SRRF ‘n’ TIRF - FCS: Simultaneous spatiotemporal super-resolution microscopy

Abstract

17Super-resolution microscopy and single molecule fluorescence spectroscopy require mutually 18 exclusive experimental strategies optimizing either time or spatial resolution. To achieve both, 19we implement a GPU-supported, camera-based measurement strategy that highly resolves 20 spatial structures (~60 nm), temporal dynamics (≤ 2 ms), and molecular brightness from the 21 exact same data set. We demonstrate the applicability and advantages of multi-parametric 22 measurements to monitor the super-resolved structure and dynamics of two different 23 biomolecules, the actin binding polypeptide LifeAct, and the epidermal growth factor receptor 24 (EGFR). Simultaneous super-resolution of spatial and temporal details leads to an improved 25 precision in estimating the diffusion coefficient of LifeAct in dependence of the cellular actin 26 network. Multi-parametric analysis suggests that the domain partitioning of EGFR is primarily 27 determined by EGFR-membrane interactions, possibly sub-resolution clustering and inter-28 EGFR interactions but is largely independent of EGFR-actin interactions. These results 29 demonstrate that pixel-wise cross-correlation of parameters obtained from different techniques 30 on the same data set enables robust physicochemical parameter estimation and provides new 31 biological knowledge that cannot be obtained from sequential measurements. 32 33 Key Words 34 35 Super-resolution, Imaging fluorescence correlation spectroscopy, Number and brightness 36 analysis, Super-resolution radial fluctuations, FCS diffusion law, Epidermal growth factor 37 receptor 38 egfr 39 Simultaneous spatiotemporal super-resolution microscopy

The dependence of EGFR oligomerization on environment and structure: A camera-based N&B study

“…The data analyses were performed on a computer equipped with a GPU with the following configuration: Windows 10 Home 64-bit operating system, Intel® Core™ i7-7800X CPU @ 3.50 GHz processor, 32 GB RAM, NVIDIA TITAN Xp GPU with 3,840 CUDA cores and 12.3 GB memory. The image stacks were loaded into the GPU-driven ImFCS 1.52 plugin (27, 38) in Fiji. A polynomial of order 2 was used to correct for photobleaching.…”

Multicolor fluorescence fluctuation spectroscopy in living cells via spectral detection

“…Notably, two-species SFSCS can not only successfully discriminate between mEGFP and mEYFP, but is also applicable when using the red FPs mApple and mCherry2. These two FPs were successfully used in several FFS studies (14,30,39), providing the best compromise between brightness, maturation and photostability among available red FPs, which generally suffer from reduced SNR compared to FPs emitting in the green or yellow part of the optical spectrum (13,14,40).…”

Multi-color fluorescence fluctuation spectroscopy in living cells via spectral detection

“…Notably, two-species SFSCS could not only successfully discriminate between mEGFP and mEYFP, but was also applicable on the red FPs mApple and mCherry2. These two FPs were successfully used in several FFS studies (14, 30, 39), providing the best compromise between brightness, maturation and photostability among available red FPs, which generally suffer from reduced SNR compared to FPs emitting in the green or yellow part of the optical spectrum (13, 14, 40).…”

“…The mp-mEYFP-(L)-mCherry2-(L)-mEGFP-(L)-mApple plasmid was generated by inserting an mEGFP-(L)-mApple cassette into mp-mEYFP-(L)-mCherry2-(L) by digestion with KpnI and EcoRI. The mEGFP-(L)-mApple construct was cloned beforehand by amplifying mApple from PMT-mApple (39) (a kind gift from Thorsten Wohland) and inserting it into mEGFP-(L)_pcDNA3.1+ by digestion with BamHI and EcoRI. The mEGFP-(L)_pcDNA3.1+ plasmid was obtained by amplifying mEGFP from an mEGFP-N1 vector (using a primer encoding a long rigid linker sequence) and inserting it into a pcDNA3.1+ vector by KpnI and BamHI restriction.…”