A Novel Method for Multiple Labeling Combining In Situ Hybridization With Immunofluorescence
Bibliographic record
Abstract
In situ hybridization (ISH) is a particularly useful method to investigate de novo mRNA expression in tissue sections. High specificity and sensitivity of this technique combined with the great preservation of tissue and cellular morphology conferred by fixatives such as 4% paraformaldehyde, pH 9.5, make ISH a tool of choice for detecting genes of interest in individual cells in the central nervous system (CNS). Here we describe a novel method that combines radioactive ISH with immunofluorescence on the same tissue section to identify cell populations expressing selected mRNA transcripts. This novel method has several major advantages over previously described double-labeling light microscopic methods combining enzymatic immunohistochemistry and ISH including (1) complete protection against loss of hybridization signal that normally occurs during the immunoenzymatic reaction, (2) improved immunolabeling sensitivity due to the proteinase K digestion step during ISH, (3) detection of several proteins specific for different cell populations on the same tissue section, and (4) counterstaining of tissue sections without affecting visualization of immunolabeling. This new method will be particularly useful for investigators looking to identify cell populations producing mRNAs expressed in low abundance such as cytokines, chemokines, and growth factors in the intact and/or injured mammalian CNS.
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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 | 0.000 | 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".