Beyond Ct Values: Why Molecular Counting Is Transforming DNA Measurement

PCR has been the workhorse of molecular biology for decades. But most PCR measurements are still analog.

Traditional qPCR does not count molecules. Instead, it infers quantity from temporal fluorescence signal curves and cycle thresholds.

This approach works well for many applications. But when measurements demand reproducibility, high precision, rare target detection, or multiplexed analysis, analog signals begin to show their limits.

A growing class of technologies now approaches PCR differently: by counting molecules directly.

The difference between analog signals, digital PCR, and molecular counting

PCR measurements can broadly be divided into three approaches: analog measurement, digital partitioning, and molecular counting.

In analog PCR methods such as qPCR, DNA quantity is inferred from fluorescence intensity and amplification curves. While widely used, this approach relies on interpreting signal strength, meaning small differences in amplification efficiency, background fluorescence, or reaction chemistry can influence the final measurement.

Digital PCR (dPCR) moves toward counting by partitioning the reaction into thousands of compartments. Molecules are estimated by counting positive partitions. However, because partitioning is statistical, some partitions contain multiple molecules while others remain empty, limiting the number of independent counting events.

Molecular counting, used in hpPCR, converts each target molecule into a rolling circle amplification product (RCP) that forms a micrometer-sized DNA microstructure. These RCPs appear as bright fluorescent spots that can be individually detected and counted by automated image analysis.

Each spot represents a single molecular event, producing an intrinsically digital readout without relying on physical compartmentalization.

This shift from analog signal interpretation to direct molecular counting has important implications for measurement precision, multiplexing, and scalability.

Why total counts determine measurement precision

In counting-based measurements, statistical uncertainty follows a simple rule.

The relative error decreases with the square root of the number of counts.

If only a small number of events are counted, statistical noise is large. As the number of counts increases, measurements become increasingly precise.

This is why counting depth matters.

In digital PCR, precision depends strongly on how many positive partitions are observed for the target of interest. For abundant targets this can be high, but for low-copy or rare targets the number of positive partitions may fall to tens or hundreds, and counting noise then becomes a major part of the measurement uncertainty.

Hyperplex PCR (hpPCR) approaches this differently.

By combining padlock probes, rolling circle amplification, and imaging-based counting, hpPCR can generate up to one million unique counts per well.

For many targets this translates to several hundreds of measurement events per molecule, dramatically increasing the statistical power of each assay.

The result is:

• Lower coefficient of variation

• More stable quantification across replicates

• Higher confidence in small differences between samples

In hpPCR, individual target molecules generate rolling circle amplification products that produce multiple detectable signals. This effectively increases the number of measurement events associated with each molecule, further improving statistical robustness compared to systems where each molecule is counted only once.

dPCR logic:

1 molecule -> 1 partition

hpPCR logic:

1 molecule -> many RCP counts

Counting depth becomes critical in multiplex assays

High-plex assays introduce an additional statistical constraint: the available counting depth must be shared across all targets measured in the reaction.

This concept is closely analogous to sequencing depth in next-generation sequencing (NGS). In sequencing, the number of reads covering a genomic position determines how precisely variants can be measured. If coverage is low, statistical noise increases and small differences become difficult to detect. The same principle applies to molecular counting assays.

When an assay measures many targets simultaneously, the total counting capacity is effectively distributed across those targets. If the total number of observable events is limited, each target receives fewer counts, increasing statistical uncertainty.

For example, consider a multiplex assay with 50 targets:

• If the system produces 20,000 total counts, each target receives on average ~400 counts. This corresponds to a statistical precision of roughly 5% CV.

• If the system produces 1,000,000 total counts, each target receives on average ~20,000 counts, improving precision to roughly 0.7% CV.

This is directly comparable to sequencing depth: 10,000 counts per target translate to roughly 10,000x sequencing depth.

Because counting events are generated from amplified rolling circle products rather than fixed physical partitions, the total counting capacity of the assay is not constrained by droplet or chamber number. This allows hpPCR to maintain substantial counting depth even as multiplex levels increase.

Why high counts matter for rare mutation detection

Rare mutation detection pushes measurement systems to their limits.

Detecting a variant present at 0.1% variant allele frequency (VAF) requires distinguishing a handful of variant molecules among thousands of wild-type molecules.

If only a small number of events are counted, the measurement becomes dominated by sampling noise.

High counting depth changes this.

By generating large numbers of measurement events, hpPCR increases the probability of observing rare variants while reducing statistical uncertainty.

This enables sensitive detection of rare alleles with high confidence, even when they represent a tiny fraction of the total DNA population.