Profiling miRNAs involved in Human Oligodendrocyte Precursor Cell Differentiation and Maturation
Barzegar, M.; Dhukhwa, A.; Patel, V. N.; Velasquez, F. C.; Das, S.; Patil, A. H.; Halushka, M. K.; Zack, D.; Chamling, X.
Show abstract
MicroRNAs (miRNAs) are evolutionarily conserved post-transcriptional regulators that play critical roles in cellular development and differentiation across species. Although the importance of miRNAs in oligodendrocyte lineage cell (OLLC) differentiation has been extensively studied in rodent models, their roles in human OL development remain less understood. To address this gap, we used a human embryonic stem cell (hESC) reporter system designed to study human OLs and OL progenitor cells (OPCs). Using an optimized differentiation protocol, we used the reporter hESCs to generate and isolate well-characterized OLLCs at specific developmental stages and performed next-generation sequencing-based miRNA profiling to identify stage-specific miRNAs enriched during OL lineage specification and maturation. In addition to canonical miRNAs known to be enriched at various stages of OL development, our study identified several lesser-known miRNAs with distinct stage-specific enrichment patterns that may serve as useful molecular markers for classifying human CNS cell types in future studies. Target analysis of OPC-and OL-enriched miRNAs revealed key genes, including transcription factors ZNF488 and DLX1, cytoskeletal regulator CSNK2B, and potassium channel gene KCNJ1, along with key signaling pathways such as AKT, SMAD2/3, estrogen receptor, and insulin signaling, which regulate OPC and OL lineage function. These findings advance our understanding of the OLLC-specific miRNAs, and miRNA-mediated regulatory networks governing human OL differentiation and maturation and provide promising therapeutic targets for future studies aimed at restoring myelin integrity and improving outcomes in demyelinating diseases.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Dual SMAD inhibition and Wnt inhibition enhances the differentiation of induced pluripotent stem cells into Retinal Ganglion cells (iPSC-RGCs) 94%
- Characterization of mitochondrial health from human peripheral blood mononuclear cells to cerebral organoids derived from induced pluripotent stem cells 94%
- Derivation of macaque trophoblast stem cells 94%
Similar papers in this journal
- Single cell transcriptomics reveals correct developmental dynamics and high-quality midbrain cell types by improved hESC differentiation. 95%
- MIXL1 Activation in Endoderm Differentiation ofHuman Induced Pluripotent Stem Cells 95%
- Modulation of the JAK2-STAT3 pathway promotes expansion and maturation of human iPSCs-derived myogenic progenitor cells 94%
Similar papers in this journal
- Bovine Formative Embryonic Stem Cell Plasticity in Embryonic and Extraembryonic Differentiation 94%
- A Serum- and Feeder-Free System to Generate CD4 and Regulatory T Cells from Human iPSCs 94%
- miR-183/96/182 cluster is an important morphogenetic factor targeting PAX6 expression in differentiating human retinal organoids 93%
Similar papers in this journal
- Schwann cell plasticity regulates neuroblastic tumor cell differentiation via epidermal growth factor-like protein 8 95%
- Generation and Trapping of a Mesoderm Biased State of Human Pluripotency 94%
- Enhanced Production of Mesencephalic Dopaminergic Neurons from Lineage-Restricted Human Undifferentiated Stem Cells 94%
Similar papers in this journal
- High-Throughput Screening for Myelination Promoting Compounds Using Human Stem Cell-derived Oligodendrocyte Progenitor Cells Identifies Novel Targets 94%
- Comparing the impact of sample multiplexing approaches for single-cell RNA-sequencing on downstream analysis using cerebellar organoids 94%
- Identity and Nature of Neural Stem Cells in the Adult Human Subventricular Zone 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.