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Background <p>The development of advanced imaging techniques and quantification methods has progressed significantly over the past 20 years; however, quantifying biological elements in three dimensions remains challenging. While recent object-based methods have emerged to analyze signal intensities and colocalization in multichannel fluorescent images, existing tools mainly focus on two-dimensional analysis. This protocol introduces Bioloc3D, a fully open-source tool designed to quantify fluorescent labeling across entire stacks and automate statistical analysis. By addressing the limitations of current methods, Bioloc3D offers a solution to the lack of tools capable of quantifying colocalization between multiple identified elements in 3D.</p> Methods <p>Bioloc3D is composed of two different components. The first one, is an ImageJ toolset called <i>Bioloc3D-Imaging</i> (<i>B3D-Img</i>) permitting to individualize fluorescent elements from different channels and identify 3D colocalizations. The second, <i>Bioloc3D-Plotting</i> (<i>B3D-Plt</i>) is a Python-based software enabling the analysis automation of Excel files produced by <i>B3D-Img</i>. As a proof of concept, this toolset was used to investigate and compare the GABAergic SOM/PV inhibitory microcircuit in the anterior cingulate cortex (ACC) and the somatosensory cortex (SSC).</p> Discussion <p>Individualizing immunolabeled profiles in 3D across multiple channels frequently requires handling large datasets. To address potential computational limitations, <i>B3D-Img </i>provides a simplified, yet accurate, segmentation solution that accelerates object-based colocalization analysis. While individualization relies on thresholding, the signal-to-noise ratio remains a critical factor that that can influence quantification time. To test this limitation, <i>B3D-Img </i>has been assessed under challenging conditions (e.g. noise &amp; background). This solution enables automated analysis of entire sets of confocal image stacks efficiently. Next, to facilitate data processing, Bioloc3D toolset includes the standalone <i>B3D-Plt </i>application, which quickly performs statistical analyses, summarizes and plots data generated by <i>B3D-Img</i>. Overall, Bioloc 3D offers an accessible, all-in-one solution for quantifying and analyzing large set of object-based colocalization data in 3D.</p> Clinical trial number <p>Not applicable.</p>

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Bioloc3D: an automated toolset to quantify fluorescent elements and their colocalizations in 3D

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Thibault Dhellemmes,
  • Rémi Kinet,
  • Fabrice Cordelières,
  • Marc Landry

摘要

Background

The development of advanced imaging techniques and quantification methods has progressed significantly over the past 20 years; however, quantifying biological elements in three dimensions remains challenging. While recent object-based methods have emerged to analyze signal intensities and colocalization in multichannel fluorescent images, existing tools mainly focus on two-dimensional analysis. This protocol introduces Bioloc3D, a fully open-source tool designed to quantify fluorescent labeling across entire stacks and automate statistical analysis. By addressing the limitations of current methods, Bioloc3D offers a solution to the lack of tools capable of quantifying colocalization between multiple identified elements in 3D.

Methods

Bioloc3D is composed of two different components. The first one, is an ImageJ toolset called Bioloc3D-Imaging (B3D-Img) permitting to individualize fluorescent elements from different channels and identify 3D colocalizations. The second, Bioloc3D-Plotting (B3D-Plt) is a Python-based software enabling the analysis automation of Excel files produced by B3D-Img. As a proof of concept, this toolset was used to investigate and compare the GABAergic SOM/PV inhibitory microcircuit in the anterior cingulate cortex (ACC) and the somatosensory cortex (SSC).

Discussion

Individualizing immunolabeled profiles in 3D across multiple channels frequently requires handling large datasets. To address potential computational limitations, B3D-Img provides a simplified, yet accurate, segmentation solution that accelerates object-based colocalization analysis. While individualization relies on thresholding, the signal-to-noise ratio remains a critical factor that that can influence quantification time. To test this limitation, B3D-Img has been assessed under challenging conditions (e.g. noise & background). This solution enables automated analysis of entire sets of confocal image stacks efficiently. Next, to facilitate data processing, Bioloc3D toolset includes the standalone B3D-Plt application, which quickly performs statistical analyses, summarizes and plots data generated by B3D-Img. Overall, Bioloc 3D offers an accessible, all-in-one solution for quantifying and analyzing large set of object-based colocalization data in 3D.

Clinical trial number

Not applicable.