Living cells rely on transport and interaction of biomolecules to perform their diverse functions. To investigate the underlying molecular processes in the native cellular environment, minimally invasive techniques are needed. Fluorescence fluctuation spectroscopy (FFS) approaches provide a powerful toolbox that fulfills this aim (Jameson et al., 2009; Weidemann et al., 2014; Petazzi et al., 2020). FFS takes advantage of inherent molecular dynamics present in biological systems, for example, diffusion, to obtain molecular parameters from fluctuations of the signal emitted by an ensemble of fluorescent molecules. More in detail, the temporal evolution of such fluctuations allows the quantification of intracellular dynamics. In addition, concentration and oligomerization state of molecular complexes can be determined by analyzing the magnitude of fluctuations. Finally, hetero-interactions of different molecular species can be detected by cross-correlation analysis of fluctuations emitted by spectrally separated fluorophores (Schwille et al., 1997). Over the last two decades, several experimental FFS schemes such as raster image (cross-) correlation spectroscopy (RI(C)CS) (Digman et al., 2005; Digman et al., 2009b), (cross-correlation) Number&Brightness analysis (Digman et al., 2008; Digman et al., 2009a), and imaging FCS (Krieger et al., 2015) have been developed, extending the concept of traditional single-point fluorescence (cross-) correlation spectroscopy (F(C)CS) (Magde et al., 1972). A further interesting example of FFS analysis relevant in the field of cell biology is represented by scanning F(C)CS (SF(C)CS). Using a scan path perpendicular to the plasma membrane (PM), this technique provides enhanced stability and the ability to probe slow membrane dynamics (Ries and Schwille, 2006), protein interactions (Ries et al., 2009b; Dunsing et al., 2017), and oligomerization (Dunsing et al., 2018) at the PM of cells.

FFS studies are conventionally limited to the analysis of two spectrally distinguished species due to (i) broad emission spectra of fluorophores with consequent cross-talk artifacts and (ii) limited overlap of detection/excitation geometries for labels with large spectral separation. Generally, only a few fluorescence-based methods are available to detect ternary or higher order interactions of proteins (Galperin et al., 2004; Sun et al., 2010; Hur et al., 2016). First in vitro approaches to perform FCS on more than two species exploited quantum dots (Burkhardt et al., 2005) or fluorescent dyes with different Stokes shifts excited with a single laser line in one- (Hwang et al., 2006) or two-photon excitation (Heinze et al., 2004; Ridgeway et al., 2012a), coupled with detection on two or more single photon counting detectors. Following an alternative conceptual approach, it was shown in vitro that two spectrally strongly overlapping fluorophore species can be discriminated in FCS by applying statistical filtering of detected photons based on spectrally resolved (fluorescence spectral correlation spectroscopy [FSCS]; Benda et al., 2014) or fluorescence lifetime (fluorescence lifetime correlation spectroscopy [FLCS]; Böhmer et al., 2002; Kapusta et al., 2007; Ghosh et al., 2018) detection. Such framework allows the minimization of cross-talk artifacts in FCCS measurements performed in living cells (Padilla-Parra et al., 2011). Recently, three-species implementations of RICCS and FCCS were successfully demonstrated for the first time in living cells. Schrimpf et al. presented raster spectral image correlation spectroscopy (RSICS), a powerful combination of RICS with spectral detection and statistical filtering based on the emission spectra of mEGFP, mVenus, and mCherry fluorophores (Schrimpf et al., 2018). Stefl et al. developed single-color fluorescence lifetime cross-correlation spectroscopy (sc-FLCCS), taking advantage of several GFP variants characterized by short or long fluorescence lifetimes (Štefl et al., 2020). Using this elegant approach, three-species FCCS measurements could be performed in yeast cells, with just two excitation lines.

Here, we explore the full potential of FSCS and RSICS. In particular, we present scanning fluorescence spectral correlation spectroscopy (SFSCS), combining SFCS and FSCS. We show that SFSCS enables cross-talk-free SFCCS measurements of two protein species at the PM of living cells tagged with strongly overlapping fluorophores in the green or red regions of the visible spectrum, excited with a single excitation line. This approach results in correct estimates of protein diffusion dynamics, oligomerization, and interactions between both species. Further, we extend our approach to the analysis of three or four interacting partners: by performing cross-correlation measurements on different fluorescent protein (FP) hetero-oligomers, we demonstrate that up to four FP species can be simultaneously analyzed. We then apply this scheme to simultaneously investigate the interaction of influenza A virus (IAV) matrix protein 2 (M2) with two cellular host factors, the tetraspanin CD9 and the autophagosome protein LC3, co-expressed in the same cell. Finally, we extend RSICS for the detection of four molecular species and quantify, for the first time directly in living cells, the complete stoichiometry of ternary IAV polymerase complexes assembling in the nucleus, using three-species fluorescence correlation and brightness analysis.

In this work, we combine FFS techniques with spectral detection to perform multicolor studies of protein interactions and dynamics in living cells. In particular, we present SFSCS, a combination of FSCS (Benda et al., 2014) and lateral scanning FCS (Ries and Schwille, 2006). We show that SFSCS allows cross-talk-free measurements of protein interactions and diffusion dynamics at the PM of cells and demonstrate that it is capable of detecting three or four species simultaneously. Furthermore, we extend RSICS (Schrimpf et al., 2018) to investigate four fluorophore species and apply this approach to determine the stoichiometry of higher order protein complexes assembling in the cell nucleus. Notably, the technical approaches can be carried out on a standard confocal microscope, equipped with a spectral photon counting detector system.

In the first part, we present two-species SFSCS using a single excitation wavelength and strongly overlapping fluorophores. Compared to the conventional implementation of FCCS with two excitation lasers and two detectors, two-species SFSCS has substantial advantages, similar to the recently presented sc-FLCCS (Štefl et al., 2020). Since it requires a single excitation line and is compatible with spectrally strongly overlapping FPs, it circumvents optical limitations such as imperfect overlap of the observation volumes. This is evident from higher rel.cc. values of 70–80% measured for mEGFP and mEYFP coupled in FP hetero-oligomers compared to 45–60% observed for mEGFP and mCherry2. Rel.cc. values around 70% are to be expected for the examined FP tandems even in the case of single-wavelength excitation, given that the p_f for such fluorophores is indeed around 0.7 (Foo et al., 2012; Dunsing et al., 2018) (see also SI, paragraph 1). On the other hand, in three- and four species measurements discussed below, FP pairs requiring two excitation wavelengths display the typical reduction of the rel.cc. due to imperfect optical volume overlap. For combinations of green and red FPs, rel.cc. values below 60% were also observed with single-wavelength excitation (Foo et al., 2012; Shi et al., 2009), indicating that overlap of both excitation and detection volumes (the latter requiring FPs with similar emission spectra) is required to maximize the achievable cross-correlation (Foo et al., 2012). 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 (Dunsing et al., 2018; Foust et al., 2019; Sankaran et al., 2021), 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 (Dunsing et al., 2017; Dunsing et al., 2018; Cranfill et al., 2016).

In comparison to sc-FLCCS, it may be more robust to discriminate fluorophores based on spectra rather than lifetimes, which can be strongly affected by FRET (Štefl et al., 2020). The emission spectra of the FPs utilized in this study did not depend on cell lines or subcellular localization (Figure 5—figure supplement 1) and showed no (mEGFP, mEYFP) or little (mApple, mCherry2) variation with pH over a range of 5.0–9.2 (Figure 5—figure supplement 2). For red FPs, specifically mApple, a red shift appeared at more acidic pH, in agreement with previous studies (Hendrix et al., 2008). This aspect should be considered for specific applications, for example, RSICS in the cytoplasm containing acidic compartments such as lysosomes. Generally, spectral approaches require accurate detection of photons in each spectral bin. A previous study using the same detection system reported intrinsic cross-talk between adjacent spectral bins (Foust et al., 2019). However, since the methodology presented here is based on temporal (SFSCS) or spatial (RSICS) correlation (both excluding the correlation at zero time or spatial lag), this issue can be neglected in our analysis.

A major limitation of SFSCS is the reduced SNR of the CFs (see Figure 1, Figure 3—figure supplement 3) caused by the statistical filtering of the signal emitted by spectrally overlapping fluorophore species (see, e.g., Figure 4—figure supplement 1). This limitation applies to all FFS methods that discriminate different fluorophore species based on spectral (e.g., FSCS [Benda et al., 2014], RSICS [Schrimpf et al., 2018]) or lifetime patterns (e.g., sc-FLCCS [Štefl et al., 2020]). The increase in noise depends on the spectral (or lifetime) overlap of different species and is more prominent for species that completely lack ‘pure’ channels, that is, detection channels in which the majority of photons can be univocally assigned to a single species (Schrimpf et al., 2018). In sc-FLCCS, this issue particularly compromises the SNR of short lifetime species (Štefl et al., 2020) since photons of longer lifetime species are detected in all ‘short lifetime’ channels at substantial relative numbers. In these conditions, sc-FLCCS could not provide reliable results with sixfold (or higher) difference in relative protein abundance, even though the lower abundant protein was tagged with the brighter, longer lifetime FP (Štefl et al., 2020). Similarly in SFSCS, CFs corresponding to mEYFP or mCherry2 were most prone to noise (Figure 1C and F) since all channels that contain, for example, mEYFP signal also contain mEGFP signal (Figure 1—figure supplement 1). In our experiments, cross-talk-free SFSCS analysis with two species excited with a single excitation wavelength could be performed for relative intensity levels as low as 1:10 (mEGFP/mEYFP) or 1:5 (mApple/mCherry2). In this range, SFSCS not only enabled the quantification of protein interactions via cross-correlation analysis, but also yielded correct estimates of protein diffusion dynamics and oligomerization at the PM. An improvement of the allowed relative concentration range can be achieved by using brighter or more photostable fluorophores, for example, organic dyes, compensating for reduced SNR due to statistical filtering. Alternatively, FP tags could be selected based on proteins’ oligomerization state. For example, monomeric proteins exhibiting low molecular brightness should be tagged with fluorophores that are less prone to noise. It should be noted that the limitation of reduced SNR due to excess signal from another species also applies to conventional dual-color FCCS: bleed-through from green to red channels can be corrected on average, but reduces the SNR in red channels (Bacia et al., 2012), unless more sophisticated schemes such as pulsed interleaved excitation (Müller et al., 2005; Hendrix et al., 2013) are applied.

Having demonstrated that two-species SFSCS is feasible with a single excitation wavelength in the green (mEGFP, mEYFP) or red (mApple, mCherry2) part of the visible spectrum, we finally implemented three- and four-species SFSCS as well as four-species RSICS. These extensions do not further compromise the SNR of CFs detected for mEGFP and mEYFP (see Figure 3—figure supplement 3A,B), but may additionally reduce the SNR of CFs corresponding to red FPs (in particular when mEGFP and/or mEYFP concentration is much higher than that of red FPs, Figure 3—figure supplement 3C). For this reason, three- and four-species analysis was restricted to cells with relative average intensity levels of 1:5 or less between species with adjacent emission spectra. In this range, the increase in noise due to statistical filtering was moderate, benefitting from the fairly large spectral separation of green/yellow and red emission (Figure 3—figure supplement 3). In addition, the higher molecular brightness of mApple (compared to mCherry2) compensated for the larger overlap of this FP with the tail of mEYFP emission. The excitation power for red FPs was generally limited by the lower photostability of mApple, which could be responsible for consistently lower rel.cc. values of mEGFP or mEYFP with mApple than with mCherry2. Nevertheless, four-species SFSCS and RSICS could successfully resolve different combinations of strongly overlapping FP hetero-oligomers, for example, a mixture of mEGFP-mCherry2 and mEYFP-mApple heterodimers, at the PM or in the cytoplasm of cells. To explore the interaction of four different FP-tagged proteins, four-species FFS may substantially reduce the experimental effort because all pair-wise interactions can be quantified in a single measurement (instead of six separate conventional two-species FCCS measurements). Yet, weak interaction of proteins, that is, a low amount of hetero-complexes compared to a high amount of unbound proteins, may not be detectable due to the large noise of the CCF in this case. The SNR might be further compromised by slow FP maturation or dark FP states, limiting the amount of complexes that simultaneously emit fluorescence of all bound FP species (Dunsing et al., 2018). Ultimately, the mentioned limitations currently restrict SFSCS and RSICS to four FP species. The approaches would thus strongly benefit from a multiparametric analysis. For instance, combining spectral and lifetime detection schemes would provide additional contrast for photons detected in the same spectral bin. This improvement could expand the range of detectable relative concentrations or might allow further multiplexing of FFS.

Conventional two-color scanning FCCS has been previously applied to quantify receptor-ligand interactions in living zebrafish embryos (Ries et al., 2009b) and CRISPR/Cas9 edited cell lines to study such interactions at endogenous protein level (Eckert et al., 2020). SFSCS is thus directly applicable in the complex environment of living multicellular organisms. In this context, spectral information could be further exploited to separate low signal levels of endogenously expressed, fluorescently tagged proteins from autofluorescence background.

As a first biological application of SFSCS, we investigated the interaction of IAV matrix protein M2 with two cellular host factors: the tetraspanin CD9 and the autophagosome protein LC3. We observed strong association of LC3 with M2, and consequent recruitment of LC3 to the PM (Figure 3—figure supplement 4), in agreement with previous in vitro and localization studies (Beale et al., 2014). Interestingly, molecular brightness analysis reported oligomerization (dimers to tetramers) of M2, but indicated a monomeric state of LC3 at the PM, that is, binding of LC3 to M2 in an apparent stoichiometry of 1:2 to 1:4. However, each M2 monomer provides a binding site for LC3 in the cytoplasmic tail (Claridge et al., 2020). A more detailed analysis of our data showed that in the analyzed cells (i.e., cells showing clear membrane recruitment of LC3, Figure 3—figure supplement 4A,B), the PM concentration of LC3 was on average only 30% compared to that of M2 (Figure 3—figure supplement 4C), although both proteins were expressed in comparable amounts in the sample in general. This suggests that not all potential binding sites in the cytoplasmic tail of M2 may be available to fluorescently tagged LC3, either due to binding of endogenous LC3, other cellular host factors, or steric hindrance. In contrast to the case of LC3, we did not detect significant binding of M2 with the tetraspanin CD9, a protein that was previously shown to be incorporated into IAV virions and supposedly plays a functional role during the infection process (Shaw et al., 2008; Hutchinson, 2014). Of note, we cannot exclude the possibility that the FP tag at the C-terminus of CD9 might hamper interactions with M2, in the specific case of M2-CD9 interaction being mediated by the C-terminal cytoplasmic tails of the two proteins. In future studies, the approach presented here may be used to further elucidate the complex interaction network of viral proteins, for example, matrix protein 1 (M1) (Hilsch et al., 2014), M2, HA, and neuraminidase, cellular host factors, and PM lipids (Bobone et al., 2017) during the assembly process of IAV at the PM of living cells (Rossman and Lamb, 2011).

Finally, we demonstrated that RSICS allows the quantification of the stoichiometry of higher order molecular complexes, based on molecular brightness analysis for each FP species. As example of an application in a biological context, we determined the stoichiometry of the IAV PC. Our data provide strong evidence for a 2:2:2 stoichiometry of the PC subunits PA, PB1, and PB2, that is, dimerization of heterotrimeric PCs. Such interactions were previously proposed based on experiments in solution using X-ray crystallography and cryo-electron microscopy (Fan et al., 2019), co-immunoprecipitation assays (Jorba et al., 2008; Nilsson-Payant et al., 2018), as well as single-channel brightness analysis of FCCS data (for the PA subunit) (Huet et al., 2010). Intermolecular interactions in the PC are hypothesized to be required for the initiation of vRNA synthesis during replication of the viral genome (Fan et al., 2019; Chen et al., 2019). The results presented here provide the first quantification of these interactions in living cells and a direct estimate of the stoichiometry of PCs in the cell nucleus. The formation of ternary PC complexes in these samples could be extrapolated from the observed high rel.cc. values for all three pair combinations, indicating very low amounts of unbound PA, PB1, or PB2 and higher order interactions (see Appendix 1, Section A1.1 for additional details). Furthermore, this observation could also be directly confirmed by performing, for the first time in living cells, a triple-correlation analysis (TRICS), indicating the presence of a considerable amount of PA-PB1-PB2 complexes. It is worth noting though that the detection of coincident triple fluctuations is prone to considerable noise and thus still limited to molecular complexes present at low concentration and characterized by high molecular brightness for each fluorophore species (Ridgeway et al., 2012a; Ridgeway et al., 2012b).

Of note, the RSICS approach presented here provides for the first time simultaneous information on molecular interactions, molecular brightness (and thus stoichiometry), diffusion dynamics, and concentration for all three complex subunits. This specific feature opens the possibility of a more in-depth analysis. For example, it is possible to quantify the relative cross-correlation of two subunits, e.g. PA and PB2, as a function of the relative concentration of the third subunit, for example, PB1 (Figure 6—figure supplement 1A). Similarly, molecular brightness and diffusion coefficients can be analyzed as a function of the abundance of each subunit (Figure 6—figure supplement 1B,C). With this approach, it is therefore possible to distinguish specific molecular mechanisms, such as inefficient PA-PB2 interactions in the presence of low PB1 concentration or efficient heterotrimer dimerization when all subunits are present at similar concentrations. The employed experimental scheme offers a powerful tool for future studies, exploring, for example, interaction of the PC with cellular host factors or the development of inhibitors that could interfere with the assembly process of the complex, as a promising therapeutic target for antiviral drugs (Massari et al., 2021).

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1-7. The analysis software is freely available on GitHub: https://github.com/VaDu8989/SpectralFFS.

1. 1. Bacia K
   2. Petrášek Z
   3. Schwille P

   (2012) Correcting for spectral cross-talk in dual-color fluorescence cross-correlation spectroscopy

   Chemphyschem 13:1221–1231.

   https://doi.org/10.1002/cphc.201100801

   • PubMed
   • Google Scholar
2. 1. Beale R
   2. Wise H
   3. Stuart A
   4. Ravenhill BJ
   5. Digard P
   6. Randow F

   (2014) A LC3-interacting motif in the influenza A virus M2 protein is required to subvert autophagy and maintain virion stability

   Cell Host & Microbe 15:239–247.

   https://doi.org/10.1016/j.chom.2014.01.006

   • PubMed
   • Google Scholar
3. 1. Benda A
   2. Kapusta P
   3. Hof M
   4. Gaus K

   (2014) Fluorescence spectral correlation spectroscopy (FSCS) for probes with highly overlapping emission spectra

   Optics Express 22:2973–2988.

   https://doi.org/10.1364/OE.22.002973

   • PubMed
   • Google Scholar
4. 1. Bobone S
   2. Hilsch M
   3. Storm J
   4. Dunsing V
   5. Herrmann A
   6. Chiantia S

   (2017) Phosphatidylserine lateral organization influences the interaction of influenza virus matrix protein 1 with lipid membranes

   Journal of Virology 91:e00267-17.

   https://doi.org/10.1128/JVI.00267-17

   • PubMed
   • Google Scholar
5. 1. Böhmer M
   2. Wahl M
   3. Rahn HJ
   4. Erdmann R
   5. Enderlein J

   (2002) Time-resolved fluorescence correlation spectroscopy

   Chemical Physics Letters 353:439–445.

   https://doi.org/10.1016/S0009-2614(02)00044-1

   • Google Scholar
6. 1. Burkhardt M
   2. Heinze KG
   3. Schwille P

   (2005) Four-color fluorescence correlation spectroscopy realized in a grating-based detection platform

   Optics Letters 30:2266–2268.

   https://doi.org/10.1364/ol.30.002266

   • PubMed
   • Google Scholar
7. 1. Chen K-Y
   2. Santos Afonso ED
   3. Enouf V
   4. Isel C
   5. Naffakh N

   (2019) Influenza virus polymerase subunits co-evolve to ensure proper levels of dimerization of the heterotrimer

   PLOS Pathogens 15:e1008034.

   https://doi.org/10.1371/journal.ppat.1008034

   • PubMed
   • Google Scholar
8. 1. Ciccotosto GD
   2. Kozer N
   3. Chow TTY
   4. Chon JWM
   5. Clayton AHA

   (2013) Aggregation distributions on cells determined by photobleaching image correlation spectroscopy

   Biophysical Journal 104:1056–1064.

   https://doi.org/10.1016/j.bpj.2013.01.009

   • PubMed
   • Google Scholar
9. 1. Claridge JK
   2. Mohd-Kipli F
   3. Florea A
   4. Gate T
   5. Schnell JR

   (2020) Ph-dependent secondary structure propensity of the influenza A virus m2 cytoplasmic tail

   Biomolecular NMR Assignments 14:157–161.

   https://doi.org/10.1007/s12104-020-09937-8

   • PubMed
   • Google Scholar
10. 1. Cranfill PJ
    2. Sell BR
    3. Baird MA
    4. Allen JR
    5. Lavagnino Z
    6. de Gruiter HM
    7. Kremers GJ
    8. Davidson MW
    9. Ustione A
    10. Piston DW

    (2016) Quantitative assessment of fluorescent proteins

    Nature Methods 13:557–562.

    https://doi.org/10.1038/nmeth.3891

    • PubMed
    • Google Scholar
11. 1. Dahmane S
    2. Doucet C
    3. Le Gall A
    4. Chamontin C
    5. Dosset P
    6. Murcy F
    7. Fernandez L
    8. Salas D
    9. Rubinstein E
    10. Mougel M
    11. Nollmann M
    12. Milhiet PE

    (2019) Nanoscale organization of tetraspanins during HIV-1 budding by correlative DSTORM/AFM

    Nanoscale 11:6036–6044.

    https://doi.org/10.1039/c8nr07269h

    • PubMed
    • Google Scholar
12. 1. Digman MA
    2. Brown CM
    3. Sengupta P
    4. Wiseman PW
    5. Horwitz AR
    6. Gratton E

    (2005) Measuring fast dynamics in solutions and cells with a laser scanning microscope

    Biophysical Journal 89:1317–1327.

    https://doi.org/10.1529/biophysj.105.062836

    • PubMed
    • Google Scholar
13. 1. Digman MA
    2. Dalal R
    3. Horwitz AF
    4. Gratton E

    (2008) Mapping the number of molecules and brightness in the laser scanning microscope

    Biophysical Journal 94:2320–2332.

    https://doi.org/10.1529/biophysj.107.114645

    • PubMed
    • Google Scholar
14. 1. Digman MA
    2. Wiseman PW
    3. Choi C
    4. Horwitz AR
    5. Gratton E

    (2009a) Stoichiometry of molecular complexes at adhesions in living cells

    PNAS 106:2170–2175.

    https://doi.org/10.1073/pnas.0806036106

    • PubMed
    • Google Scholar
15. 1. Digman MA
    2. Wiseman PW
    3. Horwitz AR
    4. Gratton E

    (2009b) Detecting protein complexes in living cells from laser scanning confocal image sequences by the Cross correlation raster image spectroscopy method

    Biophysical Journal 96:707–716.

    https://doi.org/10.1016/j.bpj.2008.09.051

    • PubMed
    • Google Scholar
16. 1. Dunsing V
    2. Mayer M
    3. Liebsch F
    4. Multhaup G
    5. Chiantia S

    (2017) Direct evidence of amyloid precursor-like protein 1 trans interactions in cell-cell adhesion platforms investigated via fluorescence fluctuation spectroscopy

    Molecular Biology of the Cell 28:3609–3620.

    https://doi.org/10.1091/mbc.E17-07-0459

    • PubMed
    • Google Scholar
17. 1. Dunsing V
    2. Chiantia S

    (2018) A fluorescence fluctuation spectroscopy assay of protein-protein interactions at cell-cell contacts

    Journal of Visualized Experiments 1:e58582.

    https://doi.org/10.3791/58582

    • Google Scholar
18. 1. Dunsing V
    2. Luckner M
    3. Zühlke B
    4. Petazzi RA
    5. Herrmann A
    6. Chiantia S

    (2018) Optimal fluorescent protein tags for quantifying protein oligomerization in living cells

    Scientific Reports 8:10634.

    https://doi.org/10.1038/s41598-018-28858-0

    • PubMed
    • Google Scholar
19. Software

    1. Dunsing V
    2. Chiantia S

    (2021) SpectralFFS, version 8942980

    Github.

    https://github.com/VaDu8989/SpectralFFS
20. 1. Eckert AF
    2. Gao P
    3. Wesslowski J
    4. Wang X
    5. Rath J
    6. Nienhaus K
    7. Davidson G
    8. Nienhaus GU

    (2020) Measuring ligand-cell surface receptor affinities with axial line-scanning fluorescence correlation spectroscopy

    eLife 9:e55286.

    https://doi.org/10.7554/eLife.55286

    • PubMed
    • Google Scholar
21. 1. Fan H
    2. Walker AP
    3. Carrique L
    4. Keown JR
    5. Serna Martin I
    6. Karia D
    7. Sharps J
    8. Hengrung N
    9. Pardon E
    10. Steyaert J
    11. Grimes JM
    12. Fodor E

    (2019) Structures of Influenza a virus RNA polymerase offer insight into viral genome replication

    Nature 573:287–290.

    https://doi.org/10.1038/s41586-019-1530-7

    • PubMed
    • Google Scholar
22. 1. Florin L
    2. Lang T

    (2018) Tetraspanin assemblies in virus infection

    Frontiers in Immunology 9:1140.

    https://doi.org/10.3389/fimmu.2018.01140

    • PubMed
    • Google Scholar
23. 1. Foo YH
    2. Naredi-Rainer N
    3. Lamb DC
    4. Ahmed S
    5. Wohland T

    (2012) Factors affecting the quantification of biomolecular interactions by fluorescence cross-correlation spectroscopy

    Biophysical Journal 102:1174–1183.

    https://doi.org/10.1016/j.bpj.2012.01.040

    • PubMed
    • Google Scholar
24. 1. Foust DJ
    2. Godin AG
    3. Ustione A
    4. Wiseman PW
    5. Piston DW

    (2019) Two-color spatial cumulant analysis detects heteromeric interactions between membrane proteins

    Biophysical Journal 117:1764–1777.

    https://doi.org/10.1016/j.bpj.2019.09.028

    • PubMed
    • Google Scholar
25. 1. Galperin E
    2. Verkhusha VV
    3. Sorkin A

    (2004) Three-chromophore fret Microscopy to analyze multiprotein interactions in living cells

    Nature Methods 1:209–217.

    https://doi.org/10.1038/nmeth720

    • PubMed
    • Google Scholar
26. 1. Ghosh A
    2. Karedla N
    3. Thiele JC
    4. Gregor I
    5. Enderlein J

    (2018) Fluorescence Lifetime Correlation Spectroscopy: Basics and applications

    Methods 140–141:32–39.

    https://doi.org/10.1016/j.ymeth.2018.02.009

    • PubMed
    • Google Scholar
27. 1. Hantak MP
    2. Qing E
    3. Earnest JT
    4. Gallagher T

    (2019) Tetraspanins: Architects of viral entry and exit platforms

    Journal of Virology 93:e01429-17.

    https://doi.org/10.1128/JVI.01429-17

    • PubMed
    • Google Scholar
28. 1. Heinze KG
    2. Jahnz M
    3. Schwille P

    (2004) Triple-color coincidence analysis: One step further in following higher order molecular complex formation

    Biophysical Journal 86:506–516.

    https://doi.org/10.1016/S0006-3495(04)74129-6

    • PubMed
    • Google Scholar
29. 1. Hendrix J
    2. Flors C
    3. Dedecker P
    4. Hofkens J
    5. Engelborghs Y

    (2008) Dark states in monomeric red fluorescent proteins studied by fluorescence correlation and single molecule spectroscopy

    Biophysical Journal 94:4103–4113.

    https://doi.org/10.1529/biophysj.107.123596

    • PubMed
    • Google Scholar
30. 1. Hendrix J
    2. Schrimpf W
    3. Höller M
    4. Lamb DC

    (2013) Pulsed interleaved excitation fluctuation imaging

    Biophysical Journal 105:848–861.

    https://doi.org/10.1016/j.bpj.2013.05.059

    • PubMed
    • Google Scholar
31. 1. Hendrix J
    2. Dekens T
    3. Schrimpf W
    4. Lamb DC

    (2016) Arbitrary-region raster image correlation spectroscopy

    Biophysical Journal 111:1785–1796.

    https://doi.org/10.1016/j.bpj.2016.09.012

    • PubMed
    • Google Scholar
32. 1. Hilsch M
    2. Goldenbogen B
    3. Sieben C
    4. Höfer CT
    5. Rabe JP
    6. Klipp E
    7. Herrmann A
    8. Chiantia S

    (2014) Influenza A matrix protein m1 multimerizes upon binding to lipid membranes

    Biophysical Journal 107:912–923.

    https://doi.org/10.1016/j.bpj.2014.06.042

    • PubMed
    • Google Scholar
33. 1. Huang L
    2. Pike D
    3. Sleat DE
    4. Nanda V
    5. Lobel P

    (2014) Potential pitfalls and solutions for use of fluorescent fusion proteins to study the lysosome

    PLOS ONE 9:e88893.

    https://doi.org/10.1371/journal.pone.0088893

    • PubMed
    • Google Scholar
34. 1. Huet S
    2. Avilov SV
    3. Ferbitz L
    4. Daigle N
    5. Cusack S
    6. Ellenberg J

    (2010) Nuclear import and assembly of influenza a virus RNA polymerase studied in live cells by fluorescence cross-correlation spectroscopy

    Journal of Virology 84:1254–1264.

    https://doi.org/10.1128/JVI.01533-09

    • PubMed
    • Google Scholar
35. 1. Hur KH
    2. Chen Y
    3. Mueller JD

    (2016) Characterization of ternary protein systems in vivo with tricolor heterospecies partition analysis

    Biophysical Journal 110:1158–1167.

    https://doi.org/10.1016/j.bpj.2016.01.009

    • PubMed
    • Google Scholar
36. 1. Hutchinson EC

    (2014) Conserved and host-specific features of influenza virIon architecture

    Nature Communications 5:1–11.

    https://doi.org/10.1038/ncomms7446

    • Google Scholar
37. 1. Hwang LC
    2. Gösch M
    3. Lasser T
    4. Wohland T

    (2006) Simultaneous multicolor fluorescence cross-correlation spectroscopy to detect higher order molecular interactions using single wavelength laser excitation

    Biophysical Journal 91:715–727.

    https://doi.org/10.1529/biophysj.105.074120

    • PubMed
    • Google Scholar
38. 1. Işbilir A
    2. Serfling R
    3. Möller J
    4. Thomas R
    5. De Faveri C
    6. Zabel U
    7. Scarselli M
    8. Beck-Sickinger AG
    9. Bock A
    10. Coin I
    11. Lohse MJ
    12. Annibale P

    (2021) Determination of G-protein-coupled receptor oligomerization by molecular brightness analyses in single cells

    Nature Protocols 16:1419–1451.

    https://doi.org/10.1038/s41596-020-00458-1

    • PubMed
    • Google Scholar
39. 1. Jameson DM
    2. Ross JA
    3. Albanesi JP

    (2009) Fluorescence fluctuation spectroscopy: Ushering in a new age of enlightenment for cellular dynamics

    Biophysical Reviews 1:105–118.

    https://doi.org/10.1007/s12551-009-0013-8

    • PubMed
    • Google Scholar
40. 1. Jorba N
    2. Area E
    3. Ortín J

    (2008) Oligomerization of the influenza virus polymerase complex in vivo

    The Journal of General Virology 89:520–524.

    https://doi.org/10.1099/vir.0.83387-0

    • PubMed
    • Google Scholar
41. 1. Kaliszewski MJ
    2. Shi X
    3. Hou Y
    4. Lingerak R
    5. Kim S
    6. Mallory P
    7. Smith AW

    (2018) Quantifying membrane protein oligomerization with fluorescence cross-correlation spectroscopy

    Methods 140:40–51.

    https://doi.org/10.1016/j.ymeth.2018.02.002

    • PubMed
    • Google Scholar
42. 1. Kapusta P
    2. Wahl M
    3. Benda A
    4. Hof M
    5. Enderlein J

    (2007) Fluorescence lifetime correlation spectroscopy

    Journal of Fluorescence 17:43–48.

    https://doi.org/10.1007/s10895-006-0145-1

    • PubMed
    • Google Scholar
43. 1. Kim SA
    2. Heinze KG
    3. Bacia K
    4. Waxham MN
    5. Schwille P

    (2005) Two-photon cross-correlation analysis of intracellular reactions with variable stoichiometry

    Biophysical Journal 88:4319–4336.

    https://doi.org/10.1529/biophysj.104.055319

    • PubMed
    • Google Scholar
44. 1. Kimura S
    2. Noda T
    3. Yoshimori T

    (2007) Dissection of the autophagosome maturation process by a novel reporter protein, tandem fluorescent-tagged LC3

    Autophagy 3:452–460.

    https://doi.org/10.4161/auto.4451

    • PubMed
    • Google Scholar
45. 1. Krieger JW
    2. Singh AP
    3. Bag N
    4. Garbe CS
    5. Saunders TE
    6. Langowski J
    7. Wohland T

    (2015) Imaging Fluorescence (cross-) correlation spectroscopy in live cells and organisms

    Nature Protocols 10:1948–1974.

    https://doi.org/10.1038/nprot.2015.100

    • PubMed
    • Google Scholar
46. 1. Linkert M
    2. Rueden CT
    3. Allan C
    4. Burel J-M
    5. Moore W
    6. Patterson A
    7. Loranger B
    8. Moore J
    9. Neves C
    10. Macdonald D
    11. Tarkowska A
    12. Sticco C
    13. Hill E
    14. Rossner M
    15. Eliceiri KW
    16. Swedlow JR

    (2010) Metadata matters: Access to image data in the real world

    The Journal of Cell Biology 189:777–782.

    https://doi.org/10.1083/jcb.201004104

    • PubMed
    • Google Scholar
47. 1. Magde D
    2. Elson E
    3. Webb WW

    (1972) Thermodynamic Fluctuations in a Reacting System—Measurement by Fluorescence Correlation Spectroscopy

    Physical Review Letters 29:705–708.

    https://doi.org/10.1103/PhysRevLett.29.705

    • Google Scholar
48. 1. Massari S
    2. Desantis J
    3. Nizi MG
    4. Cecchetti V
    5. Tabarrini O

    (2021) Inhibition of Influenza Virus Polymerase by Interfering with Its Protein-Protein Interactions

    ACS Infectious Diseases 7:1332–1350.

    https://doi.org/10.1021/acsinfecdis.0c00552

    • PubMed
    • Google Scholar
49. 1. Müller BK
    2. Zaychikov E
    3. Bräuchle C
    4. Lamb DC

    (2005) Pulsed interleaved excitation

    Biophysical Journal 89:3508–3522.

    https://doi.org/10.1529/biophysj.105.064766

    • PubMed
    • Google Scholar
50. 1. Nilsson-Payant BE
    2. Sharps J
    3. Hengrung N
    4. Fodor E

    (2018) The Surface-Exposed PA51-72-Loop of the Influenza A Virus Polymerase Is Required for Viral Genome Replication

    Journal of Virology 92:e00687-18.

    https://doi.org/10.1128/JVI.00687-18

    • PubMed
    • Google Scholar
51. 1. Padilla-Parra S
    2. Audugé N
    3. Coppey-Moisan M
    4. Tramier M

    (2011) Dual-color fluorescence lifetime correlation spectroscopy to quantify protein-protein interactions in live cell

    Microscopy Research and Technique 74:788–793.

    https://doi.org/10.1002/jemt.21015

    • PubMed
    • Google Scholar
52. 1. Palmer AG
    2. Thompson NL

    (1987) Molecular aggregation characterized by high order autocorrelation in fluorescence correlation spectroscopy

    Biophysical Journal 52:257–270.

    https://doi.org/10.1016/S0006-3495(87)83213-7

    • PubMed
    • Google Scholar
53. 1. Petazzi RA
    2. Aji AK
    3. Chiantia S

    (2020) Fluorescence microscopy methods for the study of protein oligomerization

    Progress in Molecular Biology and Translational Science 169:1–41.

    https://doi.org/10.1016/bs.pmbts.2019.12.001

    • PubMed
    • Google Scholar
54. 1. Ridgeway WK
    2. Millar DP
    3. Williamson JR

    (2012a) Quantitation of ten 30S ribosomal assembly intermediates using fluorescence triple correlation spectroscopy

    PNAS 109:13614–13619.

    https://doi.org/10.1073/pnas.1204620109

    • PubMed
    • Google Scholar
55. 1. Ridgeway WK
    2. Millar DP
    3. Williamson JR

    (2012b) The Spectroscopic Basis of Fluorescence Triple Correlation Spectroscopy

    The Journal of Physical Chemistry. B 116:1908–1919.

    https://doi.org/10.1021/jp208605z

    • PubMed
    • Google Scholar
56. 1. Ries J
    2. Schwille P

    (2006) Studying slow membrane dynamics with continuous wave scanning fluorescence correlation spectroscopy

    Biophysical Journal 91:1915–1924.

    https://doi.org/10.1529/biophysj.106.082297

    • PubMed
    • Google Scholar
57. 1. Ries J
    2. Chiantia S
    3. Schwille P

    (2009a) Accurate determination of membrane dynamics with line-scan FCS

    Biophysical Journal 96:1999–2008.

    https://doi.org/10.1016/j.bpj.2008.12.3888

    • PubMed
    • Google Scholar
58. 1. Ries J
    2. Yu SR
    3. Burkhardt M
    4. Brand M
    5. Schwille P

    (2009b) Modular scanning FCS quantifies receptor-ligand interactions in living multicellular organisms

    Nature Methods 6:643–645.

    https://doi.org/10.1038/nmeth.1355

    • PubMed
    • Google Scholar
59. 1. Rossman JS
    2. Lamb RA

    (2011) Influenza virus assembly and budding

    Virology 411:229–236.

    https://doi.org/10.1016/j.virol.2010.12.003

    • PubMed
    • Google Scholar
60. 1. Sankaran J
    2. Balasubramanian H
    3. Tang WH
    4. Ng XW
    5. Röllin A
    6. Wohland T

    (2021) Simultaneous Spatiotemporal Super-Resolution and Multi-Parametric Fluorescence Microscopy

    Nature Communications 12:1748.

    https://doi.org/10.1038/s41467-021-22002-9

    • PubMed
    • Google Scholar
61. 1. Schrimpf W
    2. Lemmens V
    3. Smisdom N
    4. Ameloot M
    5. Lamb DC
    6. Hendrix J

    (2018) Crosstalk-free multicolor rics using spectral weighting

    Methods 140–141:97–111.

    https://doi.org/10.1016/j.ymeth.2018.01.022

    • PubMed
    • Google Scholar
62. 1. Schwille P
    2. Meyer-Almes FJ
    3. Rigler R

    (1997) Dual-color fluorescence cross-correlation spectroscopy for multicomponent diffusional analysis in solution

    Biophysical Journal 72:1878–1886.

    https://doi.org/10.1016/S0006-3495(97)78833-7

    • PubMed
    • Google Scholar
63. 1. Shaw ML
    2. Stone KL
    3. Colangelo CM
    4. Gulcicek EE
    5. Palese P

    (2008) Cellular proteins in influenza virus particles

    PLOS Pathogens 4:e1000085.

    https://doi.org/10.1371/journal.ppat.1000085

    • PubMed
    • Google Scholar
64. 1. Shi X
    2. Foo YH
    3. Sudhaharan T
    4. Chong SW
    5. Korzh V
    6. Ahmed S
    7. Wohland T

    (2009) Determination of dissociation constants in living zebrafish embryos with single wavelength fluorescence cross-correlation spectroscopy

    Biophysical Journal 97:678–686.

    https://doi.org/10.1016/j.bpj.2009.05.006

    • PubMed
    • Google Scholar
65. 1. Štefl M
    2. Herbst K
    3. Rübsam M
    4. Benda A
    5. Knop M

    (2020) Single-Color Fluorescence Lifetime Cross-Correlation Spectroscopy In Vivo

    Biophysical Journal 119:1359–1370.

    https://doi.org/10.1016/j.bpj.2020.06.039

    • PubMed
    • Google Scholar
66. 1. Sun Y
    2. Wallrabe H
    3. Booker CF
    4. Day RN
    5. Periasamy A

    (2010) Three-Color Spectral FRET Microscopy Localizes Three Interacting Proteins in Living Cells

    Biophysical Journal 99:1274–1283.

    https://doi.org/10.1016/j.bpj.2010.06.004

    • PubMed
    • Google Scholar
67. 1. Weidemann T
    2. Mücksch J
    3. Schwille P

    (2014) Fluorescence fluctuation microscopy: a diversified arsenal of methods to investigate molecular dynamics inside cells

    Current Opinion in Structural Biology 28:69–76.

    https://doi.org/10.1016/j.sbi.2014.07.008

    • PubMed
    • Google Scholar
68. 1. Wohland T
    2. Rigler R
    3. Vogel H

    (2001) The standard deviation in fluorescence correlation spectroscopy

    Biophysical Journal 80:2987–2999.

    https://doi.org/10.1016/S0006-3495(01)76264-9

    • PubMed
    • Google Scholar
69. Preprint

    1. Ziegler M
    2. Yserentant K
    3. Middel V
    4. Dunsing V
    5. Gralak A
    6. Pakari K
    7. Bargstedt J
    8. Kern C
    9. Chiantia S
    10. Strähle U
    11. Herten DP
    12. Wombacher R

    (2020) A Chemical Strategy to Control Protein Networks in Vivo

    bioRxiv.

    https://doi.org/10.1101/2020.04.08.031427

    • Google Scholar

Author details

1. Valentin Dunsing

   Universität Potsdam, Institute of Biochemistry and Biology, Potsdam, Germany

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   valentin.dunsing@gmx.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2482-1498

2. Annett Petrich

   Universität Potsdam, Institute of Biochemistry and Biology, Potsdam, Germany

   Contribution

   Investigation, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2372-826X

3. Salvatore Chiantia

   Universität Potsdam, Institute of Biochemistry and Biology, Potsdam, Germany

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   chiantia@uni-potsdam.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0791-967X

• 2,096

  views

• 288

  downloads

• 9

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.