Droplet Digital Pcr
Droplet Digital Pcr - 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.
08:39
Quantification of Circular RNAs Using Digital Droplet PCR
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Cited by 9 •
2022
This protocol describes the detailed method of digital droplet PCR (dd-PCR) for precise quantification of circular RNA (circRNA) levels in cells using divergent primers.
04:07
Droplet Digital Polymerase Chain Reaction: A Method to Generate Nanodroplet PCR Reactions for Detecting Rare Tumor Mutations
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2025
In this video, we present a step-by-step procedure to generate emulsified nano-droplets to detect target nucleic acids by digital polymerase chain reaction or dPCR.
10:16
Employing Digital Droplet PCR to Detect BRAF V600E Mutations in Formalin-fixed Paraffin-embedded Reference Standard Cell Lines
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Cited by 2 •
2015
The goal of this video is to demonstrate how to perform automated DNA extraction from formalin-fixed paraffin-embedded (FFPE) reference standard cell lines and digital droplet PCR (ddPCR) analysis to detect rare mutations in a clinical setting. Detecting mutations in FFPE samples demonstrates the clinical utility of ddPCR in FFPE samples.
08:29
Profiling Epidermal Growth Factor Receptor DNA Mutations in Circulating Rare Cells Using a Cell Isolation System and Droplet Digital PCR
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2026
Here we present a protocol to illustrate the isolation of rare cancer cells from a contrived sample containing whole blood and spiked in lung cancer cells. The protocol utilizes a semi-automated, label-free cell isolation platform. Enriched cells are subsequently analyzed for epidermal growth factor receptor mutations using droplet digital PCR.
FAQs
Partitioning the sample into thousands of nanoliter-sized droplets allows each portion to undergo PCR independently. This physical separation helps distinguish droplets containing the target from those without it, which is important when a mutation or other nucleic-acid signal occurs at low frequency. The resulting positive and negative populations support precise quantification.
Endpoint fluorescence provides a binary readout: droplets are classified as positive or negative after amplification. The distribution of these two populations is then analyzed with Poisson statistics, which accounts for target molecules being randomly distributed among droplets. This calculation converts droplet counts into an absolute target concentration rather than relying only on amplification-cycle measurements.
Greater sensitivity and precision make ddPCR particularly useful when the target is rare relative to surrounding DNA or RNA. In cancer studies, that capability can reveal low-frequency mutations and quantify copy-number changes that may be difficult to resolve with less sensitive conventional amplification assays. It therefore supports analysis of subtle molecular differences in tumor-associated samples.
Unlike many conventional amplification assays, ddPCR determines target abundance from endpoint-positive and endpoint-negative droplet counts. The sample is separated into many independent reactions before fluorescence is measured, allowing rare targets to be evaluated through their distribution across droplets. This design provides the greater precision described for cancer-related nucleic acid quantification.
An analysis begins with a nucleic-acid sample that is divided into nanoliter-sized droplets. PCR proceeds independently in each droplet, followed by endpoint fluorescence measurement. The workflow then separates positive from negative droplets and applies Poisson statistics to their distribution. These stages produce an absolute concentration for the target in the analyzed sample.
ddPCR can provide an absolute target concentration and measurements of low-frequency mutations, copy-number changes, or circulating tumor DNA in blood. These outputs allow investigators to characterize tumor-associated molecular signals and compare their presence or abundance in ways relevant to tumor profiling and treatment-response studies.
Within cancer research, the method supports tumor profiling, treatment-response monitoring, and minimal residual disease assessment. Its ability to detect scarce molecular targets makes it useful when investigators need to follow tumor-associated DNA signals that may be present at low abundance. The same measurement capability also contributes to developing more sensitive molecular diagnostics.