2025/09/29 by Luis U. Aguilera, William Raymond, Rhiannon M. Sears +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Advanced Fluorescence Microscopy Techniques #Cell Image Analysis Techniques #Single-cell and spatial transcriptomics
paper · pdf · doi:10.1101/2025.09.25.678587
openalex publication_date 2025/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.