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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Spatially-smoothed quantification improves cell typing in imaging mass cytometry datasets.

Reto Gerber¹, Jake Griner², Daniel Incicau¹

¹Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich.

Biorxiv : the Preprint Server for Biology

|December 25, 2025

Summary

Accurate cell type annotation in imaging mass cytometry (IMC) relies on preprocessing. Simple spatial smoothing or cell mask resampling methods significantly improve IMC cell annotation by correcting signal spillover and enhancing marker aggregation.

Keywords:

Cell type annotation Imaging mass cytometry Marker aggregation

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Area of Science:

Single-cell biology
Computational pathology
Biomedical imaging analysis

Background:

Accurate cell type annotation in imaging mass cytometry (IMC) is crucial for biological insights.
Preprocessing steps like normalization, segmentation, and marker aggregation are vital for IMC data analysis.
Signal spillover due to limited spatial resolution and uncertain cell boundaries can distort marker intensities and lead to misannotation.

Purpose of the Study:

To systematically investigate the impact of spatial resolution and segmentation variability on per-cell marker aggregation in IMC.
To analyze technical biases in large-scale IMC studies and evaluate normalization strategies.
To benchmark spillover correction strategies for improving IMC cell type annotation.

Main Methods:

Utilized simulated IMC datasets to assess the effects of spatial resolution and segmentation on marker aggregation.
Analyzed technical biases and normalization effectiveness in large-scale IMC studies.
Benchmarked various spillover correction methods on simulated and real IMC datasets.

Main Results:

Established upper limits for reliable marker separation in cell type annotation based on spatial resolution and segmentation.
Demonstrated that appropriate normalization reduces batch effects in large-scale IMC studies without losing biological variability.
Identified two simple, effective spillover correction methods: spatial smoothing with mean aggregation or cell mask resampling with median calculation, outperforming baseline mean aggregation.

Conclusions:

Spatial resolution, normalization, and marker aggregation are critical preprocessing steps for accurate IMC single-cell annotation.
Simple preprocessing strategies like spatial smoothing or cell mask resampling can significantly enhance IMC data analysis.
Effective spillover correction is essential for robust cell type identification in IMC.