ConductVision Organoid Monitor

When a treatment acts, organoid by organoid

A pilot compares each organoid's growth and shape before and after treatment, against your controls, and shows which organoids slow, stop or keep growing.

Plan a pilotWhat a pilot measures
Line chart of organoid area over 72 hours at six gemcitabine doses, rising most at the lowest doses and staying near its starting value at the highest.
Adapted from Matthews et al. 2022 · CC BY 4.0

Measures

What a pilot measures around each treatment

You enter each treatment time by hand, or import the times from your protocol.

Phase-contrast images of an untreated cyst in a polycystic kidney disease organoid on days 1, 3, 7 and 14, growing from a small sphere to one that fills the frame.
Adapted from Gulieva et al. 2025 · CC BY 4.0

Before

  • Each organoid's baseline growth curve
Brightfield images of human kidney tubuloids grown in collagen domes with forskolin, at 0, 24, 48 and 72 hours, as their cysts grow larger.
Adapted from Rohe et al. 2026 · CC BY 4.0

Response

  • How soon a response starts
  • The largest effect
  • Change in growth rate, shape and movement
Phase-contrast images of one polycystic kidney disease organoid cyst given 10 µM everolimus on days 0, 3, 7 and 14, still round on day 7 and small and dark by day 14.
Adapted from Gulieva et al. 2025 · CC BY 4.0

Timing

  • Time to growth arrest
  • Time to shrinkage
  • Time to fragmentation
Brightfield images of colorectal cancer organoids grown from a patient-derived cell line, just after SN-38 treatment and on regrowth days 2, 4 and 8, growing back without the drug.
Adapted from Nakano et al. 2025 · CC BY 4.0

Afterward

  • Recovery, regrowth and rebound
  • Whether the response lasts or passes
Brightfield images of human kidney tubuloids in suspension, untreated at 0 and 72 hours and given DMSO or forskolin at 72 hours, where the forskolin tubuloids show larger cysts.
Adapted from Rohe et al. 2026 · CC BY 4.0

Comparisons

  • Each organoid against its own baseline
  • Untreated and vehicle controls
  • Other doses and treatments
  • The whole population

Timing

The time course an endpoint assay leaves out

Deben and colleagues used OrBITS, their brightfield time-lapse tool, to track organoids through a drug screen, and advise judging drug response by growth rate.1 With a fluorescent dye that marks damaged cells added, their time course also told cytostatic from cytotoxic responses, which the CellTiter-Glo 3D endpoint assay could not.1

Spiller and colleagues followed the drug responses of individual tumor organoids in brightfield images taken at several time points, without dyes.2 With continuous imaging, the first change after a treatment gets a time stamp.

Live-imaging pictures of human pancreatic cancer organoids, untreated and given 1000 nM gemcitabine, at 0 and 100 hours, where the treated well holds fewer organoids and more debris.
Adapted from Ferreira et al. 2024 · CC BY 4.0

Spread

Spread of responses across organoids

Spiller and colleagues note that organoids differ in size, cell makeup and response over time, which makes 3D cultures hard to evaluate.2 When Jabs and colleagues screened the same ovarian cancer cells as flat cultures and as organoids, drug effects were more diverse in the organoids.3

Borten and colleagues note that analyses largely ignore the spread of size and shape within a culture.4 A pilot reports the share of organoids growing, stable and shrinking in each well, alongside the well average.

Camera specification

The camera a treatment study needs

A treatment study compares each organoid with itself, so every image must be taken the same way.

Microscope images of the same patient-derived intestinal organoids when forskolin was added and one hour later, when their lumens had filled with fluid.
Adapted from Lefferts et al. 2024 · CC BY 4.0

Timing

  • A full stack of every well every 5 to 15 minutes before and after treatment

OrganoID's gemcitabine study imaged organoids every 4 hours over 72 hours.5 Imaging every 5 to 15 minutes dates the first response to a treatment more closely.

Phase-contrast images of one untreated medulloblastoma spheroid in its pinkish culture medium and after a rinse in buffer, when the background turns grey.
Adapted from Ivanov et al. 2014 · CC BY 4.0

Light

  • The same light and exposure for the whole study
  • Each image normalized to its own background, so a change in medium color does not read as a response

Fixed settings keep images from before and after a treatment comparable.

The same part of an organoid culture at 0 and 92 hours, each organoid outlined and numbered by OrganoID, a published tool, and keeping its number as it grows.
Adapted from Matthews et al. 2022 · CC BY 4.0

Position

  • A plate holder that puts each well back under its camera after dosing
  • Each new image aligned to the one before it
  • Images flagged until the plate has warmed back up and stopped drifting

Alignment keeps each organoid matched to its own record after the plate goes back in.

Deliverables

What the pilot delivers

  • Response curves for every organoid and condition
  • Time-to-event tables for arrest, shrinkage and fragmentation
  • The spread of responses in each well
  • Clips of organoids that responded unlike their neighbors

References

  1. Deben C, De La Hoz EC, Compte ML, et al. (2023). OrBITS: label-free and time-lapse monitoring of patient derived organoids for advanced drug screening. Cellular Oncology.
  2. Spiller ER, Ung N, Kim S, et al. (2021). Imaging-Based Machine Learning Analysis of Patient-Derived Tumor Organoid Drug Response. Frontiers in Oncology.
  3. Jabs J, Zickgraf FM, Park J, et al. (2017). Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations. Molecular Systems Biology.
  4. Borten MA, Bajikar SS, Sasaki N, et al. (2018). Automated brightfield morphometry of 3D organoid populations by OrganoSeg. Scientific Reports.
  5. Matthews JM, Schuster B, Kashaf SS, et al. (2022). OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics. PLOS Computational Biology.
  6. Gulieva RE, Ahmadvand P, Freedman BS (2025). A novel rapalog shows improved safety vs. efficacy in a human organoid model of polycystic kidney disease. Stem Cell Reports.
  7. Rohe J, Ashraf APK, Kehl M, et al. (2026). Modelling kidney cystogenesis using human kidney tubuloid cultures. BMC Molecular and Cell Biology.
  8. Nakano K, Oki E, Yamazaki M, et al. (2025). Colorectal cancer cell line-derived organoid model with stem cell properties captures the regrowing state of residual cancer cells after neoadjuvant chemotherapy. Cell Death Discovery.
  9. Ferreira N, Kulkarni A, Agorku D, et al. (2024). OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data. Cellular Oncology.
  10. Lefferts JW, Kroes S, Smith MB, et al. (2024). OrgaSegment: deep-learning based organoid segmentation to quantify CFTR dependent fluid secretion. Communications Biology.
  11. Ivanov DP, Parker TL, Walker DA, et al. (2014). Multiplexing Spheroid Volume, Resazurin and Acid Phosphatase Viability Assays for High-Throughput Screening of Tumour Spheroids and Stem Cell Neurospheres. PLOS ONE.

Measure treatment response in a pilot

Tell us your organoid type, your treatments and the response you want timed.

Plan a pilotResearch and accuracy