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: Paull, D., Sevilla, A., Zhou, H. et al. Automated, high-throughput derivation, characterization and differentiation of induced pluripotent stem cells. Nat Methods 12, 885–892 (2015). https://doi.org/10.1038/nmeth.3507 Publication: Nature Methods, 2018 DOI: https://doi.org/10.1038/nmeth.3507
Introduction The paper “Automated, high-throughput derivation, characterization and differentiation of induced pluripotent stem cells” by Paull et al. (2015) published in Nature Methods presents a groundbreaking robotic platform that transforms the traditionally manual, labor-intensive process of iPSC generation into a standardized, automated workflow. This analysis examines the technological innovation and its implications for biological research applications.
System Architecture and Capabilities The authors developed a modular automation platform comprising three integrated robotic systems.
- A fibroblast banking module utilizing Hamilton STARlet liquid handling connected to Cytomat incubators
- A reprogramming cluster with three Hamilton STAR systems linked by a Rack Runner robotic arm
- A characterization and banking module optimized for high-throughput processing
These systems operate within BSL II biosafety cabinets with HEPA filtration to maintain sterile conditions throughout all processes. The platform automates the entire iPSC workflow from skin biopsy processing to differentiation with minimal human intervention.
Figure 2 | Automated reprogramming. (a) Experimental scheme for automated fibroblast thawing and reprogramming. (b) Representative time course of mRNA transfection, with development of colonies over 22 d. (c) Representative flow cytometry analysis of 34 biological replicates of reprogrammed cultures from automated mRNA transfection, displaying a higher proportion of cells expressing the pluripotency markers TRA-1-60+ and SSEA4+ 23 d after the final mRNA transfection (left) and lack of the fibroblast surface marker CD13 (right). (d-g) Effect plots of Poisson regression analysis of factors that contribute to reprogramming success: colony count versus age (d); colony count versus fibroblast doubling time (e); colony count versus confluence (f); colony count versus recovery medium post thaw (g). Gray areas and red bars indicate confidence intervals.
Novel Biological Applications
1. Scalable iPSC Production for Population Studies The system achieves unprecedented throughput, processing several hundred samples per month with a 10-12 fold increase in productivity compared to previous methods. This scalability enables population-level studies that require large sample sizes to detect subtle genetic effects, particularly for complex diseases with modest effect sizes.
2. Standardization and Reduction of Technical Variation A significant innovation is the automated selection of polyclonal iPSC populations rather than traditional manual colony picking. Statistical analysis demonstrated that this approach eliminated more than one-third of the variability observed between manually derived lines. This finding has profound implications for disease modeling, revealing that a substantial portion of previously observed “biological” variation may actually be technical in origin.
3. Automated Differentiation with Enhanced Consistency The platform successfully automated differentiation protocols for multiple lineages, including cardiomyocytes, dopaminergic neurons, and endodermal cells. Importantly, these automated differentiations showed high efficiency and remarkable consistency across cell lines. The standardized approach to differentiation addresses a critical challenge in the field, as protocol variability has historically limited cross-laboratory reproducibility.
Figure 3 | Automated iPSC purification and arraying. (a) Flow cytometry analysis for TRA-1-60+ SSEA4+ CD13- cells before and after automated MACS purification. (b) Representative images of one well of a 96-well plate for bulk-sorted cells 9 d post sorting (9 dps), with right panel showing TRA-1-60 expression pattern captured by automated imaging. Scale bars, 500 um. (c) Clustering of sorted samples against reference hESC and fibroblast lines based upon gene expression of pluripotency and early differentiation markers. (d) Box plot of the pluripotency scores for reference hESC lines, iPSC lines and fibroblast cell lines. Numbers of unique samples shown in parentheses. The bold line represents the median, with upper and lower boundaries of the box showing the 1st and 3rd quartiles, respectively. Upper and lower whiskers represent the 75th and 25th percentiles, respectively. Circles indicate potential outliers. (e) Example growth rates of a robotically passaged iPSC plate over 5 d of culture. y axis, percentage of total well confluence from 0 to 100; x axis, time from 0 to 120 h. Each graph is of a single well of a 96-well plate. (f) Summary of flow cytometry analysis of TRA-1-60+ SSEA4+ population before and after automated passage 1:3 for control hESC lines and iPSC lines derived on the system (error bars, s.d .; n = 3 replicates per line per condition).
Integrated Quality Control and Monitoring The system incorporates continuous quality assessment through automated imaging, flow cytometry, and gene expression analysis. This integrated approach ensures consistent production of high-quality iPSCs while generating valuable data on factors affecting reprogramming efficiency, including donor age, fibroblast growth rate, and culture conditions.
Significance and Future Implications This automation platform represents a paradigm shift in stem cell technology with several important implications:
Enhanced Reproducibility: By minimizing human intervention, the system addresses a fundamental challenge in biological research—reproducibility across laboratories and experiments.
Democratization of Technology: The standardized approach could enable wider adoption of iPSC technology across research institutions.
Enabling Precision Medicine: The high-throughput capabilities make feasible the creation of large, patient-specific iPSC banks for personalized drug screening and therapeutic development.
Statistical Power for Complex Traits: The reduced technical variation and increased throughput provide improved statistical power for detecting subtle phenotypic differences in disease modeling.
Conclusion The automated platform described by Paull et al. represents a significant technological advancement that addresses critical limitations in iPSC research. By transforming manual processes into standardized, high-throughput workflows, this system enables novel applications in disease modeling, drug discovery, and personalized medicine. The approach demonstrates how automation technology can fundamentally change biological research by improving consistency, scalability, and statistical power while reducing costs and labor requirements.
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: Project - AstroMicrobes Our Space Microbe Genomics Platform will leverage laboratory automation to efficiently process and analyze microbial samples from space environments. The goal is to create a high-throughput pipeline for detecting genomic adaptations that occur in microbial strains exposed to space conditions.
Automation Components
- Automated DNA Extraction and Library Preparation
- Using Opentrons OT-2 liquid handling robot with custom protocols
- Custom 3D-printed sample holders for space-returned microbial samples
- Sequencing and Analysis Pipeline
- Integration with MinION portable sequencer for rapid analysis
- Automated bioinformatics pipeline for comparative genomics
- Visualization and Reporting System
- Automated generation of mutation maps and adaptation profiles
3D-Printed Components We will design and 3D print a specialized sample rack to hold the unique containers used for space-returned samples. The design will include,
- Stabilizing elements to prevent sample movement during robot operation
- Thermal control elements for maintaining optimal extraction conditions
- Standardized dimensions compatible with OT-2 deck positions