Hydrogel Stamping for Rapid, Multiplexed, Point-of-Care Immunostaining of Cells and Tissues
Bibliographic record
Abstract
In the era of precision oncology, multicolor fluorescence imaging has become a core technology for multiplexed molecular analysis of cellular and tissue specimens. However, conventional solution-based staining is labor-intensive and time-consuming and requires considerable expertise to yield optimal results, which creates difficulties for employing this technology in resource-limited settings. Here, we report a new immunostaining method based on hydrogel stamping, which is simple, fast, easy to use, and reproducible. We showed that a hydrophilic hydrogel stamp could effectively transfer fluorescent antibodies to targets and withdraw an excess solution when the reaction is completed, obviating the need for extra washing. This unique property allows for quality immunostaining in 5 min for cells using one-eighth of antibody consumption compared to the conventional solution-based method. Furthermore, we implemented fluorescence quenching and immunocycling with hydrogel staining for multiplexed analysis of 9 protein markers at a single cell level. Finally, we applied the immunocycling method to human breast cancer tissue samples and showed quality immunostaining over a large area (∼2 cm 2 ) in 30 min for molecular subtyping of breast cancer. The hydrogel immunostaining could open new opportunities for rapid, automated, and multiplexed profiling in compact point-of-care systems for molecular cancer diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".