OrBITS: label-free and time-lapse monitoring of patient derived organoids for advanced drug screening

Abstract

Background Patient-derived organoids are invaluable for fundamental and translational cancer research and holds great promise for personalized medicine. However, the shortage of available analysis methods, which are often single-time point, severely impede the potential and routine use of organoids for basic research, clinical practise, and pharmaceutical and industrial applications. Methods Here, we developed a high-throughput compatible and automated live-cell image analysis software that allows for kinetic monitoring of organoids, named Organoid Brightfield Identification-based Therapy Screening (OrBITS), by combining computer vision with a convolutional network machine learning approach. The OrBITS deep learning analysis approach was validated against current standard assays for kinetic imaging and automated analysis of organoids. A drug screen of standard-of-care lung and pancreatic cancer treatments was also performed with the OrBITS platform and compared to the gold standard, CellTiter-Glo 3D assay. Finally, the optimal parameters and drug response metrics were identified to improve patient stratification. Results OrBITS allowed for the detection and tracking of organoids in routine extracellular matrix domes, advanced Gri3D®-96 well plates, and high-throughput 384-well microplates, solely based on brightfield imaging. The obtained organoid Count, Mean Area, and Total Area had a strong correlation with the nuclear staining, Hoechst, following pairwise comparison over a broad range of sizes. By incorporating a fluorescent cell death marker, intra-well normalization for organoid death could be achieved, which was tested with a 10-point titration of cisplatin and validated against the current gold standard ATP-assay, CellTiter-Glo 3D. Using this approach with OrBITS, screening of chemotherapeutics and targeted therapies revealed further insight into the mechanistic action of the drugs, a feature not achievable with the CellTiter-Glo 3D assay. Finally, we advise the use of the growth rate-based normalised drug response metric to improve accuracy and consistency of organoid drug response quantification. Conclusion Our findings validate that OrBITS, as a scalable, automated live-cell image analysis software, would facilitate the use of patient-derived organoids for drug development and therapy screening. The developed wet-lab workflow and software also has broad application potential, from providing a launching point for further brightfield-based assay development to be used for fundamental research, to guiding clinical decisions for personalized medicine.

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis

“…As expected, a variation in the number and mean area of PDTOs was observed between the wells (Figure 2A,B). The total survival area (total brightfield area -total green area) combines the labelfree organoid segmentation with the fluorescence-based cell death signal and is, in our experience, the most robust parameter to study drug responses over time (Figure 2C) 8 .…”

“…In addition, the analysis is compatible with other live-cell imaging systems, as previously shown for phase-contrast images captured with an IncuCyte ZOOM system 8 . A limitation of the Spark Cyto live-cell imaging system that was used in this manuscript is the one-plate capacity for kinetic measurements.…”

“…The label-free PDTO segmentation algorithm was trained and tested on various in-house grown spheroid and PDTO models with distinct morphological differences (solid, semisolid, cystic), and can consequently detect these with high accuracy 8 . A limitation of the model is that the inclusion of cystic PDTOs increased the unwanted detection of bubbles present in the well following seeding.…”

“…This method allows for medium-to high-throughput drug screening of both tumor spheroids with established cancer cell lines (see Supplementary Table S1 for tested cell lines), as well as the PDTOs, in a 384-well microplate and multi-organoid format. By using a convolutional network machine learning process, automated identification and tracking of individual tumor spheroids or PDTOs could be performed solely from brightfield imaging and without the use of fluorescent live-cell labeling dyes 8 . This is highly advantageous, as most identification with brightfield imaging requires manual annotation (which is laborious and timeconsuming) or requires the addition of fluorescent dyes, which can confound drug responses related to photoxicity-induced oxidative stress 9 .…”

Auranofin Synergizes with the PARP Inhibitor Olaparib to Induce ROS-Mediated Cell Death in Mutant p53 Cancers

“…Three-day-old spheroids were harvested using Cultrex organoid harvesting solution and counted in a black 384-well plate using the Spark Cyto and in-house developed image analysis software Orbits [ 38 ]. Next, the spheroid seeding solution, consisting of the harvested 344SQ spheroids and 4% Cultrex Type 2, was automatically seeded under cold conditions in a precooled black 384-well plate at a concentration of 200 spheroids per well using the OT-2 pipetting robot.…”