Automated Biological Sample Analysis via Raman Spectroscopy
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Solution Overview
Problem
Current methods for analyzing biological samples on a microscopic scale are limited by the need for sample preparation, subjective identification of objects of interest, and high statistical error, leading to poor reproducibility and accuracy.
Innovation Solution
A method and device that acquire discriminant data from unstained biological samples on a glass slide using multi-spectral and multi-Z imaging, combined with Raman micro-spectroscopy, allowing for the classification of cells and cellular compartments without prior labeling, and enabling fusion of non-morphological and morphological data for improved analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual visual identification of objects of interest is used, then the analysis process is simple, but the statistical error is significant and reproducibility is poor
Solution Approach 1:
The patent replaces manual visual identification with automated image processing and analysis systems. Computers and software algorithms automatically identify and analyze objects of interest in biological samples, eliminating the need for manual visual inspection. This substitution dramatically increases the number of objects that can be analyzed (from about 100 manually to potentially thousands or millions automatically), thereby reducing statistical error and improving reproducibility while maintaining operational simplicity.
2Ease of operation
If manual visual identification of objects of interest is used, then the process is easy to operate, but the reproducibility is poor due to subjective selection
Solution Approach 1:
The patent replaces subjective manual visual identification with objective computer-based image processing systems. Automated algorithms consistently apply the same identification criteria across all samples, eliminating human subjectivity and variability. This ensures that the same objects are identified and analyzed in the same way across different experiments and operators, dramatically improving reproducibility while keeping the system easy to operate through standardized automated workflows.
3Productivity
If only about a hundred objects of interest are identified manually, then the analysis is quick, but the representativeness of results is poor
Solution Approach 1:
The patent uses automated image processing systems to analyze vastly larger numbers of objects compared to manual methods. While manual analysis can identify about 100 objects, automated systems can efficiently analyze thousands or millions of objects in the same time frame. This massive increase in sample size dramatically improves the representativeness of results, ensuring that the analyzed population accurately reflects the entire biological sample while maintaining high productivity and analysis speed.
4Ease of manufacture
If label-free techniques are used, then no prior labeling is required, but the identification of objects of interest remains dependent on manual visual location
Solution Approach 1:
The patent combines label-free techniques with automated image processing to eliminate both prior labeling requirements and manual visual identification. The automated system directly processes raw images of unstained biological samples, using computer algorithms to identify objects of interest based on their inherent visual characteristics. This integration maintains the simplicity of label-free sample preparation while fully automating the identification process, removing the remaining dependency on manual visual location.
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
This approach enhances reproducibility and accuracy by automating the identification and analysis of biological samples, reducing statistical errors, and providing a more representative and detailed characterization of biological samples without the need for extensive sample preparation.
Implementation Method 1
an acquisition, by imaging means, of an image of an observation area of the sample
Implementation Method 2
Raman micro-spectroscopy module arranged to guide a laser beam onto the biological sample and acquire Raman spectra from the biological sample
Implementation Method 3
a laser beam onto the biological sample
Implementation Method 4
acquire Raman spectra from the biological sample
Data Source
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AI summary
The present invention relates to a method of analyzing a biological sample comprising biological objects, said method comprising: -an obtaining (25) of an image of an observation zone of the sample by an optical imaging of the sample, and a storage of this image in digitized form, then, - an analysis (26) of the digitized image and a determination, on this digitized image, of biological objects of interest in the sample, then - for at least one determined object of interest, an acquisition (27) of discriminating data for this object of interest directly on the sample, said acquisition targeting a specific location of the sample where the object of interest is to be found while being guided by means of the digitized image.