In Situ Codebook Design to Minimize Optical Crowding
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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 reduced accuracy and sensitivity.
Innovation Solution
Implement codebook design strategies that minimize optical crowding by optimizing code word assignment, dilution, splitting, and attenuation, ensuring even distribution of ON states across decoding cycles and channels, and using expression data to reduce co-occurrence of highly expressed analytes.
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 resolve individual fluorescent spots is hindered by optical resolution limits, leading to optical crowding
Solution Approach 1:
The patent applies preliminary action by pre-assigning code words to target analytes based on predicted expression levels and spatial distribution patterns before the actual imaging experiment. This pre-planning optimizes the code book design to minimize optical crowding effects during imaging, allowing higher analyte densities to be imaged without losing resolution. The code word assignment strategy is determined in advance using computational models that predict which code words will cause the least crowding in specific tissue regions.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting imaging parameters such as laser power, integration time, and detector gain based on the detected density of fluorescent spots. When optical crowding is detected in certain regions, the system modifies imaging parameters to optimize signal-to-noise ratio while maintaining the ability to resolve individual spots. This adaptive parameter adjustment allows the system to handle varying analyte densities across different tissue regions.
2Measurement precision
If more decoding cycles are used to improve decoding accuracy, then the total number of ON states increases, leading to greater optical crowding across the sample
Solution Approach 1:
The patent applies preliminary action by pre-calculating optimal code book designs that achieve the required decoding accuracy with the minimum necessary number of decoding cycles. Computational algorithms determine the optimal code word assignments and code book structure before experimentation, ensuring that sufficient decoding accuracy is achieved while minimizing the total number of ON states that would cause optical crowding. This pre-optimization allows the system to achieve high decoding accuracy without unnecessarily increasing the burden on optical resolution.
Solution Approach 2:
The patent employs partial action by implementing adaptive decoding strategies where not all code words are fully decoded in all regions. In areas with low analyte density or where high accuracy is not critical, the system performs partial decoding with fewer cycles, reducing the accumulation of ON states. This selective approach maintains adequate decoding accuracy where needed while minimizing overall optical crowding across the entire sample.
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
Enhances the accuracy and sensitivity of in situ analysis by minimizing the impact of optical crowding, allowing for precise detection and decoding of barcoded target analytes.
Implementation Method 1
a different set of barcode probes (e.g., fluorescently-labeled oligonucleotides) is contacted with target analytes (e.g., mRNA sequences) or with target barcodes (e.g., nucleic acid barcodes) associated with the target analytes present in a sample (e.g., a tissue sample) under conditions that promote hybridization
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.