A Modified Polymerase Chain Reaction‐Long Serial Analysis of Gene Expression Protocol Identifies Novel Transcripts in Human CD34 <sup>+</sup> Bone Marrow Cells
Bibliographic record
Abstract
Transcriptome profiling offers a powerful approach to investigating developmental processes. Long serial analysis of gene expression (LongSAGE) is particularly attractive for this purpose because of its inherent quantitative features and independence of both hybridization variables and prior knowledge of transcript identity. Here, we describe the validation and initial application of a modified protocol for amplifying cDNA preparations from <10 ng of RNA (<10(3) cells) to allow representative LongSAGE libraries to be constructed from rare stem cell-enriched populations. Quantitative real-time polymerase chain reaction (Q-RT-PCR) analyses and comparison of tag frequencies in replicate LongSAGE libraries produced from amplified and nonamplified cDNA preparations demonstrated preservation of the relative levels of different transcripts originally present at widely varying levels. This PCR-LongSAGE protocol was then used to obtain a 200,000-tag library from the CD34+ subset of normal adult human bone marrow cells. Analysis of this library revealed many anticipated transcripts, as well as transcripts not previously known to be present in CD34+ hematopoietic cells. The latter included numerous novel tags that mapped to unique and conserved sites in the human genome but not previously identified as transcribed elements in human cells. Q-RT-PCR was used to demonstrate that 10 of these novel tags were expressed in cDNA pools and present in extracts of other sources of normal human CD34+ hematopoietic cells. These findings illustrate the power of LongSAGE to identify new transcripts in stem cell-enriched populations and indicate the potential of this approach to be extended to other sources of rare cells. Disclosure of potential conflicts of interest is found at the end of this article.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".