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Updated: Nov 6, 2025

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

MATISSE: a method for improved single cell segmentation in imaging mass cytometry.

Matthijs J D Baars¹, Neeraj Sinha¹, Mojtaba Amini¹

¹Molecular Cancer Research, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, 3584, CX, Utrecht, The Netherlands.

|May 12, 2021

Summary

We developed MATISSE, a new method for improved single-cell segmentation in imaging mass cytometry (IMC) tissue analysis. This approach enhances cell identification in complex tissues, aiding pathology research.

Keywords:

Colorectal tissue Imaging mass cytometry Immune histochemistry Microscopy Single cell segmentation

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Last Updated: Nov 6, 2025

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Published on: April 29, 2017

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

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Published on: April 8, 2015

Area of Science:

Cellular biology
Biotechnology
Pathology

Background:

Cellular heterogeneity visualization is crucial for understanding tissue complexity, development, physiology, and disease.
Imaging mass cytometry (IMC) is a powerful multiplex imaging technique for protein marker distribution analysis in tissues.
Accurate single-cell segmentation remains a challenge in IMC data analysis.

Purpose of the Study:

To develop a novel method for improved single-cell segmentation in multiplex imaging mass cytometry.
To enhance the accuracy and completeness of cell identification in complex tissue samples.

Main Methods:

Developed MATISSE (iMaging mAss cyTometry mIcroscopy Single cell SegmEntation) workflow.
Integrated high-resolution fluorescence microscopy with IMC capabilities.
Utilized commonly used open-access tools and regular fluorescence microscopy.

Main Results:

MATISSE significantly improves the quality and quantity of segmented cells compared to IMC-only segmentation.
Achieved more complete and accurate identification of various cell types (epithelial, fibroblasts, immune cells) in dense areas.
Demonstrated improved segmentation in heterogeneous tissue sections.

Conclusions:

MATISSE offers a qualitative and quantitative improvement for single-cell segmentation in densely packed cellular areas.
Implementation of MATISSE can enhance tissue microenvironment analysis in epithelial pathologies like autoimmunity and cancer.
The method is designed for easy adoption by laboratories using multiplex IMC.