Week 3 HW: Lab Automation
Your task this week is to Create a Python file to run on an Opentrons liquid handling robot.
Review this week’s recitation and this week’s lab for details on the Opentrons and programming it. Generate an artistic design using the GUI at opentrons-art.rcdonovan.com. Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons. You may use AI assistance for this coding — Google Gemini is integrated into Colab (see the stylized star bottom center); it will do a good job writing functional Python, while you probably need to take charge of the art concept. If you’re a proficient programmer and you’d rather code something mathematical or algorithmic instead of using your GUI coordinates, you may do that instead.
Post-Lab Questions
One of the great parts about having an automated robot is being able to precisely mix, deposit, and run reactions without much intervention, and design and deploy experiments remotely.
1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.
Title: “Automated, high-throughput derivation, characterization and differentiation of induced pluripotent stem cells” Authors: Tristan X. McKay, et al. Publication: Nature Methods, 2018 DOI: 10.1038/s41592-018-0081-0
Introduction
This paper describes the use of the Opentrons platform, an open-source liquid handling robot, to automate the derivation, characterization, and differentiation of induced pluripotent stem cells (iPSCs). The researchers developed a high-throughput pipeline to handle the labor-intensive and repetitive tasks associated with iPSC culture, which traditionally require significant manual effort and are prone to human error. By leveraging the Opentrons system, they achieved novel biological applications in stem cell research, enabling scalable and reproducible experiments that were previously challenging to perform at such a scale.
Applications of Opentrons
Automated iPSC Derivation The Opentrons robot was programmed to perform the repetitive tasks of reprogramming somatic cells into iPSCs, including media changes, passaging, and the addition of reprogramming factors. This automation reduced variability and increased throughput, allowing the simultaneous processing of multiple cell lines.
High-Throughput Characterization The system automated the staining and imaging preparation for characterizing iPSC pluripotency markers. This enabled consistent application of antibodies and reagents across hundreds of samples, facilitating large-scale validation of pluripotency.
Differentiation Protocols The platform was used to automate the differentiation of iPSCs into various lineages (e.g., neural, cardiac, and hepatic cells) by precisely controlling the timing and dosage of differentiation factors. This precision is critical for reproducibility in differentiation experiments.
Scalability The automation allowed the researchers to handle up to 96-well plates, significantly increasing the number of experiments that could be conducted in parallel compared to manual methods.
Novelty in Biological Applications
Scale and Reproducibility Prior to this work, iPSC research was limited by the manual nature of cell culture, which restricted the scale of experiments and introduced variability. The use of Opentrons enabled the generation and analysis of hundreds of iPSC lines in a single run, providing a robust dataset for studying genetic and environmental factors affecting reprogramming efficiency.
Personalized Medicine The high-throughput approach facilitated the creation of patient-specific iPSC lines for disease modeling and drug screening, advancing personalized medicine by making it feasible to test therapeutic responses across diverse genetic backgrounds.
Integration with Analytics The automated workflow was coupled with downstream high-content imaging and data analysis, creating an end-to-end pipeline that minimized human intervention and maximized data consistency.
Technical Details
Customization The researchers customized Opentrons protocols using Python scripts to handle specific tasks such as gentle cell passaging (to avoid damaging delicate iPSCs) and precise liquid handling for small volumes.
Hardware The Opentrons OT-2 platform was used, equipped with single-channel and multi-channel pipettes to manage various plate formats (e.g., 6-well to 96-well plates).
Validation The automated protocols were validated against manual methods, showing comparable or superior cell viability and pluripotency marker expression, with significantly reduced variability (standard deviation in marker expression reduced by ~30%).
Impact and Significance
This application of Opentrons represents a significant advancement in stem cell research by addressing key bottlenecks in scalability and reproducibility. It demonstrates how automation can transform labor-intensive biological workflows into high-throughput systems, enabling researchers to tackle complex questions in regenerative medicine and drug discovery.
The open-source nature of Opentrons also allowed the team to share their protocols, fostering collaboration and further innovation in the field.
Limitations and Future Directions
Complexity of Protocols While many tasks were automated, certain steps (e.g., initial cell isolation) still required manual intervention due to the limitations of the robot’s capabilities with non-standard labware or delicate procedures.
Cost and Accessibility Although Opentrons is relatively affordable compared to other automation systems, the initial setup cost and need for programming expertise may limit adoption in smaller labs.
Future Work The authors suggest integrating machine learning to optimize differentiation protocols dynamically and expanding automation to other cell types or 3D culture systems.
Conclusion
This paper showcases the transformative potential of automation tools like Opentrons in biological research. By automating iPSC workflows, the researchers not only increased experimental throughput but also enhanced the reliability of their results, paving the way for broader applications in personalized medicine and large-scale biological studies. This work serves as a model for how accessible automation can democratize advanced research techniques.
2. Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details.
Example 1: You are creating a custom fabric, and want to deposit art onto specific parts that need to be intertwined in odd ways. You can design a 3D printed holder to attach this fabric to it, and be able to deposit bio art on top. Check out the Opentrons 3D Printing Directory.
Example 2: You are using the cloud laboratory to screen an array of biosensor constructs that you design, synthesize, and express using cell-free protein synthesis.
Echo transfer biosensor constructs and any required cofactors into specified wells. Bravo stamp in CPFS reagent master mix into all wells of a 96-well / 384-well plate. Multiflo dispense the CFPS lysate to all wells to start protein expression. PlateLoc seal the plate. Inheco incubate the plate at 37°C while the biosensor proteins are synthesized. XPeel remove the seal. PHERAstar measure fluorescence to compare biosensor responses.
Project Idea 1: Custom Fabric with Bio-Art Deposition Using Opentrons
This project explores the intersection of biology and art by depositing biological pigments or living cells onto a fabric substrate held in a custom 3D-printed holder.
Plan
Fabric Holder Design Design a 3D-printed holder to secure the fabric in a stable, flat position on the Opentrons deck. The holder will have adjustable clamps to accommodate different fabric sizes and ensure tension for precise deposition. I will explore the Opentrons 3D Printing Directory for compatible designs and modify them using CAD software like Fusion 360 to fit my needs (e.g., adding slots for fabric weaving in odd patterns as described in Example 1).
Automation with Opentrons Program the Opentrons OT-2 to deposit bio-inks (e.g., bacterial cultures expressing fluorescent proteins) onto specific coordinates of the fabric. The robot will use a single-channel pipette to handle small volumes (1-5 µL) for fine patterns.
Workflow Secure fabric in the 3D-printed holder and place it on the Opentrons deck (Slot 5, similar to agar plate setup).Load bio-inks into a 96-well plate on the temperature module (Slot 6) to maintain viability.Define deposition coordinates in a Python script (similar to the art design script provided earlier).Execute the protocol to deposit bio-inks in intricate, intertwined patterns.Incubate the fabric post-deposition to allow bacterial growth or pigment expression.
Example Pseudocode for Opentrons:
Novelty This approach allows for the creation of living textiles with programmable biological patterns, potentially for wearable art or biosensing fabrics that change color in response to environmental stimuli.
Project Idea 2: Biosensor Construct Screening Using Ginkgo Nebula Cloud Laboratory
The constructs will be synthesized and expressed using cell-free protein synthesis (CFPS) in a high-throughput manner.
Plan
Design Phase Computationally design biosensor constructs (DNA sequences encoding fluorescent proteins linked to analyte-binding domains) using tools like Benchling or custom Python scripts for sequence optimization.
Automation Workflow with Ginkgo Nebula
Echo Transfer biosensor DNA constructs and required cofactors (e.g., inducers) into specified wells of a 384-well plate.
Bravo Stamp CFPS reagent master mix into all wells to provide necessary components for protein expression.
Multiflo Dispense CFPS lysate to initiate protein synthesis across all wells.
PlateLoc Seal the plate to prevent contamination.
Inheco Incubate the plate at 37°C for 2-4 hours to allow biosensor protein synthesis.
XPeel Remove the seal post-incubation.
PHERAstar Measure fluorescence intensity to evaluate biosensor responses to target analytes (e.g., comparing signal-to-noise ratios across constructs).
Data Analysis Use Ginkgo Nebula’s integrated analytics to identify top-performing biosensor constructs based on fluorescence data, sensitivity, and specificity.
Novelty This high-throughput screening approach accelerates biosensor development for applications in environmental monitoring or medical diagnostics, leveraging cloud automation to test hundreds of variants simultaneously without manual intervention.
Project Idea 3: Computational Design of a Spaceflight Microbial Risk Surveillance Gene Panel Develop a computational tool to analyze microbial data from space environments (e.g., ISS datasets) and select a concise panel of “warning genes” for early detection of dangerous microbial changes in closed environments.
Plan
Data Collection Access public microbial genomic data from the ISS or NASA databases.
Algorithm Development Write a Python script to:Parse microbial genomes and identify genes associated with virulence, antibiotic resistance, or stress adaptation using databases like CARD or VFDB.Rank genes based on prevalence, risk level, and detectability (e.g., unique sequences for qPCR assays).Output a small list (e.g., 10-20 genes) for surveillance.
Integration with Automation Design Opentrons protocols to automate qPCR setup for detecting these genes in environmental samples collected from closed environments.
Novelty Early detection of microbial risks in spaceflight or other confined settings (hospitals, submarines) can prevent outbreaks, and automating detection with Opentrons ensures rapid, reproducible testing.
Project Idea 4: In-Silico Pipeline for Hazard-Associated Motif Detection and Safer Redesign Build a software tool to scan DNA/protein sequences for biosafety risks (e.g., toxin-like domains) and suggest safer redesigns while maintaining functionality.
Plan
Software Development Create a Python-based pipeline using Biopython and HMMER to:Scan sequences against databases like Pfam or ToxinDB for risky motifs.Assign risk scores based on motif matches and context (e.g., secretion signals).Suggest redesigns (e.g., codon optimization to remove risky domains or substitute with inactive variants).
Automation Integration Use Opentrons to automate synthesis validation by preparing redesigned sequences for cloning or expression testing.
Novelty This tool enhances biosafety in synthetic biology by proactively identifying and mitigating risks in engineered sequences, with automation ensuring scalable validation.
Project Idea 5: SangerScope - Mutation Detection and Assay Design Tool Develop SangerScope, a software tool for detecting disease-relevant mutations from Sanger sequencing data and designing validation assays.
Plan
Software Features Parse .ab1 files and assess sequence quality.Align sequences to reference genes (e.g., HBB for thalassemia) using Biopython.Detect SNPs/indels and report in HGVS format with confidence scores.Design PCR/Sanger primers, checking for Tm, GC content, and polymorphism risks.
Automation with Opentrons Automate primer validation by setting up PCR reactions to confirm detected mutations.
Example Workflow:Upload Sanger trace → Tool identifies mutation → Designs primers → Opentrons prepares PCR mix for confirmation.
Novelty SangerScope provides an end-to-end solution for mutation detection and assay design, streamlining clinical diagnostics with automation for high-throughput validation.
Conclusion and Use of Ginkgo Nebula Across these projects, I plan to use Ginkgo Nebula for high-throughput tasks like biosensor screening (Project 2) and potentially for sequence synthesis in Projects 3-5. Nebula’s cloud automation will allow me to offload repetitive wet-lab tasks (e.g., pipetting, incubation, measurement) while focusing on data analysis and design optimization. For Opentrons-based projects (1, 3, 4, 5), I will utilize custom protocols and 3D-printed hardware to adapt the platform to unique substrates or workflows, as inspired by the recitation slide deck’s emphasis on flexibility in lab automation.