Iterative Bleaching Extends Multiplexity (IBEX) imaging facilitates simultaneous identification of all cell types in the vertebrate retina
Bibliographic record
Abstract
ABSTRACT The vertebrate retina is a complex multicellular tissue made up of distinct neuron types and glia, arranged in a stereotypic layered organisation to facilitate vision. Understanding how these cell types come together to form precise circuits during development requires the ability to simultaneously discriminate between multiple cell types and their spatial position in the same tissue. Currently, we have a limited capacity to resolve all constitutive cell types and their relationships to one another due to our limited ability to combine multiple cellular markers. To extend this capacity, we have adapted a highly multiplexed immunohistochemistry technique known as Iterative Bleaching Extends Multiplexity (IBEX) and applied it to the development of the zebrafish ( Danio rerio) retina. IBEX allows for multiple rounds of cellular labelling to be performed, before imaging and integration of data, resulting in the ability to visualise multiple markers on the same tissue. We have optimised IBEX in zebrafish using fluorescent micro-conjugation of known antibody markers to label the complete retina with up to 11 cell-specific antibodies. We have further adapted the IBEX technique to be compatible with fluorescent transgenic reporter lines, in situ hybridisation chain reaction (HCR), and wholemount immunohistochemistry (WMIHC). We then took advantage of IBEX to explore the multicellular relationships in the developing retina between glial cells and neurons and photoreceptor subtypes. Finally, we tested IBEX on retinas from the emerging ageing model, the killifish ( Nothobranchius furzeri) , and developmental model, the African clawed frog ( Xenopus laevis), demonstrating the usefulness of the technique across multiple species. The techniques described here can be applied to any tissue in any organism where antibodies are readily available to efficiently explore cellular relationships in the context of development, ageing or disease.
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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 | 0.001 | 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 | 0.001 | 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".