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Updated: Aug 11, 2026
Sample Preparation for Mass Cytometry Analysis
Published on: April 29, 2017
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.
Insights
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.
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.
Abstract:
Imaging mass cytometry (IMC) allows the detection of multiple antigens (approximately 40 markers) combined with spatial information, making it a unique tool for the evaluation of complex biological systems. Due to its widespread availability and retained tissue morphology, formalin-fixed, paraffin-embedded (FFPE) tissues are often a material of choice for IMC studies. However, antibody performance and signal to noise ratios can differ considerably between FFPE tissues as a consequence of variations in tissue processing, including fixation. In contrast to batch effects caused by differences in the immunodetection procedure, variations in tissue processing are difficult to control. We investigated the effect of immunodetection-related signal intensity fluctuations on IMC analysis and phenotype identification, in a cohort of 12 colorectal cancer tissues. Furthermore, we explored different normalization strategies and propose a workflow to normalize IMC data by semi-automated background removal, using publicly available tools. This workflow can be directly applied to previously acquired datasets and considerably improves the quality of IMC data, thereby supporting the analysis and comparison of multiple samples.
