An automation workflow for high-throughput manufacturing and analysis of scaffold-supported 3D tissue arrays

Abstract

The success rate of bringing novel cancer therapies to the clinic remains extremely low due to the lack of relevant pre-clinical culture models that capture the complexity of human tumours. Patient-derived organoids have emerged as a useful tool to model patient and tumour heterogeneity to begin addressing this need. Scaling these complex culture models while enabling stratified analysis of different cellular sub-populations remains a challenge, however. One strategy to enable higher throughput organoid cultures that also enables easy image-based analysis is the Scaffold-supported Platform for Organoid-based Tissues (SPOT) platform. SPOT allows the generation of flat, thin and dimensionally-defined microtissues in both 96- and 384-well plate footprints and is compatible with tumour organoid culture and downstream image-based readouts. SPOT manufacturing is currently a manual process however, limiting the use of SPOT to perform larger-scale screening. In this study, we integrate and optimize an automation approach to generate tumour-mimetic 3D engineered microtissues in SPOT using a liquid handler, and show comparable within-sample and between-sample variation as the standard manual manufacturing process. Furthermore, we develop a liquid handler-supported whole-cell extraction protocol and as a proof-of-value demonstration, we generate 3D complex tissues containing different proportions of tumour and stromal cells and perform single-cell-based end-point analysis to demonstrate the impact of co-culture on the tumour cell population specifically. We also demonstrate we can incorporate primary patient-derived organoids into the pipeline to capture patient-level tumour heterogeneity. We envision that this automated workflow integrated with 96/384-SPOT and multiple cell types and patient-derived organoid models will provide opportunities for future applications in high-throughput screening for novel personalized therapeutic targets. This pipeline also allows the user to assess dynamic cell responses using high-content longitudinal imaging or downstream single-cell-based analyses.