Array Imaging Decoding for Fast Low-Noise Entity Detection
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Solution Overview
Problem
Biological assays face challenges in efficiently detecting a large number of small amounts of different biological, chemical, and physical entities due to constraints on arrays, chips, microfluidic devices, flow cells, and detection systems, including material usage, density loading, and instrumentation complexity.
Innovation Solution
The method involves subjecting arrays of biological, chemical, or physical entities to binding agents, exciting them with electromagnetic radiation, acquiring pixel information using light sensing devices, and classifying the pixel information to detect components, utilizing techniques like machine learning classifiers and integrated light sensing devices to reduce noise and crosstalk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If parallel imaging without moving parts is implemented, then scanning time is reduced, but device complexity increases
Solution Approach 1:
The imaging system is divided into multiple independent sensor elements arranged in an array, where each element captures signal from a specific spatial location simultaneously. This segmentation enables parallel imaging without moving parts, as all segments operate concurrently to capture the complete image, directly reducing scanning time while managing complexity through modular sensor design
Solution Approach 2:
The system transitions from sequential scanning in one dimension to simultaneous multi-point detection in two dimensions by using an array of sensors positioned across the sample surface. This dimensional transformation allows parallel acquisition of multiple data points at once, eliminating the time required for sequential scanning while the sensor array itself remains a static structure
2Object-affected harmful factors
If the number of components in the imaging system is reduced, then noise levels are reduced, but detection sensitivity may worsen
Solution Approach 1:
Multiple sensor elements are combined into a single integrated imaging system where signals from all sensors are processed together. This merging approach reduces noise by averaging signals across multiple detection points while maintaining high detection sensitivity through the collective capability of the sensor array to identify and locate entities with high confidence
Solution Approach 2:
Instead of using a single complex sensor, the system employs multiple simplified sensor elements that replicate the basic detection function across different locations. Each sensor element is a simple copy of the basic detection unit, which reduces noise in each individual measurement while the array as a whole maintains high sensitivity through parallel detection capability
3Productivity
If density of objects to be detected is increased, then productivity is improved, but crosstalk between neighboring object signals increases
Solution Approach 1:
Each sensor element is designed with localized detection characteristics, where the signal reception is optimized for a specific spatial region. This local quality ensures that each sensor primarily detects objects in its immediate vicinity while minimizing interference from neighboring objects, allowing high-density object detection without significant crosstalk between adjacent signals
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 reduces scanning time, noise levels, and improves resolution and detection sensitivity by enabling parallel imaging without moving parts and accurate emission signal identification.
Implementation Method 1
subjecting the array of biological, chemical, or physical entities to a plurality of binding agents, wherein each of the plurality of binding agents is configured to selectively bind to at least a portion of the array of biological, chemical, or physical entities
Implementation Method 2
exposing the array of biological, chemical, or physical entities to electromagnetic radiation sufficient to excite the array, thereby producing an emission signal of the array
Implementation Method 3
exposing the array of biological, chemical, or physical entities to electromagnetic radiation sufficient to excite the array, thereby producing an emission signal of the array
Implementation Method 4
using one or more light sensing devices, acquiring a plurality of pixel information of the emission signal of the array
Data Source
AI summary
The present disclosure provides systems and methods for detecting components of an array of biological, chemical, or physical entities. In an aspect, the present disclosure provides a method for detecting an array of biological, chemical, or physical entities, comprising: (a) using one or more light sensing devices, acquiring pixel information from sites in an array, wherein the sites comprise biological, chemical, or physical entities that produce light; (b) processing the pixel information to identify a set of regions of interest (ROIs) corresponding to the sites in the array that produce the light; (c) classifying the pixel information for the ROIs into a categorical classification from among a plurality of distinct categorical classifications, thereby producing a plurality of pixel classifications; and (d) identifying one or more components of the array of biological, chemical, or physical entities based at least in part on the plurality of pixel classifications.