Crosstalk Correction in Biosensor Arrays Using Sharpening Kernels
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Solution Overview
Problem
Conventional optical systems for biological or chemical analysis face challenges in managing crosstalk, especially as the density of analytes increases, leading to unwanted light emissions from adjacent analytes.
Innovation Solution
A method involving light sensors arranged in a two-dimensional pattern, where each sensor captures illumination values and applies sharpening kernels based on a generative function determined by point spread functions (PSFs) to correct for crosstalk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the density of analytes is increased to improve productivity, then the number of detectable reactions increases, but crosstalk between adjacent analytes increases leading to reduced measurement precision
Solution Approach 1:
A generative function is introduced as an intermediary mathematical model that describes the relationship between illumination at reaction sites and detected photons at light sensors. This model includes point spread functions as intermediary components that characterize the optical response. By using this intermediary model, the system can accurately predict and compensate for crosstalk effects without requiring physical separation of analytes, thus maintaining high density while preserving measurement precision.
Solution Approach 2:
The system employs iterative deconvolution algorithms that use feedback from the generative function to progressively refine the measurement. The algorithm continuously adjusts the estimated illumination values at reaction sites based on the detected photon counts and the optical response model, effectively feedback-correcting for crosstalk. This feedback mechanism enables accurate measurement even at high analyte densities where crosstalk would otherwise be problematic.
2Measurement precision
If conventional optical systems with lenses and filters are used to improve measurement precision, then detection accuracy improves, but device complexity and benchtop footprint increase
Solution Approach 1:
The patent replaces conventional mechanical optical components (lenses, filters, beam splitters) with a direct solid-state imager detection system. Instead of using complex optical paths to separate and direct light, the system uses a simplified imager that captures light directly at multiple positions simultaneously. The optical separation function is substituted by computational deconvolution algorithms that mathematically separate overlapping signals. This substitution dramatically reduces mechanical complexity while maintaining or improving measurement precision.
Solution Approach 2:
The system creates a digital copy of the optical response characteristics through the generative function and point spread functions. Rather than physically separating light paths using optical components, the system captures the complete optical response pattern and uses computational methods to reconstruct the original signals. This digital copying approach replaces physical optical separation, reducing device complexity while preserving measurement accuracy.
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
The method effectively compensates for crosstalk, improving the accuracy of optical analysis by providing crosstalk-corrected illumination values, thereby enhancing the detection of chemical reactions.
Implementation Method 1
capturing, at each of a set of light sensors, a captured illumination value representing photons detected by that light sensor
Implementation Method 2
an optical system is used to direct an excitation light onto fluorescently-labeled analytes and to also detect the fluorescent signals that may emit from the analytes
Data Source
AI summary
Biosensor including an array of reaction sites and corresponding light sensors may experience crosstalk in which photons from one reaction site are detected by neighbors of its corresponding light sensor, and such crosstalk may be corrected using sharpening kernels corresponding to the sensors in the array. Such sharpening kernels may be derived from generative matrices, which themselves may be derived from point spread functions representing dispersion of illumination emitted from the reaction sites.