Immunohistochemistry Microarrays
Bibliographic record
Abstract
Immunohistochemistry (IHC) on tissue sections is widely used for quantifying the expression patterns of proteins and is part of the standard of care for cancer diagnosis and prognosis, but is limited to staining a single protein per tissue. Tissue microarray and microfluidics staining methods have emerged as powerful high throughput techniques, but they either only permit the analysis of a single protein per slide or require complex instrumentation and expertise while only staining isolated areas. Here, we introduce IHC microarrays (IHCμA) for multiplexed staining of intact tissues with preserved histological and spatial information. Droplets of a dextran solution containing antibodies were prespotted on a slide and snapped onto a preprocessed formalin-fixed, paraffin-embedded (FFPE) tissue section soaked in a polyethylene glycol solution. The antibodies are confined within the dextran droplets and locally stain the tissue below with a contrast similar to the one obtained by conventional IHC. The microarray of antibody droplets can be prespotted on a slide and stored, thus neither the preparation of the antibody solutions nor a sophisticated microarray spotter is needed. Sampling considerations with IHCμA were evaluated by taking three tissues with varying levels of cancer cells. A multiplex IHCμA with 180 spots targeting 8 cancer proteins was performed on a breast cancer tissue section to illustrate the potential of this method. This work opens the avenue of applying microarray technologies for conducting IHC on intact tissue slices and has great potential to be used in the discovery and validation of tissue biomarkers in human tumors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.042 |
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".