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Related Experiment Video

Updated: Jul 20, 2025

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

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

Published on: April 7, 2023

Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering.

Candace C Liu¹, Noah F Greenwald¹, Alex Kong¹

¹Department of Pathology, Stanford University, Stanford, CA, USA.

Nature Communications

|August 1, 2023

Summary

This summary is machine-generated.

Pixie is a new open-source pipeline for analyzing multiplexed imaging data. It uses unsupervised clustering to annotate pixel-level features, improving cell analysis and understanding tissue composition in health and disease.

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

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Last Updated: Jul 20, 2025

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

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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

Area of Science:

Computational Biology
Biomedical Imaging
Bioinformatics

Background:

Multiplexed imaging generates complex tissue data, posing computational challenges for interpretation.
Current cell phenotyping relies on manual adjustments, which is labor-intensive.
Extracellular matrix and other non-cellular components are crucial but often overlooked.

Purpose of the Study:

To introduce Pixie, a robust pipeline for pixel-level feature annotation in multiplexed imaging.
To demonstrate Pixie's application across diverse biological contexts and imaging platforms.
To enhance cell phenotyping by integrating pixel-level information.

Main Methods:

Utilizes unsupervised clustering for quantitative annotation of pixel-level features.
Applies a comprehensive evaluation of pre-processing steps and parameter choices.
Integrates pixel clusters within cells to refine cell annotations.

Main Results:

Pixie achieves robust and quantitative annotation of pixel-level features.
The pipeline improves cell annotations by incorporating pixel cluster data.
Demonstrates broad applicability across various biological samples and imaging technologies.

Conclusions:

Pixie offers an open-source, customizable solution for analyzing complex imaging data.
The pipeline enhances the understanding of tissue composition and spatial organization.
Pixie streamlines computational analysis, improving efficiency and accuracy in biomedical research.