Microdissection of Gonadal Tissues for Gene Expression Analyses
Bibliographic record
Abstract
Laser microdissection permits isolation of specific cell types from tissue sections or cell cultures. This may be beneficial when investigating the role of specific cells in a complex tissue or organ. In tissues with easily distinguishable morphology, a simple hematoxylin staining is sufficient, but in most cases a more specific staining is required to identify which cells to microdissect. We have established two staining protocols for frozen sections (1) Oil red O, which stains lipid droplet in fat cells and steroid-producing cells and (2) NBT BCIP, which stains cells expressing an alkaline phosphatase enzyme, such as fetal germ cells, testicular carcinoma in situ cells, and putatively also other early stem cell populations. We have applied these protocols for microdissection of rat Leydig cells, fetal human and zebrafish germ cells, and human testicular germ cell tumors, but the staining protocols could also be used in other species and for other cell types containing lipid droplets or expressing alkaline phosphatase. Both protocols ensure a morphology that enables microdissection of single cells with RNA quality sufficient for subsequent gene expression analysis. However, RNA yields after microdissection and purification are small, and therefore, two rounds of linear amplification are recommended prior to gene expression analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".