Spatial-dependent biological analysis using multi-photon laser labeling
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Solution Overview
Problem
Current molecular biology-based tissue analysis methods lose spatial context and face limitations such as low sequencing depth, sample volume restrictions, and time-consuming read-out, with no methods combining precision of isolation with depth of analysis.
Innovation Solution
A method for three-dimensional labeling of cells or tissue regions using multi-photon laser technology after contacting the region with a photo-activatable label, allowing for precise imaging and isolation of labeled regions for further analysis like DNA sequencing or proteomic analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular biology-based methods are used for tissue analysis, then depth of analysis is improved, but spatial context is lost
Solution Approach 1:
The tissue sample is divided into discrete regions of interest that are individually labeled and analyzed. The multi-photon laser selectively activates photo-activatable labels in specific three-dimensional regions, enabling sequential analysis of different tissue segments while preserving their spatial coordinates through imaging documentation.
Solution Approach 2:
Different regions of the tissue sample receive different treatments or labels based on their specific characteristics and research questions. The method allows selective labeling of particular cell types, tissue layers, or anatomical regions with appropriate markers, enabling localized analysis tailored to specific research objectives.
2Manufacturing precision
If photo-activatable labeling with multi-photon laser is used, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
A photo-activatable label acts as an intermediary between the multi-photon laser and the target molecules. The label absorbs the laser energy and transfers it to nearby molecules, enabling precise spatial activation without requiring direct laser-molecule interaction. This intermediary approach simplifies the overall system by decoupling the laser parameters from the labeling specificity.
Solution Approach 2:
The method exploits changes in optical parameters (wavelength, intensity, focal position) of the multi-photon laser to achieve precise three-dimensional control of labeling. By varying these parameters, the system can selectively activate labels at different depths and locations within the tissue sample, achieving high precision without mechanical manipulation.
3Loss of information
If intact tissue samples are analyzed, then spatial context is preserved, but isolation precision is reduced
Solution Approach 1:
Regions of interest within the intact tissue sample are pre-labeled with photo-activatable markers before isolation procedures. This preliminary labeling allows subsequent precise identification and isolation of the marked regions, ensuring that spatial context is maintained while achieving high isolation precision through the guide provided by the labels.
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 detailed three-dimensional expression profiling and precise isolation of cells or nuclei from tissues, providing comprehensive spatial and compositional data that was previously unattainable.
Implementation Method 1
subjecting a region of the tissue or tissue sample to a multi-photon laser, thereby labeling the region
Data Source
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AI summary
Biological research requires isolation and analysis of material, for example, RNA, DNA and protein, from tissue samples. The methods and compositions described herein allow for high resolution imaging of large and intact tissue samples, and subsequent isolation of material in a precise and location dependent-manner. The methods and compositions described herein may be used, for example, for biomarker discovery, identification of cell populations, pathology analysis, and generation of expression data in specific regions of interest.