Why does lifetime measurement matter for target validation?

Measuring fluorescence lifetime provides a robust, quantitative readout of protein interactions via FRET, enabling detection of interaction population fractions in a single measurement. This supports target validation by delivering mechanistic insight into binding events and conformational changes in live or fixed cells. The method’s sensitivity to lifetime changes down to 20 picoseconds allows detection of subtle interaction shifts critical for early-stage target de-risking.

How does isolating the independent variable improve discovery pipeline efficiency?

Isolating the independent variable—such as inhibitor concentration or genetic perturbation—enables clear attribution of observed lifetime changes to specific experimental conditions. This is achieved through controlled multiwell plate acquisition where each well represents a defined condition, minimizing confounding variables. The approach supports reproducible structure-activity relationship (SAR) mapping and hit confirmation in screening campaigns.

What do quantitative dependent variable measurements enable in FRET assays?

Quantitative dependent variable measurements—fluorescence lifetime and FRET population fraction—enable objective comparison of protein interaction states across conditions. These outputs are derived from mono-exponential decay fitting of FLIM data using FLIMfit, providing numerical values for hit selection and lead optimization. The data supports statistical analysis and cross-well comparison essential for assay robustness.

Why are replication requirements important for cross-functional collaboration?

Replication requirements—acquiring instrument response function, reference dye, and background images for each experiment—ensure data integrity and comparability across runs and users. This standardization allows discovery, assay development, and preclinical teams to trust and build upon shared FLIM datasets. Consistent replication supports technology transfer and multi-site assay deployment in global R&D organizations.

What statistical analysis capabilities are required before implementation?

Before implementation, teams must be capable of performing global analysis on thousands of cells across hundreds of fields of view to extract mean lifetime values per well. This includes fitting fluorescence decay models (e.g., mono-exponential) using tools like FLIMfit and calculating interaction population fractions from lifetime shifts. The capability to generate lifetime plate maps and perform statistical comparisons (e.g., t-tests, ANOVA) is essential for hit calling and data interpretation.

自動化された蛍光寿命イメージング顕微鏡を利用してオープンソースのハイコンテンツな解析

14.7K 閲覧数

被引用数 14

09:30 分

2017年1月18日

10.3791/55119-v

2017年1月18日

14.7K 閲覧数

¹Photonics Group, Department of Physics, Imperial College London, ²Institute for Chemical Biology, Department of Chemistry, Imperial College London, ³MRC Clinical Sciences Centre, Hammersmith Hospital, ⁴Chemical Biology Section, Department of Chemistry, Imperial College London, ⁵Retroscreen Virology Ltd, ⁶Pfizer Global Research and Development, Pfizer Limited, Sandwich, Kent, UK, ⁷Centre for Histopathology, Imperial College London

^* These authors contributed equally

我々は、蛍光共鳴エネルギー移動（FRET）ベースの読み出しを用いてタンパク質相互作用をアッセイするための自動化された蛍光寿命イメージング（FLIM）を利用するオープンソースの高含有量分析（HCA）装置を提示します。このopenFLIM-HCAの楽器のためのデータ取得がマイクロマネージャーとデータ分析で書かれたソフトウェアによって制御されFLIMfitで行われています。