‘Iterative Bleaching Extends Multiplexity’ facilitates simultaneous identification of all major retinal cell types
Bibliographic record
Abstract
To understand the multicellular composition of tissues, and how it is altered during development, ageing and/or disease, we must visualise the complete cellular landscape. Currently, this is hindered by our limited ability to combine multiple cellular markers. To overcome this, we adapted a highly multiplexed immunofluorescence (IF) technique called 'Iterative Bleaching Extends Multiplexity' (IBEX) to the zebrafish retina. We optimised fluorescent antibody micro-conjugation to perform sequential rounds of labelling on a single tissue to simultaneously visualise all major retinal cell types with 11 cell-specific antibodies. We further adapted IBEX to be compatible with fluorescent transgenic reporter lines, in situ hybridisation chain reaction (HCR), and whole-mount immunofluorescence (WMIF). We applied IBEX at multiple stages to study the spatial and temporal relationships between glia and neurons during retinal development. Finally, we demonstrate the utility of IBEX across species by testing it on the turquoise killifish (Nothobranchius furzeri) and African clawed frog (Xenopus laevis) to glean large amounts of information from precious tissues. These techniques will revolutionise our ability to visualise multiple cell types in any organism where antibodies are readily available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".