A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at...

Area of Science:

• Biotechnology
• Computational Biology
• Bioinformatics

Background:

• Multiplexed imaging technologies offer high-resolution spatial insights into biological tissues.
• Current analysis methods are fragmented, technology-specific, and hinder scalability and reproducibility.

Purpose of the Study:

• To introduce SIMPLI (Single-cell Identification from MultiPLexed Images), a unified and flexible software for multiplexed imaging data analysis.
• To provide a technology-agnostic solution addressing the limitations of existing analytical tools.

Main Methods:

• SIMPLI unifies raw image processing, spatially resolved single-cell analysis, and cell-independent marker expression quantification.
• The software is customizable for desktop and high-performance computing environments, supporting workflow parallelization.
• Containerized implementation ensures portability and reproducibility.

Main Results:

• SIMPLI enables comprehensive analysis of multiplexed imaging data, including features not detectable at the single-cell level.
• The software generates tabular and graphical outputs at each analysis stage.
• It facilitates scalable and reproducible analysis of large datasets.

Conclusions:

• SIMPLI offers a portable, reproducible, and scalable solution for multiplexed imaging data analysis.
• The software democratizes advanced spatial biology analysis by unifying diverse tools into a single platform.