Microrna Quantification
Microrna Quantification - Related Videos
07:37
Circulating MicroRNA Quantification Using DNA-binding Dye Chemistry and Droplet Digital PCR
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Cited by 9 •
2016
A sensitive and accurate method for cell-free microRNAs quantification using a dye-based chemistry and droplet digital PCR technology is described.
01:22
MicroRNAs
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2019
MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA ends...
09:06
MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as a Novel Detection and Quantification Method
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Cited by 1 •
2025
The protocol describes a tandem immunofluorescence hybridization assay with formalin-fixed paraffin-embedded tissue to detect and quantify microRNA and multiplexed proteins simultaneously.
14:41
RNA Blot Analysis for the Detection and Quantification of Plant MicroRNAs
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Cited by 11 •
2020
This method demonstrates use of the northern hybridization technique to detect miRNAs from total RNA extract.
09:05
MicroRNA-based Regulation of Picornavirus Tropism
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Cited by 4 •
2017
We describe here a method for regulating picornavirus tropism by incorporating sequences complementary to specific microRNAs into the viral genome. This protocol can be adapted to all different classes of viruses with modifications based upon the length and nature of their life cycle.
FAQs
Reference controls provide the baseline needed to compare microRNA abundance across samples or experimental conditions. In quantitative reverse-transcription PCR and related measurements, the control helps distinguish a genuine difference from variation introduced during sample processing, complementary DNA conversion, or measurement. Consistent controls therefore strengthen comparisons of expression patterns and support more reliable genetic interpretation.
These approaches measure microRNA abundance through different analytical platforms. Quantitative reverse-transcription PCR performs measurement after complementary DNA conversion, while sequencing and hybridization-based assays provide alternative ways to assess microRNA levels. Because each method produces measurements through a different workflow, researchers should consider assay type and reference controls when comparing expression patterns.
The measurements identify changes in small regulatory RNA abundance that can be associated with post-transcriptional control of gene expression. In genetics, comparing these patterns across tissues, cells, organisms, or conditions helps reveal regulatory networks and context-dependent changes. The resulting data can therefore connect microRNA expression patterns with broader studies of gene regulation and biological state.
A useful workflow links the biological sample, microRNA isolation, complementary DNA conversion when required, measurement platform, and reference controls. Researchers should decide which assay format fits the intended comparison and then apply the same control strategy across samples. Coordinating these stages makes the resulting abundance measurements more interpretable across treatments, tissues, or experimental groups.
It is useful when researchers need to compare microRNA expression across cells, tissues, organisms, developmental stages, diseases, or environmental conditions. These comparisons can expose tissue-specific patterns and changes associated with biological states. The data also support biomarker discovery and functional studies investigating how post-transcriptional regulation contributes to genetic and cellular processes.
Depending on the study design and assay, researchers can obtain comparative abundance measurements that reveal expression patterns between samples or conditions. These results may identify microRNAs associated with development, disease, or environmental responses, and can help map regulatory networks. Appropriate reference controls are important for interpreting whether observed differences reflect meaningful biological variation.