Heterogeneity in intrahepatic macrophage populations and druggable target expression in patients with steatotic liver disease-related fibrosis.

Cardiometabolic risk factors Chemokines Infiltrating monocytes Inflammation MASLD Multispectral imaging NanoString nCounter Visiopharm

Journal

JHEP reports : innovation in hepatology
ISSN: 2589-5559
Titre abrégé: JHEP Rep
Pays: Netherlands
ID NLM: 101761237

Informations de publication

Date de publication:
Jan 2024
Historique:
received: 10 03 2023
revised: 18 08 2023
accepted: 25 09 2023
medline: 2 1 2024
pubmed: 2 1 2024
entrez: 1 1 2024
Statut: epublish

Résumé

Clinical trials for reducing fibrosis in steatotic liver disease (SLD) have targeted macrophages with variable results. We evaluated intrahepatic macrophages in patients with SLD to determine if activity scores or fibrosis stages influenced phenotypes and expression of druggable targets, such as CCR2 and galectin-3. Liver biopsies from controls or patients with minimal or advanced fibrosis were subject to gene expression analysis using nCounter to determine differences in macrophage-related genes (n = 30). To investigate variability among individual patients, we compared additional biopsies by staining them with multiplex antibody panels (CD68/CD14/CD16/CD163/Mac387 or CD163/CCR2/galectin-3/Mac387) followed by spectral imaging and spatial analysis. Algorithms that utilize deep learning/artificial intelligence were applied to create cell cluster plots, phenotype profile maps, and to determine levels of protein expression (n = 34). Several genes known to be pro-fibrotic ( Patients with SLD have markedly varied macrophage- and druggable target-related gene and protein expression in their livers. Several patients had relatively high expression, while others were like controls. Overall, patients with more advanced disease had significantly higher expression of CCR2 and galectin-3 at both the gene and protein levels. Appreciating individual differences within the hepatic microenvironment of patients with SLD may be paramount to developing effective treatments. These results may explain why such a small percentage of patients have responded to macrophage-targeting therapies and provide additional support for precision medicine-guided treatment of chronic liver diseases.

Sections du résumé

Background & Aims UNASSIGNED
Clinical trials for reducing fibrosis in steatotic liver disease (SLD) have targeted macrophages with variable results. We evaluated intrahepatic macrophages in patients with SLD to determine if activity scores or fibrosis stages influenced phenotypes and expression of druggable targets, such as CCR2 and galectin-3.
Methods UNASSIGNED
Liver biopsies from controls or patients with minimal or advanced fibrosis were subject to gene expression analysis using nCounter to determine differences in macrophage-related genes (n = 30). To investigate variability among individual patients, we compared additional biopsies by staining them with multiplex antibody panels (CD68/CD14/CD16/CD163/Mac387 or CD163/CCR2/galectin-3/Mac387) followed by spectral imaging and spatial analysis. Algorithms that utilize deep learning/artificial intelligence were applied to create cell cluster plots, phenotype profile maps, and to determine levels of protein expression (n = 34).
Results UNASSIGNED
Several genes known to be pro-fibrotic (
Conclusions UNASSIGNED
Patients with SLD have markedly varied macrophage- and druggable target-related gene and protein expression in their livers. Several patients had relatively high expression, while others were like controls. Overall, patients with more advanced disease had significantly higher expression of CCR2 and galectin-3 at both the gene and protein levels.
Impact and implications UNASSIGNED
Appreciating individual differences within the hepatic microenvironment of patients with SLD may be paramount to developing effective treatments. These results may explain why such a small percentage of patients have responded to macrophage-targeting therapies and provide additional support for precision medicine-guided treatment of chronic liver diseases.

Identifiants

DOI: 10.1016/j.jhepr.2023.100958 PMID: 38162144 PMC: PMC10757256
pubmed: 38162144
doi: 10.1016/j.jhepr.2023.100958
pii: S2589-5559(23)00289-6
pmc: PMC10757256
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100958

Informations de copyright

© 2023 The Authors.

Déclaration de conflit d'intérêts

HSL and OAS have a filed patent titled “Systems and methods for spectral imaging characterization of macrophages for use in personalization of targeted therapies to prevent fibrosis development in patients with chronic liver disease." (Board or Regents, The University of Texas System, United States, Galveston, Texas; Pub. No: US 20210293814.) AR serves as a member of Voxel Analytics LLC and consults for Tempus Labs Inc, and Tata Consultancy Services Ltd. The remaining authors who have taken part in this study declared that they do not have anything to disclose regarding funding or conflict of interest with respect to this manuscript. Please refer to the accompanying ICMJE disclosure forms for further details.

Auteurs

Omar A Saldarriaga (OA)

Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA.

Timothy G Wanninger (TG)

Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA.

Esteban Arroyave (E)

Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA.

Joseph Gosnell (J)

Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA.

Santhoshi Krishnan (S)

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.

Morgan Oneka (M)

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Daniel Bao (D)

School of Medicine, University of Texas Medical Branch, Galveston, TX, USA.

Daniel E Millian (DE)

Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA.

Michael L Kueht (ML)

Department of Surgery, University of Texas Medical Branch, Galveston, TX, USA.

Akshata Moghe (A)

Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX, USA.

Jingjing Jiao (J)

Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Jessica I Sanchez (JI)

Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Heidi Spratt (H)

Department of Biostatistics and Data Science, University of Texas Medical Branch, Galveston, TX, USA.

Laura Beretta (L)

Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Arvind Rao (A)

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
Departmen of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Department of Biomedical Engineering, Rice University, Ann Arbor, MI, USA.

Jared K Burks (JK)

Department of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Heather L Stevenson (HL)

Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA.

Classifications MeSH