Background

Confocal microscopy is a cornerstone of modern molecular and cell biology research, providing high-resolution imaging capabilities and generating large volumes of data, especially when performing Z-stack imaging. For example, systems like the Olympus FV10i-LIV or Zeiss LSM series can automatically produce hundreds of images per sample, enabling detailed spatial and temporal studies of biological specimens [1,2]. Among the many applications of confocal microscopy, colocalization analysis is a widely used technique to assess the spatial association between biomolecules, helping to infer protein interactions, co-occurrence, and functional relationships [3]. The Manders coefficient is a commonly employed metric for colocalization studies, offering a quantitative measure of overlap between two signals in fluorescence microscopy datasets [4]. Fiji ImageJ’s Bioimaging and Optics Platform (BIOP) Just Another Colocalization Plugin (JACoP) has been developed for colocalization analysis on confocal images by using the Manders coefficient method [5,6].

Despite its importance, conducting accurate and efficient colocalization analysis remains challenging due to the lack of automated workflows to handle the substantial datasets generated by confocal microscopy. To the best of our knowledge, no comprehensive, automated solution exists in the literature to address these issues, creating an urgent and unmet need for a step-by-step protocol for automated workflows. The primary challenge lies in the preprocessing of Z-stack images, which often include blank slices at the beginning and end of the stack due to the nature of sample preparation and imaging conditions. Including these signal-free slices in Manders coefficient calculations may disrupt thresholding accuracy and lead to inconsistent results [3]. Manually identifying and excluding these blank images is not only time-consuming but also prone to observer bias, which can introduce errors and inconsistencies across datasets.

The current protocol addresses these limitations by providing a detailed, step-by-step automated workflow that integrates a Python-based script to refine raw confocal images. The script identifies and removes signal-free Z-slices, creating refined substacks for further analysis. By coupling this preprocessing step with Fiji ImageJ’s BIOP-JACoP plugin for Manders coefficient calculations, the protocol ensures reliable and reproducible colocalization measurements. This automated approach minimizes human intervention, enhances the accuracy of results, and significantly improves the efficiency of high-throughput studies.

Organic anion transport polypeptide (OATP) 1B1 is a plasma membrane transport protein that plays an important role in drug disposition [7]. Plasma membrane localization of OATP1B1 has often been assessed by quantification of its colocalization with sodium-potassium ATPase (Na/K-ATPase), a plasma membrane protein marker, as we previously published [8,9]. The current protocol uses colocalization of transiently transfected FLAG-tagged OATP1B1 with endogenous Na/K-ATPase in human embryonic kidney (HEK) 293 cells from confocal images as an example, as shown in our original publication [9].