Part of this research was funded by donations from different donors, including Dedert Schilde vzw and Mr Willy Floren. This work is partially funded by the Flemish Research Foundation, 12S9221N (A.L.), G044420N (S.V., A.L., E.G), 1S27021N (M.L), and by the Industrial Research Fund of the University of Antwerp, PS ID 45151 (S.V., A.L., C.D.). Figure 1 was created with BioRender.com.

Human pancreatic ductal adenocarcinoma (PDAC) patient-derived organoids were used. Tissue resection fragments were obtained from patients undergoing curative surgery at the Antwerp University Hospital. Written informed consent was obtained from all patients, and the study was approved by the UZA Ethical Committee (ref. 14/47/480). Details related to all materials, reagents, equipment, and software used in this protocol are provided in the Table of Materials. An overview of the workflow is presented in Figure 1. Example data is provided in the supplementary material to reproduce the protocol.
1. Day 0: Preparation of 2- or 3-day old organoids
<ol>
	<li>Preheat the microplates at 37 &#176;C overnight and thaw the extracellular matrix (ECM) at 4 &#176;C.</li>
	<li>Prepare full PDAC organoid culture medium: supplement ADF+++ (Advanced DMEM/F12, 1% glutamine supplement, 1% HEPES, and 1% penicillin/streptomycin) with 0.5 nM WNT surrogate-Fc-Fusion protein, 4% Noggin-Fc Fusion Protein conditioned medium, 4% Rpso3-Fc Fusion Protein conditioned medium, 1x B27, 1 mM N-acetyl cysteine (NAC), 5 mM nicotinamide, 500 nM A83-01, 100 ng/mL FGF10, and 10 nM Gastrin).</li>
	<li>Establish the PDTOs according to the method of choice. 
	NOTE: A detailed protocol is provided by Driehuis et al., which describes the conventional method to establish, culture, and passage PDTOs in ECM domes4.</li>
	<li>Enzymatically dissociate the organoids in ECM domes.
	<ol>
		<li>Aspirate the medium and wash 1x with phosphate-buffered saline (PBS). Add dissociation enzyme (e.g., 2 mL in a 6-well microplate) and pipette up and down 10x with a 1 mL pipette to mechanically dissociate the organoids and ECM domes.</li>
		<li>Incubate for 10 min at 37 &#176;C, pipette up and down, and check whether the organoids are dissociated to single cells. Repeat this step if necessary.</li>
		<li>Collect the cell suspension in a 15 mL tube, add ADF+++ to a volume of 10 mL, centrifuge for 5 min at 450 &#215; g at room temperature, and aspirate the supernatant with a Pasteur pipette and suction pump.</li>
		<li>Resuspend the pellet in 100-200 &#181;L of full medium depending on the size of the pellet and count the number of cells using the method of choice. For example: mix 10 &#181;L of the cell suspension + 10 &#181;L of Trypan Blue and count with an automated cell counter.</li>
	</ol>
	</li>
	<li>Plate single cells in ECM domes.
	<ol>
		<li>Dilute the cell suspension and add 2/3 ECM according to Table 1. Pipette up to ten 20 &#181;L droplets per well in a preheated 6-well plate. Invert the plate and incubate for 30 min at 37 &#176;C.</li>
		<li>Overlay with full medium supplemented with 10 &#181;M Y-27632 and incubate for 2-3 days in an incubator. 
		NOTE: Ten domes containing 75,000 cells each are usually enough to fill one 384-well microplate at a concentration of 200 organoids/well, excluding the wells at the edge.</li>
	</ol>
	</li>
</ol>
2. Days 2 - 3: Harvest and seed 2- or 3-day old organoids
<ol>
	<li>Collect intact organoids from the ECM domes. 
	NOTE: Organoids tend to stick to plastic surfaces (e.g., tubes, pipette tips). To avoid this, plasticware can be prerinsed with a 0.1% bovine serum albumin (BSA)/PBS solution.
	<ol>
		<li>Aspirate the medium and wash 1x with PBS. Add 1-2 mL of cold (4 &#176;C) organoid harvesting solution to a 6-well plate depending on the number of ECM domes and incubate on ice on a shaking platform for 10 min.</li>
		<li>Pipette up and down with a 1 mL pipette to dissociate the ECM domes, incubate for an additional 10 min on ice, and visually check under a microscope whether the ECM is dissociated.</li>
		<li>Optional: if a more uniform size distribution is preferred, filter the suspension through a 70 &#181;m cell strainer before centrifugation.</li>
		<li>Collect the organoids in a 15 mL tube precoated with 0.1% BSA/PBS, add ADF+++ up to 10 mL, and centrifuge for 5 min at 200 &#215; g at 4 &#176;C. Aspirate the supernatant and resuspend the pellet in up to 1,000 &#181;L of full PDAC organoid medium depending on the size of the pellet to obtain a concentration of &#62;6,000 organoids/mL.</li>
		<li>Count the organoids using any counting method of choice, preferably an image-based one.</li>
	</ol>
	</li>
	<li>Seed the organoids. 
	NOTE: See the Table of Materials for the minimum volume/well for two different types of 384-well microplates used in this protocol.
	<ol>
		<li>Precool all plasticware at -20 &#176;C or on ice for at least 20 min before use to avoid solidification of the ECM.</li>
		<li>Prepare the seeding solution from the 1 mL of organoid stock solution (step 2.1.6) using full medium to seed ~200 organoids per well in 50 &#181;L, the minimum volume used to fill a well. Use Supplementary File 1 to calculate the amount of organoid seeding solution. Add a residual volume of 1,500 &#181;L when using a 25 mL reservoir and multichannel pipette or pipetting robot.</li>
		<li>Seed the organoids using a pipetting robot. 
		NOTE: Both the seeding solution and microplate need to be cooled at 4 &#176;C during pipetting to avoid solidification of the ECM. Therefore, a 25 mL reservoir and microplate holder were 3D printed to be used in combination with the pipetting robot that can hold cooling elements listed in the Table of Materials. STL files for 3D printing the custom labware (Supplementary File 2 and Supplementary File 3) and custom labware JSON files for the pipetting robot (Supplementary File 4 and Supplementary File 5) are provided.
		<ol>
			<li>Design the dispensing protocol using the online Protocol Designer Tool. An example JSON file (Supplementary File 6) is provided, in which the custom labware is already loaded and an eight-channel p300 (Gen2) pipette with corresponding pipette tips is used.</li>
			<li>Open the pipetting robot control app, select protocols, click on Import, and drag and drop Supplementary File 6 into the designated field.</li>
			<li>Select the imported protocol and place all the labware, including cooling elements and plasticware, in the decks according to the layout shown in the Deck Setup field. Use the left slot for the 25 mL reservoir and cooling element as shown in Supplementary File 7.</li>
			<li>Click on Run Protocol and Proceed to Setup. Open the Labware Setup tab, click on Run Labware Position Check, and follow the instructions to calibrate the pipetting robot to the new hardware. 
			NOTE: Labware offset data can be stored for later, but it is recommended to run the labware position check before each run.</li>
			<li>Fill the 25 mL reservoir (placed on top of the cooling element) with the cooled organoid seeding solution and click on Start Run. 
			&#8203;NOTE: The top and bottom wells also become filled with organoid suspension solution due to the use of the eight-channel pipette.</li>
			<li>Centrifuge the microplate for 1 min at 100 &#215; g at 4 &#176;C.</li>
			<li>Incubate at 37 &#176;C for at least 30 min.</li>
			<li>Fill the outer blank wells with at least 50 &#181;L of H2O to avoid evaporation.</li>
			<li>Incubate at 37 &#176;C overnight to remove any bubbles in the well that can interfere with the image analysis.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Day 4: Drug treatment and reagent dispensing with digital drug dispenser
<ol>
	<li>Create the drug dispensing protocol using the digital drug dispenser control software.
	<ol>
		<li>Hover over Plate 1 above the plate layout, select edit plate attributes, and fill in plate type: 384 well, additional volume (&#181;L): 50, and DMSO limit (%): 1.</li>
		<li>Add fluids by clicking on the + button next to Fluids. Double-click on the newly created fluid and name it; select class (DMSO-based or aqueous+Tween 20) and concentration. 
		NOTE: All drugs and reagents must be dissolved in 100% DMSO or 0.3% Tween-20. A 1-10 mM stock solution can be used, taking into account a maximum DMSO concentration of &#60;1%. Table 2 provides examples of the required dilutions for common fluorescent reagents and therapies.</li>
		<li>Plate layout
		<ol>
			<li>For drug titration, select wells and click on Titration. For fluid, select the drug of interest, choose the highest concentration (e.g., 2,000 nM), and the lowest concentration (e.g., 10 nM); for replicates, choose a minimum of 2, and choose the desired titration pattern. 
			NOTE: The titration pattern will depend on many factors, including how much compound is to be fit in a single plate, whether the wells are to be randomized, and the number of replicates and controls.</li>
			<li>For the positive control, select three wells, click on Set Value, and fill in 2 &#181;M staurosporine from 10 mM stock in DMSO, which will induce maximum cell death.</li>
			<li>For Cytotox Green, select all the used wells, click on Set Value, and enter 60 nM/well. 
			NOTE: The Cytotox Green fluorescent staining indicates cells that have died, and therefore will not interfere with drug response monitoring. Here, no fluorescent marker for live cells is required.</li>
			<li>For the negative control and DMSO normalization, select all the wells with an additional four wells for the vehicle control, right-click, select normalization, select normalize fluid class: DMSO-based, and normalize to the highest class volume to obtain an equal DMSO concentration in each well. 
			NOTE: DMSO concentrations should be &#60;1%. An example TDD drug titration file (Supplementary File 8) is provided.</li>
			<li>Click on the arrow under Run in the top left corner, select Always Simulate, and click on Simulate to identify any errors and obtain the volumes of each drug to be prepared. 
			NOTE: To overcome a warning when the initial dispensing volume is too low, &#34;Dispense Warning well of 30 nL or greater is recommended for each fluid on each plate,&#34; select two wells on the edge that are filled with water, select Set Value, and enter 10 &#181;M of the drug for which the warning occurs. This primes the drug cartridge with a volume higher than 30 nL. These same wells can be used to prime the DMSO cartridge by setting a normalize to % of total volume value (e.g., 0.5%).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Uncheck Always Simulate under the Run button; click on Run to start the drug dispensing protocol and follow the instructions.</li>
	<li>Apply the sealing membrane to the microplate to prevent evaporation.</li>
	<li>Incubate the Cytotox Green dye 1-2 h at 37 &#176;C in the incubator and proceed to step 4.</li>
</ol>
4. Acquire images with the live-cell imager
NOTE: For the growth rate and NDR, a scan at timepoint 0 (T0 = start treatment) must be acquired 1-2 h after adding Cytotox Green.
<ol>
	<li>Open the live-cell imager Control software, select Method Editor New, go to file &#62; import, and select the example method XML file (Supplementary File 9). Alternatively, create a new file and select Plate: (CORE384fb_OpticalImaging) - Corning 384 Flat Black (Corning #4588), No lid and No humidity cassette; Application: Images-only; Objective: 4x; Pattern: Central; Check channels Brightfield and Green (Led intensity (%) = 40; Exposure Time (ms) = 200). 
	NOTE: The green channel settings work well for a concentration of 60 nM Cytotox Green. The Live viewer option can be used to adjust the focus offset and/or the led settings in real time.</li>
	<li>Click on Start to initiate scanning at T0.</li>
	<li>Repeat the scan every 24 h for up to 5 days using the same method. Alternatively, to run the timelapse measurement automatically, adjust the method in the live-cell imager Control software to a kinetic experiment by clicking and dragging the Kinetic Loop tab into the method field. Similarly, the Temperature and Gas tabs have to be dragged into the method field to set the system to 37 &#176;C and 5% CO2 to ensure the correct conditions within the live-cell imager during the experiment.</li>
</ol>
5. Image and data analysis
<ol>
	<li>Merging and compressing data
	<ol>
		<li>The live-cell imager Control software generates a folder for each scan at each timepoint. Create a new folder, copy the individual experiment folders into this new parent folder, and add _0h, _24h, _48h, _72h, _96h, and _120h to the corresponding experiment folder names.</li>
		<li>Prepare an XLSX plate map from the digital drug dispenser Control software by right-clicking on the plate map layout from the drug dispensing protocol and copy all the wells; paste the data in an XLSX file. Remove Cytotox Green and staurosporine data and add a matrix for Cell Line and Replicate. Enter ctrl- and ctrl+. See Supplementary File 10 for an example plate map.</li>
		<li>Open the Data Compression Tool, click on Browse, select the parent folder, and click on Run to initiate image data compression. All TIFF image files for the different timepoints are compressed into a single HDF5 for each well in a new datasets folder within the parental folder.</li>
	</ol>
	</li>
	<li>Image analysis
	<ol>
		<li>Go to the image analysis webapp platform, log in, and click on Add New Project in the Home tab. Enter the project name, continue, select Add New Experiment, and upload the datasets folder containing the HDF5 files.</li>
		<li>After uploading, go to the project and experiment folder and click on Upload Platemap for additional functionalities. Click on Run Analysis, select Multi-Organoid analysis, Default Parameters, and click on Analyze to initiate image analysis.</li>
		<li>Click on Download Results to download the raw data tables which contain the measurements for each well (e.g., total brightfield area, total fluorescence green area, etc.) and the segmented images/videos to confirm the accuracy of the analysis and further data processing.</li>
	</ol>
	</li>
	<li>Growth rate-based drug response metrics and normalized drug response
	<ol>
		<li>Select the Raw_NDR.xlsx file from the results folder (plate map required) (Supplementary File 11) and load this into the Official_NDR_7point R-script (Supplementary File 12) to automatically generate GR (normalized to ctrl-) and NDR (normalized to ctrl- and ctrl+) value tables (Supplementary File 13, Supplementary File 14, Supplementary File 15, and Supplementary File 16). GR and NDR values are calculated from the parameter as shown in equation (1) using the R-script (Supplementary File 12). 
		Total Survival Area = Total Brightfield Area - Total Green Area&#160; &#160; (1) 
		Where 0 &#60; NDR &#60;1 = cytostatic effect (growth arrest), and NDR &#60; 0 = cytotoxic response (cell death). 
		NOTE: The R-script was adapted from Gupta et al.6.</li>
		<li>From the clonal_data.xlsx table, retrieve single-organoid response data and plot them as a bubble plot.</li>
		<li>Use the Z-factor10 to assess the drug screen quality of a run (see equation (2)). Discard an experiment with a Z-factor &#60; 0.5. 
		<img alt="Equation 1" src="/files/ftp_upload/64434/64434eq01.jpg" style="margin: auto;" />&#160; &#160; (2)</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Drost, J., Clevers, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organoids+in+cancer+research.">Organoids in cancer research.</a> Nature Reviews Cancer. 18 (7), 407-418 (2018).</li><li>Veninga, V., Voest, E. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumor+organoids:+Opportunities+and+challenges+to+guide+precision+medicine.">Tumor organoids: Opportunities and challenges to guide precision medicine.</a> Cancer Cell. 39 (9), 1190-1201 (2021).</li><li>Le Compte, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Patient-derived+organoids+as+individual+patient+models+for+chemoradiation+response+prediction+in+gastrointestinal+malignancies.">Patient-derived organoids as individual patient models for chemoradiation response prediction in gastrointestinal malignancies.</a> Critical Reviews in Oncology/Hematology. 157, 103190 (2021).</li><li>Driehuis, E., Kretzschmar, K., Clevers, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Establishment+of+patient-derived+cancer+organoids+for+drug-screening+applications.">Establishment of patient-derived cancer organoids for drug-screening applications.</a> Nature Protocols. 15 (10), 3380-3409 (2020).</li><li>Hafner, M., Niepel, M., Chung, M., Sorger, P. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growth+rate+inhibition+metrics+correct+for+confounders+in+measuring+sensitivity+to+cancer+drugs.">Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs.</a> Nature Methods. 13 (6), 521-527 (2016).</li><li>Gupta, A., Gautam, P., Wennerberg, K., Aittokallio, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+normalized+drug+response+metric+improves+accuracy+and+consistency+of+anticancer+drug+sensitivity+quantification+in+cell-based+screening.">A normalized drug response metric improves accuracy and consistency of anticancer drug sensitivity quantification in cell-based screening.</a> Communications Biology. 3 (1), 42 (2020).</li><li>Rios, A. C., Clevers, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+organoids:+a+bright+future+ahead.">Imaging organoids: a bright future ahead.</a> Nature Methods. 15 (1), 24-26 (2018).</li><li>Deben, C., Cardenas, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OrBITS:+label-free+and+time-lapse+monitoring+of+patient+derived+organoids+for+advanced+drug+screening.">OrBITS: label-free and time-lapse monitoring of patient derived organoids for advanced drug screening.</a> Cellular Oncology. , (2022).</li><li>Icha, J., Weber, M., Waters, J. C., Norden, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phototoxicity+in+live+fluorescence+microscopy,+and+how+to+avoid+it.">Phototoxicity in live fluorescence microscopy, and how to avoid it.</a> Bioessays. 39 (8), (2017).</li><li>Zhang, J. H., Chung, T. D., Oldenburg, K. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simple+statistical+parameter+for+use+in+evaluation+and+validation+of+high+throughput+screening+assays.">A simple statistical parameter for use in evaluation and validation of high throughput screening assays.</a> Journal of Biomolecular Screening. 4 (2), 67-73 (1999).</li><li>Engel, R. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Patient-derived+colorectal+cancer+organoids+upregulate+revival+stem+cell+marker+genes+following+chemotherapeutic+treatment.">Patient-derived colorectal cancer organoids upregulate revival stem cell marker genes following chemotherapeutic treatment.</a> Journal of Clinical Medicine. 9 (1), 128 (2020).</li><li>Hennig, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+drug+resistance+in+pancreatic+cancer+organoids+guides+optimized+chemotherapy+treatment.">Detecting drug resistance in pancreatic cancer organoids guides optimized chemotherapy treatment.</a> The Journal of Pathology. 257 (5), 607-619 (2022).</li><li>D'Alterio, C., Scala, S., Sozzi, G., Roz, L., Bertolini, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Paradoxical+effects+of+chemotherapy+on+tumor+relapse+and+metastasis+promotion.">Paradoxical effects of chemotherapy on tumor relapse and metastasis promotion.</a> Seminars in Cancer Biology. 60, 351-361 (2020).</li><li>Njoroge, R. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organoids+model+distinct+Vitamin+E+effects+at+different+stages+of+prostate+cancer+evolution.">Organoids model distinct Vitamin E effects at different stages of prostate cancer evolution.</a> Scientific Reports. 7 (1), 16285 (2017).</li><li>Godet, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fate-mapping+post-hypoxic+tumor+cells+reveals+a+ROS-resistant+phenotype+that+promotes+metastasis.">Fate-mapping post-hypoxic tumor cells reveals a ROS-resistant phenotype that promotes metastasis.</a> Nature Communications. 10 (1), 4862 (2019).</li><li>Letai, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+precision+cancer+medicine-moving+beyond+pure+genomics.">Functional precision cancer medicine-moving beyond pure genomics.</a> Nature Medicine. 23 (9), 1028-1035 (2017).</li></ol>

The authors declare no conflict of interest.

Medium- to high-throughput PDTO drug screening often relies on readouts that only extract a fraction of information that organoids could potentially provide. It has become increasingly clear that, in order for the rapidly evolving organoid technology to realize greater scientific and clinical potential, more advanced 3D assays, readouts, and analysis methods are critically required. Here, an advanced screening pipeline is described, which not only increases the reproducibility, but also considerably enhances the clinical translatability by incorporating an AI-driven, live-cell imaging readout. In addition to analysis software developed in-house, the use of the normalized drug response metric (NDR) is implemented, which clearly demonstrates its ability to define patient-specific differences in treatment response6. 
The inclusion of this normalization metric will undoubtedly be of tremendous value, recalling that numerous studies aim to delineate treatment responses based on minor differences in area under the curve (AUC) or half-maximal inhibitory concentration (IC50) (as most of the dose-response curves overlap/are located close to each other)11,12. Growth rate metrics have already been implemented in organoid drug screening protocols using the ATP-based assay but rely on the normalization of reference wells lysed at timepoint 04. In contrast, this method allows for intrawell growth-rate normalization, which not only accounts for interpatient differences in PDTO growth rate but also interwell differences resulting from variations in seeding density and plate location-dependent effects to increase reproducibility. Furthermore, we adapted the NDR to further increase the separation of interpatient PDTO response by including a positive control for normalization6,8. 
Furthermore, the analysis, which is compatible with high-throughput and automation formats, can accurately detect individual organoid responses, enabling the quantification of subclonal resistance-the major driving force of tumor relapse and progression13. For example, although PDAC052 and PDAC060 showed a good response to the treatment in vitro (based on the NDR), the additional single-organoid analysis was able to detect a small (bigger population with PDAC060) population of subclones that do not respond to the treatment. Interestingly, this corresponded highly with the clinical observation, given that PDAC052 and PDAC060 had a durable response (no tumor activity detected) but eventually were both diagnosed with local tumor progression (due to the presence of resistant clones). Compared to the conventional 3D readouts (ATP-based assay and size/numbers), this advanced screening pipeline is expected to increase the predictive performance by extracting more clinically relevant information out of these 'patients-in-the-lab'. This hypothesis is now being tested by screening clinical PDTO samples in the authors' laboratory with this method to correlate ex vivo with in vivo response and clinical outcome. 
To obtain more insights into the mechanisms of a drug response, conventional fluorescent live-cell imaging reagents, in addition to cytotoxicity dyes, are compatible with this method to study mechanisms of cell death. We have previously shown the compatibility of this method with the Sartorius Caspase 3/7 Green Reagent to study caspase-dependent induction of apoptosis following cisplatin treatment8. The compatibility with other dyes to study oxidative stress (CellROX reagents) or hypoxia (Image-iT Hypoxia reagents) remains to be tested. However, these reagents have already successfully been used in 3D in vitro models14,15. 
The image analysis software is also compatible with other plate formats or culturing methods (e.g., microcavity plates, ECM domes) if clear, in-focus images of the organoids can be captured. This is often challenging for organoids cultured in domes since they grow in different z-planes, which requires z-stacking functionality of the microscope that is not always available. Therefore, we advise the use of flat-bottom ULA 384-well microplates to ensure images of sufficient quality. 
In addition, the analysis is compatible with other live-cell imaging systems, as previously shown for phase-contrast images captured with an IncuCyte ZOOM system8. A limitation of the Spark Cyto live-cell imaging system that was used in this manuscript is the one-plate capacity for kinetic measurements. However, the Spark Motion expansion increases its capacity to up to 40 microplates that can be screened in bulk. The compatibility of the software developed in-house will be expanded to these and other systems to offer a platform-agnostic solution, with the goal to standardize and automate image and data analysis pipelines. The web-based application will also include interactive graphing tools and automated drug metric calculations, as shown in this paper, to reduce manual analysis time.
The label-free PDTO segmentation algorithm was trained and tested on various in-house grown spheroid and PDTO models with distinct morphological differences (solid, semi-solid, cystic), and can consequently detect these with high accuracy8. A limitation of the model is that the inclusion of cystic PDTOs increased the unwanted detection of bubbles present in the well following seeding. However, overnight incubation was sufficient to remove most of these bubbles, allowing for a qualitative timepoint 0 scan. The accuracy of the organoid image segmentation and the method needs to be validated by other users, and based on their feedback, the software can be trained further to obtain a robust and automated image analysis algorithm. In addition, we aim to obtain more clinical data to correlate the ex vivo drug response quantified by this method to the clinical response in the patient to identify the best parameters to predict therapy response and further develop this method for functional precision cancer medicine16.

In recent years, in vitro cancer drug discovery, drug screening, and fundamental research have been transitioning from the use of traditional two-dimensional (2D) cancer models with immortalized cell lines to more physiologically relevant three-dimensional (3D) cancer models. This has spurred the adoption of tumor spheroids with established cancer cell lines, which recreate more complex cell-to-cell interactions and structures present in solid tumors. Currently, patient-derived tumor organoids (PDTOs) are the most advanced and physiologically relevant 3D cancer model available for in vitro cancer research, as they provide additional advantages over tumor spheroids, namely the heterogeneity found in cancer patients1. PDTOs are established from tumor tissue originating from cancer patients, and therefore retain both the tumor phenotype and genotype. As such, PDTOs are becoming invaluable for fundamental and translational cancer research and have the potential to greatly improve precision oncology2. 
Despite their promising potential, these sophisticated 3D in vitro cancer models are often underutilized due to a lack of advanced analysis methods. The most commonly used assay determines the number of viable cells in the PDTO via the quantification of intracellular ATP3. These assays are normally single-timepoint, bulk analyses, thus overlooking critical time-dependent responses and neglecting clonal responses. Specifically, the ability to monitor the growth of PDTOs (growth rate) and their response to specific therapies is of high interest4,5. The normalized drug response (NDR), which scores drug response based on the growth rate normalized to a positive (ctrl+) and negative control (ctrl-) condition, has also recently been reported to be a crucial metric for evaluating cancer drug sensitivity with cell-based screening, though this was predominantly done for 2D cell lines6. Therefore, more sophisticated analysis methods are needed to fully take advantage of these more clinically representative and complex 3D cancer models. Microscopy is considered a powerful approach to study the complexity of these organoid models7.
This paper describes a method for monitoring kinetic drug responses in 3D cancer models, using conventional widefield microscopes and live-cell imaging systems. Adaptations were made to the protocol described by Driehuis et al.4 to be compatible with automation using a pipetting robot, digital drug dispenser, and live-cell imaging system to increase reproducibility and reduce the number of 'hands-on' hours of labor. 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 dyes8. This is highly advantageous, as most identification with brightfield imaging requires manual annotation (which is laborious and time-consuming) or requires the addition of fluorescent dyes, which can confound drug responses related to photoxicity-induced oxidative stress9.
The resulting image analysis software developed in-house extends the functionality of conventional live-cell imaging systems, as 3D image analysis modules are either not available, platform-restricted, or not compatible with 384-well microplates and whole-well imaging. In addition, these modules are often highly priced and offer limited bulk organoid readouts. Therefore, this method is highly relevant for scientists who have access to widely available live-cell imaging systems and aim to extract more information about a drug response compared to the gold-standard but rudimentary ATP-based assay. With the addition of specific cell death indicators, cytostatic drug responses can be distinguished from cytotoxic responses, thus providing further insight into mechanistic drug actions currently unattainable from single-timepoint analysis. Finally, live-cell imaging allows for individual organoid tracking to obtain single organoid drug response metrics to capture response heterogeneity and identify potential resistant subclones.
The goal of this method and the associated image analysis software is to implement low-cost automation in organoid drug screening to limit user intervention and reduce variability in handling, image analysis, and data analysis. To make this software available to researchers, it is microscope- and platform-agnostic, and a cloud-based application is made available. Thus, by supporting conventional live-cell imaging systems, we also aim to improve their functionality for 3D culturing applications and analysis.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>6-well plate</td>
			<td>Greiner</td>
			<td>657160</td>
			<td></td>
		</tr>
		<tr>
			<td>8-Channel p300 (GEN 2) pipette</td>
			<td>Opentrons</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>300 &#181;L Tips</td>
			<td>Opentrons</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>384-well flat-bottom ULA microplate</td>
			<td>Corning</td>
			<td>4588</td>
			<td>minimum volume 50 &#181;L&#160;</td>
		</tr>
		<tr>
			<td>384-well flat-bottom ULA Phenoplate</td>
			<td>Perkin Elmer</td>
			<td>6057802</td>
			<td>minimum volume 75 &#181;L&#160;</td>
		</tr>
		<tr>
			<td>A8301</td>
			<td>Tocris Bioscience</td>
			<td>2939</td>
			<td></td>
		</tr>
		<tr>
			<td>ADF+++</td>
			<td></td>
			<td></td>
			<td>Advanced DMEM/F12, 1% GlutaMAX, 1% HEPES, 1% penicillin/streptomycin</td>
		</tr>
		<tr>
			<td>Advanced DMEM/F-12</td>
			<td>ThermoFisher Scientific</td>
			<td>12634</td>
			<td></td>
		</tr>
		<tr>
			<td>B27</td>
			<td>ThermoFisher Scientific</td>
			<td>17504044</td>
			<td></td>
		</tr>
		<tr>
			<td>Breathe easy sealing membrane</td>
			<td>Sigma-Aldrich</td>
			<td>Z380059</td>
			<td></td>
		</tr>
		<tr>
			<td>Caspase 3/7 Green&#160;</td>
			<td>Sartorius</td>
			<td>4440</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell Counting Slides for TC10/TC20</td>
			<td>Bio-Rad Laboratories</td>
			<td>1450017</td>
			<td></td>
		</tr>
		<tr>
			<td>CellTiter-Glo 3D</td>
			<td>Promega</td>
			<td>G9681</td>
			<td>ATP-assay</td>
		</tr>
		<tr>
			<td>Cooler for 25 mL reservoir</td>
			<td>VWR (Diversified Biotech)</td>
			<td>490006-908</td>
			<td></td>
		</tr>
		<tr>
			<td>Cooling element 12 x 8 x 3 cm</td>
			<td>Bol.com</td>
			<td>9200000107744702</td>
			<td>For custom microplate holder OT-2</td>
		</tr>
		<tr>
			<td>Cultrex Organoid Harvesting Solution</td>
			<td>R&#38;D systems</td>
			<td>3700-100-01</td>
			<td></td>
		</tr>
		<tr>
			<td>Cultrex PathClear Reduced Growth Factor BME, Type 2&#160;</td>
			<td>R&#38;D systems</td>
			<td>3533-010-02</td>
			<td>extracellular matrix (ECM)</td>
		</tr>
		<tr>
			<td>Cytotox Green</td>
			<td>Sartorius</td>
			<td>4633</td>
			<td></td>
		</tr>
		<tr>
			<td>Cytotox Red</td>
			<td>Sartorius</td>
			<td>4632</td>
			<td></td>
		</tr>
		<tr>
			<td>D300e</td>
			<td>Tecan</td>
			<td></td>
			<td>Digital drug dispenser</td>
		</tr>
		<tr>
			<td>D300e Control v3.3.5</td>
			<td>Tecan</td>
			<td></td>
			<td>Control software D300e</td>
		</tr>
		<tr>
			<td>FGF10</td>
			<td>Peprotech</td>
			<td>100-26</td>
			<td></td>
		</tr>
		<tr>
			<td>Full Medium</td>
			<td></td>
			<td></td>
			<td>ADF+++ supplemented with 0.5 nM WNT surrogate-Fc-Fusion protein, 4% Noggin-Fc Fusion Protein conditioned medium, 4% Rpso3-Fc Fusion Protein conditioned medium, 1x B27, 1 mM N-acetyl cysteine (NAC), 5 mM nicotinamide, 500 nM A83-01, 100 ng/mL FGF10, and 10 nM Gastrin</td>
		</tr>
		<tr>
			<td>Gastrin</td>
			<td>Sigma-Aldrich</td>
			<td>G9145</td>
			<td></td>
		</tr>
		<tr>
			<td>Gemcitabine</td>
			<td>Selleck Chemicals</td>
			<td>S1714</td>
			<td></td>
		</tr>
		<tr>
			<td>GlutaMAX</td>
			<td>ThermoFisher Scientific</td>
			<td>35050</td>
			<td></td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>ThermoFisher Scientific</td>
			<td>15630056</td>
			<td></td>
		</tr>
		<tr>
			<td>Hoechst 33342 Solution (20 mM)</td>
			<td>ThermoFisher Scientific</td>
			<td>62259</td>
			<td></td>
		</tr>
		<tr>
			<td>Human pancreatic ductal adenocarcinoma (PDAC) patient-derived organoids</td>
			<td>Biobank@uza (Antwerp, Belgium; ID: BE71030031000; Belgian Virtual Tumorbank funded by the National Cancer Plan)</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>N-acetyl-cysteine</td>
			<td>Sigma-Aldrich</td>
			<td>A9165-25G</td>
			<td></td>
		</tr>
		<tr>
			<td>Nicotinamide</td>
			<td>Sigma-Aldrich</td>
			<td>N0636-100G</td>
			<td></td>
		</tr>
		<tr>
			<td>Noggin-Fc Fusion Protein conditioned medium</td>
			<td>Immunoprecise</td>
			<td>N002</td>
			<td></td>
		</tr>
		<tr>
			<td>Opentrons App v6.0.1</td>
			<td>Opentrons</td>
			<td></td>
			<td>OT-2 control software</td>
		</tr>
		<tr>
			<td>Opentrons Protocol Designer Tool</td>
			<td>Opentrons</td>
			<td></td>
			<td>https://designer.opentrons.com/</td>
		</tr>
		<tr>
			<td>Orbits data compression tool</td>
			<td></td>
			<td></td>
			<td>www.orbits-oncology.com or contact corresponding author</td>
		</tr>
		<tr>
			<td>Orbits image analysis webapp</td>
			<td>University of Antwerp</td>
			<td></td>
			<td>www.orbits-oncology.com or contact corresponding author</td>
		</tr>
		<tr>
			<td>OT-2</td>
			<td>Opentrons</td>
			<td></td>
			<td>Pipetting robot</td>
		</tr>
		<tr>
			<td>Paclitaxel</td>
			<td>Selleck Chemicals</td>
			<td>S1150</td>
			<td></td>
		</tr>
		<tr>
			<td>Pasteur Pipette 230 mm&#160;</td>
			<td>Novolab</td>
			<td>A33696</td>
			<td></td>
		</tr>
		<tr>
			<td>Peniciline-Streptomycin</td>
			<td>ThermoFisher Scientific</td>
			<td>15140</td>
			<td></td>
		</tr>
		<tr>
			<td>Prism 9</td>
			<td>GraphPad</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Rspo3-Fc Fusion Protein conditioned medium</td>
			<td>Immunoprecise</td>
			<td>N003</td>
			<td></td>
		</tr>
		<tr>
			<td>Spark Cyto 600</td>
			<td>Tecan</td>
			<td></td>
			<td>Live-cell imaging and multi-mode platereader</td>
		</tr>
		<tr>
			<td>SparkControl v3.1</td>
			<td>Tecan</td>
			<td></td>
			<td>Spark Cyto control software</td>
		</tr>
		<tr>
			<td>Staurosporine</td>
			<td>Tocris Bioscience</td>
			<td>1285</td>
			<td></td>
		</tr>
		<tr>
			<td>Sterile 25 mL reservoir</td>
			<td>VWR (Diversified Biotech)</td>
			<td>10141-922</td>
			<td></td>
		</tr>
		<tr>
			<td>T8 plus cassette</td>
			<td>Tecan</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>TC20</td>
			<td>Bio-Rad Laboratories</td>
			<td></td>
			<td>automated cell counter</td>
		</tr>
		<tr>
			<td>TrypLE</td>
			<td>ThermoFisher Scientific</td>
			<td>12604-021</td>
			<td>dissociation enzyme</td>
		</tr>
		<tr>
			<td>Tween-20</td>
			<td>Acros Organics</td>
			<td>233360010</td>
			<td></td>
		</tr>
		<tr>
			<td>WNT Surrogate-Fc-Fusion protein</td>
			<td>Immunoprecise</td>
			<td>N001</td>
			<td></td>
		</tr>
		<tr>
			<td>Y-27632</td>
			<td>Selleck Chemicals</td>
			<td>S1049</td>
			<td></td>
		</tr>
	</tbody>
</table>

multiparametric tumor organoid drug screening using widefield live-cell imaging for bulk and single-organoid analysis

Patient-derived tumor organoids (PDTOs) hold great promise for preclinical and translational research and predicting the patient therapy response from ex vivo drug screenings. However, current adenosine triphosphate (ATP)-based drug screening assays do not capture the complexity of a drug response (cytostatic or cytotoxic) and intratumor heterogeneity that has been shown to be retained in PDTOs due to a bulk readout. Live-cell imaging is a powerful tool to overcome this issue and visualize drug responses more in-depth. However, image analysis software is often not adapted to the three-dimensionality of PDTOs, requires fluorescent viability dyes, or is not compatible with a 384-well microplate format. This paper describes a semi-automated methodology to seed, treat, and image PDTOs in a high-throughput, 384-well format using conventional, widefield, live-cell imaging systems. In addition, we developed viability marker-free image analysis software to quantify growth rate-based drug response metrics that improve reproducibility and correct growth rate variations between different PDTO lines. Using the normalized drug response metric, which scores drug response based on the growth rate normalized to a positive and negative control condition, and a fluorescent cell death dye, cytotoxic and cytostatic drug responses can be easily distinguished, profoundly improving the classification of responders and non-responders. In addition, drug-response heterogeneity can by quantified from single-organoid drug response analysis to identify potential, resistant clones. Ultimately, this method aims to improve the prediction of clinical therapy response by capturing a multiparametric drug response signature, which includes kinetic growth arrest and cell death quantification.

The automated pipetting protocol ensures an even distribution of PDAC_060 PDTOs in all the columns of the 384-well microplate (Figure 2A). 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 label-free 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. To account for variations in cell seeding and organoid size, growth rate-based metrics should be used to reduce variations between replicates, as shown by the reduced error bars in Figure 2D versus Figure 2C, and a higher Z-factor indicating a strongly improved drug screen quality (Figure 2E).
The NDR dose-response curve (Figure 2G), normalized to ctrl- and ctrl+, is clearly superior to the GR dose-response curve (Figure 2F), normalized to ctrl-, as it increases the separation of the drug response curves and more accurately represents cytotoxic drug responses. An example of the associated images for ctrl-, ctrl+, and 400 nM gemcitabine/80 nM paclitaxel-treated PDTO is shown in Figure 3. An interesting observation is that the cytotoxic effect of gemcitabine was dominant in the combination therapy as no added value of paclitaxel was observed.
Next, two additional PDTO lines, PDAC_052 and PDAC_087, were used. A clear difference in growth rate between these lines was observed (Figure 4A), which supports the use of GR metrics. Again, NDR dose-response curves (Figure 4C) resulted in an increased dynamic range and separation between the three different patients compared to the GR curves (Figure 4B). Furthermore, the protocol allows for the determination of NDR over time and shows that PDAC_052 and PDAC_060 had a very similar cytostatic drug response to a low dose of gem-pac (Figure 4D), while a clear differential cytostatic versus cytotoxic response could be observed for the middle (Figure 4E) and high doses (Figure 4F) of gem-pac. These drug responses were consistent with the clinical responses observed in the patients (Figure 4G).
Finally, a major benefit of the approach and software is that single-organoid drug responses can be quantified to study response heterogeneity and identify potentially resistant subclones. Figure 5 provides a clear overview of the clonal dynamics of the different patients and shows that PDAC-087 had the most resistant subclones following treatment, which is consistent with the aggressive and highly resistant disease observed in the patient. Interestingly, this patient was also the least sensitive to the ctrl+ staurosporin.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64434/64434fig01.jpg" /> 
Figure 1: Workflow overview. <a href="https://www.jove.com/files/ftp_upload/64434/64434fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64434/64434fig02v2.jpg" /> 
Figure 2: Seeding accuracy and drug response metrics. (A) Organoid counts/well of PDAC_060 PDTOs seeded in a 384-well microplate using the pipetting robot. Each dot represents the count in a single well and plots are separated by the 384-well microplate columns. (B) Mean PDTO area/well. (C) Total survival area (total brightfield area - total green area) and (D) growth rate (total survival area normalized to T0 = 1) of PDAC_060 PDTOs treated with a 5:1 ratio of gemcitabine/paclitaxel. (E) Z-factor as a metric for assay quality. (F) Growth rate-dose response curve normalized to ctrl- and (G) normalized drug response curve normalized to ctrl- and ctrl+. Error bars indicate mean &#177; SD of two wells. Abbreviations: PDAC = pancreatic ductal adenocarcinoma; PDTO = patient-derived tumor organoid; GR = growth rate; NDR = normalized drug response. <a href="https://www.jove.com/files/ftp_upload/64434/64434fig02largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/64434/64434fig03.jpg" /> 
Figure 3: Example images. Representative images of PDAC_060 PDTO treated with vehicle (ctrl-), 400 nM gemcitabine/80 nM paclitaxel, and 2 &#181;M staurosporin (ctrl+). The left column shows brightfield images, the middle column shows the Cytotox Green fluorescent signal, and the right column shows the label-free annotated brightfield images using the organoid analysis module. Scale bars = 100 &#181;m. Abbreviations: PDAC = pancreatic ductal adenocarcinoma; PDTO = patient-derived tumor organoid; GemPac = gemcitabine/paclitaxel. <a href="https://www.jove.com/files/ftp_upload/64434/64434fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/64434/64434fig04.jpg" /> 
Figure 4: Comparing interpatient drug response. (A) Comparison of growth rate (based on total survival area) of PDAC_052, PDAC_060, and PDAC_087 PDTO lines. (B) Growth rate-dose response curve normalized to ctrl- and (C) normalized drug response curve normalized to ctrl- and ctrl+. Kinetic NDR of a (D) low, (E) middle, and (F) high dose of gemcitabine/paclitaxel (5:1 ratio). (G) PDAC patients&#39; clinical characteristics. Error bars indicate mean &#177; SD of two wells. Abbreviations: PDAC = pancreatic ductal adenocarcinoma; PDTO = patient-derived tumor organoid; GR = growth rate; NDR = normalized drug response; FFX = folfirinox. <a href="https://www.jove.com/files/ftp_upload/64434/64434fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/64434/64434fig05.jpg" /> 
Figure 5: Single organoid metrics. Single organoid dose response based on cell death (green area/brightfield area) and area (brightfield) of PDAC_052, PDAC_060, and PDAC_087 PDTOs treated with vehicle (ctrl-), 400 nM gemcitabine/80 nM paclitaxel, and 2 &#181;M staurosporine (ctrl+). Green regions indicate viable organoids; blue regions indicate x-as range of GemPac and ctrl+ plots. Abbreviations: PDAC = pancreatic ductal adenocarcinoma; PDTO = patient-derived tumor organoid; GemPac = gemcitabine/paclitaxel. <a href="https://www.jove.com/files/ftp_upload/64434/64434fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Cell suspension stock</td>
			<td>Cells/Drop</td>
			<td># Drops (20 &#181;L)</td>
			<td>Stock (1/3)</td>
			<td>ECM (2/3)</td>
		</tr>
		<tr>
			<td>1.13 &#215; 107 cells/mL</td>
			<td>75,000</td>
			<td>10</td>
			<td>75 uL</td>
			<td>150 &#181;L</td>
		</tr>
		<tr>
			<td>1.13 &#215; 107 cells/mL</td>
			<td>75,000</td>
			<td>5</td>
			<td>40 uL&#160;</td>
			<td>80 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 1: Dilution for plating in ECM domes. Abbreviation: ECM = extracellular matrix.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Compound</td>
			<td>Stock concentration</td>
			<td>Dilution</td>
			<td>Working concentration</td>
			<td>Solvent</td>
			<td>Well concentration</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Cytotox Green</td>
			<td>1 mM (DMSO)</td>
			<td>1/10</td>
			<td>10 &#181;M</td>
			<td>DMSO</td>
			<td>60 nM</td>
			<td>Cell death marker</td>
		</tr>
		<tr>
			<td>Cytotox Red&#160;</td>
			<td>1 mM (DMSO)</td>
			<td>1/10</td>
			<td>10 &#181;M</td>
			<td>DMSO</td>
			<td>250 nM</td>
			<td>Cell death marker</td>
		</tr>
		<tr>
			<td>Caspase 3/7 Green</td>
			<td>5 mM (DMSO)</td>
			<td>1/2</td>
			<td>2.5 mM</td>
			<td>DMSO</td>
			<td>2.5 &#181;M&#160;</td>
			<td>Apoptotic marker</td>
		</tr>
		<tr>
			<td>Hoechst</td>
			<td>20 mM (H2O)</td>
			<td>1/200</td>
			<td>100 &#181;M</td>
			<td>0.33% Tween/PBS</td>
			<td>50 nM</td>
			<td>Nuclear marker</td>
		</tr>
		<tr>
			<td>Staurosporin</td>
			<td>10 mM (DMSO)</td>
			<td>/</td>
			<td>1 - 10 mM</td>
			<td>/</td>
			<td>2 &#8211; 5 &#181;M</td>
			<td>Positive control</td>
		</tr>
		<tr>
			<td>Gemcitabine</td>
			<td>10 mM (DMSO)</td>
			<td>/</td>
			<td>1 - 10 mM</td>
			<td>/</td>
			<td>Titration</td>
			<td>Chemotherapy</td>
		</tr>
		<tr>
			<td>Paclitaxel</td>
			<td>10 mM (DMSO)</td>
			<td>/</td>
			<td>1 - 10 mM</td>
			<td>/</td>
			<td>Titration</td>
			<td>Chemotherapy</td>
		</tr>
		<tr>
			<td>Cisplatin</td>
			<td>5 mM (0.9% NaCl)</td>
			<td>1/2</td>
			<td>2.5 mM</td>
			<td>0.6% Tween/PBS</td>
			<td>Titration</td>
			<td>Chemotherapy</td>
		</tr>
	</tbody>
</table>
Table 2: Example dilutions of frequently used drugs and fluorescent reagents. Each compound needs to be dissolved in either 100% DMSO or 0.3% Tween/PBS.
Supplementary Table S1: Overview of compatible cancer cell lines. Static: spheroids are not migratory. Merge: spheroids migrate toward each other and merge together. <a href="https://www.jove.com/files/ftp_upload/64434/64434_SuppTableS1 (1).xlsx" target="_blank">Please click here to download this File.</a>
Supplementary File 1: Organoid seeding solution calculation tool. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 1.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary File 2: STL file for 3D printing custom labware &#39;Microplate Holder&#39;. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 2.stl" target="_blank">Please click here to download this File.</a>
Supplementary File 3: STL file for 3D printing custom labware &#39;2 x 25 mL Reservoir Holder&#39;. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 3.stl" target="_blank">Please click here to download this File.</a>
Supplementary File 4: JSON file for custom labware pipetting robot &#39;Microplate Holder&#39;. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 4.json" target="_blank">Please click here to download this File.</a>
Supplementary File 5: JSON file for custom labware pipetting robot &#39;2 x 25 mL Reservoir Holder_WithCooler&#39;. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 5.json" target="_blank">Please click here to download this File.</a>
Supplementary File 6: JSON file for pipetting robot protocol &#39;Plating_ PDO_384well_Cooled_Row2-23&#39;. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 6.json" target="_blank">Please click here to download this File.</a>
Supplementary File 7: Overview of the pipetting robot desk setup. (A) Cooling elements and (B) reservoir and microplate. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 7.png" target="_blank">Please click here to download this File.</a>
Supplementary File 8: TDD file for protocol of the digital drug dispenser. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 8.tdd" target="_blank">Please click here to download this File.</a>
Supplementary File 9: XML file for protocol of the live-cell imager for brightfield and fluorescence imaging. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 9.xml" target="_blank">Please click here to download this File.</a>
Supplementary File 10: Example plate map. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 10.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary File 11: Example input file for NDR R script. Abbreviation: NDR = normalized drug response. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 11.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary File 12: Normalized drug response NDR R script. Abbreviation: NDR = normalized drug response. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 12.R" target="_blank">Please click here to download this File.</a>
Supplementary File 13: Example output file of NDR R script GR values. Abbreviations: GR = growth rate; NDR = normalized drug response. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 13.csv" target="_blank">Please click here to download this File.</a>
Supplementary File 14: Example output file of NDR R script with GR values transposed. Abbreviations: GR = growth rate; NDR = normalized drug response. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 14.csv" target="_blank">Please click here to download this File.</a>
Supplementary File 15: Example output file of NDR R script NDR values. Abbreviation: NDR = normalized drug response. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 15.csv" target="_blank">Please click here to download this File.</a>
Supplementary File 16: Example output file of NDR R script with NDR values transposed. Abbreviation: NDR = normalized drug response. <a href="https://www.jove.com/files/ftp_upload/64434/Supplementary File 16.csv" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis at JoVE.com

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

This protocol describes a semi-automated method for medium- to high-throughput organoid drug screenings and microscope-agnostic, automated image analysis software to quantify and visualize multiparametric, single-organoid drug responses to capture intratumor heterogeneity.

Patient-derived tumor organoids (PDTOs) hold great promise for preclinical and translational research and predicting the patient therapy response from ex ...

multiparametric-tumor-organoid-drug-screening-using-widefield-live

Research

JoVE Journal

Cancer Research

4.6K Views.  University of Antwerp. This protocol describes a semi-automated method for medium- to high-throughput organoid drug screenings and microscope-agnostic, automated image analysis software to quantify and visualize multiparametric, single-organoid drug responses to capture intratumor heterogeneity. 

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

Simple and Rapid Method to Obtain High-quality Tumor DNA from Clinical-pathological Specimens Using Touch Imprint Cytology

It is critical to determine the mutational status in cancer before administration and treatment of specific molecular targeted drugs for cancer patients. In the clinical setting, formalin-fixed paraffin-embedded (FFPE) tissues are widely used for genetic testing. However, FFPE DNA is generally damaged and fragmented during the fixation process with formalin. Therefore, FFPE DNA is sometimes not adequate for genetic testing because of low quality and quantity of DNA. Here we present a method of touch imprint cytology (TIC) to obtain genomic DNA from cancer cells, which can be observed under a microscope. Cell morphology and cancer cell numbers can be evaluated using TIC specimens. Furthermore, the extraction of genomic DNA from TIC samples can be completed within two days. The total amount and quality of TIC DNA obtained using this method was higher than that of FFPE DNA. This rapid and simple method allows researchers to obtain high-quality DNA for genetic testing (e.g., next generation sequencing analysis, digital PCR, and quantitative real time PCR) and to shorten the turnaround time for reporting results.

Obtaining high-quality genomic DNA from tumor tissues is an essential first step for analyzing genetic alterations using next generation sequencing. In this article, we present a simple and rapid method to enrich tumor cells and obtain intact DNA from touch imprint cytology specimens.

It is critical to determine the mutational status in cancer before administration and treatment of specific molecular targeted drugs for cancer patients. ...

Radionuclide-fluorescence Reporter Gene Imaging to Track Tumor Progression in Rodent Tumor Models

Metastasis is responsible for most cancer deaths. Despite extensive research, the mechanistic understanding of the complex processes governing metastasis remains incomplete. In vivo models are paramount for metastasis research, but require refinement. Tracking spontaneous metastasis by non-invasive in vivo imaging is now possible, but remains challenging as it requires long-time observation and high sensitivity. We describe a longitudinal combined radionuclide and fluorescence whole-body in vivo imaging approach for tracking tumor progression and spontaneous metastasis. This reporter gene methodology employs the sodium iodide symporter (NIS) fused to a fluorescent protein (FP). Cancer cells are engineered to stably express NIS-FP followed by selection based on fluorescence-activated cell sorting. Corresponding tumor models are established in mice. NIS-FP expressing cancer cells are tracked non-invasively in vivo at the whole-body level by positron emission tomography (PET) using the NIS radiotracer [18F]BF4-. PET is currently the most sensitive in vivo imaging technology available at this scale and enables reliable and absolute quantification. Current methods either rely on large cohorts of animals that are euthanized for metastasis assessment at varying time points, or rely on barely quantifiable 2D imaging. The advantages of the described method are: (i) highly sensitive non-invasive in vivo 3D PET imaging and quantification, (ii) automated PET tracer production, (iii) a significant reduction in required animal numbers due to repeat imaging options, (iv) the acquisition of paired data from subsequent imaging sessions providing better statistical data, and (v) the intrinsic option for ex vivo confirmation of cancer cells in tissues by fluorescence microscopy or cytometry. In this protocol, we describe all steps required for routine NIS-FP-afforded non-invasive in vivo cancer cell tracking using PET/CT and ex vivo confirmation of in vivo results. This protocol has applications beyond cancer research whenever in vivo localization, expansion and long-time monitoring of a cell population is of interest.

We describe a protocol for preclinical in vivo tracking of cancer metastasis. It is based on a radionuclide-fluorescence reporter combining the sodium iodide symporter, detected by non-invasive [18F]tetrafluoroborate-PET, and a fluorescent protein for streamlined ex vivo confirmation. The method is applicable for preclinical in vivo cell tracking beyond tumor biology.

Metastasis is responsible for most cancer deaths. Despite extensive research, the mechanistic understanding of the complex processes governing metastasis ...

Generation of High-Throughput Three-Dimensional Tumor Spheroids for Drug Screening

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is exposed to in vivo. Solid tumors grow not as a sheet attached to plastic, but instead as a collection of clonal cells in a three-dimensional (3D) space interacting with their neighbors, and with distinct spatial properties such as the disruption of normal cellular polarity. These interactions cause 3D-cultured cells to acquire morphological and cellular characteristics which are more relevant to in vivo tumors. Additionally, a tumor mass is in direct contact with other cell types such as stromal and immune cells, as well as the extracellular matrix from all other cell types. The matrix deposited is comprised of macromolecules such as collagen and fibronectin.
In an attempt to increase the translation of research findings in oncology from bench to bedside, many groups have started to investigate the use of 3D model systems in their drug development strategies. These systems are thought to be more physiologically relevant because they attempt to recapitulate the complex and heterogeneous environment of a tumor. These systems, however, can be quite complex, and, although amenable to growth in 96-well formats, and some now even in 384, they offer few choices for large-scale growth and screening. This observed gap has led to the development of the methods described here in detail to culture tumor spheroids in a high-throughput capacity in 1536-well plates. These methods represent a compromise to the highly complex matrix-based systems, which are difficult to screen, and conventional 2D assays. A variety of cancer cell lines harboring different genetic mutations are successfully screened, examining compound efficacy by using a curated library of compounds targeting the Mitogen-Activated Protein Kinase or MAPK pathway. The spheroid culture responses are then compared to the response of cells grown in 2D, and differential activities are reported. These methods provide a unique protocol for testing compound activity in a high-throughput 3D setting.

Across a wide variety of disease indications, more physiologically relevant models are being developed and implemented into drug discovery programs. The new model system described here demonstrates how three-dimensional tumor spheroids can be cultured and screened in a high-throughput 1536-well plate-based system to search for new oncology drugs.

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is ...

Live-Cell Imaging Assays to Study Glioblastoma Brain Tumor Stem Cell Migration and Invasion

Glioblastoma (GBM) is an aggressive brain tumor that is poorly controlled with the currently available treatment options. Key features of GBMs include rapid proliferation and pervasive invasion into the normal brain. Recurrence is thought to result from the presence of radio- and chemo-resistant brain tumor stem cells (BTSCs) that invade away from the initial cancerous mass and, thus, evade surgical resection. Hence, therapies that target BTSCs and their invasive abilities may improve the otherwise poor prognosis of this disease. Our group and others have successfully established and characterized BTSC cultures from GBM patient samples. These BTSC cultures demonstrate fundamental cancer stem cell properties such as clonogenic self-renewal, multi-lineage differentiation, and tumor initiation in immune-deficient mice. In order to improve on the current therapeutic approaches for GBM, a better understanding of the mechanisms of BTSC migration and invasion is necessary. In GBM, the study of migration and invasion is restricted, in part, due to the limitations of existing techniques which do not fully account for the in vitro growth characteristics of BTSCs grown as neurospheres. Here, we describe rapid and quantitative live-cell imaging assays to study both the migration and invasion properties of BTSCs. The first method described is the BTSC migration assay which measures the migration toward a chemoattractant gradient. The second method described is the BTSC invasion assay which images and quantifies a cellular invasion from neurospheres into a matrix. The assays described here are used for the quantification of BTSC migration and invasion over time and under different treatment conditions.

Here, we describe live-cell imaging techniques to quantitatively measure the migration and invasion of glioblastoma brain tumor stem cells over time and under multiple treatment conditions.

Glioblastoma (GBM) is an aggressive brain tumor that is poorly controlled with the currently available treatment options. Key features of GBMs include ...

Analysis of Cancer Cell Invasion and Anti-metastatic Drug Screening Using Hydrogel Micro-chamber Array (HMCA)-based Plates

Cancer metastasis is known to cause 90% of cancer lethality. Metastasis is a multistage process which initiates with the penetration/invasion of tumor cells into neighboring tissue. Thus, invasion is a crucial step in metastasis, making the invasion process research and development of anti-metastatic drugs, highly significant. To address this demand, there is a need to develop 3D in vitro models which imitate the architecture of solid tumors and their microenvironment most closely to in vivo state on one hand, but at the same time be reproducible, robust and suitable for high yield and high content measurements. Currently, most invasion assays lean on sophisticated microfluidic technologies which are adequate for research but not for high volume drug screening. Other assays using plate-based devices with isolated individual spheroids in each well are material consuming and have low sample size per condition. The goal of the current protocol is to provide a simple and reproducible biomimetic 3D cell-based system for the analysis of invasion capacity in large populations of tumor spheroids. We developed a 3D model for invasion assay based on HMCA imaging plate for the research of tumor invasion and anti-metastatic drug discovery. This device enables the production of numerous uniform spheroids per well (high sample size per condition) surrounded by ECM components, while continuously and simultaneously observing and measuring the spheroids at single-element resolution for medium throughput screening of anti-metastatic drugs. This platform is presented here by the production of HeLa and MCF7 spheroids for exemplifying single cell and collective invasion. We compare the influence of the ECM component hyaluronic acid (HA) on the invasive capacity of collagen surrounding HeLa spheroids. Finally, we introduce Fisetin (invasion inhibitor) to HeLa spheroids and nitric oxide (NO) (invasion activator) to MCF7 spheroids. The results are analyzed by in-house software which enables semi-automatic, simple and fast analysis which facilitates multi-parameter examination.

Analysis of Cancer Cell Invasion and Anti-metastatic Drug Screening Using Hydrogel Micro-chamber Array HMCA-based Plates

A HMCA-based imaging plate is presented for invasion assay performance. This plate facilitates the formation of three-dimensional (3D) tumor spheroids and the measurement of cancer cell invasion into the extracellular matrix (ECM). The invasion assay quantification is achieved by semi-automatic analysis.

Cancer metastasis is known to cause 90% of cancer lethality. Metastasis is a multistage process which initiates with the penetration/invasion of tumor ...

A Novel Stromal Fibroblast-Modulated 3D Tumor Spheroid Model for Studying Tumor-Stroma Interaction and Drug Discovery

Tumor-stroma interactions play an important role in cancer progression. Three-dimensional (3D) tumor spheroid models are the most widely used in vitro model in the study of cancer stem/initiating cells, preclinical cancer research, and drug screening. The 3D spheroid models are superior to conventional tumor cell culture and reproduce some important characters of real solid tumors. However, conventional 3D tumor spheroids are made up exclusively of tumor cells. They lack the participation of tumor stromal cells and have insufficient extracellular matrix (ECM) deposition, thus only partially mimicking the in vivo conditions of tumor tissues. We established a new multicellular 3D spheroid model composed of tumor cells and stromal fibroblasts that better mimics the in vivo heterogeneous tumor microenvironment and its native desmoplasia. The formation of spheroids is strictly regulated by the tumor stromal fibroblasts and is determined by the activity of certain crucial intracellular signaling pathways (e.g., Notch signaling) in stromal fibroblasts. In this article, we present the techniques for coculture of tumor cells-stromal fibroblasts, time-lapse imaging to visualize cell-cell interactions, and confocal microscopy to display the 3D architectural features of the spheroids. We also show two examples of the practical application of this 3D spheroid model. This novel multicellular 3D spheroid model offers a useful platform for studying tumor-stroma interaction, elucidating how stromal fibroblasts regulate cancer stem/initiating cells, which determine tumor progression and aggressiveness, and exploring involvement of stromal reaction in cancer drug sensitivity and resistance. This platform can also be a pertinent in vitro model for drug discovery.

A novel three-dimensional spheroid model based on the heterotypic interaction of tumor cells and stromal fibroblasts is established. Here, we present coculture of tumor cells and stromal fibroblasts, time-lapse imaging, and confocal microscopy to visualize the formation of spheroids. This three-dimensional model offers a pertinent platform to study tumor-stroma interactions and test cancer therapeutics.

Tumor-stroma interactions play an important role in cancer progression. Three-dimensional (3D) tumor spheroid models are the most widely used in vitro ...

Drug Screening of Primary Patient Derived Tumor Xenografts in Zebrafish

Patient derived xenograft models are critical in defining how different cancers respond to drug treatment in an in vivo system. Mouse models are the standard in the field, but zebrafish have emerged as an alternative model with several advantages, including the ability for high-throughput and low-cost drug screening. Zebrafish also allow for in vivo drug screening with large replicate numbers that were previously only obtainable with in vitro systems. The ability to rapidly perform large scale drug screens may open up the possibility for personalized medicine with rapid translation of results back to clinic. Zebrafish xenograft models could also be used to rapidly screen for actionable mutations based on tumor response to targeted therapies or to identify new anti-cancer compounds from large libraries. The current major limitation in the field has been quantifying and automating the process so that drug screens can be done on a larger scale and be less labor-intensive. We have developed a workflow for xenografting primary patient samples into zebrafish larvae and performing large scale drug screens using a fluorescence microscope equipped imaging unit and automated sampler unit. This method allows for standardization and quantification of engrafted tumor area and response to drug treatment across large numbers of zebrafish larvae. Overall, this method is advantageous over traditional cell culture drug screening as it allows for growth of tumor cells in an in vivo environment throughout drug treatment, and is more practical and cost-effective than mice for large scale in vivo drug screens.

Zebrafish xenograft models allow for high-throughput drug screening and fluorescent imaging of human cancer cells in an in vivo microenvironment. We developed a workflow for large scale, automated drug screening on patient-derived leukemia samples in zebrafish using an automated fluorescence microscope equipped imaging unit.

Patient derived xenograft models are critical in defining how different cancers respond to drug treatment in an in vivo system. Mouse models are the ...

Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments is crucial to advancing healthcare and improving patient outcomes. Organoids are in-vitro three-dimensional multicellular miniature organs. Patient-derived organoid (PDO) models of ovarian cancer may be optimal for drug screening because they more accurately recapitulate tissues of interest than two-dimensional cell culture models and are inexpensive compared to patient-derived xenografts. In addition, ovarian cancer PDOs mimic the variable tumor microenvironment and genetic background typically observed in ovarian cancer. Here, a method is described that can be used to test conventional and novel drugs on PDOs derived from ovarian cancer tissue and ascites. A luminescence-based adenosine triphosphate (ATP) assay is used to measure viability, growth rate, and drug sensitivity. Drug screens in PDOs can be completed in 7-10 days, depending on the rate of organoid formation and drug treatments.

We present a protocol that can be used to conduct therapeutic drug testing with patient-derived ovarian cancer organoids.

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments ...

Automated Kinetic Imaging for Drug Assessment in Patient-Derived Tumor Organoids

Multiplexed Live-Cell Imaging for Drug Responses in Patient-Derived Organoid Models of Cancer

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell lines. PDO models can be generated by culturing patient tumor cells in extracellular basement membrane extracts (BME) and plating them as three-dimensional domes. However, commercially available reagents that have been optimized for phenotypic assays in monolayer cultures often are not compatible with BME. Herein, we describe a method to plate PDO models and assess drug effects using an automated live-cell imaging system. In addition, we apply fluorescent dyes that are compatible with kinetic measurements to quantify cell health and apoptosis simultaneously. Image capture can be customized to occur at regular time intervals over several days. Users can analyze drug effects in individual Z-plane images or a Z Projection of serial images from multiple focal planes. Using masking, specific parameters of interest are calculated, such as PDO number, area, and fluorescence intensity. We provide proof-of-concept data demonstrating the effect of cytotoxic agents on cell health, apoptosis, and viability. This automated kinetic imaging platform can be expanded to other phenotypic readouts to understand diverse therapeutic effects in PDO models of cancer.

Author Spotlight: Developing Multiplexed Kinetic Assays for Organoid-Based Drug Response Analysis

Patient-derived tumor organoids are a sophisticated model system for basic and translational research. This methods article details the use of multiplexed fluorescent live-cell imaging for simultaneous kinetic assessment of different organoid phenotypes.

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell ...