Quantification and analysis of multiplexed fluorescence insitu hybridization data using open-source tools.

Area of Science:

• Molecular Biology
• Bioinformatics
• Image Analysis

Background:

• Multiplexed fluorescence in situ hybridization (mFISH) is a powerful technique for visualizing gene expression patterns within tissues.
• Analyzing complex mFISH data requires robust computational tools for accurate quantification and interpretation.
• Existing methods may lack the flexibility or accessibility needed for comprehensive spatial gene expression analysis.

Purpose of the Study:

• To present a standardized protocol for quantifying and analyzing multiplexed fluorescence in situ hybridization (mFISH) data.
• To introduce and demonstrate the utility of two open-source software tools, FijiFISH and RUHi, for mFISH data analysis.
• To enable multimodal, single-cell resolution insights into spatial gene expression, including experimentally induced exogenous fluorophores.

Main Methods:

• Utilized FijiFISH, an ImageJ-based plugin, for essential image processing steps including registration, cell segmentation, and gene expression quantification.
• Employed RUHi, an R-based package, for advanced data analysis, including dimensionality reduction, clustering, and visualization via code and a Shiny application.
• Integrated experimental data with exogenous fluorophores to achieve multimodal spatial gene expression analysis.

Main Results:

• Successfully quantified and analyzed mFISH data using the FijiFISH and RUHi pipeline.
• Achieved accurate cell segmentation and gene expression quantification with FijiFISH.
• Demonstrated effective dimensionality reduction, clustering, and visualization of spatial gene expression data with RUHi.
• Provided multimodal, single-cell resolution insights into spatial gene expression patterns.

Conclusions:

• The FijiFISH and RUHi protocol offers a comprehensive and accessible solution for mFISH data analysis.
• This approach facilitates deeper understanding of spatial gene expression at the single-cell level.
• The open-source nature of these tools promotes wider adoption and further development in the field of spatial transcriptomics.