Determination of the exact copy numbers of particular mRNAs in a single cell by quantitative real-time RT-PCR
Bibliographic record
Abstract
Gene expression is differently regulated in every cell even though the cells are included in the same tissue. For this reason, we need to measure the amount of mRNAs in a single cell to understand transcription mechanism better. However, there are no accurate, rapid and appropriate methods to determine the exact copy numbers of particular mRNAs in a single cell. We therefore developed a procedure for isolating a single, identifiable cell and determining the exact copy numbers of mRNAs within it. We first isolated the cerebral giant cell of the pond snail Lymnaea stagnalis as this neuron plays a key role in the process of memory consolidation of a learned behavior brought about by associative learning of feeding behavior. We then determined the copy numbers of mRNAs for the cyclic AMP-responsive element binding proteins (CREBs). These transcription factors play an important role in memory formation across animal species. The protocol uses two techniques in concert with each other: a technique for isolating a single neuron with newly developed micromanipulators coupled to an assay of mRNAs by quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). The molecular assay determined the mRNA copy numbers, each of which was compared with a standard curve prepared from cDNA solutions corresponding to the serially diluted solutions of Lymnaea CREB mRNA. The standard curves were linear within a range of 10 to 10(5) copies, and the intra-assay variation was within 15%. Each neuron removed from the ganglia was punctured to extract the total RNA directly and was used for the assay without further purification. Using this two-step procedure, we found that the mRNA copy number of CREB repressor (CREB2) was 30-240 in a single cerebral giant cell, whereas that of CREB activator (CREB1) was below the detection limits of the assay (< 25). These results suggest that the CREB cascade is regulated by an excess amount of CREB2 in the cerebral giant cells. Our procedure is the only quantitative analysis for elucidation of the dynamics of gene transcription in a single cell.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".