Novel Organoid Platform for High-Throughput Drug Discovery
App Note / Case Study
Published: October 25, 2023
|
Last Updated: November 6, 2023
credit: iStock
Studies show that patients and their derived organoids (PDOs) respond similarly to drugs. PDOs are therefore an advanced and biologically relevant in vitro model for the prediction of therapeutic efficacy and toxicity.
However, challenges such as assay reproducibility, scalability and cost have limited the use of PDOs in mainstream drug discovery pipelines.
This app note highlights the utility of highly standardized, assay-ready PDOs in high-throughput applications.
Download this app note to learn how to:
- Reduce organoid model culture time using assay-ready, PDOs
- Detect organoid morphological changes over time in response to compound exposure
- Create robust, automated image analysis pipelines with deep-learning tools for morphological readouts
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APPLICATION NOTE
Novel patient-derived colorectal
cancer organoid platform for
automated high-throughput
drug discovery applications
Angeline Lim, Jason Baade, Aditya Katiyar, Prathyushakrishna Macha,
Zhisong Tong, Oksana Sirenko | Molecular Devices
Elizabeth Fraser | Cellesce
Introduction
Patient derived organoids (PDOs) represent a promising
tool to reduce pipeline attrition in drug discovery. These
tumor organoids are multicellular mini replicas of the
3D tumor and has been shown to retain its in vivo
characteristics.1 Studies show that patients and their
derived organoids respond similarly to drugs. PDOs
are therefore an advanced and biologically relevant, in
vitro model for the prediction of therapeutic efficacy and
toxicity. However, challenges such as assay reproducibility,
scalability, and cost have limited the use of PDOs in
mainstream drug discovery pipelines.
To address the challenge associated with scalability,
Cellesce developed a unique industrial bioprocess for
the large-scale expansion of PDOs. The organoids are
grown in an environment that ensures constant delivery
of nutrients and growth factors while preventing the
accumulation of toxins in the culture, which can lead to
cell death. This proprietary method generates highly
standardized assay ready PDOs at scale, enabling
applications such as high-throughput screening for
drug discovery.
To demonstrate the utility of these PDOs in highthroughput
applications, colorectal cancer (CRC) PDOs
were seeded in multi-well plates with both manual
(384 well plate) and automated approach. For automation,
Benefits
• Reduce organoid model culture time by using
assay-ready, patient-derived organoids
• Detect organoid morphological changes over
time in response to compound exposure using
high-content imaging systems
• Create robust, automated image analysis pipelines
with deep-learning tools for morphological readouts
the pipetting function of the BAB400 bioprinter was
used to dispense Matrigel domes mixed with organoids
in the center of each well of a 96-well plate. Organoids
were treated with selected anti-cancer drugs at various
concentrations. The PDOs were monitored over time using
transmitted light imaging. A deep learning-based image
segmentation model was developed and used for the
analysis of the PDOs. Measurements such as size, texture,
intensity and other morphological and phenotypic readouts
were obtained. A viability assay was carried out using live/
dead cell dyes and the PDOs were imaged in 3D on a high
content confocal imager.
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Figure 2. Workflow for using assay ready colorectal cancer organoids.
Figure 1. Patient derived CRC organoid (Cellesce) are supplied in cryopreserved vials (100,000 organoids).
Information such as mutational profiles, tumor characteristics and morphology are available for each line.
Gene ISO 34 ISO 38 ISO 48 ISO 49 ISO 50 ISO 57 ISO 68 ISO 72 ISO 75 ISO 78 Gene
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R1450*
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E1286*
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▲
Q1291*
--- ▲
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APC
TP53 --- ▲
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--- ▲
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▲
R282W
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--- --- ▲
H193D
TP53
KRAS --- --- --- ▲
G12D
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--- ▲
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KRAS
BRAF ▲
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--- --- --- --- --- --- --- ▲
V600E
--- BRAF
PIK3CA --- --- ▲
E542K
▲
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--- --- --- --- --- --- PIK3CA
CTNNB1 --- --- ▲
S33C
--- --- --- --- --- --- --- CTNNB1
FBXW7 --- --- --- ▲
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--- --- --- --- --- --- FBXW7
ARID1A --- --- --- --- --- --- --- --- ▲
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--- ARID1A
SMAD4 --- ▲
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ARID2 --- --- --- --- --- --- --- --- --- --- ARID2
AXIN2 --- --- --- --- --- --- --- --- --- --- AXIN2
ERBB3 --- --- --- --- --- --- --- ▲
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--- --- ERBB3
MSH3 --- --- --- --- --- --- --- --- ▲
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--- MSH3
NRAS --- --- --- --- --- --- --- --- --- --- NRAS
POLE --- --- --- --- --- --- --- --- --- --- POLE
SMAD2 --- --- --- --- ▲
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--- --- --- --- --- SMAD2
TCF7L2 --- --- --- --- --- --- --- --- --- --- TCF7L2
RNF43 --- --- --- --- --- --- --- --- ▲
G659GX
--- RNF43
Gene ISO 34 ISO 38 ISO 48 ISO 49 ISO 50 ISO 57 ISO 68 ISO 72 ISO 75 ISO 78 Gene
ISO 68
Assay-ready
CRC organoids
48
hours
1 4 5
Thaw
Mix organoids
with Matrigel and
seed in wells
Add compounds
Monitor organoids
End-point assay
Image acquisition
and analysis
2 3
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Materials and methods
Cell culture
Colorectal cancer organoids (ISO68 line, Cellesce) were
handled according manufacturer’s instructions (Figure 1).
Briefly, organoids were thawed quickly at 37°C, gently
resuspended and washed in media. Pellet containing
organoids were resuspended in Matrigel and then seeded
in 384 well plate, at 200 organoids per well. Organoids
were incubated with media containing ROCK inhibitor
for 48hours to improve recovery. Organoids were then
treated with selected compounds for 5 days, at varying
concentrations and in quadruplicates (Figure 2).
For automated seeding, the organoid Matrigel suspension
was seeded into 96-well plates using BAB400 (Advanced
Solution) pipette tool. Gripper (PnP tool) Sequence can
be used to de-lid and lid the plates, and to move the
plates to the integrated ImageXpress® Micro Confocal
High-Content Imaging System for imaging. 7μl of organoid
suspension was dispensed in the middle of each well
to form “domes”. The automation path mapping the tool
coordinates was set and recorded before the run.
High throughput Imaging and analysis
The effects of compound treatment was monitored over
time using the ImageXpress® Micro Confocal system. CRC
organoids were imaged using 4X objective, with z-stacks
enabled. For viability assay, organoids were incubated with
Hoechst, Calcein AM and ethidium homodimer for 2hrs
at 37°C. Images were acquired at 10X with Z-stacking. For
staining with phalloidin, organoids were fixed in 4% PFA
and then incubated with phalloidin488. Images were then
acquired at 10X or 20X (with water immersion objectives).
The IN Carta® Image Analysis Software was used to
analyze images acquired during monitoring. A deep
learning-based approach was used to create a model
for organoids segmentation.
Results
Assay set up with colorectal
cancer organoids
To evaluate the use of assay ready cancer organoids, we
designed a proof-of-concept study in a 384 microwell
plate assay. Organoids were thawed, mixed with Matrigel
and seeded. After 48 hours, CRC organoids were treated
with a selection of nine compounds at 7 concentrations
with four technical replicates (Figure 3). After 5 days, a
live/dead assay was carried out to determine the effects
of the various compounds on organoid viability (Figure
5). Following the viability assay, organoids were fixed and
then stained with phalloidin to observe compound effects
on organoid morphology (Figure 6).
Figure 3. Assay setup. Plate map view of organoids in 384W plate. Inset shows example image (top: fluorescent, bottom: transmitted light) from one of the
wells. Organoids were labelled with Hoechst (nuclei, blue), phalloidin 488 (actin, green) and ethidium homodimer (dead cell marker, red).
Concentration
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Figure 4. A) Example images of CRC organoids in Matrigel. Organoids were treated with the indicated compounds and monitored over 5 days. B) Overview
of the SINAP workflow in IN Carta software to generate a model for organoid segmentation. C) Images acquired in transmitted light usually have high,
non-homogenous background, edge effects and artifacts (such as bubbles) which prevents robust object segmentation. Shown here are example organoid
images overlaid with segmentation mask. A SINAP model was created to segment CRC organoids. D) Graph showing the average CRC organoid area
over 5 days between DMSO control and CRCs exposed to compounds (5-FU 5uM, cisplatin 2.5μM, cytarabin 50μM, doxorubicin 15μM, romidepsin 2.5μM,
trametinib 5μM). Day 0 refers to images acquired prior to compound exposure. Error bars represent standard deviation between replicate wells.
A B
C D
Control Romidepsin Ibrutinib 5-Fluorouracil
Pre-treatment Day 5 post treatment
Control Doxorubicin Trametinib
Days after treatment
Area
5-FU
Cisplatin
Cytarabin
Doxorubicin
Romidepsin
Trametinib
DMSO
Monitoring of phenotypic effects of
compounds on organoids using
deep learning
Because PDOs do not express any fluorescent markers,
brightfield imaging was used to monitor the growth of
PDOs over time (Figure 4). To monitor the quality of
developing organoids, we used a deep learning-based
segmentation approach to analyze the acquired images.
Growth of CRC organoids can be monitored by measuring
their diameters or areas over time. The effects of
compounds on organoids can also be quantified. Here,
the growth of CRC organoids was inhibited by romidepsin
and trametinib.
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Viability assay to quantify compound effects
A
B
Figure 5. Effects of compounds on CRC organoids. A) Viability assay was carried out on CRC organoids after compound treatment and then imaged.
Organoids were stained with Calcein AM for live cells (green), ethidium homodimer for dead cells (red) and Hoechst for all nuclei (blue). Shown here are
representative images of organoids. B) The ratio of dead cells (Hoechst and ethidium homodimer positive) for each compound shown. Romidepsin and
trametinib treated organoids showed significant increase in dead cells compared to the controls (p<0.001).
Compound-induced phenotypic changes in CRC organoids
Figure 6. Phenotypic changes in CRC organoids. Organoids were fixed and stained with phalloidin after 5 days after compound treatment.
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Automation of CRC organoids seeding
To explore the feasibility of automating the workflow, the BAB400 bioprinter was optimized for CRC organoid seeding in a
96W plate. A dome of matrigel mixed with organoids was seeded in the center of each well using the pipette tool (Figure 7).
Conclusions
• Our results support the use of PDOs for high throughput assays such as compound screening. The availability of
standardized, assay ready PDOs provides significant time savings for screens using 3D models
• We show that an AI-based approach can be successfully used to generate robust segmentation for the analysis of
label-free biological modes such as 3D organoids.
References
1. Vlachogiannis G, Hedayat S, Vatsiou A, et al. Patient-derived organoids model treatment response of metastatic gastrointestinal
cancers. Science. 2018;359(6378):920-926. doi:10.1126/science.aao2774
A B
Figure 7. A) CRCs were treated on Day 2 with trametinib, fluorouracil (5-FU), and staurosporine at 50μM and 25μM, and then stained and imaged on Day 6.
B) Quantification of the average number of live cells in per organoid following drug treatment.
Staurosporine
Live cells (FITC positive)
Trametinib 5-FU Untreated
Human Colorectal Organoids
Drug Response (96h) – 96 wells
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