A Scalable 3D High-Content Imaging Protocol for Measuring a Drug Induced DNA Damage Response Using Immunofluorescent Subnuclear γH2AX Spots in Patient Derived Ovarian Cancer Organoids (2024)

Abstract

The high morbidity rate of ovarian cancer has remainedunchangedduring the past four decades, partly due to a lack of understandingof disease mechanisms and difficulties in developing new targetedtherapies. Defective DNA damage detection and repair is one of thehallmarks of cancer cells and is a defining characteristic of ovariancancer. Most in vitro studies to date involve viability measurementsat scale using relevant cancer cell lines; however, the translationto the clinic is often lacking. The use of patient derived organoidsis closing that translational gap, yet the 3D nature of organoid culturespresents challenges for assay measurements beyond viability measurements.In particular, high-content imaging has the potential for screeningat scale, providing a better understanding of the mechanism of actionof drugs or genetic perturbagens. In this study we report a semiautomatedand scalable immunofluorescence imaging assay utilizing the developmentof a 384-well plate based subnuclear staining and clearing protocoland optimization of 3D confocal image analysis for studying DNA damagedose response in human ovarian cancer organoids. The assay was validatedin four organoid models and demonstrated a predictable response toetoposide drug treatment with the lowest efficacy observed in theclinically most resistant model. This imaging and analysis methodcan be applied to other 3D organoid and spheroid models for use inhigh content screening.

Keywords: organoids, ovarian cancer, 3D high-contentimaging, high content screening, DNA damage response

Ovarian cancer is known to bea heterogeneous disease that consists of multiple distinct malignanciesthat share a common anatomical site. Of these, the type II high-gradeserous (HGS) subtype dominates in the clinical setting and is responsiblefor over 70% of all cases.¹⁻⁴ The high morbidity rate and the finding that the5-year overall survival from ovarian cancer has remained virtuallyunchanged since about the 1980s is due to factors including late detectionof the disease, a lack of understanding of disease mechanisms, anddifficulties in developing new targeted therapies.^1,2 Currently,the primary route of therapy is surgery and chemotherapy, with targetedtherapies mainly utilized in recurrent disease, both in chemotherapysensitive and resistant cases.¹

Effectivetargeted therapies rely on knowledge of the molecularfeatures of the disease. Defective DNA damage detection and repair(DDR) is one of the hallmarks of cancer cells and is a defining characteristicof HGS ovarian cancer.⁵ Defects in thehomologous recombination repair (HRR) pathway have been found in morethan 50% of patients with HGS ovarian cancer, with BRCA1 and BRCA2playing particularly pronounced roles.⁶⁻⁸ This feature of HGS ovariancancers has been utilized for targeted therapies as part of the conceptof synthetic lethality, where functional loss of individual genesis tolerated in isolation but not in combination.⁵ Inhibition of poly(ADP-ribose) polymerase proteins (PARPs)—nuclearenzymes activated by DNA damage and integral to DNA repair—isone such example, where ovarian cancers with defects in the HRR pathwaydisplay synthetic lethality in the context of PARP inhibition.⁹

Preclinical human models of ovarian cancerinclude cancer celllines, patient derived xenografts (PDXs), and organoids. Cancer celllines serve well in terms of high scalability and low cost and (still)contribute to the discovery of novel targets.¹⁰ However, they lack translatability to the patient due to numerousfactors ranging from their initial derivation, clonality, adaptationto in vitro 2D growth, genetic drift, and cross contaminationamong other factors.¹¹ PDXs offer increasedgenetic and histological stability¹² andhave been used successfully in preclinical research;¹³⁻¹⁵ however, their use is ethically challenged, labor intensive, andlacks the scalability needed for early drug screens.

Patientderived organoids demonstrate stable phenotype expressionin a 3D culture platform that has been shown to recapitulate tumorheterogeneity.^2,3 They are scalable and recapitulatethe patient’s response to chemotherapeutics in vitro.^4,16,17 The origin of the majority ofHGS is widely acknowledged to be from the epithelium of the fallopiantube,¹ which is highly relevant for organoidculture, as these are derived from the epithelium. Organoids are inuse in preclinical screening assays;¹⁷ however,such assays have typically been limited to cell viability at sucha scale. Although viability assays provide a robust and reliable readoutfor oncology, additional information regarding the cellular machineryor pathways involved in cell killing could increase our understandingof the disease and aid in the identification of novel molecular targetsfor potential therapies. Due to the 3D and heterogeneous architectureof organoids and the frequent use of basement membrane extracts fortheir culture, the options of cellular interrogation at scale in amultiwell format are limited. Automated confocal high-content imagingis one option that can be used to overcome this challenge in an organoidculture system; however, it requires thoughtful consideration insofaras how to amend conventional 2D high content imaging workflows toaccommodate this challenging 3D context.

High content screening(HCS) is an integral part of drug discoveryplatforms, and it is widely used to identify and validate compoundsby classification of drug induced phenotypes at high throughput. AnHCS method for the quantification of DNA damage utilizing γH2AXlabeled double-strand DNA break foci as a marker was reported in cancercells with a large-scale chemical library in 2D imaging format.¹⁸ However, with increasing evidence of improvedclinical relevance in the assays utilizing primary human tissue inthe form of organoids compared to that of 2D cell lines, there isgrowing need for scalable methods for high content confocal imagingscreens in 3D. The multivariate nature of the phenotypic imaging datais crucial for cross-validating established assays such as viabilityor similarly in patient derived 3D cellular models like organoids.¹⁹

However, utilization of 3D cell culturein HCS is still challengingprimarily due to difficulties in scalability to 384-well plates, whichare crucial for leveraging the full benefits in terms of assay robustnessand throughput that automated screening setups provide²⁰ as well as for minimizing excessive use of difficultto scale organoid source cultures. As such, liquid handling protocolsneed to be tuned not only to handle often delicate complex culturesbut also to do so in low volumes. With respect to imaging, the combinationof organoid size, mismatched refractive indices of cell and organoidsized structures, and encapsulation in hydrogels such as Matrigelpresent unique challenges that require careful optimization of labelingprotocols as well as optical clearing to ensure sufficient label andlaser penetration.

Recent publications demonstrate some advancesin the industrializationof 3D complex cell based assays, for example, monitoring kidney organoiddifferentiation from human pluripotent stem cells,²¹ monitoring growth in response to Tankyrase inhibitors,²² and quantifying compound related cytoskeletaland nuclear phenotypic changes in colorectal organoids.²³ However, to the best of our knowledge, thereis still a need for a reproducible multiwell plate-based immunofluorescenceimaging protocol for compound induced 3D DNA damage response imagingat scale in ovarian cancer organoids.

Utilization of organoidsin a 3D image based HTS in 384-well platesrequires optimization of cell culture parameters, compound treatmentregimen, assay time course, and end point staining conditions. Furthermore,such protocol optimization also requires robust and scalable assaysthat are amenable to multiple liquid handling devices and data pipelinesin place to enable optimized image acquisition parameters and imageanalysis workflows for a scalable 3D immunofluorescence method.

Several organoid or spheroid fixing and staining protocols arereported to improve signal-to-noise ratio (SNR) in 3D fluorescencemicroscopy images with increased laser penetration depth facilitatingquantification of the fluorescence signal in 3D multicellular structures.²⁴ We have modified and adapted a method reportedfor cells on slides or dishes for high resolution confocal laser scanningmicroscopy. 384-well assay plates were utilized for automated confocalmicroscopy with automated liquid handling to enable the scale requiredfor screening.

Here, we report the development of a scalable3D imaging assayusing patient derived HGS ovarian cancer organoids. We quantifiedthe compound-initiated 3D DNA damage response using a subnuclear immunofluorescenceassay using a γH2AX antibody. 69 intensity, morphology, andtexture related parameters were then calculated for organoids, organoidnuclei, and subnuclear γH2AX spots, in batch mode through anautomated analysis script.

The DNA damage response to etoposidewas used to validate the high-contentimaging method by calculating Z-prime scores, using DMSO and 30 μMetoposide, as negative and positive controls, respectively. The largesteffect size for DNA damage response was seen for the parameter “Totalnuclear γH2AX spot area- mean per well” for the organoidlines studied. In addition, phenotypic parameters such as those relatedto γH2AX spot texture, organoid nuclear size, and nuclear roundnesswere seen to be dependent on compound concentration, thus demonstratingthe value of phenotypic high-content imaging in preclinical drug discoveryas a multivariate screening tool. Furthermore, one of the organoidlines did not respond to etoposide, confirming its resistant nature.

Materials and Methods

Patient-Derived Ovarian Cancer Organoid Culture

Cryopreservedovarian tumor organoids were obtained from Hubrecht Organoid Technology(HUB; Netherlands). Figure 1 shows brightfield microscopic images of the models in culture;they show a variety of compact morphologies and were derived fromHGS carcinomas from different patients, sites, and stages of disease(Table 1). The humanbiological samples were sourced ethically, and their research usewas in accord with the terms of the informed consent under an IRB/ECapproved protocol.

Figure 1.

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Table 1. List of Organoid Lines Used.

HUB code	abbrevation	patient	disease	donor (sex,age, site of collection)	organoid morphology
HUB-19-C2-008	HUB008	1	HGS carcinoma,original tumor	F, 70, omentum	compact
HUB-19-C2-014	HUB014	1	HGS carcinoma,recurrenttumor	F, 71, ascites	compact
HUB-19-B2-010 I	HUB010	2	HGS carcinoma	F, 67, adnex, right	compact
HUB-19-B2-010 II	HUB010II	2	HGS carcinoma	F, 67,adnex, left	compact

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In Figure 1, allorganoids have the expected compact morphology with a wide range insize between approximately 30 and 300 μm in diameter dependingon the extent of shearing used during subculture, subsequent growthrate, and aggregation. Patient 2 organoids (HUB010, HUB010II) wereless compact than Patient 1 (HUB008, HUB014), making them easier toshear into smaller fragments. The concentration of the organoids isvariable but controlled and adjusted as needed for optimum growthby the split ratio applied at the time of subculture.

Cultureswere maintained and expanded in 10 μL droplets of80% Matrigel (Corning, Cat# 356231) in six-well suspension plates(Greiner, Cat# 657185) and cultured in ovarian cancer (OC) mediumwithout Wnt as previously described,³ exceptrecombinant human R-spondin 3 (R&D Systems, Cat# 3500-RS-025/CF)was used at 25 μg and recombinant human Noggin (Peprotech, Cat#120–10C) was used at 20 μg for 100 mL of complete medium.

Ovarian cancer organoids were subcultured at a ratio of 1:3 or1:2, dependent on the donor line, every 7 days by mechanical shearingusing a filtered P1000 pipet tip with a nonfiltered P10 pipet tipon the end. Large and dense organoids were sheared as before but inprewarmed (37 °C) 50% TrypLE (Gibco, Cat# 12604013) + 50% ADF+++(Advanced DMEM/F-12 (Thermo Fisher, Cat# 12634010), PenStrep (ThermoFisher, Cat# 15140122) and GlutaMax (Gibco, Cat# 35050061)) buffer.Sheared organoid pellets were washed with ADF+++, spun at 450 g at room temperature for 5 min, and any old remaining Matrigelwas removed before resuspending in fresh 80% Matrigel and replating.The plates containing freshly plated droplets were inverted and incubatedat 37 °C and 5% CO₂ in the air for 30 min until Matrigelhad polymerized and then turned the right way up before adding 2 mLof OC medium and reincubating. OC medium was replaced every 2–3days.

Organoid Expansion for HCS

Organoids were expandedas described above to generate 1 × 10⁶ organoids of20–70 μm diameter needed for the assay. This equatedto seven confluent six-well suspension plates per donor line (totalof 42 wells with 18 × 10 μL droplets per well). Two daysbefore seeding for the assay, the organoids were subcultured and shearedwith a ratio of 1:1 into 10 μL droplets of 50% Matrigel.

1. Collect culture plates of ovarian organoids from the incubator,and check confluency, organoid size, growth, and general health ofthe cultures by light microscopy at 4× to 10× magnification.

2. Add 20 mL of Dispase stock (100 mg/mL) to each well (alreadycontaining 2 mL culture medium), and return it to incubation (37 °C,5% CO₂, in air) for 60 min.

3. Prepare a sufficientOC medium containing 5% Matrigel, and storeit on ice for use in step 16.

4. Prepare the Multidrop combiinstrument; use ice cold DPBS tochill the tubing just prior to plating in step 17.

5. Prewetsufficient 70 μm cell strainers (pluriSelect, Cat#43-50070-01) with 1 mL of D-BSA (DMEM with GlutaMax (Thermo Fisher,Cat# 31966047) supplemented with 10% BSA (Sigma-Aldrich, Cat#A6003)and PenStrep (Thermo Fisher, Cat# 15140122)) on each side of the filter.One filter is used for up to four full six-well plates.

6. Collectcultures after 60 min of incubation, and check thatthe Matrigel domes have dissolved, releasing the organoids into theculture supernatant.

7. Aspirate the supernatant containingorganoids from all wells,and pass it through the 70 μm cell strainer into a 50 mL centrifugetube.

8. Wash each culture well with 2 mL of D-BSA, and useit to washthrough the cell strainer, keeping the final volume less than 50 mL.

9. Prewet a sufficient number of 20 μm cell strainers (pluriSelect,Cat# 43-50070-01) with 1 mL of D-BSA on each side of the filter. Onefilter is used for up to four full six-well plates.

10. Passthe <70 μm flowthrough over the 20 μm cellstrainers; wash the strainers with 10 mL of ADF+++.

11. Collectthe 20–70 μm sized organoids by back-washingthem from the top surface of the 20 μm cell strainer into aseparate 50 mL centrifuge tube with ADF+++.

12. Spin these organoidsinto a single pellet at 450g for 5 min at room temperature.

13. Discard the supernatant, and resuspend the pellet in 0.5 to2 mL of ice cold OC media containing 5% Matrigel depending on theexpected count. Keep the tube on ice during the count.

Etoposide Treatment

Etoposide (DNA Topoisomerase II)was prepared and stored frozen as a concentrated DMSO (dimethyl sulfoxide)stock prior to dilution and addition to the organoids. The compoundwas serially diluted in two-fold steps from the highest test concentration(specified in Table 2) and assayed over seven concentrations with a final assay v/v DMSOconcentration of 0.5%.

Table 2. Etoposide Dilution Range.

compound number	CAS no.	compoundname	highest final concentration	lowest final concentration	dilution series
GR87698X	33419-42-0	etoposide	30 μM	470 nM	1in2

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Compound plate preparation for addition to organoidswas performedusing the following method:

1. Thaw the compound plate at roomtemperature for 30 min.

2. During this time, prepare sufficientOC media containing 5%Matrigel, and store it on ice for use in step 4.

3. After 30min, centrifuge the compound plates at 50g for 1min at room temperature.

4. Dilute the compound plate to 2×the final assay concentrationwith OC media containing 5% Matrigel using the BRAVO (Agilent) liquidhandler.

5. Centrifuge the diluted compound plates at 50g for 1 min at room temperature.

6. Mix the well contentsand transfer 20 μL of diluted compoundto the organoid plate using the BRAVO liquid handler. Total volumeper well will be 40 μL with the compounds at their final assayconcentration.

7. Image the plates with a 5× objectivein brightfield transmissionmode using a PerkinElmer Opera Phenix high content screening system.

8. Incubate the plates as described previously for 48 h.

CellTiter-Glo 3D Cell Viability Assay

Organoid viabilitywas measured in one of the five replicate plates using the CellTiter-Glo3D Cell Viability kit (Promega, Cat#G9683) using the following method:

1. Image the whole 384-well plate using the PerkinElmer Opera Phenixhigh content screening system in brightfield transmission mode withthe 5× lens to check well contents.

2. Thaw 40 mL of CellTiter-Glo3D, and equilibrate it to room temperature.

3. Dispense andmix 30 μL of CellTiter-Glo reagent in eachwell using the BRAVO liquid handler.

4. Incubate it in the darkat room temperature for 30 min by wrappingthe plate in foil.

5. After 30 min, mix and transfer 10 μLto a solid whitelow-volume assay plate (Greiner, Cat# 784080) using the BRAVO liquidhandler.

6. Measure the luminescence from the whole 384-wellplate usinga PHERAstar FSX plate reader (BMG Labtech).

7. Calculate thepercent viability of treated wells using DMSOas the high control.

Organoid Staining and Clearing for Imaging

Organoidswere fixed after 48 h of incubation with etoposide in media followingthe steps below, using the consumables, reagents, and devices listed.A visual summary of this workflow can be seen in Supplementary Figure S1.

Reagents

All reagents were prepared fresh and filteredon that day using consumables listed in Supplementary Table S1:

1. OWB (organoid washing buffer): 500 mL DPBS+ 1 g BSA + 0.5 mL Triton X-100

2. PBT (DPBS with Tween): 500mL DPBS + 0.5 mL Tween

3. DPBS-BSA: 500 mL DPBS + 1 g BSA

4. Clearing Stock 1: 50% (v/v) MeOH + 25% (v/v) DPBS + 25% (v/v)H₂O

5. Clearing Stock 2: 80% (v/v) MeOH + 20% (v/v)H₂O

6. Clearing Stock 3: 100% MeOH

Equipment

The following equipment was used:

1.Corning LSE Digital Microplate Shaker (kept and used in a 4 °Ccold room).

2. BRAVO automated liquid handler (Agilent Technologies).

3. Viaflo384 liquid handler (Integra Biosciences).

Plateswere fixed, stained, and cleared after 48 h of compoundincubation as below:

1. Image the plates with a 5× objectivein brightfield transmissionmode using the PerkinElmer Opera Phenix high content screening system.

2. Visually inspect the plates for viability, size, and distributionof organoids in each well (40 μL media) before further processing.

3. For fixing, spin the plates at 200 rpm for 10 s, and then placethem on the BRAVO liquid handler for 5 min to settle.

4. Washthe BRAVO tips with 40 μL of DPBS-BSA once; dispense40 μL of 4% PFA ready solution into each well.

5. Incubate in situ for 15 min.

6. Aspirate 40 μL from eachwell and discard.

7. Add another 40 μL of 4% PFA to eachwell as before.

8. Incubate in situ for 15min.

9. Aspirate 40 μL from each well and discard.

10. Wash wells with 40 μL of PBT twice in a fume hood usinga BRAVO.

11. Add 40 μL of PBT, and if required store theplates at4 °C (for up to 72 h).

12. Aspirate 40 μL of PBT;add 40 μL of OWB.

13. Incubate in situ for 15 min.

14. Aspirate and replace with another 40 μLof OWB.

15. Incubate on a shaker (70 rpm, no tilt) at 4 °Cin a fridgefor 60 min.

16. Spin the plates at 200 rpm for 10 s.

17.Incubate for 15 min at room temperature, and aspirate 40 μLof OWB.

18. Add 40 μL of primary antibody solution (preparedin OWB),i.e., γH2AX (2:1000), and incubate overnight on a shaker (70rpm, no tilt) at 4 °C in a dark fridge.

19. Spin the platesat 200 rpm for 10 s.

20. Incubate on BRAVO at least 15 min;then aspirate and discard40 μL of the primary antibody solution.

21. Add 40 μLof OWB and incubate in situ for 15 min; then aspirateand discard.

22. Add 40 μL of OWB and incubate on a shaker(70 rpm, notilt) at 4 °C in a fridge for 1 h.

23. Place the plateson the BRAVO for at least 15 min; then aspirate40 μL of OWB.

24. Add 40 μL of the secondary antibody,i.e., Alexa 647(2:1000) solution and dapi (2:1000) stain, and incubate overnighton a shaker (70 rpm, no tilt) at 4 °C in a dark fridge.

25. Spin the plates at 200 rpm for 10 s.

26. Incubate on BRAVOat least 15 min; then aspirate and discard40 μL of secondary antibody solution.

27. Add 40 μLof OWB, and incubate it in situ for 15 min; thenaspirate and discard.

28. Add 40 μL of OWB, and incubateit on a shaker (70 rpm,no tilt) at 4 °C in a fridge for 1 h.

29. Place the plateson the BRAVO for at least 15 min; then aspirate40 μL of OWB.

30. Add 40 μL of OWB, and incubateit in situ for 15 min; then aspirate and discard.

31. Add 40 μL of stock 1 using a Viaflo384 in a fume hood.Incubate it at room temperature (RT) 15 min.

32. Aspirate 40μL of stock 1; add 40 μL of stock 2using a Viaflo384 in a fume hood, and incubate it at RT for 15 min.

33. Aspirate 40 μL of stock 2; add 40 μL of stock 3using a Viaflo384 in a fume hood, and incubate it at RT for 15 min.

34. Aspirate 40 μL of stock 3; add 40 μL of clearingreagent, and incubate the plate at room temperature for 15 min.

35. Record a 5× brightfield image of the plate for visualchecks of losses after the protocol.

36. Seal the plate, andmove to imaging and analysis.

Automated 3D High-Content Imaging

A PerkinElmer OperaPhenix high content screening system coupled with a collaborativerobot designed for pharmaceutical screening (the plate:handler FLEX,PerkinElmer) was used to image the 384-well plates in spinning diskconfocal mode. A 20× water immersion objective (NA = 1.0) wasused to collect 16 fields of view from each well covering ∼70%of the total well area, utilizing two fluorescence channels comprisingnuclei (excitation, 405 nm; emission, 435–480 nm) and γH2AXspots (excitation, 640 nm; emission, 650–760 nm). A Z-stackof 14 focal planes was collected from each field of view with 10 μmZ steps. Two sCMOS cameras of the system were employed with a pixelbinning of 2. Each camera acquired one fluorescence channel at a time,resulting in a 3 h scan time per full 384-well plate (∼270GB file size). All fluorescence imaging data were collected, visualized,and analyzed using PerkinElmer Harmony (version 4.9.2137.273; Revision147881; Acapella version 5.0.1.124082) software installed on the imaginginstrument and analysis computers. Raw image files were annotatedwithin the software via definition of links for electronic experimentrecords and plate maps with compound names, compound concentration,and cell donor codes ensuring that metadata was FAIR (Findable, Accessible,Interpretable, and Reusable).

Image Analysis

Images were analyzed using a secondcopy of the Harmony software package installed on a local PC (HP Z8G4 workstation, Windows 10 (64-bit), dual intel Xeon 6248R with a3.0 GHz CPU, Nvidia QDR 16 GB TRX 5000 Graphics, an HP Z turbo 4 ×1 TB SSD, and 192 GB of DDR4 RAM) connected to the imager PC (DELLworkstation, similar specification) via a 10 Gigabit ethernet. Analysisof a full 384-well plate took ∼3 h, which at the time of thisstudy was relatively shorter than running the analysis on a clusterusing PerkinElmer’s Columbus 2.9 platform, mainly due to alow speed of file mounting from Harmony to Columbus. Raw images wereflat field corrected using the basic correction algorithm availablein the Harmony image analysis package. A maximum intensity projectionof the collected Z-stacks was obtained for each field of view. A setof representative images showing nuclei in blue and γH2AX inred, from both negative (DMSO) and positive (30 μM Etoposide)control wells, can be seen in Figure 2. In Figure 2D, DNA damage can be seen expressed as various size spotsand larger areas in some nuclei or colocalized with up to 100% ofthe individual nuclei. This variation in γH2AX expression isexpected compared to other more often communicated DNA damage inducingmethods such as irradiation. Therefore, the spot detection algorithmthat is able to distinguish such spots with different morphology andintensity needs to be implemented by editing available options inthe Harmony software.

Figure 2.

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Figure 3 outlinesthe image analysis steps. Briefly, the nuclear channel in the projectedimages was used to detect organoids larger than 800 μm², and those touching edges of the images were removed from analysis.The rest of the organoids were further filtered based on roundness(>0.01), average DAPI intensity (>500), and width to lengthratio(>0.03) in order to remove debris from the wells (dust, fibers,deplatedorganoids etc.). Selected organoids were then used for nuclear segmentationusing nuclei detection method “M,” and spots were detectedwithin individual nuclei using the spot detection method “D,”which was seen to be distinguishing the variable shapes and spot sizes,avaiable in the Harmony analysis software. Intensity, morphology,and texture related parameters were then calculated for organoids,organoid nuclei, and subnuclear γH2AX spots utilizing the automatedanalysis pipeline that was running in parallel to imaging.

Figure 3.

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Plate and Well Quality Control

Image analysis resultfiles, comprising a list of intensity and morphology features foreach plate with annotations readily added in Harmony software, wereexported in text format and imported into the High Content Profilerv2.0.0 application (PerkinElmer) in Tibco Spotfire package v11.4 tobe used for secondary analysis and data visualization. All plateswere analyzed at once, using default settings in the high contentprofiler application, mainly in that an interplate normalization wasapplied using the “median method,” and feature selectionwas done comparing Z-prime scores (also known as Z-prime factors).²⁵

All of the calculated features were usedindividually to calculate a Z-prime score indicating effect size ineach plate using the difference between negative (DMSO) and positive(30 μM Etoposide) controls. For high content screening assays,Z-prime scores above 0 are considered sufficient, depending on thecomplexity of the assay.²⁵ Total nuclearγH2AX spot area (mean per well) was found to be the highestZ-prime scoring parameter across all responding organoid lines. Alist of parameters can be seen in Supplementary Table S2B. All dose related plots and donor comparisons werethen generated based on the highest Z-prime scoring parameter “Totalnuclear γH2AX spot area- mean per well.”

Each organoidline was imaged in four replicate plates comprising32 × DMSO, 32 × etoposide (30 μM) replicates, and8 × 7-point etoposide dose response curves. Raw etoposide 7-pointdose response data plotted for the highest Z-prime scoring parameter(across all lines) is shown in Supplementary Figure S4. A dose response curve can readily be seen for HUB008 (Patient1 original tumor), HUB010 (Patient 2 adnex right), and HUB010 II (Patient2 adnex left). No remarkable response was observed in the HUB014 line(Patient 1 recurrent tumor). The raw data for HUB010 II shows thelargest amount of variability overall, including outliers.

Supplementary Table S2A shows the listof Z-prime and SNR scores calculated for each plate for the threeorganoid lines that showed a dose response in raw data shown in Supplementary Figure S2. A quality control thresholdwas applied such that outlier wells that showed edge effects, includingless than five organoids, were removed from analysis to cut falsesignal contribution to the rest of the wells (on average ∼50organoids per well was detected). An average signal-to-noise ratioof 4.2 was achieved across all of the plates, which is acceptableconsidering the 3D nature of the assay, since projecting images formaximum intensity per binned pixel across all Z-planes results inan increased signal but also increases noise for some binned pixelsif they do not contain organoids in that particular field of view.Selective imaging (i.e., prescan with a low magnification lens (5×or 10×) to locate and rescan with a high magnification lens (20×water) to resolve individual organoids or volumetric analysis) canbe implemented to improve SNR; however, these options come at theexpense of computational power and time, which at the time of ourstudy risked the practicality and scalability of the reported assay;therefore, it was not implemented.

Results and Discussion

The patient derived organoidsused in this study exhibited noticeablemorphological differences in size, shape, and aggregation propertiesduring expansion, as shown and described in Figure 1. This brings a challenge for confocal fluorescenceimaging as the aforementioned parameters influence redistributionof the organoids during assay plating, culture, and washing stepsafter fixing. The extent of this variation was reduced by controllingthe size of the organoids to between 20 and 70 μm in diameterbefore seeding. However, optimization was still required of not onlythe assay culture conditions and time course but also the imagingsettings to develop a global work flow to facilitate scaling-up andscreening. Our work reported here, therefore, was focused on findinguniversal conditions such that the fixed end point parameters werecovering detection of the morphological, textural, and intensity propertiesof all four ovarian cancer organoid lines with the same 3D imagingand analysis settings. It is also worth noting that although cultureparameters and the dosing regimen may be different, it is possibleto utilize this assay once organoid size and morphology is optimizedto be somewhat consistent at the point of fixing. Further studiesinvolving the use of patient derived organoids from other tissue typesand a variety of other compounds, not reported here, indicated a broadapplication of the protocol, which can facilitate a wider comparisonof targets.

Cell viability is one of the most common assaysin drug screeningin organoids,¹⁹ and compared to cell-specific in situ fluorescent-dye-based viability measurements, forexample, plate-reader-based luminescence measurements reporting oncell health, is more often preferred as it generally facilitates morerobust data, when only well-based measurements are of interest. Organoidlines were tested for viability in replicates in parallel to the imagingassay ensuring DNA damage response measurements were done mostly inviable cells. Figure 4 shows the percent viability (CellTiter-Glo 3D) normalized to DMSOsolvent control of the four organoid lines that were exposed to themaximum concentration of etoposide (30 μM) over the dosing periodof 2 days in parallel to the imaging assay. Although variable, viabilitywas maintained above 72% for all four models, demonstrating sufficientlygood health of the cells within the organoids after 2 days treatmentfor DNA damage response analysis.

Figure 4.

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Figure 5 shows thenormalized etoposide DNA damage dose response curves fitted usingthe linear regression option in the High Content Profiler package,per organoid line with standard deviation. A good correlation betweenincreasing etoposide concentration and increasing DNA damage detectioncan be seen from different plates for HUB008, HUB010, and HUB010 II.There is relatively less of a response in the HUB014 line, particularlyat the lower to mid concentrations tested below 3.75 μM. Thiscan also be observed in individual graphs plotted per plate for eachorganoid line in Supplementary Figure S3. Overall, poor curve fits for HUB014 were seen. This finding couldbe explained by the fact that the HUB014 organoid line is derivedfrom a recurrent tumor following the standard of care treatment andcould therefore indicate establishment of drug resistance. Dose responsecurves for each individual organoid line are plotted in Supplementary Figure S4 for alternative visualization.

Figure 5.

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Conclusions

A subnuclear 3D imaging assay that is unbiasedbetween four morphologicallydifferent ovarian cancer organoid lines was outlined in the form ofa method aiming for broad application for wider use. Although thedata reported herein are tissue specific, the 3D imaging assay istransferable between tissue types if cell density, dosing regimen,and organoid size can be relatively controlled as demonstrated. Afixing, staining, and clearing protocol that is applicable to allmultiwell plate based immunofluorescence organoid or spheroid assaysis validated for DNA damage response. However, it should be furthertested for other applications as antibody penetration and bindingproperties may vary and compromise quantification at scale for othermarkers, in particular for cytoplasmic proteins, which ought to facilitateless SNR when images are projected prior to cell segmentation. Thefour organoid lines studied here showed an expected response to etoposide,and HUB014 has shown no quantifiable response, possibly explainedby its clinical origin. Our current work at GSK also involves exploringsuitability of this protocol for screening other tissue types andvalidating targets for drug combinations in CRISPR edited organoids.

Acknowledgments

The authors would like to thank Katja Remlingerfor statisticsdiscussions.

Supporting Information Available

The Supporting Informationis available free of charge at https://pubs.acs.org/doi/10.1021/acsptsci.2c00200.

Figure S1: Organoid fixing, staining, clearing, andimaging analysis workflow. Figure S2: Raw dose response data. TableS1: List of consumables. Table S2: Z-prime and SNR scores per plate.Figure S3: Etoposide dose response curves per plate. Figure S4: Etoposidedose response curves per organoid line (PDF)

Author Contributions

H.K., C.S.,and H.R. designed, performed, and analyzed the experimental work andwrote the manuscript. L.M. and E.E.E. contributed to the design andwriting of the manuscript.

The authorsdeclare no competing financial interest.

Supplementary Material

pt2c00200_si_001.pdf^{(1,001.9KB, pdf)}

References

Lisio M. A.; Fu L.; Goyeneche A.; Gao Z. H.; Telleria C.High-Grade Serous OvarianCancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int. J. Mol. Sci.2019, 20 (4), 952. 10.3390/ijms20040952. [DOI] [PMC free article] [PubMed] [Google Scholar]
Hoffmann K.; Berger H.; Kulbe H.; Thillainadarasan S.; Mollenkopf H. J.; Zemojtel T.; Taube E.; Darb-Esfahani S.; Mangler M.; Sehouli J.; Chekerov R.; Braicu E. I.; Meyer T. F.; Kessler M.Stable expansion of high-grade serousovarian cancer organoids requires a low-Wnt environment. EMBO J.2020, 39 (6), e104013. 10.15252/embj.2019104013. [DOI] [PMC free article] [PubMed] [Google Scholar]
Kopper O.; de Witte C. J.; Lohmussaar K.; Valle-Inclan J. E.; Hami N.; Kester L.; Balgobind A. V.; Korving J.; Proost N.; Begthel H.; van Wijk L. M.; Revilla S. A.; Theeuwsen R.; van de Ven M.; van Roosmalen M. J.; Ponsioen B.; Ho V. W. H.; Neel B. G.; Bosse T.; Gaarenstroom K. N.; Vrieling H.; Vreeswijk M. P. G.; van Diest P. J.; Witteveen P. O.; Jonges T.; Bos J. L.; van Oudenaarden A.; Zweemer R. P.; Snippert H. J. G.; Kloosterman W. P.; Clevers H.An organoid platform for ovarian cancer captures intra-and interpatient heterogeneity. Nat. Med.2019, 25 (5), 838. 10.1038/s41591-019-0422-6. [DOI] [PubMed] [Google Scholar]
deWitte C. J.; Espejo Valle-Inclan J.; Hami N.; Lohmussaar K.; Kopper O.; Vreuls C. P. H.; Jonges G. N.; van Diest P.; Nguyen L.; Clevers H.; Kloosterman W. P.; Cuppen E.; Snippert H. J. G.; Zweemer R. P.; Witteveen P. O.; Stelloo E.Patient-Derived Ovarian Cancer Organoids Mimic ClinicalResponse and Exhibit Heterogeneous Inter- and Intrapatient Drug Responses. Cell Rep2020, 31 (11), 107762. 10.1016/j.celrep.2020.107762. [DOI] [PubMed] [Google Scholar]
Gee M. E.; Faraahi Z.; McCormick A.; Edmondson R. J.DNA damagerepair in ovarian cancer: unlocking the heterogeneity. J. Ovarian Res.2018, 11 (1), 50. 10.1186/s13048-018-0424-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
Milanesio M. C.; Giordano S.; Valabrega G.Clinical Implicationsof DNA RepairDefects in High-Grade Serous Ovarian Carcinomas. Cancers (Basel)2020, 12 (5), 1315. 10.3390/cancers12051315. [DOI] [PMC free article] [PubMed] [Google Scholar]
Lord C. J.; Ashworth A.PARP inhibitors: Synthetic lethality in the clinic. Science2017, 355 (6330), 1152. 10.1126/science.aam7344. [DOI] [PMC free article] [PubMed] [Google Scholar]
Kurman R. J.; Shih I.-M.The Dualistic Model of Ovarian Carcinogenesis: Revisited,Revised, and Expanded. Am. J. Pathol.2016, 186 (4), 733. 10.1016/j.ajpath.2015.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
Chiang Y. C.; Lin P. H.; Cheng W. F.HomologousRecombination DeficiencyAssays in Epithelial Ovarian Cancer: Current Status and Future Direction. Front Oncol2021, 11, 675972. 10.3389/fonc.2021.675972. [DOI] [PMC free article] [PubMed] [Google Scholar]
Greshock J.; Bachman K. E.; Degenhardt Y. Y.; Jing J.; Wen Y. H.; Eastman S.; McNeil E.; Moy C.; Wegrzyn R.; Auger K.; Hardwicke M. A.; Wooster R.Molecular target classis predictive of in vitro response profile. Cancer Res.2010, 70 (9), 3677. 10.1158/0008-5472.CAN-09-3788. [DOI] [PubMed] [Google Scholar]
Domcke S.; Sinha R.; Levine D. A.; Sander C.; Schultz N.Evaluatingcell lines as tumour models by comparison of genomic profiles. Nat. Commun.2013, 4, 2126. 10.1038/ncomms3126. [DOI] [PMC free article] [PubMed] [Google Scholar]
Thu K. L.; Papari-Zareei M.; Stastny V.; Song K.; Peyton M.; Martinez V. D.; Zhang Y. A.; Castro I. B.; Varella-Garcia M.; Liang H.; Xing C.; Kittler R.; Milchgrub S.; Castrillon D. H.; Davidson H. L.; Reynolds C. P.; Lam W. L.; Lea J.; Gazdar A. F.A comprehensively characterized cell line panel highlyrepresentative of clinical ovarian high-grade serous carcinomas. Oncotarget2017, 8 (31), 50489. 10.18632/oncotarget.9929. [DOI] [PMC free article] [PubMed] [Google Scholar]
Cybula M.; Wang L.; Wang L.; Drumond-Bock A. L.; Moxley K. M.; Benbrook D. M.; Gunderson-Jackson C.; Ruiz-Echevarria M. J.; Bhattacharya R.; Mukherjee P.; Bieniasz M.Patient-Derived Xenografts of High-Grade Serous OvarianCancer Subtype as a Powerful Tool in Pre-Clinical Research. Cancers (Basel)2021, 13 (24), 6288. 10.3390/cancers13246288. [DOI] [PMC free article] [PubMed] [Google Scholar]
Cybula M.; Bieniasz M.Patient-derived tumor models are attractive tools torepurpose drugs for ovarian cancer treatment: pre-clinical updates. Oncotarget2022, 13, 553. 10.18632/oncotarget.28220. [DOI] [PMC free article] [PubMed] [Google Scholar]
Kondo J.; Ekawa T.; Endo H.; Yamazaki K.; Tanaka N.; Kukita Y.; Okuyama H.; Okami J.; Imamura F.; Ohue M.; Kato K.; Nomura T.; Kohara A.; Mori S.; Dan S.; Inoue M.High-throughput screeningin colorectal cancer tissue-originated spheroids. Cancer science2019, 110 (1), 345. 10.1111/cas.13843. [DOI] [PMC free article] [PubMed] [Google Scholar]
Vlachogiannis G.; Hedayat S.; Vatsiou A.; Jamin Y.; Fernández-Mateos J.; Khan K.; Lampis A.; Eason K.; Huntingford I.; Burke R.; Rata M.; Koh D.-M.; Tunariu N.; Collins D.; Hulkki-Wilson S.; Ragulan C.; Spiteri I.; Moorcraft S. Y.; Chau I.; Rao S.; Watkins D.; Fotiadis N.; Bali M.; Darvish-Damavandi M.; Lote H.; Eltahir Z.; Smyth E. C.; Begum R.; Clarke P. A.; Hahne J. C.; Dowsett M.; de Bono J.; Workman P.; Sadanandam A.; Fassan M.; Sansom O. J.; Eccles S.; Starling N.; Braconi C.; Sottoriva A.; Robinson S. P.; Cunningham D.; Valeri N.Patient-derived organoidsmodel treatment response of metastatic gastrointestinal cancers. Science2018, 359 (6378), 920. 10.1126/science.aao2774. [DOI] [PMC free article] [PubMed] [Google Scholar]
Price S.; Bhosle S.; Gonçalves E.; Li X.; McClurg D. P.; Barthorpe S.; Beck A.; Hall C.; Lightfoot H.; Farrow L.; Ansari R.; Jackson D. A.; Allen L.; Roberts K.; Beaver C.; Francies H. E.; Garnett M. J.A suspensiontechnique for efficient large-scale cancer organoid culturing andperturbation screens. Sci. Rep.2022, 12 (1), 5571. 10.1038/s41598-022-09508-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
Kim S.; Jun D. H.; Kim H. J.; Jeong K.-C.; Lee C.-H.Developmentof a High-Content Screening Method for Chemicals Modulating DNA DamageResponse. Journal of biomolecular screening2011, 16, 259. 10.1177/1087057110392993. [DOI] [PubMed] [Google Scholar]
Lukonin I.; Zinner M.; Liberali P.Organoidsin image-based phenotypicchemical screens. Experimental & MolecularMedicine2021, 53 (10), 1495. 10.1038/s12276-021-00641-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
Janzen W. P.ScreeningTechnologies for Small Molecule Discovery: The State of the Art. Chemistry & Biology2014, 21 (9), 1162. 10.1016/j.chembiol.2014.07.015. [DOI] [PubMed] [Google Scholar]
Czerniecki S. M.; Cruz N. M.; Harder J. L.; Menon R.; Annis J.; Otto E. A.; Gulieva R. E.; Islas L. V.; Kim Y. K.; Tran L. M.; Martins T. J.; Pippin J. W.; Fu H.; Kretzler M.; Shankland S. J.; Himmelfarb J.; Moon R. T.; Paragas N.; Freedman B. S.High-ThroughputScreening Enhances Kidney Organoid Differentiation from Human PluripotentStem Cells and Enables Automated Multidimensional Phenotyping. Cell Stem Cell2018, 22 (6), 929. 10.1016/j.stem.2018.04.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
Badder L. M.; Hollins A. J.; Herpers B.; Yan K.; Ewan K. B.; Thomas M.; Shone J. R.; Badder D. A.; Naven M.; Ashelford K. E.; Hargest R.; Clarke A. R.; Esdar C.; Buchstaller H.-P.; Treherne J. M.; Boj S.; Ramezanpour B.; Wienke D.; Price L. S.; Shaw P. H.; Dale T. C.3D imagingof colorectal cancer organoids identifies responses to Tankyrase inhibitors. PLoS One2020, 15 (8), e0235319. 10.1371/journal.pone.0235319. [DOI] [PMC free article] [PubMed] [Google Scholar]
Betge J.; Rindtorff N.; Sauer J.; Rauscher B.; Dingert C.; Gaitantzi H.; Herweck F.; Srour-Mhanna K.; Miersch T.; Valentini E.; Boonekamp K. E.; Hauber V.; Gutting T.; Frank L.; Belle S.; Gaiser T.; Buchholz I.; Jesenofsky R.; Härtel N.; Zhan T.; Fischer B.; Breitkopf-Heinlein K.; Burgermeister E.; Ebert M. P.; Boutros M.The drug-induced phenotypiclandscape of colorectal cancer organoids. Nat.Commun.2022, 13 (1), 3135. 10.1038/s41467-022-30722-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
Dekkers J. F.; Alieva M.; Wellens L. M.; Ariese H. C. R.; Jamieson P. R.; Vonk A. M.; Amatngalim G. D.; Hu H.; Oost K. C.; Snippert H. J. G.; Beekman J. M.; Wehrens E. J.; Visvader J. E.; Clevers H.; Rios A. C.High-resolution 3D imaging of fixedand cleared organoids. Nat. Protoc2019, 14 (6), 1756. 10.1038/s41596-019-0160-8. [DOI] [PubMed] [Google Scholar]
Choo N.; Ramm S.; Luu J.; Winter J. M.; Selth L. A.; Dwyer A. R.; Frydenberg M.; Grummet J.; Sandhu S.; Hickey T. E.; Tilley W. D.; Taylor R. A.; Risbridger G. P.; Lawrence M. G.; Simpson K. J.High-ThroughputImaging Assay forDrug Screening of 3D Prostate Cancer Organoids. SLAS Discovery2021, 26 (9), 1107. 10.1177/24725552211020668. [DOI] [PMC free article] [PubMed] [Google Scholar]

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