Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - PEX5 applied experiment

This is a dataset of Fluorescence Correlation Spectroscopy (FCS) Time-Correlated Single Photon Counting (TCSPC) data with and without peak artifacts. The provenance of the data is recorded in this file (see a rendered version here). This parent project (https://github.com/aseltmann/fluotracify) also contains examples of how to use these models and related Python code. The following connected paper is currently under review and should be cited together with these model versions: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review) Note on the file formats and notation: "Primary data" refers to the .ptu files, so the actual TCSPC data. "Secondary data" refers to the .pqres files, which are derived files by the proprietary PicoQuant software. Note on sample preparation (from the Supplementary Note of the paper above): The peak artifacts measurements were produced by 20 nM Trypanosoma brucei-PEX5 N-term fused to eGFP in solution, and the corresponding control measurements by 5 nM Homo sapiens-PEX5 N-term fused to eGFP in solution. The detailed sample preparation is described elsewhere. We prepared the samples on #1.5 coverslips mounted on Attofluor Cell Chambers (Thermo Fisher Scientific). We acquired the data on a MicroTime 200 microscope (PicoQuant) equipped with an Olympus UPlanSApo 60× 1.2NA water immersion objective lens and a HydraHarp 400 TCSPC module (PicoQuant). Excitation was achieved with a 488 nm pulsed laser (PicoQuant) with a power of 5 mW measured at the sample plane. We used a 20 nM solution of Alexa Fluor for calibrating the correction collar. One TCSPC measurement had a length of 20 s for PEX5 experiments.本数据集包含带有和不带有峰伪影的荧光相关光谱（Fluorescence Correlation Spectroscopy, FCS）时间相关单光子计数（Time-Correlated Single Photon Counting, TCSPC）数据。数据的溯源信息已在本文件中记录（此处可查看渲染版本）。该父项目（https://github.com/aseltmann/fluotracify）还包含了使用这些模型的示例以及相关Python代码。 以下相关论文目前处于审稿阶段，使用该模型版本时请一并引用：Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. 神经网络辅助光子过滤可减少荧光相关光谱数据中的伪影. 2023（目前处于审稿中） 关于文件格式与标注的说明： “原始数据”指.ptu文件，即实际的TCSPC数据；“二级数据”指.pqres文件，为专有PicoQuant软件生成的衍生数据。 关于样品制备的说明（摘自上述论文的补充说明）： 峰伪影测量样品为20 nM的布氏锥虫（Trypanosoma brucei）-PEX5 N端与增强绿色荧光蛋白（enhanced Green Fluorescent Protein, eGFP）融合蛋白溶液，对照测量样品为5 nM的智人（Homo sapiens）-PEX5 N端与eGFP融合蛋白溶液。详细的样品制备流程已在其他文献中描述。我们将样品制备于#1.5盖玻片上，并安装在Attofluor细胞培养池（Thermo Fisher Scientific）中。数据采集使用MicroTime 200型显微镜（PicoQuant），配备奥林巴斯UPlanSApo 60× 1.2NA水浸物镜以及HydraHarp 400 TCSPC模块（PicoQuant）。激发采用488 nm脉冲激光器（PicoQuant），在样品平面处测得的激光功率为5 mW。我们使用20 nM的Alexa Fluor溶液校准物镜校正环。PEX5相关实验的单次TCSPC测量时长为20秒。