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Related Experiment Video

Updated: Jul 3, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

A flexible open-source processing workflow for multiplexed fluorescence imaging based on cycles.

Guillaume Potier^1,2, Aurélie Doméné^1,2,3, Perrine Paul-Gilloteaux^3,4

¹Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, F-44000, France.

|January 22, 2024

Summary

This summary is machine-generated.

This study introduces an open-source software workflow for preprocessing multiplexing tissue imaging data. The software addresses challenges in spatial biology analysis, improving cell population identification with spatial context.

Keywords:

Bio image analysis fluorescence microscopy multiplexing registration segmentation signal processing workflow

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Area of Science:

Spatial biology
Biomedical imaging
Computational pathology

Background:

Multiplexing tissue imaging complements single-cell analysis by providing spatial context.
Advanced imaging techniques enable measurement of multiple cellular parameters simultaneously.
Cyclical immunofluorescence and microfluidics overcome spectral overlap issues in reporter imaging.

Purpose of the Study:

To develop and implement an open-source software workflow for preprocessing multiplexing tissue imaging data.
To provide a robust solution for essential preprocessing steps including registration, artifact correction, and mosaicking.
To facilitate spatial analysis of cell populations in complex tissue environments.

Main Methods:

A novel preprocessing workflow implemented as open-source software (library, command-line tool, standalone application).
Utilizes wide-field microscopy and mosaicking for large field-of-view acquisition.
Incorporates image processing for inter-cycle registration, autofluorescence correction, and mosaicking.

Main Results:

The workflow was exemplified using the PhenoCycler system, with a reduced dataset provided for testing.
The open-source processor was compared to the commercial processor.
The developed software effectively addresses known issues in multiplexing imaging data preprocessing.

Conclusions:

The proposed open-source workflow offers a viable alternative for preprocessing multiplexing tissue imaging data.
The software enhances the analysis of spatial cell populations, similar to cytometry but with added spatial information.
This tool aims to improve the accessibility and reliability of spatial biology data analysis.