MicroLive: an image processing toolkit for quantifying live-cell single-molecule microscopy
Year: 2026
Type: article
Abstract: Advances in live-cell fluorescence microscopy have enabled us to visualize single molecules (such as mRNAs and nascent proteins) in real time with high spatiotemporal resolution. However, these experiments generate large datasets that require complex computational processing pipelines to derive meaningful and quantitative information, which is a technical barrier for many researchers. To address this barrier, here, we introduce MicroLive, an open-source Python-based application for quantifying live-cell microscopy images. MicroLive provides an interactive Graphical User Interface (GUI) to perform key tasks, including cell segmentation, photobleaching correction, single-particle detection/tracking, spot intensity quantification, inter-channel colocalization, and time-series correlation analysis. As a ground-truth testing dataset, we used synthetic live-cell imaging data generated with the rSNAPed toolkit, demonstrating accurate extraction of biologically relevant parameters. Microscopy images of U-2 OS cells expressing a gene construct smHA-KDM5B-BoxB-MS2 were used to demonstrate the use of this software. Availability and implementation: MicroLive is distributed under a GPLv3 license and available on GitHub. https:/github.com/ningzhaoAnschutz/microlive.
Source: Bioinformatics Advances
Institutions: University of Colorado Anschutz, Colorado State University
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,726
Awards: G6557079271, Investigating local protein co-translational folding in situ with high spatiotemporal resolution, Imaging cotranslational protein folding with high spatiotemporal resolution in living cells, Integrating Multi-Scale Imaging, Reaction-Diffusion Simulation, and Markov Model Inference to Enhance Predictive Design and Interpretation of Single-Molecule Gene Regulation Experiments
OpenAlex ID: W7146955801