Stochastic Molecular Tagging for Digital Molecule Counting
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Solution Overview
Problem
Current methods for measuring the absolute number of biological molecules, such as gene copy number or expression levels, face challenges in accuracy and specificity, especially when dealing with low numbers of molecules in a background of many other species, as they often rely on analog signals and require extensive sequencing to distinguish specific from non-specific sequences.
Innovation Solution
The method involves stochastic labeling of molecules with diverse label-tags, allowing each molecule to randomly choose from a non-depleting reservoir, creating unique identities based on the statistics of random choice, which enables high-sensitivity digital counting of individual molecules by transforming the problem into a series of yes/no digital questions, facilitating precise relative and absolute counting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analog methods such as microarray hybridization are used to measure gene expression levels, then the measurement can be performed, but the accuracy and precision of counting are limited due to variability in probe hybridization and cross-reactivity
Solution Approach 1:
The patent replaces the mechanical/chemical hybridization-based analog measurement system with a digital counting system based on stochastic molecular tagging. Instead of relying on probe hybridization signals, the invention uses random assignment of unique molecular tags to individual target molecules, followed by digital counting of tag sequences. This substitution eliminates hybridization variability and enables precise digital quantification of gene expression levels.
Solution Approach 2:
The invention changes the measurement parameter from continuous analog hybridization signal intensity to discrete digital tag counts. By converting the measurement from an analog parameter (hybridization signal strength) to a digital parameter (number of unique tags detected), the system achieves higher precision and eliminates the variability inherent in analog measurements.
2Measurement precision
If digital counting methods with stochastic labeling are used, then the accuracy of molecule counting is improved, but the device complexity and process complexity increase
Solution Approach 1:
The patent segments the measurement process into distinct modular steps: (1) stochastic tagging of target molecules with unique molecular identifiers, (2) amplification of tagged molecules, (3) sequencing or detection of tag sequences, and (4) digital counting of unique tags. This segmentation allows each step to be optimized independently and simplifies the overall system architecture despite the increased precision requirements.
Solution Approach 2:
The invention introduces molecular tags as intermediary elements that mediate between the target molecules and the detection system. These tags serve as surrogates that carry unique identifiers, allowing the complex task of directly measuring and distinguishing individual target molecules to be simplified into the task of counting and identifying tag sequences, which can be performed using standard sequencing or detection technologies.
3Measurement precision
If extensive sequencing is performed to distinguish specific from non-specific sequences, then the specificity of measurement is improved, but the loss of time and productivity decrease
Solution Approach 1:
The patent performs preliminary stochastic tagging of target molecules before amplification and detection. By assigning unique molecular tags to individual target molecules in advance, the system enables specific identification of target sequences during subsequent sequencing or detection steps. This preliminary action reduces the need for extensive sequencing to distinguish specific from non-specific sequences, as the tags provide built-in specificity markers.
Solution Approach 2:
The invention extracts and amplifies only the tag sequences from the full target molecule sequences. Instead of sequencing entire target sequences to achieve specificity, the method isolates and counts only the shorter tag regions that contain the unique identifiers. This extraction approach maintains high specificity while significantly reducing the sequencing burden and time required.
Data Source
AI summary
Compositions, methods and kits are disclosed for high-sensitivity counting of individual molecules by stochastic labeling of a identical molecules in mixtures of molecules by attachment of a unique label-tags from a diverse pool of label tags to confer uniqueness to otherwise identical or indistinguishable events. Individual occurrences of target molecules randomly choose from a non-depleting reservoir of diverse label-tags. Labeled molecules may be detected by hybridization or sequencing based methods. Molecules that would otherwise be identical in information content are labeled to create a separately detectable product that can be distinctly detected. The disclosed stochastic transformation methods reduce the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed label-tags are present. The methods may be used, for example, to count a given species of molecule within a sample.