Cell-Free Dot Blot as a Practical and Adaptable Immunoassay Platform for the Detection of Antibody Response in Human and Animal Sera
Bibliographic record
Abstract
The string of global pathogenic outbreaks over the past two decades has highlighted the importance of serosurveillance strategies. Immunoassay platforms that serve to detect disease-specific antibodies in patients' sera are at the core of serosurveillance. Common examples include enzyme-linked immunosorbent assays and lateral flow assays; however, while these are gold standard methods, they require pathogen-specific consumables and specialized equipment, which limits their use outside of well-resourced laboratories. We have recently developed a novel immunoassay platform called Cell-Free Dot-Blot (CFDB) and validated it using human and animal sera against SARS-CoV-2. Unlike conventional immunoassays, CFDB patient serum samples are immobilized to a solid phase (nitrocellulose membrane), while the target antigen is suspended in the mobile phase of the assay. To improve access to serosurveillance capabilities, CFDB antigens are produced on demand and with low-burden infrastructure using in vitro protein expression. Here, the antigen is fused with a peptide tag that can be detected using a single universal reporter protein for any CFDB assay. The result is that the CFDB does not require access to a multi-well plate reader or purified commercial molecular assay components. With these design considerations, CFDB addresses the shortcomings of existing immunoassay platforms by providing accessibility to non-centralized laboratories, adaptability for emerging pathogens, and affordability for lower-income communities. In the current article, we will provide a step-by-step protocol to prepare and perform a CFDB immunoassay. Using our recent work on SARS-CoV-2 CFDB as an example, we will cover antigen DNA design for on-demand cell-free production, followed by preparation of the CFDB reporter protein, immobilization of serum samples on the solid phase, and finally, antigen-binding and detection steps of the assay. We anticipate that by following these instructions, researchers will be able to adapt the CFDB assay to detect immune responses in human and animal sera to any given pathogen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".