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How Opentrons Helped NYU IGEM Enable Higher Flavonoid Production

NYU’s iGEM team conceived of a more scalable way to create flavonoids – with a little help from Opentrons’ OT-2 pipetting robot.

Out of the ten teams Opentrons sponsored at this year’s iGEM Giant Jamboree, we wanted to showcase the efforts of New York University’s iGEM team. Their project was ambitious and practical, embracing Opentrons open-source ethos as well as iGEM’s tenet to use synthetic biology to improve real-world situations by finding a more scalable way to manufacture flavonoids.

NYU iGEM Team. CREDIT: NYU iGEM Team Wiki.

Improving Accessibility and Scalability With Lab Automation

Flavonoids, a naturally-occurring compound found in plants, contain antioxidants, anticarcinogens, and are anti-inflammatory as well as anti-mutagenic. This makes them desirable to a wide range of industries from pharmaceuticals to cosmetics to textiles.

But marketplace availability for flavonoids is limited right now due to costly and challenging extraction methods. Plants take a long time to grow, and the low concentration of flavonoids in any given plant results in low-yield harvests. NYU wondered if lab production could speed up a plant’s metabolic process—and, if so, could it enable a more efficient, reproducible, and scalable method of flavonoid extraction.

The team set out to design a reproducible, scalable method of engineering E. coli to produce flavonoids. They aimed to metabolically engineer pathways to produce recombined flavonoids, utilizing a novel optogenetics approach that reduced reliance upon more traditional chemical induction process.

“I’ve only had experience with one other pipetting robot, but the OT-2 was actually a dream to use. It was stellar.”

“The plan was to put together the NEB biolabs protocol for restriction enzyme digestion and ligation and make that completely automated,” said NYU alumnus and Ph.D. candidate Steven Ionov, who led the team of eight undergraduates. The team knew that increasing lab efficiency would serve their goals, so they aimed to incorporate the Opentrons OT-2 pipetting robot in the most central procedures of their project: digestion and ligation with a 3A assembly protocol.

Their procedure entailed “preparing the digestion reaction in a thermocycler with the 200 mL tubes,” said Ionov. He and undergraduate Neelam Pandyas learned how to use the OT-2, while the other team members designed a bioreactor to activate and grow the cells using green light, reducing traditional reliance upon chemical inducers.

“I’ve only had experience with one other pipetting robot,” Ionov said, “but the OT-2 was actually a dream to use. It was stellar. Not only was it an amazing experience for me to learn Python, but the actual ease of calibration, and the intuitive nature of how Opentrons actually programmed their material, made it really awesome.”

Pandyas concurred with Ionov’s enthusiastic assessment. “Getting to work with Opentrons was very exciting for our team,” she said. “The robot was really easy to calibrate; we did a bunch of trials with it, relying on the Opentrons website and the API for guidance.” Besides customizing the project to work with the 300 mL pipette tips, NYU iGEM easily incorporated the Opentrons into their existing lab workflows, with little need for additional customization.

Adaptive Problem Solving and Project Results

Despite all that, the NYU iGEM discovered that smooth technological operations couldn’t overcome protocol challenges. The 3A assembly ligation didn’t work and the team spent much of their time troubleshooting it: was the fault in the enzymes, the cells, or the protocol itself? The team painstakingly documented their troubleshooting process with these questions to ensure future teams could make fewer errors—and more progress.

“This was our first year,” Pandyas explained. “We had a learning curve, like every first iGEM team.” Given those issues and the time constraints of the competition, NYU iGEM was forced to pivot from their original plan and use the OT-2 for sampling instead of preparing the digestion reaction as originally planned. Thankfully, the OT-2 performed that task well, too. Mostly. “In one case there was just a drop of EcoRI instead of one microliter, which could make a bit of a difference,” Ionov said. “We had this trouble at the bench as well,” added Pandyas. “Even if you’re sampling with a handheld pipette, it’s really hard to get such a small viscous amount out of the pipette tips. I can see why the robot had a similar problem.”

Temporary Setbacks, Long-Term Progress

Despite the wet lab setbacks, the project’s overall architecture reflected a long-term vision that makes the limited OT-2 use a temporary setback. NYU iGEM arrived at a better understanding of their field, without undermining their project’s long-term viability. The OT-2 will continue to serve them in future endeavors, and stands in their lab today as a trusted technology to help automate their processes.

We applaud NYU’s hard work, and look forward to seeing how they move forward.

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