Week 3 HW: Lab Automation

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.

For this week, we’d like for you to do the following:

  1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.
  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. While your description/project idea doesn’t need to be set in stone, we would like to see core details of what you would automate. This is due at the start of lecture and does not need to be tested on the Opentrons yet.

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.

1. Paper using Opentrons for novel biology

One example is AssemblyTron: flexible automation of DNA assembly with Opentrons OT‑2 lab robots by Eno et al. (Synthetic Biology, 2023). The authors developed AssemblyTron, an open‑source Python package that takes DNA assembly designs (from the j5 design software) and converts them into executable protocols for the Opentrons OT‑2 liquid‑handling robot. The biological focus is on accelerating the Build step of the Design–Build–Test–Learn cycle in synthetic biology by fully automating PCR setup and multi‑part DNA assemblies such as Golden Gate and in vivo assembly (IVA). academic.oup

2. What I intend to automate for my final project

Core biological idea

I want to build a small, automated platform to probe whether ABO blood‑group–like glycan patterns (especially type A–like structures) influence biological responses relevant to gastrointestinal disease risk. The concept is to combine:

  • Engineered mammalian or microbial cells that express defined ABO‑like glycan patterns (e.g., via glycosyltransferase expression or synthetic glycan coatings).
  • Simple reporter circuits or biosensors that respond to inflammatory cues (e.g., NF‑κB activation, cytokine mimics) or pathogen‑associated signals.
  • An automated liquid‑handling workflow that sets up and runs multi‑factor experiments varying glycan background, inflammatory stimulus, and microbial or ligand exposure.

The aim for this course project is not a full mechanistic explanation, but a robot‑friendly experimental scaffold that could, in principle, be scaled to test whether “type A‑like” contexts behave systematically differently from “type O/B‑like” contexts.

2.1. What I will automate

For the scope of the class, I would focus on plate‑based assays with three main automated modules:

  1. Automated plate setup (Opentrons OT‑2)

    • Distribute different “glycan conditions” across a 96‑well plate:
      • Rows = glycan backgrounds (e.g., mock, A‑like, B‑like, O‑like mimics or different lectin/glycopolymer coatings).
      • Columns = inflammatory or microbial stimuli (e.g., LPS analog, TNFα mimic, conditioned media).
    • Prepare master mixes for:
      • Reporter cells (or cell‑free biosensor system).
      • Media plus defined concentrations of stimuli.
    • Dispense appropriate combinations into each well according to a CSV design file (similar spirit to AssemblyTron linking design → pipetting plan).

  2. Automated time‑course perturbations

    • Use the robot to:
      • Add secondary stimuli at defined timepoints (e.g., addition of microbial supernatant after pre‑conditioning in inflammatory cues).
      • Perform serial dilutions of stimuli across the plate to get dose–response curves.

  3. Automated sampling / readout prep

    • For fluorescent reporters: set up plates with consistent volumes and controls so they can be read on a plate reader.
    • For secreted markers (e.g., simulated “cytokines” using fluorescent reporters or colorimetric substrates): aliquot supernatant into a separate plate for endpoint assays.

This pipeline mirrors the “DBTL” idea in the AssemblyTron paper: design a matrix of conditions, automatically build the experiment on the robot, then test by measuring reporter outputs.

2.2. Example automation workflow (high‑level steps)

Here is a concrete plan for a 96‑well plate experiment:

  1. Design phase

    • Create a CSV “experiment map” specifying:
      • Factor A: Glycan context (e.g., 4 levels: mock, A‑mimic, B‑mimic, O‑mimic).
      • Factor B: Inflammatory stimulus (e.g., 6 concentrations of LPS analog or TNFα mimic).
      • Factor C: Microbial cue (e.g., presence/absence of microbial supernatant or defined ligand).
    • Encode which wells are controls: no cells, no stimulus, glycan only, stimulus only.

  2. Robot setup

    • Deck layout:
      • Slot 1: 96‑well assay plate (flat‑bottom).
      • Slot 2: Reservoir with media and reporter cell suspension (or CFPS mix if using a cell‑free biosensor).
      • Slot 3: 96‑well “stimulus source” plate with concentrated stocks of inflammatory agents and microbial components.
      • Slots 4–5: Tip racks for P20 and P300 single/multi‑channel pipettes.
      • Optional: Temperature module holding cells at 37 °C or 30 °C depending on chassis.

  3. Automated protocol

    • Step 1: Seed reporter cells
      • Robot mixes cell suspension and dispenses a fixed volume (e.g., 50–100 µL) into each experimental well.
    • Step 2: Apply glycan context
      • Option A (simple): Pre‑coat wells manually with glycopolymers or lectins; robot only has to track which wells are which.
      • Option B (more advanced): Robot dispenses defined concentrations of soluble glycoconjugates or lectins to appropriate wells.
    • Step 3: Add inflammatory stimuli
      • Robot performs serial dilutions from stimulus stock plate into media to generate a gradient.
      • Dispenses the correct volume to each well according to the design map.
    • Step 4: Incubation
      • Plate incubated off‑deck (incubator).
    • Step 5: Secondary perturbation (if included)
      • Plate returned to deck; robot adds microbial supernatant or additional ligands to specified wells.
    • Step 6: Sampling / preparation for readout
      • For fluorescence: robot mixes wells, optionally transfers aliquots to a clean plate for reading, and adds stop buffer if needed.
      • For colorimetric assays: robot dispenses substrate and halts reactions after defined times.

  4. Readout

    • Plate reader measures fluorescence or absorbance corresponding to biosensor activation (e.g., NF‑κB reporter, general stress reporter).
    • Data analysis (offline): compare response curves between glycan backgrounds to see whether “A‑like” context shifts sensitivity or maximum response to inflammatory/microbial cues.

2.3. Example pseudocode / Python sketch (Opentrons‑style)

This is illustrative pseudocode in a Python‑like style for an Opentrons OT‑2 protocol:

metadata = {
    "protocolName": "ABO glycan–inflammation screen",
    "author": "Your Name",
    "apiLevel": "2.15"
}

def run(protocol):
    # Load labware
    plate = protocol.load_labware("corning_96_wellplate_360ul_flat", "1")
    stimulus_plate = protocol.load_labware("nest_96_wellplate_200ul_flat", "3")
    reservoir = protocol.load_labware("nest_12_reservoir_15ml", "2")
    tiprack_p300 = protocol.load_labware("opentrons_96_tiprack_300ul", "4")
    tiprack_p20 = protocol.load_labware("opentrons_96_tiprack_20ul", "5")

    # Load instruments
    p300 = protocol.load_instrument("p300_multi", "left", tip_racks=[tiprack_p300])
    p20 = protocol.load_instrument("p20_multi", "right", tip_racks=[tiprack_p20])

    # Reagents in reservoir
    cells = reservoir.wells()[0]      # reporter cell suspension
    media = reservoir.wells() [academic.oup](https://academic.oup.com/synbio/article/8/1/ysac032/6956284?searchresult=1)      # base media

    # Simple map for glycan contexts and stimulus columns
    glycan_rows = {
        "A_mimic": ["A", "B"],
        "B_mimic": ["C", "D"],
        "O_mimic": ["E", "F"],
        "mock":    ["G", "H"]
    }

    # Step 1: seed cells in all wells
    p300.pick_up_tip()
    for col in range(1, 13):  # columns 1–12
        dest = plate.columns()[col - 1]
        p300.transfer(80, cells, dest, new_tip="never", mix_after=(3, 80))
    p300.drop_tip()

    # Step 2: add stimuli (example: gradient from stimulus_plate row A)
    stimulus_source_row = stimulus_plate.rows_by_name()["A"]
    for idx, col in enumerate(plate.columns()):
        p20.pick_up_tip()
        # transfer from corresponding source well in stimulus plate
        p20.transfer(20, stimulus_source_row[idx], col, new_tip="never")
        p20.drop_tip()

    # (Optional) Step 3: add secondary microbial cue at later time point
    # protocol.pause("Incubate plate, then return to deck to resume.")
    # ...additions go here...

    # End: user moves plate to reader for fluorescence measurement

In a more complete version, the layout and volumes would be read from a CSV (like AssemblyTron reads design files), allowing you to change the entire experimental design without rewriting the protocol.

2.4. Possible 3D‑printed holders / hardware

To better mimic gastrointestinal contexts and make the automation physically robust, I could incorporate simple 3D‑printed pieces:

  • Custom plate lid / insert that:
    • Holds gas‑permeable membranes or films coated with different glycan patterns above cell layers.
    • Keeps multiple inserts aligned so the OT‑2 can still accurately access wells.
  • Fabricated “gut chip” carriers that fit into a standard plate footprint:
    • Thin channels or membranes printed into a carrier that snaps into a 96‑well frame, allowing the robot to seed cells on one side and add glycan/microbial stimuli on the other.

These holders would be designed to preserve compatibility with standard SBS plate dimensions, so the robot’s calibration remains valid.

2.5. Possible use of a cloud lab (e.g., Ginkgo Nebula)

If access to a cloud automation platform such as Ginkgo Nebula is available, an extended version of the project could:

  • Use the local Opentrons workflow to prototype the condition matrix and reporter constructs (small panel).
  • Upload the best‑performing biosensor designs and condition matrix to the cloud system to:
    • Scale the screen to many more glycan contexts and pathogen‑related ligands.
    • Incorporate robotics like:
      • Acoustic droplet transfer to miniaturize reaction volumes.
      • Automated incubation and kinetic plate reading.
  • Use the returned data to refine hypotheses about how “A‑like” glycans modulate inflammatory or infection‑related responses.

For this class assignment, the concrete deliverable is the Opentrons protocol plus experimental design, but the architecture is chosen so it could later be ported to a higher‑throughput cloud system.

2.6. What is “novel” about this automation

  • It uses automation not just to speed up a routine assay, but to systematically explore a multi‑factor space: glycan background × inflammatory state × microbial cues.
  • It is explicitly designed to test whether statistical associations between blood type and GI disease risk have plausible biological correlates in controlled, engineered systems.
  • The workflow is modular: swapping in different glycan mimics or reporter circuits does not require changing the overall automated structure—only the design file and a few reagent definitions.