Simple Elimination of Background Fluorescence in Formalin-Fixed Human Brain Tissue for Immunofluorescence Microscopy
Bibliographic record
Abstract
Immunofluorescence is a common method used to visualize subcellular compartments and to determine the localization of specific proteins within a tissue sample. A great hindrance to the acquisition of high quality immunofluorescence images is endogenous autofluorescence of the tissue caused by aging pigments such as lipofuscin or by common sample preparation processes such as aldehyde fixation. This protocol describes how background fluorescence can be greatly reduced through photobleaching using white phosphor light emitting diode (LED) arrays prior to treatment with fluorescent probes. The broad-spectrum emission of white phosphor LEDs allow for bleaching of fluorophores across a range of emission peaks. The photobleaching apparatus can be constructed from off-the-shelf components at very low cost and offers an accessible alternative to commercially available chemical quenchers. A photobleaching pre-treatment of the tissue followed by conventional immunofluorescence staining generates images free of background autofluorescence. Compared to established chemical quenchers which reduced probe as well as background signals, photobleaching treatment had no effect on probe fluorescence intensity while it effectively reduced background and lipofuscin fluorescence. Although photobleaching requires more time for pre-treatment, higher intensity LED arrays may be used to reduce photobleaching time. This simple method can potentially be applied to a variety of tissues, particularly postmitotic tissues that accumulate lipofuscin such as the brain and cardiac or skeletal muscles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".