proteins from 38 individuals with histological confirmed MASLD and in vitro models. EVs were characterized using nano-flow cytometry, ExoView, and high-resolution microscopy (ONI), with liver-specific markers confirming their origin. Key proteins, including SLC27A5, HP, and CXCL7, were elevated in patient samples, while PHH and HLO models exhibited upregulation of ASGPR1, HP, and CXCL7 under MASH conditions. Proteomic analysis revealed shared pathways across models, with machine learning models achieving AUROC values of 0.97, supporting the diagnostic potential of these protein signatures. This integrative approach advances MASH biomarker discovery by linking localized liver dysfunction with systemic disease mechanisms. These findings highlight clinically relevant EV protein signatures that support the development of non-invasive diagnostics and personalized treatment strategies, including prediction of outcomes for bariatric surgery patients.

Article Details

Volume / Issue Vol. 16, Issue 1
Published June 03, 2026
ISSN 2045-2322
Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (14)

A

Aleksandra Leszczynska

B

Benedikt Kaufmann

H

Hana Sung

C

Christian Stoess

A

Agustina Reca

A

Andrea Kim

Y

Yeon-Kyung Choi

C

Chelsea Tran

S

Sung-Eun Kim

D

Davide Povero

B

Bruce Wolfe

Oregon Health and Science University

T

Trevor Crafts

A

Akiko Eguchi

A

Ariel E. Feldstein