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pmid:36359782
Multiplexed Digital Spatial Protein Profiling Reveals Distinct Phenotypes of Mononuclear Phagocytes in Livers with Advanced Fibrosis.
Jaejun Lee · Chang Min Kim · Jung Hoon Cha · Jin Young Park · Yun Suk Yu · Hee Jung Wang · Pil Soo Sung · Eun Sun Jung · Si Hyun Bae
Cells · 2022 doi:10.3390/cells11213387
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Experiment
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Analysis
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Complete file
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In this study, using multiplexed digital spatial protein profiling, we aimed to derive a unique protein signature predicting advanced liver fibrosis. Methods: Snap-frozen liver tissues from various chronic liver diseases were subjected to spatially defined protein-based multiplexed profiling (Nanostring GeoMXTM). A single-cell RNA sequencing analysis was performed using Gene Expression Omnibus (GEO) datasets from normal and cirrhotic livers. Results: Sixty-four portal regions of interest (ROIs) were selected for the spatial profiling. Using the results from the CD68+ area, a highly sensitive and specific immune-related protein signature (CD68, HLA-DR, OX40L, phospho-c-RAF, STING, and TIM3) was developed to predict advanced (F3 and F4) fibrosis. A combined analysis of single-cell RNA sequencing data from GEO datasets (GSE136103) and spatially-defined, protein-based multiplexed profiling revealed that most proteins upregulated in F0-F2 livers in portal CD68+ cells were specifically marked in tissue monocytes, whereas proteins upregulated in F3 and F4 livers were marked in scar-associated macrophages (SAMacs) and tissue monocytes. Internal validation using mRNA expression data with the same cohort tissues demonstrated that mRNA levels for TREM2, CD9, and CD68 are significantly higher in livers with advanced fibrosis. Conclusions: In patients with advanced liver fibrosis, portal MPs comprise of heterogeneous populations composed of SAMacs, Kupffer cells, and tissue monocytes. This is the first study that used spatially defined protein-based multiplexed profiling, and we have demonstrated the critical difference in the phenotypes of portal MPs between livers with early- or late-stage fibrosis.", "claims": [{"id": "claim-method", "statement": "The predictive accuracy of the threshold values for classifying fibrosis stages 0–2 and stages 3 and 4 was assessed using a receiver operating characteristic (ROC) curve analysis. The independence of the protein signature was analyzed using a logistic regression analysis of the protein signature and clinicopathological variables.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36359782", "locator": "sections[10] \"2.8. Statistical Analysis\" paragraph 2", "quote": "The predictive accuracy of the threshold values for classifying fibrosis stages 0–2 and stages 3 and 4 was assessed using a receiver operating characteristic (ROC) curve analysis. 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