Multiomics Reveals How Macrophages Contribute to Liver Disease

Macrophages may be best known as the first responders of the immune system, and the ones that reside in the liver are no different. They protect the liver from infections, but when liver cells start to accumulate fat, these macrophages can become activated and contribute to the chronic inflammatory condition, metabolic dysfunction-associated steatotic liver disease (MASLD).¹

MASLD ranges in severity from simple fat buildup called metabolic dysfunction-associated steatosis (MASL) to chronic inflammation and fibrosis called metabolic dysfunction-associated steatohepatitis (MASH), which can progress to cirrhosis and liver cancer, eventually necessitating a liver transplant. While mouse models have helped scientists better understand the different ways macrophage populations contribute to the progression of MASLD, they have led to oversimplified models due to differences between mice and humans.

To take a more detailed and spatially resolved look at how liver macrophages change as MASL progresses to MASH, researchers at KU Leuven, UZ Leuven, and Newcastle University used spatial multi-omics, single-nucleotide transcriptomics (snRNA-seq), and functional assays to characterize the macrophages in human liver samples.² They observed distinct macrophage populations that changed with disease progression and identified macrophage markers that corresponded with disease activity, which could help stratify patients for clinical trials. The team reported their results in Nature Genetics.

To better characterize the macrophages involved in different stages of MASLD, the researchers first performed snRNA-seq on human liver samples sorted as healthy-lean, healthy-obese, MASL, and MASH. They identified eight transcriptionally distinct groups of macrophages, including a group that they called metabolically active macrophages or MetMacs. They noticed that the different populations of macrophages changed in their relative abundance as the disease progressed: Kupffer cell numbers decreased and MetMacs increased. In MASH samples, MetMacs upregulated genes involved in inflammatory responses and those involved in cell-cell interactions with other liver cells.

Spatial transcriptomics further revealed that macrophages in specific locations had distinct gene expression profiles, and in particular, the team noticed that macrophages associated with liver cell fat accumulation highly expressed the gene glycoprotein nmb (GPNMB). When they looked closer at these GPNMB⁺ macrophages, they found that most fell into the MetMac group. In particular, GPNMB⁺ cells increased from 5.7 to 12 percent in MASL and MASH samples respectively. Proteomics analysis of independent liver samples validated these findings.

To better understand the role of GPNMB⁺ macrophages in MASLD, the researchers focused on the cytokine interleukin-32 (IL32). Their snRNA-seq data had shown high expression of IL32 in MASH liver cells samples. Using spatial transcriptomics, they saw that as MASLD progressed, liver cells expressing IL32 increased in parallel with GPNMB⁺ cells, suggesting a link between liver cell signaling and GPNMB⁺ cell function.

Finally, the team assessed three independent omics data sets to see if liver macrophage markers could predict MASLD severity. They identified distinct spatial transcriptomic markers between MASL and MASH samples and serum proteome markers that differed among various stages of MASH.

“Our study sheds new light on the diversity of macrophage identities and their metabolic phenotypes during the progression of MASLD,” the authors wrote in the paper. “We showed that spatial and temporal changes occur in mature macrophage markers, which adds complexity to the old dogma of macrophage-monocytes in chronic liver disease.”