Simultaneous imaging of microRNA or mRNA territories with protein territory in mammalian cells at single cell resolution
Bibliographic record
Abstract
Cellular mechanisms that inhibit mRNA translation by regulatory molecules involving microRNAs (miRNAs), a class of noncoding RNAs (ncRNAs), are well recognized in recent days. However, methodologies that measure these changes in cell populations lack the capabilities to observe such effects at single cell resolution. This is mostly due to the low level of transcript abundance and the heterogeneity of cell populations, together with the inability to measure transcripts and proteins at the same time. Here, we combine an in situ TaqMan PCR method with immunostaining so as to amplify low abundance transcripts in cellular compartments and image these efficiently at single cell resolution. The method offers flexibility to end-users for further fine-tuning of this optimized protocol based on the number of PCR cycles for individual genes in any cell type. After immunostaining, confocal microscopy is performed to detect the fluorescence of TaqMan probes (representing amplified transcripts/miRNA) and fluorophores tagged to antibodies (representing proteins) simultaneously. The presented technique offers an important tool to understand functional genomics as well as molecular mechanism of transcriptional and translational regulation so as to map these at single cell resolution.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".