Microscope Illumination Path Planning for Rapid ROI Photolabeling
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Solution Overview
Problem
Current methods for de novo spatial proteomics lack the sensitivity and specificity to identify low-abundant proteins in biological samples, and existing photolabeling techniques are inefficient for rapid illumination of multiple regions of interest within a reasonable duration.
Innovation Solution
A microscope-based system and method that uses a processing module to identify regions of interest, determine an efficient illumination sequence, and control a light source to illuminate these regions rapidly by minimizing region-to-region traveling distances and optimizing illumination paths within each field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional photolabeling techniques are used to illuminate regions of interest, then labeling precision is achieved, but illumination time becomes excessively long (at least one day to illuminate ten of thousands of FOVs)
Solution Approach 1:
The system segments the large number of fields of view into multiple groups that can be illuminated in parallel. The light source is controlled to illuminate multiple regions simultaneously rather than sequentially, dividing the total illumination task into concurrent segments that reduce overall processing time while maintaining labeling precision in each segment.
Solution Approach 2:
The system implements continuous illumination across multiple fields of view by coordinating the light source to operate simultaneously on multiple regions. This eliminates the idle time between sequential illuminations, maintaining continuous useful action throughout the photolabeling process and dramatically reducing total illumination time.
2Ease of operation
If laser-capture microdissection is used for protein isolation, then spatial protein identification is enabled, but beam size is too large to achieve spatial precision
Solution Approach 1:
The system applies local quality by using a focused light source that can be precisely positioned at specific coordinates within each field of view. The illumination is concentrated exactly where needed (at the regions of interest) rather than using a large diffuse beam, achieving high spatial precision while maintaining ease of operation through automated positioning.
Solution Approach 2:
The system replaces the mechanical laser-cutting approach with an optical photolabeling approach. Instead of using a large-beam laser for physical cutting that sacrifices precision, the system uses controlled photolabeling with precisely positioned light to mark regions of interest, achieving both spatial precision and operational ease.
3Adaptability or versatility
If spatially targeted optical microproteomics is used, then de novo spatial proteomics is enabled, but sensitivity and specificity are insufficient to reach mass spectrometry requirements
Solution Approach 1:
The system performs preliminary photolabeling action at the exact locations of regions of interest before mass spectrometry analysis. By pre-marking the specific spatial locations with photolabels, the system ensures that subsequent MS analysis focuses on the correct proteins with high sensitivity and specificity, enabling reliable de novo spatial proteomics.
Solution Approach 2:
The system introduces photolabels as an intermediary between the optical imaging system and the mass spectrometry system. These photolabels serve as markers that bridge the two techniques, allowing the MS to detect and identify proteins at the precisely marked spatial locations with the required sensitivity and specificity.
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 rapid and precise photolabeling of multiple regions of interest, overcoming the limitations of existing technologies by significantly reducing illumination time while ensuring maximum area coverage within the regions of interest.
Implementation Method 1
applies proximity photolabeling to tag the proteins accurately in the target area. After photolabeling, the biotinylated proteins are extracted from the samples
Data Source
AI summary
A microscope-based system is provided. The microscope-based system includes an illumination assembly comprising an illumination light source and a pattern illumination device, and a processing module coupled to the illumination light source and the pattern illumination device. The processing module is configured to identify regions of interest in a sample to generate a two-dimensional illumination mask for each of the multiple fields of view, and for each field of view, determine an illumination sequence of the regions of interest by minimizing a sum of a plurality of region-to-region traveling distances between sequential regions of interest, determine an illumination path following the illumination sequence within each of the regions of interest, and control the illumination light source and the pattern illumination device to illuminate the regions of interest based on the illumination sequence and the illumination path for each of the multiple fields of view. Methods of use are also provided.