Engineered human hepatocyte organoids enable CRISPR-based target discovery and drug screening for steatosis.

The lack of registered drugs for nonalcoholic fatty liver disease (NAFLD) is partly due to the paucity of human-relevant models for target discovery and compound screening. Here we use human fetal hepatocyte organoids to model the first stage of NAFLD, steatosis, representing three different triggers: free fatty acid loading, interindividual genetic variability (PNPLA3 I148M) and monogenic lipid disorders (APOB and MTTP mutations). Screening of drug candidates revealed compounds effective at resolving steatosis. Mechanistic evaluation of effective drugs uncovered repression of de novo lipogenesis as the convergent molecular pathway. We present FatTracer, a CRISPR screening platform to identify steatosis modulators and putative targets using APOB-/- and MTTP-/- organoids. From a screen targeting 35 genes implicated in lipid metabolism and/or NAFLD risk, FADS2 (fatty acid desaturase 2) emerged as an important determinant of hepatic steatosis. Enhancement of FADS2 expression increases polyunsaturated fatty acid abundancy which, in turn, reduces de novo lipogenesis. These organoid models facilitate study of steatosis etiology and drug targets.

Rapid tissue prototyping with micro-organospheres.

In vitro tissue models hold great promise for modeling diseases and drug responses. Here, we used emulsion microfluidics to form micro-organospheres (MOSs), which are droplet-encapsulated miniature three-dimensional (3D) tissue models that can be established rapidly from patient tissues or cells. MOSs retain key biological features and responses to chemo-, targeted, and radiation therapies compared with organoids. The small size and large surface-to-volume ratio of MOSs enable various applications including quantitative assessment of nutrient dependence, pathogen-host interaction for anti-viral drug screening, and a rapid potency assay for chimeric antigen receptor (CAR)-T therapy. An automated MOS imaging pipeline combined with machine learning overcomes plating variation, distinguishes tumorspheres from stroma, differentiates cytostatic versus cytotoxic drug effects, and captures resistant clones and heterogeneity in drug response. This pipeline is capable of robust assessments of drug response at individual-tumorsphere resolution and provides a rapid and high-throughput therapeutic profiling platform for precision medicine.