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Related Experiment Video

Updated: Feb 24, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

mViSE: A visual search engine for analyzing multiplex IHC brain tissue images (spatial proteomics).

Liqiang Huang¹, Rachel Mills¹, Saikiran Mandula¹

¹University of Houston, Houston, TX, 77204, USA.

Scientific Reports

|February 22, 2026

Summary

This summary is machine-generated.

We developed mVISE, a visual search engine for analyzing massive spatial proteomics brain images. This tool enables programming-free, query-driven retrieval and profiling of cell communities and tissue niches.

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Last Updated: Feb 24, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

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Published on: April 7, 2023

Area of Science:

Neuroscience
Computational Biology
Biotechnology

Background:

Whole-slide spatial proteomics images of brain tissue are large and complex, posing analytical challenges.
Current methods often require programming expertise or lack comprehensive analysis capabilities.

Purpose of the Study:

To introduce mVISE, an interactive visual search engine for programming-free analysis of multiplex spatial proteomics data.
To enable query-driven retrieval and profiling of cellular communities and multicellular niches within brain tissue.

Main Methods:

Developed multiplex encoders trained on cell morphology, chemoarchitecture, cytoarchitecture, and myeloarchitecture without human annotation.
Integrated visual cues across imaging channels to overcome foundation model limitations.
Combined multiple encoders for specialized search functionalities.

Main Results:

Validated mVISE for retrieving single cells, cell pairs, and tissue patches.
Demonstrated accurate delineation of cortical layers, brain regions, and sub-regions.
Showcased programming-free, query-driven analysis of complex spatial proteomics data.

Conclusions:

mVISE provides an effective, accessible alternative for analyzing massive spatial proteomics datasets.
The tool facilitates exploratory analysis, comparative profiling, and delineation of brain structures.
mVISE is available as an open-source QuPath plug-in, promoting wider adoption in neuroscience research.