Two new open-source tools are making fluorescence lifetime imaging microscopy (FLIM) faster, more accessible, and better suited to live imaging research. Developed by Ph.D. student Sofia Kapsiani in Professor Gabi Kaminski Schierle’s Molecular Neuroscience Group at the University of Cambridge, the tools FLIMPA and FLIMngo tackle longstanding barriers in biomedical imaging.
The first, FLIMPA, is a standalone software for phasor analysis of FLIM data. Published in Analytical Chemistry, it allows users to visualize, compare, and analyze lifetime changes across a wide range of sample types without relying on expensive commercial packages. FLIMPA is open source, user-friendly, and compatible with multiple file formats. Kapsiani showcased its capabilities by developing a cell-based assay to measure microtubule depolymerization, a key mechanism in cancer drug research. The second tool, FLIMngo, described in Journal of the American Chemical Society, uses deep learning to drastically reduce FLIM data acquisition time. Trained on extremely low-photon images, it enables high-throughput in vivo imaging with reduced light exposure critical when working with live samples. Kapsiani successfully tracked disease-related protein aggregates in C. elegans across their lifespan, without anesthesia.
“With these tools, we’re trying to remove those barriers and make FLIM a more flexible option,” said Kapsiani.
Together, FLIMPA and FLIMngo aim to shift FLIM from a specialist technique to a widely usable platform in live imaging and health research.
Photo credit: Journal of the American Chemical Society (2025). DOI: 10.1021/jacs.5c03749
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By Department of Chemical Engineering and Biotechnology, University of Cambridge. Edited by Stephanie Baum, reviewed by Robert Egan
Article can be accessed on: MedicalXpress
