Comprehensive microRNA expression profiling of the hematopoietic hierarchy
Bibliographic record
Abstract
The hematopoietic system produces a large number of highly specialized cell types that are derived through a hierarchical differentiation process from a common stem cell population. miRNAs are critical players in orchestrating this differentiation. Here, we report the development and application of a high-throughput microfluidic real-time quantitative PCR (RT-qPCR) approach for generating global miRNA profiles for 27 phenotypically distinct cell populations isolated from normal adult mouse hematopoietic tissues. A total of 80,000 RT-qPCR assays were used to map the landscape of miRNA expression across the hematopoietic hierarchy, including rare progenitor and stem cell populations. We show that miRNA profiles allow for the direct inference of cell lineage relations and functional similarity. Our analysis reveals a close relatedness of the miRNA expression patterns in multipotent progenitors and stem cells, followed by a major reprogramming upon restriction of differentiation potential to a single lineage. The analysis of miRNA expression in single hematopoietic cells further demonstrates that miRNA expression is very tightly regulated within highly purified populations, underscoring the potential of single-cell miRNA profiling for assessing compartment heterogeneity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".