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MIST-Explorer: The Comprehensive Toolkit for Spatial Omic Analysis and Visualization of Single-Cell MIST Array Data.

Clark Fischer, Jianxin Chen, Arafat Meah

Biorxiv : the Preprint Server for Biology

|July 14, 2025

Summary

MIST-Explorer is a new software toolkit that simplifies the analysis of spatial proteomics data from Multiplex In Situ Tagging (MIST) experiments. It enables researchers to visualize and analyze complex spatial omics data efficiently.

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Area of Science:

Proteomics
Spatial Omics
Bioinformatics

Background:

Spatial proteomics enables high-dimensional protein analysis in tissues.
Existing tools lack dedicated software for spatial MIST data analysis.
Low-abundance functional protein detection remains a challenge.

Purpose of the Study:

To develop a comprehensive, user-friendly software toolkit for spatial MIST data.
To streamline the entire spatial omics workflow from image processing to analysis.
To empower researchers in deriving biological insights from complex spatial omics datasets.

Main Methods:

Developed MIST-Explorer, a Python-based toolkit with a PyQt6 graphical interface.
Integrated tile-based image registration (Astroalign, PyStackReg) and deep learning segmentation (StarDist).
Implemented multi-channel visualization, ROI selection, and interactive analysis modules (histograms, heatmaps, UMAP).

Main Results:

MIST-Explorer supports both preprocessed and raw image data inputs.
The software facilitates cell segmentation and protein quantification at single-cell resolution.
Generated spatially resolved expression tables compatible with downstream single-cell analysis pipelines.

Conclusions:

MIST-Explorer addresses the need for dedicated spatial MIST data analysis software.
The toolkit simplifies complex spatial omics workflows, making them accessible to researchers without extensive computational expertise.
Facilitates deeper biological insights from spatial proteomics data.