实时开源 FLIM 分析。

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in bioinformatics Pub Date : 2023-11-30 eCollection Date: 2023-01-01 DOI:10.3389/fbinf.2023.1286983
Kevin K D Tan, Mark A Tsuchida, Jenu V Chacko, Niklas A Gahm, Kevin W Eliceiri
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引用次数: 0

摘要

荧光寿命成像显微镜(FLIM)为荧光团的化学微环境提供了宝贵的定量洞察力。由于计算时间长,且缺乏可访问的开源实时分析工具包,传统的 FLIM 数据分析,特别是广泛使用的时间相关单光子计数(TCSPC)方法,通常是在采集后进行的。因此,即使在采集之后,FLIM 数据质量的不确定性依然存在,经常需要延长成像时间。遗憾的是,延长成像时间不仅有可能错过重要的生物事件,还会造成光漂白和光损伤。为了应对这些挑战,我们推出了首个开源程序,用于在标本扫描过程中进行实时 FLIM 分析。我们的方法将采集与实时计算和可视化功能相结合,使我们能够即时评估 FLIM 数据质量。我们的开源实时 FLIM 查看器集成了 Napari 插件,可显示相位分析和快速寿命测定 (RLD) 结果,这些结果是通过基于开源 Micro-Manager 的 OpenScan 软件包等采集软件传输的实时数据计算得出的。我们的方法通过在采集过程中提供初步分析,有助于早期识别 FLIM 信号和数据质量评估。这不仅加快了成像过程,而且在对敏感的活体生物样本进行成像时尤其有用。
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Real-time open-source FLIM analysis.

Fluorescence lifetime imaging microscopy (FLIM) provides valuable quantitative insights into fluorophores' chemical microenvironment. Due to long computation times and the lack of accessible, open-source real-time analysis toolkits, traditional analysis of FLIM data, particularly with the widely used time-correlated single-photon counting (TCSPC) approach, typically occurs after acquisition. As a result, uncertainties about the quality of FLIM data persist even after collection, frequently necessitating the extension of imaging sessions. Unfortunately, prolonged sessions not only risk missing important biological events but also cause photobleaching and photodamage. We present the first open-source program designed for real-time FLIM analysis during specimen scanning to address these challenges. Our approach combines acquisition with real-time computational and visualization capabilities, allowing us to assess FLIM data quality on the fly. Our open-source real-time FLIM viewer, integrated as a Napari plugin, displays phasor analysis and rapid lifetime determination (RLD) results computed from real-time data transmitted by acquisition software such as the open-source Micro-Manager-based OpenScan package. Our method facilitates early identification of FLIM signatures and data quality assessment by providing preliminary analysis during acquisition. This not only speeds up the imaging process, but it is especially useful when imaging sensitive live biological samples.

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