Protocol for antibody optimization and panel design in high-dimensional multiplexed immunofluorescence imaging
Bibliographic record
Abstract
The rise of spatial biology technologies is advancing our ability to study cellular interactions within native tissue contexts using antibody-based multiplexed imaging. Here, we present a protocol for designing and optimizing a suitable antibody panel, with a specific focus on the PhenoCycler-Fusion system. We outline steps for antibody selection, optimization, and validation, as well as reporter plate preparation and cycle generation for image acquisition. This protocol incorporates key considerations for robust panel development for spatial profiling of tissues. For complete details on the use and execution of this protocol, please refer to Abraham et al. 1 • Steps for selecting antibodies for immunofluorescence imaging of tissues • Guidance for validating antibody performance in single and multiplexed imaging formats • Recommendations for assigning barcodes based on autofluorescence and antigen abundance • Steps for reporter plate setup and cycle design for imaging with PhenoCycler-Fusion Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The rise of spatial biology technologies is advancing our ability to study cellular interactions within native tissue contexts using antibody-based multiplexed imaging. Here, we present a protocol for designing and optimizing a suitable antibody panel, with a specific focus on the PhenoCycler-Fusion system. We outline steps for antibody selection, optimization, and validation, as well as reporter plate preparation and cycle generation for image acquisition. This protocol incorporates key considerations for robust panel development for spatial profiling of tissues.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.052 |
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".