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Updated: Jan 11, 2026

Application of Membrane and Cell Wall Selective Fluorescent Dyes for Live-Cell Imaging of Filamentous Fungi

Application of Membrane and Cell Wall Selective Fluorescent Dyes for Live-Cell Imaging of Filamentous Fungi

Published on: November 28, 2019

MicroLive: An Image Processing Toolkit for Quantifying Live-cell Single-Molecule Microscopy.

Luis U Aguilera¹, William S Raymond², Rhiannon M Sears¹

¹Department of Biochemistry and Molecular Genetics, University of Colorado-Anschutz Medical Campus, 80045, CO, USA.

Biorxiv : the Preprint Server for Biology

|November 19, 2025

Summary

This summary is machine-generated.

Researchers can now quantify live-cell microscopy images more easily with MicroLive, an open-source Python application. This tool simplifies complex data analysis for visualizing single molecules like mRNAs and proteins in real time.

Keywords:

autocorrelation function colocalization image processing live-cell imaging single-molecule tracking transcription translation

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Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence

Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence

Published on: March 5, 2022

Area of Science:

Cell biology
Biophysics
Microscopy

Background:

Live-cell fluorescence microscopy allows real-time visualization of single molecules (e.g., mRNAs, nascent proteins) with high resolution.
Analyzing the large datasets generated by these experiments requires complex computational pipelines, posing a barrier for many researchers.

Purpose of the Study:

To introduce MicroLive, an open-source Python application with a Graphical User Interface (GUI) designed to simplify the quantification of live-cell microscopy images.
To provide researchers with an accessible tool for analyzing complex microscopy data.

Main Methods:

Development of MicroLive, an interactive Python-based GUI application.
Implementation of key image analysis tasks: cell segmentation, photobleaching correction, single-particle detection/tracking, spot intensity quantification, inter-channel colocalization, and time-series correlation analysis.
Validation using synthetic live-cell imaging data generated with the rSNAPed toolkit and microscopy images of U-2 OS cells.

Main Results:

MicroLive successfully performs essential image quantification tasks through an intuitive GUI.
Testing with synthetic data demonstrated accurate extraction of biologically relevant parameters.
Demonstrated application on real microscopy data of U-2 OS cells.

Conclusions:

MicroLive significantly lowers the technical barrier for quantitative analysis of live-cell microscopy data.
This open-source application facilitates real-time, single-molecule visualization and analysis in live cells.
MicroLive is available on GitHub under a GPLv3 license.