Comprehensive Profiling of Micrornas in Murine Hematopoietic Stem Cells and Lineages Using a Microfluidics Approach
Bibliographic record
Abstract
Abstract MicroRNAs (miRNAs) have been shown to be developmental regulators in various organisms and tissues such as the hematopoietic system. miRNA profiling studies have been primarily performed on specific aspects of hematopoiesis like lymphocyte or red blood cell development. However, a comprehensive study including rare hematopoietic stem cell populations and various lineages has yet to be published. MiRNA expression profiling within the hematopoietic tree is challenging due to difficulties in obtaining highly purified samples of stem and progenitor cell populations as well as the high cost and labour associated with global profiling approaches. The combined requirements of high sensitivity, dynamic range and efficient throughput pose serious obstacles to the use of established methods including Northern Blot, cloning, miRNA microarrays, and deep sequencing. Real time PCR offers the requisite dynamic range and sensitivity, but is labour intensive and prohibitive in cost using conventional formats. To overcome these limitations we combined new high throughput microfluidic technologies with a 288-plex real time PCR approach to quantify miRNAs in hematopoietic stem cells and lineage positive cells. This approach allowed us the simultaneous detection of 288 miRNAs in small numbers (≤3000) of cells across multiple subpopulations of the murine hematopoietic tree. Twenty unique murine hematopoietic cell populations were isolated through current FACS sorting strategies, including hematopoietic stem cells (HSCs) based on SLAM and LSK markers, myeloid and lymphoid progenitor cells as well as mature populations of all lineages. The cells were immediately lysed after sorting and reverse transcribed using 3 pools of 96 stemloop RT-primers, followed by a PCR pre-amplification step. In order to detect individual amplified products, we used BioMark™ 48.48 Dynamic Arrays (Fluidigm Corp, San Francisco, USA) and miRNA specific TaqMan probes. For each miRNA, a series of synthetic miRNA dilutions was used as a standard to determine the absolute number of miRNA molecules per cell. This analysis further revealed systematic and miRNA-specific variations in the sensitivities of Taqman assays, highlighting that RT-PCR analysis without the inclusion of an absolute standard may be misrepresentative of the true molecular abundance. Hierarchical clustering analysis and comparison between hematopoietic stem cell (HSC) populations and mature populations revealed miRNAs that are critical for hematopoietic development and maturation. In general, miRNAs detected at the highest abundance were miR-706 and miR-720, which is likely due to highly reactive Taqman assays for these targets. Of the tested 288 miRNAs, only 133 were detected across all cell populations. Most of these miRNAs exhibited a mixed expression profile, with expression peaks in the differentiated populations. Consistent with previous results, we detected a strong increase of miR-223 within myeloid differentiated populations. The highest levels were detected in neutrophils and monocytes, but surprisingly low levels were found in mast cells, supporting the specific role of miR-223 in myeloid differentiation. Other miRNAs highly enriched in differentiated cells were miR-142 and miR-16. Clustering revealed a distinct miRNA expression pattern for the profiled HSC populations including miRNAs located in the Hox cluster and the miR-181 family. In conclusion, we applied a novel technical approach to quantify a broad range of miRNAs in rare cell populations. With this approach we can further define the expression patterns of miRNAs from hematopoietic stem cells to mature lineages.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".