An autocrine signaling circuit in hepatic stellate cells underlies advanced fibrosis in non-alcoholic steatohepatitis

Advanced hepatic fibrosis, driven by the activation of hepatic stellate cells (HSCs), affects millions worldwide and is the strongest predictor of mortality in nonalcoholic steatohepatitis (NASH); however, there are no approved antifibrotic therapies. To identify antifibrotic drug targets, we integrated progressive transcriptomic and morphological responses that accompany HSC activation in advanced disease using single-nucleus RNA sequencing and tissue clearing in a robust murine NASH model. In advanced fibrosis, we found that an autocrine HSC signaling circuit emerged that was composed of 68 receptor-ligand interactions conserved between murine and human NASH. These predicted interactions were supported by the parallel appearance of markedly increased direct stellate cell-cell contacts in murine NASH. As proof of principle, pharmacological inhibition of one such autocrine interaction, neurotrophic receptor tyrosine kinase 3–neurotrophin 3, inhibited human HSC activation in culture and reversed advanced murine NASH fibrosis. In summary, we uncovered a repertoire of antifibrotic drug targets underlying advanced fibrosis in vivo. The findings suggest a therapeutic paradigm in which stage-specific therapies could yield enhanced antifibrotic efficacy in patients with advanced hepatic fibrosis. This is all of the human data samples and analysis for the paperAn autocrine signaling circuit in hepatic stellate cells underlies advanced fibrosis in nonalcoholic steatohepatitis. GEO data here: GSE212837 The folderliver_data_cellranger_all.zipcontains the raw and filtered cellranger output for every sample. This allows reprocessing of all cells including the use of methods that use empty droplets to calculate ambient RNA. The folderAutocrine_liver_paper_jupyter_notebooks.zipcontains all of the notebooks used for analysis: Autocrine_signaling_liver_data_merge_all_select_celltype_subsets.ipynb is the initial noteboook for processing all datasets and merging. It also contains in the first cell instructions for creating a conda environment that will allow all of the tools to run. There are 5 notebooks for qc and cleaning of each celltype: Autocrine_signaling_liver_data_Stellate_cell_recluster.ipynb,Autocrine_signaling_liver_data_Hepatocyte_cell_recluster.ipynb, Autocrine_signaling_liver_data_Endo_cell_recluster.ipynb,Autocrine_signaling_liver_data_Cholangiocyte_cell_recluster.ipynb, and Autocrine_signaling_liver_data_NKTcell_cell_recluster.ipynb There is a final notebook for re-merging the cleaned celltype objects and final clustering, DE and analysis:Autocrine_signaling_liver_data_recluster_all_cleaned_celltypes_analysis.ipynb The fileraw_all_cellranger.h5ad.gzis all cells passing cellranger filter annotated with celltype and ambient RNA and doublet scoring, but otherwise no cells or genes have been removed. The foldercelltype_subsets_v1.zipcontains the roughly filtered celltypes for each of the celltypes. The foldercleaned_celltype_subsets_for_merge.zipcontains the final qc'd and filtered celltypes that are merged for final analysis. It also contains the final merged and batch normalized object. The python scriptcellphone_db_liver_all_cells_clean.pycontains a script for running cellphoneDB on final data.