Stefan Guldin, Associate Professor, leads the Adaptive & Responsive Nanomaterials Group (AdReNa Group), where Alaric Taylor (Honorary Research Fellow and CTO of the UCL start-up Vesynta) and Yann Mamie (Research Engineer) are accelerating materials innovation by lab automation using open-source platforms with the Opentrons OT-2 liquid handling robot and the Opentrons Python Protocol API.
Opentrons: Please tell us a little about your background.
Alaric Taylor: I started as a physicist at Imperial College London. Then, I moved into engineering and earned my master’s degree from the University of Cambridge and, subsequently, a PhD in electronic and electrical engineering from University College London. After that, I joined the AdReNa group and worked with Stefan on nanomaterial self-assembly at the air-water interface as an EPSRC Research Fellow. I was inspired by a former group member (Stuart Ibsen, now a professor at Oregon Health & Science University) and switched my focus to bioanalytical device development in the support of children with cancer. AdReNa was an early adopter of the OT-One (and, later, the OT-2) and we are strong believers in the foundations of the Opentrons platform for customizable lab automation.
Yann Mamie: I have a master’s degree in materials science and engineering from EPFL (Switzerland) and worked with the AdReNa group at UCL for my final research project. After graduating, I returned to London to rejoin the group on a research and development project supported by the UCL start-up Vesynta, in which we are developing innovative uses of lab automation for processing biological samples for therapeutic drug monitoring (TDM).
Stefan Guldin: I studied applied physics with an emphasis on soft matter at TU Karlsruhe and TU Munich. For my PhD, I moved to the University of Cambridge to conduct research on nanostructuring inorganic materials by organic self-assembly. I then joined EPFL in Switzerland for postdoctoral research on the interplay of liquid crystals and nanoparticles for biosensing applications. At UCL, I am an Associate Professor in the Department of Chemical Engineering, lead the AdReNa research group, and teach molecular engineering and soft nanotechnology. I am also the co-lead of the UCL Soft Materials Network, I’m actively involved in the Doctoral Training Center in Transformative Pharmaceutical Technologies, and I’m developing a new master’s program in Digital Manufacturing of Advanced Materials.
Opentrons: Please tell us more about the research you do.
Stefan Guldin: We’re using molecular self-assembly to create materials that can interact with the environment—most prominently for sensing chemicals or biomarkers. We also apply our findings toward functional coatings, like self-cleaning applications, and those with interesting optical properties. Advancing capabilities of high-throughput sample preparation, materials characterization, and analysis drive much of our work.
Opentrons: What do you use the OT-2 to do, and how does it fit into the workflow?
Yann Mamie: We’re working on extending the Opentrons OT-2’s liquid pipetting functionalities by tracking volume and liquid height in containers. Using our custom functions, we can track the meniscus and determine the aspiration depths. With this consistency and repeatability, we can facilitate advanced protocols. We are also using specific labware, which we are designing and building for use on the OT-2.
Stefan Guldin: Custom pipetting is helpful to more rapidly disperse a compound or make it more soluble. Aqueous two-phase systems are a hot topic, and custom pipetting is useful for these biphasic mixtures, where you can aspirate in one phase and disperse in the other. We also examined the accuracy of pipetting as a measure of position: we found that the Opentrons OT-2 is accurate, and the position of the pipette in the well is not so important for accuracy if you handle solutions with regular viscosity.
Alaric Taylor: It’s important to recognize and celebrate that many of our capabilities are derived from the willingness of our predecessors and peers’ open-source developments. I hope our developments will outlive our current applications and may super-charge people on the other side of the planet.
Opentrons: Are you planning any other automation?
Yann Mamie: We are working on automatic error propagation calculations for each pipette stroke of a protocol to minimize error when creating optimized pipetting protocols. If we know there’s a certain uncertainty on pipetting, we can track that uncertainty on the volume contained in each well and the volume transferred from one well to another. With a simple printing inside the protocol, we can review all the well volumes and concentrations.
Opentrons: How is the OT-2 supporting specific projects at AdReNa?
Stefan Guldin: We are using the OT-2 with qTLC—our web app that quantifies chemical compounds via thin layer chromatography. It has been used in 37 countries across six continents, supporting analytical chemistry with very simple means. Users follow the protocol of separating molecules, take and upload an image from their smartphone to the server, and then we help them analyze and quantify their compounds.
Alaric Taylor: The ability to automate liquid handling on a user-friendly, code-flexible platform like the OT-2 has supported many proof-of-principle studies in our lab. The applications have ranged from controlled and repeatable bioanalytical sample preparation through to screening of chemical compositions for reactions. Based upon these capabilities, others in the department, across the university, and at other institutions regularly ask us to collaborate with them.
Opentrons: What else do you use the OT-2 for?
Stefan Guldin: We are leading a NIHR-funded project called ChromaDose. The goal is to establish a novel method of assessing chemotherapeutic drug concentrations in fluids. Current oncology administration is based on body surface area, but that has nothing to do with personal metabolism. That means that there’s a large variation of extra concentration inside patients, especially children. This is a huge problem, because it can lead to severe side effects and limited efficacy. We are trying to use Opentrons to automate analysis of blood samples within a relatively short time, and then do the quantification. This helps doctors assess the patient’s concentration of chemotherapeutic drugs for more accurate dosage. The Opentrons platform is important for us to automate our procedures, and it’s serving us well as a workhorse for our prototyping.
Yann Mamie: We also created a debugger that simulates Opentrons protocols and creates easily readable logs. In addition, it also provides guidance about labware requirements with their respective positioning and content. The user just has to input the file of the respective pipetting protocol and the folder for any custom labware definitions. Below are two examples of printouts obtained with this debugger:
Opentrons: What was it like to get your OT-2 up and running?
Yann Mamie: The OT-2 was already here when I arrived in September 2020. I started using it and figuring out the coding of protocols using the Opentrons Python API. I’ve been developing custom functions that do, for example, meniscus-tracking, video, types of mixing or transfer functions, and also designing some custom labware for our own containers.
Opentrons: Did you encounter any challenges working with your OT-2?
Alaric Taylor: The OT-One was our first pipetting robot–and we loved it! But when the OT-2 came along, we recognized all the engineering that had gone into improving the user experience from mechanical components to software. That’s not to say we don’t get a little frustrated from time to time: we like to go a little deeper into the code than perhaps an average user would. Updates to the Opentrons Python API can be challenging for us to identify and debug errors from previous protocols that worked without any problems. We know it’s because we’re working with an actively developed tool, and we’re grateful for the enhancements these API updates bring to the general functionality of the OT-2.
Opentrons: Since you’re affiliated with the University, will students ever be using the OT-2?
Stefan Guldin: In our new Digital Manufacturing of Advanced Materials master’s program, we want to teach the new generation of engineers a holistic approach, which combines the manufacturing of materials with high-throughput characterization and data analysis. We will host eight Opentrons robots in our experimental lab, where students will solve problems in a fully integrated way. They’ll design and plan out the experiments; use the Opentrons robots to generate a large set of samples; gather data by high-throughput characterization; apply statistical methods including machine learning to analyze the data; and go through this cycle in an iterative fashion to map out the experimental domain.
Opentrons: You are also committed to open source sharing of your findings. Can you please share your perspective on that?
Stefan Guldin: We think we can make the most impact on the field by sharing our tools. We are trying to create something that is useful to us, and then sense-check it with the community. We are deeply committed to open source for both hardware and software developments in our group. That is why we share all of our work on the GitHub platform. Ultimately, using, adopting, and sharing experiences helps all of us progress—and since Opentrons believes the same and provides tools to help with that, we really like it.