Binary-Coded HiPR-FISH Imaging for Dense Microbial Biofilms
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Solution Overview
Problem
Current methods for studying microbial communities lack the ability to provide high phylogenetic and spatial resolution, are limited by multiplexity in fluorescence in-situ hybridization (FISH) techniques, and struggle with single-cell segmentation in densely packed biofilms, leading to incomplete taxonomic coverage and poor understanding of microbial interactions in environments like the human oral cavity and colorectal cancer.
Innovation Solution
A method utilizing n-bit binary encoding of fluorophores for microbial taxa, combined with custom-designed encoding and decoding probes, allows for high phylogenetic resolution fluorescence in-situ hybridization (HiPR-FISH) to identify up to 1023 unique species by spectral imaging, enabling accurate spatial and phylogenetic analysis of microbial communities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional FISH methods use one fluorophore per taxa or combination of fluorophores, then different taxa can be distinguished, but the number of taxa that can be probed is limited due to spectral overlap and limited wavelength range
Solution Approach 1:
The patent transitions from using multiple fluorophores in the spectral domain to using binary codes in the digital domain. Each taxon is assigned a unique binary code, and the presence/absence of specific fluorophores represents binary digits. This dimensional shift allows exponential increase in multiplexing capacity (2^n taxa with n fluorophores) while simplifying the optical system design.
Solution Approach 2:
The patent changes the encoding parameter from spectral wavelength (continuous variable) to binary code (discrete variable). By using the presence or absence of fluorophores to represent binary 1s and 0s, the system can distinguish 2^n taxa using only n fluorophores, dramatically increasing the number of detectable taxa without adding more fluorophores or expanding the spectral range.
2Measurement precision
If fluorescence imaging is used to study spatial organization of biofilms, then spatial structure can be visualized, but multiplexity is significantly limited by spectral overlap of fluorophores
Solution Approach 1:
The patent moves the information encoding from the spectral dimension to the digital binary code dimension. Each taxon's binary code is read out using a small number of fluorophores, and the binary pattern is decoded computationally. This allows high spatial resolution imaging while simultaneously detecting hundreds or thousands of taxa, as the multiplexing capacity is determined by the binary code length rather than the number of fluorophores.
Solution Approach 2:
The patent introduces binary code as an intermediary between the biological target (taxa) and the detection system (fluorophores). The binary code encoding scheme acts as a mediator that translates taxonomic identity into a detectable fluorescent pattern, enabling both high spatial resolution and high multiplexing capacity without direct conflict between these requirements.
3Measurement precision
If image segmentation algorithms are used to measure spatial organization, then quantitative metrics can be obtained, but single-cell segmentation is challenging in densely packed biofilms with high dynamic range
Solution Approach 1:
The patent applies preliminary binary code encoding to each taxon before imaging. By pre-assigning unique binary codes to different taxa and using decoding probes to reveal these codes, the system creates distinct fluorescent patterns for each cell type before segmentation is attempted. This preliminary encoding simplifies the segmentation task by providing clear, distinguishable markers for different taxa even in densely packed regions.
Solution Approach 2:
The patent replaces traditional intensity-based segmentation methods with binary code-based identification. Instead of relying on subtle intensity variations that are difficult to resolve in dense biofilms, the system uses discrete binary patterns (presence/absence of fluorophores) that can be clearly distinguished. This substitution of the detection mechanism from continuous intensity measurement to discrete binary pattern recognition dramatically improves segmentation accuracy in challenging conditions.
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
Enables accurate identification and quantification of multiple microbial species in complex environments, providing detailed spatial and phylogenetic insights into microbial communities, including those associated with diseases like colorectal cancer and the human oral microbiome.
Implementation Method 1
Each taxon from a list of taxa of microorganisms is probed with a custom designed taxon-specific targeting sequence, flanked by a subset of n unique encoding sequences. A mixture of n decoding probes, each complementary to one of the n encoding sequences and conjugated to a unique fluorophore, is then allowed to hybridize to their complementary encoding sequences. The spectrum of labels for each cell is then detected using spectral imaging
Data Source
AI summary
Micron scale biogeography is a major driver of physiology and ecology of complex microbial biofilm communities, which remains elusive largely due to the lack of tools for spatially resolved phylogenetic mapping. This disclosure provides methods, computer-readable storage devices and kits that allow highly multiplexed and spatially resolved imaging of microbial community spatial organization. The disclosure provides a highly-multiplexed approach to resolve the spatial structure of complex microbial community at high taxonomic resolution.