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Updated: Apr 18, 2026

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

Published on: March 18, 2021

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, Aurora, CO, 80045, United States.

Bioinformatics Advances

|April 17, 2026

Summary

This summary is machine-generated.

MicroLive is a new Python application that simplifies the analysis of live-cell microscopy images. This open-source tool quantifies single molecules, overcoming computational barriers for researchers.

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Area of Science:

Cellular and Molecular Imaging
Biophysics
Computational Biology

Background:

Live-cell fluorescence microscopy allows real-time visualization of single molecules like mRNAs and proteins.
Large datasets from these experiments require complex computational analysis, posing a barrier for many researchers.

Purpose of the Study:

To introduce MicroLive, an open-source Python application designed to simplify the quantification of live-cell microscopy images.
To provide researchers with an accessible tool for extracting quantitative data from complex imaging experiments.

Main Methods:

Developed an open-source Python application, MicroLive, featuring an interactive Graphical User Interface (GUI).
Implemented key image analysis functions: cell segmentation, photobleaching correction, single-particle detection/tracking, spot intensity quantification, colocalization, and time-series correlation.
Validated MicroLive using synthetic live-cell imaging data generated with the rSNAPed toolkit and real microscopy data from U-2 OS cells.

Main Results:

MicroLive successfully performs essential image quantification tasks through an intuitive GUI.
The software accurately extracts biologically relevant parameters from both synthetic and real microscopy data.
Demonstrated the application's utility in analyzing gene expression constructs in U-2 OS cells.

Conclusions:

MicroLive significantly lowers the technical barrier for quantitative analysis of live-cell microscopy data.
This open-source tool empowers researchers to derive meaningful insights from complex single-molecule imaging experiments.
MicroLive is readily available, installable via pip, and distributed under a GPLv3 license.