Using enhanced number and brightness to measure protein oligomerization dynamics in live cells.
Francesco CutraleDaniel RodriguezVerónica HortigüelaChi-Li ChiuJason OtterstromStephen MieruszynskiAnna SeriolaEnara LarrañagaÁngel RayaMelike LakadamyaliScott E FraserElena MartínezSamuel OjosnegrosPublished in: Nature protocols (2019)
Protein dimerization and oligomerization are essential to most cellular functions, yet measurement of the size of these oligomers in live cells, especially when their size changes over time and space, remains a challenge. A commonly used approach for studying protein aggregates in cells is number and brightness (N&B), a fluorescence microscopy method that is capable of measuring the apparent average number of molecules and their oligomerization (brightness) in each pixel from a series of fluorescence microscopy images. We have recently expanded this approach in order to allow resampling of the raw data to resolve the statistical weighting of coexisting species within each pixel. This feature makes enhanced N&B (eN&B) optimal for capturing the temporal aspects of protein oligomerization when a distribution of oligomers shifts toward a larger central size over time. In this protocol, we demonstrate the application of eN&B by quantifying receptor clustering dynamics using electron-multiplying charge-coupled device (EMCCD)-based total internal reflection microscopy (TIRF) imaging. TIRF provides a superior signal-to-noise ratio, but we also provide guidelines for implementing eN&B in confocal microscopes. For each time point, eN&B requires the acquisition of 200 frames, and it takes a few seconds up to 2 min to complete a single time point. We provide an eN&B (and standard N&B) MATLAB software package amenable to any standard confocal or TIRF microscope. The software requires a high-RAM computer (64 Gb) to run and includes a photobleaching detrending algorithm, which allows extension of the live imaging for more than an hour.
Keyphrases
- high resolution
- induced apoptosis
- single molecule
- optical coherence tomography
- deep learning
- cell cycle arrest
- amino acid
- machine learning
- high speed
- oxidative stress
- endoplasmic reticulum stress
- magnetic resonance
- computed tomography
- label free
- data analysis
- convolutional neural network
- magnetic resonance imaging
- electronic health record
- artificial intelligence
- small molecule
- contrast enhanced
- solar cells
- diffusion weighted imaging