An Automation Workflow for High-Throughput Manufacturing and Analysis of Scaffold-Supported 3D Tissue Arrays

Adv Healthc Mater. 2023 Jul;12(19):e2202422. doi: 10.1002/adhm.202202422. Epub 2023 May 10.

Abstract

Patient-derived organoids have emerged as a useful tool to model tumour heterogeneity. Scaling these complex culture models while enabling stratified analysis of different cellular sub-populations, however, remains a challenge. One strategy to enable higher throughput organoid cultures is the scaffold-supported platform for organoid-based tissues (SPOT). SPOT allows the generation of flat, thin, and dimensionally-defined microtissues in both 96- and 384-well plate footprints that are compatible with longitudinal image-based readouts. SPOT is currently manufactured manually, however, limiting scalability. In this study, an automation approach to engineer tumour-mimetic 3D microtissues in SPOT using a liquid handler is optimized and comparable within- and between-sample variation to standard manual manufacturing is shown. Further, a liquid handler-supported cell extraction protocol to support single-cell-based end-point analysis using high-throughput flow cytometry and multiplexed cytometry by time of flight is developed. As a proof-of-value demonstration, 3D complex tissues containing different proportions of tumour and stromal cells are generated to probe the reciprocal impact of co-culture. It is also demonstrated that primary patient-derived organoids can be incorporated into the pipeline to capture patient-level tumour heterogeneity. It is envisioned that this automated 96/384-SPOT workflow will provide opportunities for future applications in high-throughput screening for novel personalized therapeutic targets.

Keywords: 3D in vitro cancer models; automation; tumour microenvironments.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Automation
  • Coculture Techniques
  • High-Throughput Screening Assays / methods
  • Humans
  • Neoplasms* / pathology
  • Organoids
  • Workflow