Digital PCR Quantification via Machine Learning Image Analysis
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Solution Overview
Problem
Existing methods for quantifying target concentrations in biological samples using PCR analysis face challenges such as uncertainties and inaccuracies in analog PCR, particularly in detecting low initial concentrations of target analytes and dealing with non-exponential amplification cycles.
Innovation Solution
The use of digital PCR, which involves partitioning a biological sample into smaller test samples, allowing for individual detection and quantification of target analytes, and employing machine-learning models for image analysis and signal classification to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If analog PCR quantification is used, then the measurement process is simpler, but the measurement precision and reliability deteriorate due to uncertainties in extrapolation and non-exponential amplification
Solution Approach 1:
The biological sample is partitioned into a large number of individual partitions (e.g., droplets, wells, or chambers), each containing a small volume of the sample. This segmentation transforms the continuous analog measurement into discrete digital measurements, where each partition is independently analyzed for the presence or absence of target analytes, thereby eliminating extrapolation uncertainties and improving quantification accuracy.
2Measurement precision
If digital PCR with partitioning is used, then the measurement precision and detection capability improve, but the device complexity and processing time increase
Solution Approach 1:
A machine-learning model serves as an intermediary between the raw image data from the partitioned sample and the final quantification results. The model automatically identifies partition locations, classifies positive versus negative partitions, and handles image analysis tasks, thereby reducing the need for complex manual processing while maintaining high measurement precision.
3Reliability
If digital PCR with machine-learning models is used, then the detection of rare targets and low concentrations improves, but the extent of automation and computational requirements increase
Solution Approach 1:
The machine-learning model enables the system to perform self-service in image analysis and partition classification. The automated identification and classification of partitions based on image data reduces manual intervention, improves consistency, and enhances the reliable detection of rare targets by systematically processing large numbers of partitions without human error.
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
Digital PCR provides more precise and accurate quantification of target concentrations, enabling the detection of rare targets and improving the reliability of PCR analysis, particularly in cases of low initial concentrations and non-exponential amplification.
Implementation Method 1
PCR amplifies nucleic acids with the DNA polymerase enzyme responsible for forming new copies of DNA. Based on the theory that such amplification is exponential, a specific segment of DNA can be amplified millions or billions of times using PCR
Implementation Method 2
When the samples are thermally cycled using a PCR apparatus, the samples containing the target concentration are amplified and produce a positive detection signal
Implementation Method 3
After multiple PCR amplification cycles, the samples are imaged and analyzed for fluorescence, which is used to quantify the target concentration in the samples
Data Source
AI summary
Methods and systems are disclosed for quantifying one or more target concentrations in a biological sample using an analyte detection apparatus configured to analyze an array of partitions of the biological sample. A disclosed method comprises calculating expected locations of partitions in a representation of the array of partitions such as an image, based on corner locations of the array of partitions and analyzing images representing partitions associated with the expected locations of the partitions. The method further comprises determining observed locations of the partitions based on an analysis result of the images and quantifying the one or more target concentrations in the biological sample based on the observed locations of the partitions. These and other methods and systems are disclosed herein.