Background and Aims. In clinical trials for reducing fibrosis in NASH patients, therapeutics that target macrophages have had variable results. We evaluated intrahepatic macrophages in patients with non-alcoholic steatohepatitis to determine if fibrosis influenced phenotypes and expression of CCR2 and Galectin-3. Approach & Results. We used nCounter to analyze liver biopsies from well-matched patients with minimal (n=12) or advanced (n=12) fibrosis to determine which macrophage-related genes would be significantly different. Known therapy targets (e.g., CCR2 and Galectin-3) were significantly increased in patients with cirrhosis. However, several genes (e.g., CD68, CD16, and CD14) did not show significant differences, and CD163, a marker of pro-fibrotic macrophages was significantly decreased with cirrhosis. Next, we analyzed patients with minimal (n=6) or advanced fibrosis (n=5) using approaches that preserved hepatic architecture by multiplex-staining with anti-CD68, Mac387, CD163, CD14, and CD16. Spectral data were analyzed using deep learning/artificial intelligence to determine percentages and spatial relationships. This approach showed patients with advanced fibrosis had increased CD68+, CD16+, Mac387+, CD163+, and CD16+CD163+ populations. Interaction of CD68+ and Mac387+ populations was significantly increased in patients with cirrhosis and enrichment of these same phenotypes in individuals with minimal fibrosis correlated with poor outcomes. Evaluation of a final set of patients (n=4) also showed heterogenous expression of CD163, CCR2, Galectin-3, and Mac387, and significant differences were not dependent on fibrosis stage or NAFLD activity. Conclusions. Approaches that leave hepatic architecture intact, like multispectral imaging, may be paramount to developing effective treatments for NASH. In addition, understanding individual differences in patients may be required for optimal responses to macrophage-targeting therapies.

A.R. serves as a member for Voxel analytics LLC.and consults for Genophyll, LLC. 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.

This study was funded by a R01 from NIDDK (1R01DK125730-01A1) (H.L.S., O.A.S., M.O., S.K., L.B. and A.R.). H.L.S. and O.A.S. were also supported by a Moody Endowment Grant (2014-07). Initial optimization studies were supported in part by the National Center for Advancing Translational Services Clinical and Translational Science Awards Grant NCATS CTSA Grant KL2 Scholars Program (KL2TR001441-06). The Vectra 3 microscope was purchased with funds from the UT Systems Faculty Science and Technology Acquisition and Retention (STARs) Program. A.R. and S.K. were supported by CCSG Bioinformatics Shared Resource 5 P30 CA046592, a gift from Agilent technologies, and a Precision health Investigator award from U-M Precision Health. S.K. and A.R. were partially supported by the NCI Grant R37-CA214955. S.K. and A.R. were also partially supported by The University of Michigan (U-M) startup institutional research funds. S.K. and A.R. were also supported by a Research Scholar Grant from the American Cancer Society (RSG-16-005-01). M.O. was supported by the Advanced Proteome Informatics of Cancer Training Grant (T32 CA140044).

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The Institutional Review Board of the University of Texas Medical Branch gave ethical approval for this work.

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