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Related Experiment Video

Updated: Oct 31, 2025

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Semi-automated background removal limits data loss and normalizes imaging mass cytometry data.

Marieke E Ijsselsteijn¹, Antonios Somarakis², Boudewijn P F Lelieveldt²

¹Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|July 1, 2021

Summary

This study addresses signal variations in imaging mass cytometry (IMC) using formalin-fixed, paraffin-embedded (FFPE) tissues. A new workflow improves data quality for better analysis of colorectal cancer tissues.

Keywords:

CyTOF background removal imaging mass cytometry multiplex immunophenotyping

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Last Updated: Oct 31, 2025

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

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Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

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Area of Science:

Biotechnology
Cancer Research
Immunology

Background:

Imaging mass cytometry (IMC) is valuable for analyzing complex biological systems using spatial information.
Formalin-fixed, paraffin-embedded (FFPE) tissues are widely used for IMC due to preserved morphology.
Variations in FFPE tissue processing affect antibody performance and signal-to-noise ratios in IMC.

Purpose of the Study:

To investigate the impact of signal intensity fluctuations on IMC analysis and phenotype identification in colorectal cancer.
To explore and propose effective normalization strategies for IMC data from FFPE tissues.

Main Methods:

Analysis of 12 colorectal cancer FFPE tissue samples.
Evaluation of immunodetection-related signal intensity variations.
Development and application of a semi-automated background removal workflow for IMC data normalization using public tools.

Main Results:

Signal intensity fluctuations significantly impact IMC analysis and phenotype identification.
The proposed normalization workflow effectively reduces variations and improves data quality.
The workflow is applicable to existing IMC datasets.

Conclusions:

Standardized normalization is crucial for reliable IMC analysis of FFPE tissues.
The developed workflow enhances the quality and comparability of IMC data.
This approach supports more robust evaluation of complex biological samples, including colorectal cancer.