Optimization of the fluorescence <i>in situ</i> hybridization (FISH) technique for high detection efficiency of very small proportions of target interphase nuclei
Bibliographic record
Abstract
Using commercially available fluorochrome-labeled probes specific for chromosomes X, Y, 13, 18, and 21, we optimized the technical protocols for fluorescence in situ hybridization (FISH) so that the highest sensitivity and specificity were achieved. Also, we compared the optical properties of different types of fluorescent labels in an effort to develop the most efficient FISH protocol, including the determination of which types of labels are the easiest to count accurately. The lymphocytes were purified from blood of normal male and female newborns, normal male and female adults, and a trisomy 21 male adult. Male and female lymphocytes were mixed in five different combinations. For each combination, the male lymphocytes either from newborns or from adults were diluted with female lymphocytes in seven different proportions. For each of these 35 different cell mixtures, 100,000 nuclei were analyzed and scored in a blind fashion. Among the different fluorochrome-labeled probes, the highest sensitivity and specificity were achieved when SpectrumAqua CEP-Y/SpectrumOrange CEP X probe mixture, SpectrumAqua CEP-18, SpectrumOrange LSI-13, and SpectrumOrange LSI-21 were hybridized. The hybridization sensitivity and specificity were higher than 99% for the identification of chromosomes X, Y, 13, and 18, and higher than 98% for the detection of trisomy 21. The proportion of false-positive signals was under 0.005% for XY detection and lower than 0.14% for autosome detection. With these high hybridization sensitivities and specificities, the optimized FISH protocol developed in our laboratory has the potential to detect very rare events, e.g., when the proportion of cells being sought is lower than 0.01%. In other words, our protocol allows the specific detection of one male cell sunken among 10,000 female cells.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".