We propose an advanced ELISA platform composed of three integrated innovations:
- 2.1. Organoid-Embedded Microplates
- 2.2. CRISPR-Based Signal Transduction
- 2.3. AI-Augmented Optical and Kinetic Interpretation
Each innovation is modular and compatible with existing ELISA microplate readers and automation platforms.
2.1. Organoid-Embedded Microplates
Microplates are pre-loaded with miniaturized organoid cultures (e.g., liver, gut, immune system) that simulate tissue-level responses to analytes. Organoids are embedded in hydrogel matrices with oxygen-permeable membranes, allowing long-term viability and exposure to environmental inputs.
Advantages:
- Recapitulation of human-specific physiology without animal models (NIH ORIP 3Rs)
- Detection of not only biomarker presence, but functional response (e.g., cytokine release, metabolic activity)
- Suitable for longitudinal monitoring of therapeutic response in vitro (NCBI Organoid Review)
Applications include real-time hepatotoxicity screening, gut microbiota interaction assays, and personalized drug response profiling.
2.2. CRISPR-Based Molecular Biosensors
Integration of CRISPR-Cas systems (e.g., Cas12a, Cas13) enables the ELISA to transition from antibody-based recognition to nucleic acid-guided molecular detection. CRISPR sensors can be lyophilized and embedded in microplate wells for point-of-use rehydration.
Functionality:
- Detection of specific mRNA, DNA, or viral RNA sequences with single-nucleotide specificity (NIH CRISPR Diagnostic Test)
- Enzyme activation (collateral cleavage) produces amplified signal (fluorescent, colorimetric)
- Multi-target detection with minimal cross-reactivity
CRISPR-based detection has shown utility in rapid SARS-CoV-2 assays (FDA EUA CRISPR Assays), making it ideal for expanding ELISA applications beyond protein quantification.
2.3. AI-Augmented Signal Interpretation
Traditional ELISA outputs require manual calibration curve construction and interpretation. AI-based software modules can automate and enhance interpretation using supervised models trained on kinetic absorbance data, sample history, and environmental metadata.
Capabilities:
- Curve fitting optimization and noise rejection
- Early anomaly detection across large batch runs
- Predictive diagnostics based on time-series data (NIH Bridge2AI)
- Integration with cloud-based analysis for epidemiological insights
This transforms the ELISA platform from a static endpoint reader to a continuous data acquisition and interpretation system.