Week 3 HW: Lab Automation

1. Published Article on Automation: Apta-MIP Biosensors for Water Pathogen Detection

Title: Aptamer-Molecular Imprinted Polymer Hybrid Biosensors for Pathogen Detection in Water [citation:1]

Author: Meltem Agar

Journal/Institution: Doctoral Thesis, University of Bath, 2025 [citation:1]

Summary of the Work: This thesis addresses the critical global problem of detecting waterborne pathogens quickly and cost-effectively. Traditional methods like culturing take 2-4 days and require trained personnel. To solve this, the research developed novel electrochemical biosensors that combine two powerful technologies: aptamers and Molecularly Imprinted Polymers (MIPs) [citation:1].

This hybrid “Apta-MIP” sensor was designed to detect Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) in water samples. The sensor works by using the aptamer’s high specificity and the MIP’s ability to create precise, antibody-like cavities for the bacteria. This dual-recognition system makes the detection incredibly sensitive and selective [citation:1][citation:4][citation:9].

The performance was remarkable. For E. coli, the sensor achieved a detection limit as low as 2 CFU/mL in buffer solution and 3.5 CFU/mL in real tap water [citation:1]. It also successfully detected both bacteria simultaneously in a single test, making it a powerful tool for water quality monitoring [citation:1][citation:9].

Why Automation Was Essential (or could be): While the sensor itself is the novel achievement, the development and validation process highlight the need for automation. To move this technology from the lab to a real-world, point-of-care device, automation is key for several reasons:

  • High-Throughput Screening: Testing the sensor’s performance against a wide range of bacterial concentrations and in different water matrices (tap water, river water, etc.) requires processing hundreds of samples. A liquid handler like the Opentrons OT-2 could automate the preparation of these samples and the application of bacteria to the sensor arrays [citation:1].
  • Reproducibility: Automating the electrochemical measurements and the sensor fabrication process would ensure every sensor performs identically, a critical factor for regulatory approval and reliable commercial production.

This study is a perfect example of a “new biological application” enabled by advanced materials, and it represents an ideal candidate for future automation to make it a deployable, high-throughput diagnostic tool [citation:1][citation:9].

2. Proyecto de arte biológico con Opentrons

Diseño: Rana bioluminiscente

Creé un diseño artístico que representa una rana bioluminiscente utilizando dos colores:

  • Verde: Cuerpo de la rana (usando ~200 puntos de coordenadas)
  • Rojo: Ojos, lengua y detalles bioluminiscentes (~60 puntos)

El script de Python controla el Opentrons OT-2 para dispensar gotas de 1μL en coordenadas específicas (x, y) sobre una placa de agar, creando una imagen que simula la detección colorimétrica de bacterias.

https://colab.research.google.com/drive/1Z-UmyG9dBfAS33tIwYKGWMPEat7lgpKv#scrollTo=_CrnFLU2Zf7k

Also, you can see the result here:

featured-rana-design.png

3. Final Project Idea: Automated Colorimetric UTI Screening (U-ColorTest)

For my final project, I plan to design an automated workflow for the rapid, culture-free detection of E. coli in urine samples, inspired by the principles of the Apta-MIP sensor but adapted for a simpler, colorimetric readout.

Concept

The goal is to eliminate the 24-48 hour culture step for urinary tract infection (UTI) diagnosis. The system will detect E. coli directly in a urine sample using a colorimetric reaction. The intensity of the color will be proportional to the bacterial load (CFU/mL), providing a same-day result.

Why Automation?

Manual pipetting of 96-well plates with patient samples is tedious, error-prone, and a contamination risk. The Opentrons OT-2 will provide the precision, sterility, and throughput needed for a clinical lab setting.

Automated Workflow with Opentrons OT-2

The protocol will be divided into three main stages:

  1. Sample Preparation:

    • The robot receives a 2 mL tube of urine (loaded in a custom 3D-printed tube rack).
    • It pipettes 1 mL of urine into a deep-well plate.
    • Pause: The user places the plate in a centrifuge.
    • The robot then resuspends the bacterial pellet in 100 μL of a reaction buffer.

  2. Colorimetric Reaction:

    • The robot transfers 50 μL of the concentrated sample to a 96-well assay plate.
    • It adds 50 μL of a chromogenic substrate (e.g., a substrate for the enzyme β-glucuronidase, which is specific to E. coli).
    • The robot mixes the well contents.
    • The plate is then transferred to an on-deck thermal module for a 30-minute incubation at 37°C.

  3. Detection and Interpretation:

    • After incubation, the plate is moved to a plate reader (or a simple camera setup within the robot’s deck).
    • The robot triggers the reader to measure absorbance at a specific wavelength (e.g., 405nm).
    • A Python script analyzes the data, comparing the optical density to a standard curve to estimate the CFU/mL.
    • Results are compiled into a report (e.g., “Negative,” “Low-Grade Infection,” “Positive UTI”).

Pseudocode Example

# Pseudocode for the U-ColorTest workflow on the OT-2

DEFINE PLATE_96, TIP_RACK, TUBE_RACK, RESERVOIR

LOAD pipette
LOAD thermal_module with assay_plate

# Sample Preparation
FOR EACH sample_tube IN TUBE_RACK:
    pipette.pick_up_tip()
    pipette.transfer(1000, sample_tube, deepwell_plate)  # Transfer urine
    pipette.drop_tip()

PROTOCOL.pause("Please centrifuge the deepwell plate, then place it back.")

FOR EACH sample_well IN deepwell_plate:
    pipette.pick_up_tip()
    pipette.transfer(50, sample_well, assay_plate)        # Transfer concentrated bacteria
    pipette.transfer(50, RESERVOIR['substrate'], assay_plate) # Add colorimetric substrate
    pipette.mix(3, 50, assay_plate)                      # Mix the reaction
    pipette.drop_tip()

# Incubation
thermal_module.set_temperature(37)
PROTOCOL.delay(minutes=30)

# Detection
trigger_plate_reader(assay_plate, wavelength=405)
results = get_data_from_plate_reader()

# Report Generation
FOR EACH result IN results:
    IF result.OD < 0.2:
        REPORT "Sample Negative"
    ELIF result.OD < 0.5:
        REPORT "Low Bacteria Count"
    ELSE:
        REPORT "POSITIVE for UTI"