Digital Multiplex Analysis for Ambiguous PCR Partition Signals
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Solution Overview
Problem
Digital PCR methods face challenges in accurately quantifying multiple nucleic acid targets due to inconsistencies in sample preparation and limitations of sample types, particularly when partitions contain more than one target, leading to ambiguous signals and reduced sensitivity.
Innovation Solution
The method involves partitioning a sample containing multiple nucleic acid targets into partitions, generating signals from oligonucleotide probes, and quantifying targets without determining the exact number of partitions with multiple targets, using statistical models to calculate target concentrations based on signal probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital PCR methods are used to detect multiple nucleic acid targets, then the sensitivity and selectivity are improved, but the accuracy of quantification deteriorates when partitions contain more than one target
Solution Approach 1:
The method segments the detection process by analyzing signal intensities from individual partitions without requiring classification of partitions containing multiple targets. Each partition's signal intensity is measured and used in statistical calculations to determine target concentrations, avoiding the ambiguity of classifying multi-target partitions.
Solution Approach 2:
Statistical models serve as an intermediary between the raw signal intensities and the final quantification results. These models calculate target concentrations based on the distribution of signal intensities across all partitions, mediating the transition from ambiguous individual partition signals to accurate overall quantification.
2Productivity
If partitions containing multiple targets are excluded from analysis, then the quantification accuracy is improved, but the throughput and productivity are reduced
Solution Approach 1:
The method changes the analytical parameter from binary classification (positive/negative) to continuous signal intensity measurement. By analyzing the actual signal intensities rather than excluding partitions based on classification, the method maintains high throughput while improving quantification accuracy through statistical analysis of the full signal distribution.
3Ease of manufacture
If sample preparation is optimized to reduce partitions with multiple targets, then the quantification accuracy is improved, but the complexity of sample preparation increases
Solution Approach 1:
The statistical analysis method performs self-correction by automatically accounting for partitions containing multiple targets through mathematical modeling. The system uses the distribution of signal intensities across all partitions to calculate accurate target concentrations without requiring manual optimization or complex sample preparation protocols.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for high sensitivity and accurate quantification of multiple nucleic acid targets in a sample, even when partitions contain ambiguous signals, enhancing the reliability and throughput of digital PCR assays.
Implementation Method 1
a first plurality of oligonucleotide probes, which hybridizes to the first nucleic acid target, and a second plurality of oligonucleotide probes, which hybridizes to the second nucleic acid target
Implementation Method 2
The plurality of signals may be detectable in one or more color channels
Data Source
AI summary
The present disclosure provides methods, systems, and compositions for the multiplexed detection and quantification of multiple analytes from a sample. Analytes may be nucleic acid analytes. Detection of analytes may comprise contacting one or more sample subsets with hybridization probes to generate cumulative signal measurements. The methods may comprise digital PCR or may comprise partitioning a sample into multiple partitions.