Comprehensive Profiling of Micrornas in Murine Hematopoietic Stem Cells and Lineages Using a Microfluidics Approach
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».