Choline-deficient high-fat diet-induced MASH in Göttingen Minipigs: Characterisation and effects of a chow reversal period.

The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) is increasing, and translational animal models are needed to develop novel treatments for this disease. The physiology and metabolism of pigs have a relatively high resemblance to humans, and the present study aimed to characterise choline-deficient, and high-fat diet (CDAHFD) fed Göttingen Minipigs as a novel animal model of MASLD/MASH. Göttingen Minipigs were fed CDAHFD for up to 5 months, and the phenotype was investigated by analysis of plasma parameters and repeated collection of liver biopsies. Furthermore, changes in hepatic gene expression during the experiment were explored by RNA sequencing. For a subset of the minipigs, the diet was changed from CDAHFD back to chow to investigate if the liver pathology was reversible. Göttingen Minipigs on CDAHFD gained bodyweight, and plasma levels of cholesterol, AST, ALT, ALP and GGT were increased. CDAHFD-fed minipigs developed hepatic steatosis, inflammation, and fibrosis, which in 5 of 16 animals progressed to cirrhosis. During an 11-week chow reversal period, steatosis regressed while fibrosis persisted. Regarding inflammation, the findings were less clear, depending on the type of readout. MASH Human Proximity Scoring (combined evaluation of transcriptional, phenotypic and histopathological parameters) showed that CDAHFD-fed Göttingen Minipigs resemble human MASLD/MASH better than most rodent models. In conclusion, CDAHFD-fed minipigs develop a MASH-like phenotype which in several aspects resemble the changes observed in human patients with MASLD/MASH. Furthermore, repeated collection of liver biopsies allow detailed characterisation of histopathological changes over time in individual animals.

A systematic evaluation of state-of-the-art deconvolution methods in spatial transcriptomics: insights from cardiovascular disease and chronic kidney disease.

A major challenge in sequencing-based spatial transcriptomics (ST) is resolution limitations. Tissue sections are divided into hundreds of thousands of spots, where each spot invariably contains a mixture of cell types. Methods have been developed to deconvolute the mixed transcriptional signal into its constituents. Although ST is becoming essential for drug discovery, especially in cardiometabolic diseases, to date, no deconvolution benchmark has been performed on these types of tissues and diseases. However, the three methods, Cell2location, RCTD, and spatialDWLS, have previously been shown to perform well in brain tissue and simulated data. Here, we compare these methods to assess the best performance when using human data from cardiovascular disease (CVD) and chronic kidney disease (CKD) from patients in different pathological states, evaluated using expert annotation. In this study, we found that all three methods performed comparably well in deconvoluting verifiable cell types, including smooth muscle cells and macrophages in vascular samples and podocytes in kidney samples. RCTD shows the best performance accuracy scores in CVD samples, while Cell2location, on average, achieved the highest performance across all test experiments. Although all three methods had similar accuracies, Cell2location needed less reference data to converge at the expense of higher computational intensity. Finally, we also report that RCTD has the fastest computational time and the simplest workflow, requiring fewer computational dependencies. In conclusion, we find that each method has particular advantages, and the optimal choice depends on the use case.

Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics.

BACKGROUND: Chronic Kidney Disease (CKD) and Metabolic dysfunction-associated steatohepatitis (MASH) are metabolic fibroinflammatory diseases. Combining single-cell (scRNAseq) and spatial transcriptomics (ST) could give unprecedented molecular disease understanding at single-cell resolution. A more comprehensive analysis of the cell-specific ligand-receptor (L-R) interactions could provide pivotal information about signaling pathways in CKD and MASH. To achieve this, we created an integrative analysis framework in CKD and MASH from two available human cohorts. RESULTS: The analytical framework identified L-R pairs involved in cellular crosstalk in CKD and MASH. Interactions between cell types identified using scRNAseq data were validated by checking the spatial co-presence using the ST data and the co-expression of the communicating targets. Multiple L-R protein pairs identified are known key players in CKD and MASH, while others are novel potential targets previously observed only in animal models. CONCLUSION: Our study highlights the importance of integrating different modalities of transcriptomic data for a better understanding of the molecular mechanisms. The combination of single-cell resolution from scRNAseq data, combined with tissue slide investigations and visualization of cell-cell interactions obtained through ST, paves the way for the identification of future potential therapeutic targets and developing effective therapies.