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Fluorescence datasets from investigations into intracellular trafficking compartments produce images of variable quality, scales, and complexities. Investigators are therefore confronted with a choice of how to analyze this information. Here, we have used confocal immunofluorescence images of lysosomes from retinal pigment epithelial cells as an exemplar dataset, and employed three freely accessible computational approaches (Fiji, CellProfiler and Icy) to showcase their workings. A step-by-step workflow for each pipeline is described with non-specialist users in mind. These produce results including lysosomal number and shape, but also 3D outputs such as volume. Features of the three methods alongside their advantages and limitations are subsequently summarized. An important consideration, however, is that results generated from the different approaches are not necessarily comparable. Hence, users should adopt only a single method to analyze their dataset which best suit their specific requirements.

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Computational Approaches for Delineating Lysosomes and Related Intracellular Trafficking Vesicles in Confocal and Other Fluorescence Datasets

  • 实验方案

  • 发表日期：

    2025年

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  • Charles Ellis,
  • David S. Chatelet,
  • J. Arjuna Ratnayaka

摘要

Fluorescence datasets from investigations into intracellular trafficking compartments produce images of variable quality, scales, and complexities. Investigators are therefore confronted with a choice of how to analyze this information. Here, we have used confocal immunofluorescence images of lysosomes from retinal pigment epithelial cells as an exemplar dataset, and employed three freely accessible computational approaches (Fiji, CellProfiler and Icy) to showcase their workings. A step-by-step workflow for each pipeline is described with non-specialist users in mind. These produce results including lysosomal number and shape, but also 3D outputs such as volume. Features of the three methods alongside their advantages and limitations are subsequently summarized. An important consideration, however, is that results generated from the different approaches are not necessarily comparable. Hence, users should adopt only a single method to analyze their dataset which best suit their specific requirements.