Quantification and analysis of multiplexed fluorescence insitu hybridization data using open-source tools | JoVE Visualize

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.