Júlia Vallverdú Ginés, PhD
Lead ScientistTaking a model from a promising result to routine use involves two distinct stages: translating the method and operationalizing it. The right starting point depends on how mature the method is, not on the type of organization running it.
Translate a New Model: Develop, Transfer, Validate
→ For teams taking an expert-developed model toward reproducibility, measurable performance, and transferability
Many 3D models begin as expert-developed protocols that perform well in the hands of the scientists who built them. Moving them beyond that original context requires making them reproducible, measurable, and transferable, without flattening the biology that makes them valuable in the first place.
This stage is typical of disease modeling. In this application, protocol steps that depend on individual operator technique become a source of variability once other teams or sites need to run the model. The MO:BOT converts manual protocols into defined, automated routines that can be executed identically across runs.
Internal data demonstrate that:
· The MO:BOT can translate an iPSC-derived kidney organoid workflow from 6-well to 96-well U-bottom plates, enhancing organoid viability.
· Organoid area variability drops from a coefficient of variation of 15.2% with manual handling to 10.4% on the MO:BOT.
Operationalize an Established Model: Control it. Visualize it. Scale it. Document it.
→ For teams that need established 3D workflows to run routinely, consistently, and at greater capacity.
Once a model has a defined use, the priority shifts. The goal is to run it as a repeatable process and add capacity without adding expert hands at the same rate. This feature is particularly useful in drug discovery: screening, toxicity and safety testing, and routine culture quality control.
The MO:BOT automates seeding, medium exchange, treatment delivery, and monitoring on a single modular platform. Monitoring is built into the workflow through the MO:CROSCOPE, an on-deck brightfield imaging module that delivers per-well morphology quality control in approximately 10 minutes per 96-well plate. Imaging data is linked to MO:BOT workflow execution records in a single audit trail, so every result remains traceable to its culture history.
| Our published evidence has demonstrated that: · The MO:BOT-generated HepG2 spheroids show substantially lower variability in size across independent runs (8.3% CV vs 15.3% in manual workflows). · Improved uniformity translates into a more sensitive and robust toxicity APAP assay. |
