This article and video describe how to perform medium-throughput drug screening using PDOs. This protocol can, with optimization, be adopted to screen organoids derived from different tissue types from those described here. Determining the ideal passage timeframe prior to the screen is important as this will vary for individual organoid cultures and depend on the tissue type. The density and size of organoids seeded per well is an important factor to optimize as faster growing models will require more space within the well, and size differences may result in more variation. To ensure that only the test compounds affect organoid viability, it is important to make sure that untreated (negative control) organoids do not suffer from any deprivation during the course of the experiment. As untreated organoids will increase unhampered in volume compared to the treated populations, this assay assesses both treatment-induced cell death as well as inhibition of cell proliferation. Using this method, compounds and treatments, such as radiotherapy, can therefore be applied to organoid models to investigate in vitro responses in a three-dimensional format.
In this protocol, we make use of the CTG assay that uses ATP levels as a proxy for cell viability. Although this assay, in most instances, robustly reports the number of viable cells at the end of the experiment, it is important to realize that this assay could also be affected by severe changes in cell metabolism. Other viability assays are available that are dependent on, e.g., total DNA, protease activity, or leakage of lactate dehydrogenase, and may function as alternative or additive readouts in this assay.
Here, we discuss some additional recommendations and steps for optimization and upscaling of organoid drug screening experiments, as well as some considerations for drug screen analysis.
First, a challenge to performing (larger) drug screening is expanding the organoid culture enough to obtain a sufficient number of organoids to perform the experiment. It is therefore important to realize that many organoids tend to stick to untreated plastic, resulting in potential loss of critical cell mass. We recommend using low-retention plastics wherever possible and pre-wetting pipets/filters with aDF+++ or washing buffer prior to using them with concentrated organoid suspension.
Second, as increasing the handling time of organoids can be detrimental to their viability, we recommend working as efficiently as possible. In our experience, prolonged incubation of organoids in suspension while preparing them for dispensing negatively impacts their viability to a greater degree when kept on ice as compared to when kept at room temperature. Adding ROCK inhibitor during organoid preparation increases their viability. Moreover, in our experience, a healthy culture at the start of an experiment is absolutely required for a meaningful outcome.
Third, when processing multiple organoid models at once, there is a risk of swapping and contaminating the cultures. We therefore recommend only handling a single organoid model at a time. This could mean that multiple researchers need to work in parallel when processing larger amounts of organoid models or larger volumes of fewer organoid models. The identity of the organoid models should be verified through SNP analysis15 prior to and after screens and comparing the data to early passage and/or patient blood SNP data.
Some (targeted) therapies/compounds potentially interact/compete with growth factors/ECM present in the medium. Ideally, these potential interactions need to be identified, and the concentrations of these growth factors need to be minimized and tightly controlled to ensure that results are consistent. An example of such an interaction is epidermal growth factor (EGF)- and EGF receptor (EGFR)-targeting therapies, such as Cetuximab and Panitumumab, wherein high EGF concentrations in the medium potentially suppress EGFR expression16 or can compete with compound-EGFR binding. Ways to identify these interactions include performing a titration of the compound and a control compound against a titration of ECM or growth factors intended for the drug screen, or if the compound targets a membrane receptor (e.g., EGFR), using flow cytometry to assess receptor expression and confirm whether the ECM or compound affects antibody binding. Adjust the growth factor or ECM concentration such that there is no specific inhibition by the targeting compound compared to inhibition by the control compound.
In addition, we make the following recommendations regarding the experimental (plate) setup. First, use at least three technical replicates in each plate and ideally, two biological replicates for each screen. As seeding by most liquid handlers happens per row, we suggest putting replicates in columns to allow the detection of potential seeding issues. Second, we suggest including at least 6 (ideally >9) negative (vehicle) control wells and at least 3 (ideally >6) positive control wells on each plate. Lower numbers will likely negatively impact the Z' of the experiment. If multiple organoid models and/or solvents are used throughout the plate, include controls for each one. Ideally, the maximum concentration of solvent is 0.8% v/v for DMSO and 3% v/v for PBS/0.3% Tween. Greater than 1% and 5% solvent, respectively, will induce cell death and thus decrease the quality of screening results. Third, for accurate IC50 calculations, it is recommended to measure the organoid response to at least 9 drug concentrations, of which 2 fall in the upper plateau and 2 in the lower plateau, leaving 5 concentrations in the sigmoid phase17.
Users must note that the 384-well (or any multi-well) plate setup is sensitive to edge-effects (i.e., different results in the edges of the plate due to evaporation of medium). Practices to prevent this from affecting the results are the following: ensure a good humidification of the CO2 incubator (>85%), place the plates in the back of the incubator and prevent repetitive opening and closing, fill the outer wells with (cell-less) medium (water/PBS does not prevent the effect), or use permeable plate seals. Note that dispensing machines can only select per two rows in a 384-well format, meaning 4 rows are lost when dismissing the outer two tubes.
Recommendations for optimization for screening using novel organoid models are as follows. First, different organoid cultures have different growth kinetics and different basal ATP levels, resulting in different CTG readings. We therefore recommend optimizing the number of organoids per well for each organoid type (recommended range: 100-1,000 organoids/well), using Table 1 for reference. For optimization, assess Z' by measuring negative and positive controls with different organoid numbers.
Moreover, monitor the morphology of the organoids at the end of the screening period, and ensure the protocol results in healthy-looking organoids in the negative controls; any sign of deprivation during the course of the experiment should be avoided. It is important to note that different numbers of organoids/well can affect drug sensitivity. Further, different compound incubation times can affect the optimal amount of organoids/well. If organoids tend to fuse, using a higher percentage of ECM (v/v, up to 10%) could be beneficial. Additionally, some organoid models do not cope well with low ECM concentrations. If negative controls do not show regular proliferation or exhibit a changed morphology compared to normal expansion conditions, one should consider using 10% (v/v) ECM instead. It is worth mentioning that high ECM concentration can influence compound/antibody binding and effectiveness, lysis efficiency, and therefore activity in the CTG assay and subsequently, IC50 values. Once the screening assay performs well in smaller (pilot) screens, it is possible to increase the throughput of the assay.
Considerations to take into account when upscaling the screening assay are as follows. First, proliferation and differentiation of organoids are influenced not only by handling, but also by batch differences in ECM and growth factors. For screens performed over time to be comparable, it is important to ensure that sufficient amounts of growth factors, medium, and ECM from the same batch (comparable quality) are available to perform the complete screen. Additionally, we recommend including 1-2 organoid cultures that are used in every drug screen. These organoid cultures can act as controls to check and ensure reproducibility across drug screens. Shearing of organoids becomes less efficient in the presence of high concentrations of organoids and extracellular ECM. Increasing the number of organoids in a tube can therefore lead to a more than proportional increase in handling time. As prolonged handling of the organoids is often detrimental to their viability, the use of a dispase at critical steps can greatly improve the quality of the resulting organoid cultures. The D300e drug dispenser described in this protocol is a very flexible system and suited for smaller screens with only a few screening plates and a limited number of compounds. The software is however limited to drug addition to four screening plates per run, and the stock compounds need to be manually added to the cassette for each run.
When performing a screen with large numbers of plates or large compound libraries, it could be worthwhile using a liquid handler instead. Within the described protocol, there is space to add up to 5 µL of compound diluted in medium to each well without compromising the readout (taking solvent limits into account).
Downscaling of throughput is possible as well. This protocol can easily be adapted to a lower-throughput screen using a 96-well plate format. Both the cell dispenser and the robot drug printer described here are adaptable to dispense in a 96-well plate format. Manual pipetting is possible, but should be accompanied by careful quality assurance as there are higher chances of mistakes and of organoids being less evenly dispensed. Where the 384-well plate format uses 40 µL per well, doubling this in a 96-well plate format would serve as a good starting point; however, this should be further optimized prior to the screen.
The following points should be considered regarding drug screen data analysis. First, we recommend always taking a quick glance at the data by colorizing the raw data min/max, as seeding and edge-effects will become easily apparent this way. Second, to reiterate, Z' is an important metric to assess the dynamic range of each drug screen assay13 and should be calculated for each plate. Excluding drug screen results with a Z' lower than 0.3 and using data with a Z' > 0.5 is recommended. For the Z' to be informative, it is important that all organoids in the positive control wells have died. Third, it is recommended to always check curve-fitting after IC50 calculations. If curve-fitting appears difficult, the AUC parameter should be analyzed instead. Both IC50 and AUC reflect the effects of the tested drugs on both cell proliferation and cell death. Alternatively, organoid growth can be taken out of this equation by calculating the proliferation rate for normalization. The so-called GR metric requires an extra measurement at day 2 (described in section 7) and allows for easier comparison between fast- and slow-growing organoid line responses. However, for many drugs, proliferation effects are also to be expected, and these are dismissed by looking at this metric. Therefore, careful analysis of multiple metrices allows for the best output of this type of experiments.