The instrument measures fluorescence generated by target-specific probes as each droplet passes through its optical detection system. Droplets with detectable target-associated fluorescence are classified as positive, while those without the relevant signal are classified as negative. This binary classification converts many individual reactions into a structured dataset that can be analyzed quantitatively.
Poisson analysis links the number of positive and negative droplets to an estimate of absolute target concentration. Because the measurement is based on counts across partitioned reactions, the statistical calculation converts the observed distribution of target-containing and target-free droplets into quantitative results. This allows concentration estimation without relying only on a relative signal comparison.
Target-specific probes provide the fluorescence signal used to identify droplets containing the sequence of interest. Their signal gives the optical system a basis for separating positive droplets from negative ones, which is essential for accurate counting. In biological assays, this targeting supports measurements focused on particular nucleic acids rather than undifferentiated material.
Partitioning distributes the sample across many individual droplets, allowing the reader to examine a large number of separate reactions. A small number of positive droplets can therefore be distinguished within the overall count and incorporated into the concentration estimate. This supports sensitive analysis of low-abundance mutations and other uncommon nucleic-acid signals.
A typical measurement sequence begins with individually partitioned droplets entering the reader, followed by optical interrogation of each droplet and fluorescence measurement from the relevant probes. The system then assigns positive or negative classifications and applies statistical analysis to the resulting counts. The final output is a quantitative estimate of the nucleic-acid target.
This approach is useful when researchers need sensitive quantification of nucleic acids, particularly when the target may occur at low abundance. Supported applications include detecting low-abundance mutations, testing for pathogens, analyzing gene expression, and measuring copy number. These use cases benefit from converting thousands of droplet-level observations into precise quantitative data.
The results provide counts of target-positive and target-negative droplets that can be converted into an estimate of absolute nucleic-acid concentration. Depending on the assay, the same measurement framework can support mutation detection, pathogen testing, gene-expression analysis, or copy-number measurement. The resulting data help researchers assess rare signals and compare the quantity of biological targets.