In Situ Barcode Codebook Design for Optical Crowding Reduction
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Solution Overview
Problem
Optical crowding in in situ detection and analysis techniques hinders the accurate decoding and detection of barcoded target analytes due to the limits of optical resolution and density in biological samples, leading to difficulties in resolving individual fluorescently-labeled probes or spots.
Innovation Solution
Codebook design strategies are employed to minimize optical crowding by optimizing code word assignment, dilution, splitting, and gene attenuation, ensuring even distribution of ON states across decoding cycles and channels, and using decision rules to reduce the maximum predicted density of ON signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the density of fluorescing barcoded target analytes is increased to improve detection sensitivity, then the ability to detect target analytes is improved, but optical crowding increases making it difficult to resolve individual probes or spots
Solution Approach 1:
The patent segments the detection process into multiple decoding cycles, where different sets of barcode probes are contacted in each cycle. This temporal segmentation allows the same physical probes to be used multiple times without simultaneous optical crowding, as only one set of probes is active at a time. The segmentation of detection cycles resolves the contradiction by separating the high-density detection requirement from the optical resolution requirement.
Solution Approach 2:
The patent introduces a temporal dimension to the detection process by using multiple decoding cycles. Instead of detecting all target analytes simultaneously in a single image (spatial dimension only), the system distributes detection across multiple time points. This dimensional transition from 2D spatial detection to 3D spatio-temporal detection allows high density to be maintained while preserving optical resolution through sequential imaging.
2Difficulty of detecting and measuring
If code word weight is reduced to minimize optical crowding, then the distribution of ON states is improved, but the number of detection cycles required increases
Solution Approach 1:
The patent changes the parameter of code word weight (number of ON bits) to optimize the balance between optical crowding and detection efficiency. By selecting code words with appropriate weights and distributing them across multiple decoding cycles, the system achieves even distribution of ON states that minimizes optical crowding while maintaining acceptable decoding time through controlled parameter selection.
3Difficulty of detecting and measuring
If code words are assigned to highly expressed target analytes to reduce their weight, then optical crowding is minimized, but the complexity of code word assignment increases
Solution Approach 1:
The patent performs preliminary analysis of single-cell expression data to identify highly expressed target analytes before assigning code words. This preliminary action enables the system to proactively assign lower-weight code words to these high-expression targets, preventing optical crowding issues before they occur. The preliminary characterization allows for optimized code word assignment without increasing operational complexity during the actual detection process.
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
These methods enhance the accuracy and sensitivity of in situ analysis by reducing the impact of optical crowding, allowing for precise detection and decoding of barcoded target analytes even in high-density biological samples.
Implementation Method 1
the density of fluorescing barcoded target analytes (e.g., detectably labeled probes, fluorescing rolling circle amplification products (RCPs) of barcoded gene transcripts)
Data Source
AI summary
Methods and systems for performing in situ decoding are described that minimize optical crowding, thereby improving decoding accuracy. The methods may comprise, e.g., receiving images of a biological sample acquired during a cyclical decoding process; detecting a series of detectable signals (ON signals) or absence thereof (OFF signals) at one or more locations in the biological sample corresponding to one or more barcoded target analytes; determining a code word based on the series of ON and OFF signals that corresponds to a barcode for each of the one or more barcoded target analytes, where the one or more code words are assigned to the one or more barcoded target analytes based on a minimax decision rule to minimize a density of ON signals detected in the images of the series of images; and identifying the one or more barcoded target analytes based on the one or more determined code words.