Automated multimodal fluorescence microscopy for hyperplex spatial-proteomics: Coupling microfluidic-based...

Area of Science:

• Biomedical research
• Molecular biology
• Cancer research

Background:

• In situ multiplexing and transcriptomics are crucial for understanding cancer's molecular basis and advancing personalized medicine.
• Current complex analyses necessitate technological integration throughout experimental workflows.
• There is a need for novel tools with characterized performance and limitations to maximize resolution and sensitivity.

Purpose of the Study:

• To develop a cost-effective experimental pipeline for multiplexing analysis.
• To optimize the ratio of costs to benefits in multiplexing experiments.
• To improve resolution, sensitivity, and efficiency in cancer molecular analysis.

Main Methods:

• An integrated pipeline combining automated and manual procedures for sample preparation, staining, image collection, and analysis.
• Comparison of ultra-fast, automated immunofluorescence staining with standard protocols, evaluating antigen saturation, background, signal-to-noise ratio, and duration.
• Development of computational tools for automated analysis-driven fluorescence microscopy, including computer-assisted selection of areas for variable magnification and resolution.
• Utilization of confocal microscopy for 3D high-resolution analysis.

Main Results:

• The proposed pipeline offers an optimized cost-to-benefit ratio.
• Automated immunofluorescence staining demonstrated comparable or improved performance over standard protocols in key metrics.
• Computer-assisted microscopy enabled maximized spatial resolution and sensitivity.
• Significant reduction in data storage and total experimental time compared to standard approaches.

Conclusions:

• The developed experimental pipeline enhances multiplexing analysis efficiency and effectiveness for cancer research.
• Integration of automated and manual techniques, coupled with advanced computational tools, optimizes resource utilization.
• This approach facilitates deeper comprehension of cancer's molecular landscape and supports personalized medicine strategies.