Linear Fiducials for Scan Path Deviation Detection
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Solution Overview
Problem
Current sequencing systems face challenges in detecting and compensating for deviations from linear movement during scanning, which can lead to inaccurate sample cluster localization and deteriorated sequencing data, especially with higher density flow cells requiring precision down to 40 nm or better.
Innovation Solution
The implementation of a patterned flow cell with a substrate featuring a periodic arrangement of sample sites and linear fiducials, where fiducials comprise sample sites and 'blank' regions, allowing for real-time detection and correction of deviations from a linear scan path using one-dimensional Fourier transforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional readout approaches are used for scanning, then the scanning operation can be performed, but deviations from linear movement occur that affect localization accuracy
Solution Approach 1:
The patent introduces linear fiducials as intermediary reference features embedded in the flow cell that mediate between the scanning system and the sample clusters. These fiducials serve as a stable reference framework that allows the system to detect and correct for deviations in the scan path, thereby maintaining localization accuracy despite mechanical imperfections in the scanning motion.
Solution Approach 2:
The patent implements a feedback mechanism where the positions of linear fiducials are continuously monitored during scanning, and the detected deviations from the expected linear positions are used to calculate correction factors. These correction factors are then applied to the sample cluster localization calculations, creating a closed-loop system that compensates for scan path errors in real-time.
2Productivity
If higher density flow cells are used to increase throughput, then more samples can be processed, but the required localization accuracy becomes more difficult to achieve
Solution Approach 1:
The patent replaces reliance on purely mechanical precision of the scanning system with an optical/computational solution. Instead of depending on the mechanical scanner to maintain perfect linearity, the system uses optical detection of linear fiducials combined with computational correction methods to achieve the required localization precision, thereby enabling higher density flow cells to be used effectively.
Solution Approach 2:
The patent addresses the localization problem by introducing an additional dimensional reference framework. The linear fiducials create a reference grid that spans multiple dimensions, allowing the system to detect and correct errors in both horizontal and vertical scan directions simultaneously, thereby maintaining accuracy across the entire high-density flow cell surface.
3Measurement precision
If linear fiducials are implemented to detect deviations, then localization accuracy improves, but the device complexity increases
Solution Approach 1:
The patent designs the linear fiducials to serve multiple functions: they act as structural elements of the flow cell, provide reference markers for scan path detection, and serve as calibration features for the imaging system. This multi-functionality reduces the need for separate dedicated reference features, thereby limiting the increase in device complexity while still achieving improved measurement precision.
Solution Approach 2:
The patent optimizes the physical parameters of the linear fiducials (such as their spacing, size, and optical contrast) to achieve maximum detection accuracy with minimal structural complexity. By carefully selecting these parameters, the system achieves high precision scan path detection without requiring excessive fiducial features or complex flow cell structures.
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 enables accurate detection and correction of transverse movements, ensuring precise sample cluster localization and improved sequencing data quality, even at high densities, by utilizing linear fiducials to enhance the resolution and accuracy of the scanning process.
Implementation Method 1
a linear fiducial may be said to comprise a pattern of light and dark areas 304, 306, 308, 310
Implementation Method 2
deviations from a linear scan path can be detected using one or more linear fiducials and one-dimensional (1-D) Fourier transforms
Data Source
AI summary
The present approach relates generally to image-based approaches for detecting deviations from a linear movement when scanning a surface. More particularly, the approach relates to the use of linear fiducials to detect, in real-time, deviations from a linear scan path during operation of a scanning imaging system. Such linear fiducials may include both sample sites and blank regions or sites or, in certain embodiments, may utilize elongated sample sites (e.g., linear features) within the linear fiducial.