Multiplexed FISH Barcode Identification Using Multi-Channel CNN
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Solution Overview
Problem
Conventional fluorescence in-situ hybridization (FISH) techniques are limited in their ability to efficiently identify multiple RNA species simultaneously due to the slow process of targeting individual RNA species and the limited number of distinguishable colors, while multiplexed FISH techniques face challenges with error robustness and accuracy in barcode identification.
Innovation Solution
Employing a multi-channel convolutional neural network (CNN) to analyze FISH image data, utilizing spatial and temporal features to segment barcodes, and training the CNN to recognize complex barcodes by rearranging input channels, thereby improving accuracy and efficiency in identifying genes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional FISH techniques target individual RNA species sequentially, then measurement precision for each species is improved, but productivity decreases due to the slow process
Solution Approach 1:
The patent segments the identification process into two independent stages: (1) detecting fluorescence spots using traditional FISH methods, and (2) identifying RNA species through barcode sequence analysis. This segmentation allows parallel processing of multiple RNA species simultaneously while maintaining identification accuracy through computational methods.
Solution Approach 2:
The patent introduces barcode sequences as an intermediary mechanism between the physical fluorescence detection and the biological RNA identification. The barcodes serve as a mediating layer that enables multiplexed detection by encoding species information that can be read computationally without interfering with the physical detection process.
2Productivity
If multiplexed FISH techniques use multiple probes simultaneously, then productivity increases by detecting multiple RNA species at once, but device complexity increases due to the need for multiple probe types
Solution Approach 1:
The patent creates universal probe sets that can detect multiple RNA species simultaneously. Instead of requiring separate probe sets for each species, the system uses a single universal probing mechanism combined with species-specific barcode sequences, reducing the complexity of probe preparation while maintaining high multiplexing capability.
Solution Approach 2:
The patent utilizes fluorescence color coding to distinguish between different RNA species detected by the same probe set. Each RNA species is assigned a unique barcode that can be read through color detection, allowing multiple species to be detected simultaneously using the same physical probes without increasing probe complexity.
3Measurement precision
If combinatorial FISH uses sequential hybridizations to read barcodes, then measurement precision for barcode identification is improved, but loss of time increases due to the multiple rounds of imaging required
Solution Approach 1:
The patent performs preliminary barcode encoding during the probe design stage, where barcode sequences are integrated into the probe sets before the actual detection experiment. This preliminary action eliminates the need for time-consuming sequential hybridization rounds, as the barcodes are already in place to be read in a single imaging process.
Solution Approach 2:
The patent maintains continuous barcode visibility throughout the detection process by designing the probe sets so that barcode sequences remain bound to the target RNA molecules during the entire hybridization and imaging process. This continuity allows the barcode to be read immediately after a single imaging round, eliminating the time loss associated with multiple sequential hybridization steps.
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
The CNN approach enhances the accuracy and speed of gene identification in multiplexed FISH experiments by addressing intensity variations, misaligned spots, and overlapping spots, while reducing processing time and resource requirements.
Implementation Method 1
The probes bind to the sequence of interest when it is present in the sample and then are caused to fluoresce, thereby allowing researchers to identify the presence and location of the sequence of interest in the sample.
Data Source
AI summary
Exemplary embodiments provide methods, mediums, and systems for processing multiplexed image data from a fluorescence in-situ hybridization (FISH) experiment. According to exemplary embodiments, a convolutional neural network (CNN) may be applied to the image data to localize and identify hybridization spots in images corresponding to different sets of targeting probes. The CNN is configured in such a way that it is able to discriminate hybridization spots in situations that are difficult for conventional techniques. The CNN may be trained on a relatively small amount of data by exploiting the nature of the FISH codebook.