FLIM Playground: An interactive, end-to-end graphical user interface for analyzing single-cell fluorescence lifetime data
Zhao, W.; Samimi, K.; Skala, M. C.; Datta, R.
Show abstract
Fluorescence lifetime imaging microscopy (FLIM) is a cellular-resolution molecular imaging technique. Yet, the journey from raw photon decays to biological insight remains fragmented by multi-step data extraction and siloed analyses. This work presents FLIM Playground, the first interactive graphical platform that unifies single-cell FLIM workflows, embeds user checks at each stage, and offers diverse user options. Built in Python and available open-source, FLIM Playground runs on major operating systems as a ready-to-run application and is web deployable. Its Data Extraction section collects and checks field-of-view metadata, calibrates via instrument response function shift or fluorescence lifetime standard, and extracts single-cell fluorescence lifetime features, along with morphology and texture features across channels. Multiple datasets can be merged through an interface that assigns categorical labels. The Data Analysis section provides real-time visual analytic modules for outputs from Data Extraction or user-provided datasets. Lifetime extraction by fitting and phasor were validated by comparison with a commercial software and published results, respectively, and both sections were demonstrated on a FLIM dataset of cancer cell lines to obtain biological insights. By adopting best practices and offering interactivity, FLIM Playground accelerates hypothesis-driven discovery and promotes reproducibility, and its modular design can incorporate new imaging modalities, extraction methods, and analysis modules.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Genome-wide analysis of the dynamic and biophysical properties of chromatin and nuclear proteins in living cells with Hi-D 97%
- An end-to-end workflow for multiplexed image processing and analysis 93%
- Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS 93%
Similar papers in this journal
Similar papers in this journal
- Mesmerize: a dynamically adaptable user-friendly analysis platform for 2D & 3D calcium imaging data. 96%
- Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis 95%
- Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis 94%
Similar papers in this journal
- CellPhePy: a Python implementation of the CellPhe toolkit for automated cell phenotyping from microscopy time-lapse videos 96%
- ModularImageAnalysis (MIA): Assembly of modularised image and object analysis workflows in ImageJ 95%
- LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.