Stochastic Molecular Labeling for Precise Single-Molecule Counting
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Solution Overview
Problem
Existing methods for counting individual molecules, particularly nucleic acids, struggle with accuracy and precision, especially when dealing with low numbers in a background of many other species, as they often provide relative abundance rather than absolute counts and are prone to noise and variability.
Innovation Solution
A method of stochastic labeling of molecules with diverse labels followed by amplification and detection, where each molecule is randomly assigned a unique identifier from a non-depleting reservoir, allowing for digital counting through a series of yes/no questions, reducing the need for statistical depth and enhancing precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional relative abundance measurement methods are used, then the measurement process is simple, but the counting precision and accuracy deteriorate due to noise and variability
Solution Approach 1:
The patent segments the measurement process into distinct stages: stochastic labeling of individual molecules with unique identifiers, amplification of labeled molecules, and digital detection. This segmentation allows each stage to be optimized independently, improving counting precision while managing overall complexity through modular design
Solution Approach 2:
The patent introduces stochastic labels as intermediary elements that attach to target molecules before amplification and detection. These labels serve as mediators that enable digital counting by providing unique identifiers for each original molecule, thereby improving measurement precision without directly complicating the detection system
2Measurement precision
If digital counting methods with stochastic labeling are implemented, then counting precision improves, but the device and process complexity increases
Solution Approach 1:
The patent performs preliminary stochastic labeling of molecules with unique identifiers before amplification and detection. This preliminary action assigns distinct identities to each target molecule, enabling accurate digital counting later while simplifying the detection process by pre-organizing the sample with identifiable markers
Solution Approach 2:
The patent uses amplification to create copies of labeled molecules, where each copy retains the original stochastic label. This copying process preserves the unique identifier information through multiple generations, allowing digital counting of original molecules based on their labeled copies without requiring direct detection of each original molecule
3Reliability
If conventional analog methods are used, then the ease of operation is maintained, but the reliability of absolute counting deteriorates
Solution Approach 1:
The patent replaces conventional analog measurement mechanisms with a digital counting system based on stochastic labeling and amplification. This substitution transitions from continuous signal measurement to discrete digital counting of labeled molecules, significantly improving reliability while the automated nature of the process maintains ease of operation
Solution Approach 2:
The patent fundamentally changes the measurement parameter from continuous analog signal intensity to discrete digital counts of uniquely labeled molecules. This parameter change from analog to digital domain improves counting reliability by eliminating analog noise and variability, while the standardized protocol maintains operational simplicity
Data Source
AI summary
Compositions, methods and kits are disclosed for high-sensitivity single molecule digital counting by the stochastic labeling of a collection of identical molecules by attachment of a diverse set of labels. Each copy of a molecule randomly chooses from a non-depleting reservoir of diverse labels. Detection may be by a variety of methods including hybridization based or sequencing. Molecules that would otherwise be identical in information content can be labeled to create a separately detectable product that is unique or approximately unique in a collection. This stochastic transformation relaxes the problem of counting molecules from one of locating and identifying identical molecules to a series of binary digital questions detecting whether preprogrammed labels are present. The methods may be used, for example, to estimate the number of separate molecules of a given type or types within a sample.