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

2020 ◽  
Author(s):  
Valentin Dunsing ◽  
Annett Petrich ◽  
Salvatore Chiantia

AbstractFluorescence fluctuation spectroscopy provides a powerful toolbox to quantify transport dynamics and interactions between biomolecules in living cells. For example, cross-correlation analysis of spectrally separated fluctuations allows the investigation of inter-molecular interactions. This analysis is conventionally limited to two fluorophore species that are excited with a single or two different laser lines and detected in two non-overlapping spectral channels. However, signaling pathways in biological systems often involve interactions between multiple biomolecules, e.g. formation of ternary or quaternary protein complexes. Here, we present a methodology to investigate such interactions at the plasma membrane (PM) of cells, as encountered for example in viral assembly or receptor-ligand interactions. To this aim, we introduce scanning fluorescence spectral correlation spectroscopy (SFSCS), a combination of scanning fluorescence correlation spectroscopy with spectrally resolved detection and decomposition. We first demonstrate that SFSCS allows cross-talk-free cross-correlation analysis of PM-associated proteins labeled with strongly overlapping fluorescent proteins (FPs), such as mEGFP and mEYFP, excited with a single excitation line. We then verify the applicability of SFSCS for quantifying diffusion dynamics and protein oligomerization (based on molecular brightness analysis) of two protein species tagged with spectrally overlapping FPs. Adding a second laser line, we demonstrate the possibility of three- and four-species (cross-) correlation analysis using mApple and mCherry2, as examples of strongly overlapping FP tags in the red spectral region. Next, we apply this scheme to investigate the interactions of influenza A virus (IAV) matrix protein 2 (M2) with two cellular host factors simultaneously. Using the same set of fluorophores, we furthermore extend the recently presented raster spectral image correlation spectroscopy (RSICS) approach to four species analysis, successfully demonstrating multiplexed RSICS measurements of protein interactions in the cell cytoplasm. Finally, we apply RSICS to investigate the assembly of the ternary IAV polymerase complex and report a 2:2:2 stoichiometry of these protein assemblies in the nucleus of living cells.

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Valentin Dunsing ◽  
Annett Petrich ◽  
Salvatore Chiantia

Signaling pathways in biological systems rely on specific interactions between multiple biomolecules. Fluorescence fluctuation spectroscopy provides a powerful toolbox to quantify such interactions directly in living cells. Cross-correlation analysis of spectrally separated fluctuations provides information about inter-molecular interactions but is usually limited to two fluorophore species. Here, we present scanning fluorescence spectral correlation spectroscopy (SFSCS), a versatile approach that can be implemented on commercial confocal microscopes, allowing the investigation of interactions between multiple protein species at the plasma membrane. We demonstrate that SFSCS enables cross-talk-free cross-correlation, diffusion and oligomerization analysis of up to four protein species labeled with strongly overlapping fluorophores. As an example, we investigate the interactions of influenza A virus (IAV) matrix protein 2 with two cellular host factors simultaneously. We furthermore apply raster spectral image correlation spectroscopy for the simultaneous analysis of up to four species and determine the stoichiometry of ternary IAV polymerase complexes in the cell nucleus.


2021 ◽  
Author(s):  
Annett Petrich ◽  
Valentin Dunsing ◽  
Sara Bobone ◽  
Salvatore Chiantia

Influenza A virus (IAV) is a respiratory pathogen that causes seasonal epidemics and occasional pandemics of severe illnesses with significant mortality. One of the most abundant proteins in IAV particles is the matrix protein 1 (M1), which is essential for the structural stability of the virus. M1 organizes virion assembly and budding at the plasma membrane (PM), where it can interact with other viral components and cellular membrane factors (i.e. lipids and host proteins). Of interest, the recruitment of M1 to the PM as well as its interaction with the other viral envelope proteins (hemagglutinin (HA), neuraminidase, matrix protein 2 (M2)) is controversially discussed in previous studies. Therefore, we used fluorescence fluctuation microscopy techniques (i.e. (cross-correlation) number and brightness, and scanning fluorescence cross-correlation spectroscopy) to quantify the oligomeric state of M1 and its interaction with other viral proteins in co-transfected as well as infected cells. Our results indicate that M1 is recruited to the PM by M2, as a consequence of the strong interaction between the two proteins. In contrast, only a weak interaction between M1 and HA was observed. M1-HA interaction occurred only in the case that M1 was already bound to the PM. We therefore conclude that M2 initiates the assembly of IAV by recruiting M1 to the PM, possibly allowing its further interaction with other viral proteins.


2019 ◽  
Vol 11 (1) ◽  
pp. 01025-1-01025-5 ◽  
Author(s):  
N. A. Borodulya ◽  
◽  
R. O. Rezaev ◽  
S. G. Chistyakov ◽  
E. I. Smirnova ◽  
...  

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1571 ◽  
Author(s):  
Jhonatan Camacho Navarro ◽  
Magda Ruiz ◽  
Rodolfo Villamizar ◽  
Luis Mujica ◽  
Jabid Quiroga

2010 ◽  
Vol 09 (02) ◽  
pp. 203-217 ◽  
Author(s):  
XIAOJUN ZHAO ◽  
PENGJIAN SHANG ◽  
YULEI PANG

This paper reports the statistics of extreme values and positions of extreme events in Chinese stock markets. An extreme event is defined as the event exceeding a certain threshold of normalized logarithmic return. Extreme values follow a piecewise function or a power law distribution determined by the threshold due to a crossover. Extreme positions are studied by return intervals of extreme events, and it is found that return intervals yield a stretched exponential function. According to correlation analysis, extreme values and return intervals are weakly correlated and the correlation decreases with increasing threshold. No long-term cross-correlation exists by using the detrended cross-correlation analysis (DCCA) method. We successfully introduce a modification specific to the correlation and derive the joint cumulative distribution of extreme values and return intervals at 95% confidence level.


2021 ◽  
Vol 27 (S1) ◽  
pp. 1540-1541
Author(s):  
Tristan O'Neill ◽  
B. C. Regan ◽  
Matthew Mecklenburg

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