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.
Matthews et al. 2022 · Fig. 3b, cropped, rearranged, legend from Fig. 3c · CC BY 4.0Total organoid area over 72 hours, as a fold change from the first image, at six gemcitabine doses from 3 to 1,000 nM, measured by OrganoID, another group's published tool. Above 3 nM, area grew for the first several hours and then fell, ending lower at higher doses. A pilot reports curves like these for every organoid as well as for each dose.

Measures

What a pilot measures around each treatment

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

Before

  • Each organoid's baseline growth curve

Response

  • How soon a response starts
  • The largest effect
  • Change in growth rate, shape and movement

Timing

  • Time to growth arrest
  • Time to shrinkage
  • Time to fragmentation

Afterward

  • Recovery, regrowth and rebound
  • Whether the response lasts or passes

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.

Brightfield images of pancreatic cancer organoids at 0, 36 and 72 hours, untreated in the top row and given 30 nM gemcitabine in the bottom row, where red staining spreads by 72 hours.
Matthews et al. 2022 · Fig. 3a, cropped, rearranged, labels added · CC BY 4.0Pancreatic cancer organoids, untreated or given 30 nM gemcitabine, at 0, 36 and 72 hours, in brightfield with a fluorescence overlay. Red marks propidium iodide, a dye that stains cells with damaged membranes. A pilot's standard setup does not record fluorescence such as the red dye.

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.

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.

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.

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.

Measure treatment response in a pilot

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

Plan a pilotResearch and accuracy