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Tracking molecular dynamics without tracking: image correlation of photo-activation microscopy

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Methods and Applications in Fluorescence
Authors:
Elvis Pandzic at UNSW Sydney
Elvis Pandzic
Jérémie Rossy at Biotechnology Institute Thurgau
Jérémie Rossy
  • Biotechnology Institute Thurgau
Katharina Gaus
Katharina Gaus
Katharina Gaus
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Abstract and Figures

Measuring protein dynamics in the plasma membrane can provide insights into the mechanisms of receptor signaling and other cellular functions. To quantify protein dynamics on the single molecule level over the entire cell surface, sophisticated approaches such as single particle tracking (SPT), photo-activation localization microscopy (PALM) and fluctuation-based analysis have been developed. However, analyzing molecular dynamics of fluorescent particles with intermittent excitation and low signal-to-noise ratio present at high densities has remained a challenge. We overcame this problem by applying spatio-temporal image correlation spectroscopy (STICS) analysis to photo-activated (PA) microscopy time series. In order to determine under which imaging conditions this approach is valid, we simulated PA images of diffusing particles in a homogeneous environment and varied photo-activation, reversible blinking and irreversible photo-bleaching rates. Further, we simulated data with high particle densities that populated mobile objects (such as adhesions and vesicles) that often interfere with STICS and fluctuation-based analysis. We demonstrated in experimental measurements that the diffusion coefficient of the epidermal growth factor receptor (EGFR) fused to PAGFP in live COS-7 cells can be determined in the plasma membrane and revealed differences in the time-dependent diffusion maps between wild-type and mutant Lck in activated T cells. In summary, we have developed a new analysis approach for live cell photo-activation microscopy data based on image correlation spectroscopy to quantify the spatio-temporal dynamics of single proteins.
Time-resolved STICS analysis applied to a photo-activation localization microscopy image time series. (a) From an image time series of 256 by 256 by 10 000 images regions of interest (yellow box) of 16 by 16 pixels by 1024 frames (ROI-TOI) are sampled and analyzed. (b) STICS correlation function (CF) of the ROI-TOI for 3 different temporal lags. (c) For each ROI-TOI, the square width of CF is fitted versus τ and the diffusion coefficient and maximum displacements are extracted. (d) The resulting 61 × 61 × 16 ROI-TOIs over the full time series produce a diffusion map. (e) STICS applied to a time series containing flowing particles produces translating CF. A linear fit of peak positions (blue or red dashed lines) provides the measure of particles' velocity.
Time-resolved STICS analysis applied to a photo-activation localization microscopy image time series. (a) From an image time series of 256 by 256 by 10 000 images regions of interest (yellow box) of 16 by 16 pixels by 1024 frames (ROI-TOI) are sampled and analyzed. (b) STICS correlation function (CF) of the ROI-TOI for 3 different temporal lags. (c) For each ROI-TOI, the square width of CF is fitted versus τ and the diffusion coefficient and maximum displacements are extracted. (d) The resulting 61 × 61 × 16 ROI-TOIs over the full time series produce a diffusion map. (e) STICS applied to a time series containing flowing particles produces translating CF. A linear fit of peak positions (blue or red dashed lines) provides the measure of particles' velocity.
… 
Simulation results for free 2D diffusion simulations of photo-activation of particles. (a) The kinetics reactions diagram simulated by the de Gilespie algorithm that involves photo-activation (N inactive to N active), a reversible dark state (N off) and irreversible photo-bleaching (N bleached). (b) Fractions of the different particle populations over time with k 1 = 0.04, k 2 = 0.01, k 3 = 0.02 and k 4 = 0.04 particles s⁻¹. (c) The simulated time series from scenario in (b) for particles diffusing at D = 0.002 µm² s⁻¹. SNR was set to 4 and scale bar equals 5 µm. (d) STICS measured diffusion coefficient as a function of kinetic rates. Blue, green and red symbols represent k14 > , k12 > and 3k14 > . Dashed black line represents data at D = 0.01 µm² s⁻¹.
Simulation results for free 2D diffusion simulations of photo-activation of particles. (a) The kinetics reactions diagram simulated by the de Gilespie algorithm that involves photo-activation (N inactive to N active), a reversible dark state (N off) and irreversible photo-bleaching (N bleached). (b) Fractions of the different particle populations over time with k 1 = 0.04, k 2 = 0.01, k 3 = 0.02 and k 4 = 0.04 particles s⁻¹. (c) The simulated time series from scenario in (b) for particles diffusing at D = 0.002 µm² s⁻¹. SNR was set to 4 and scale bar equals 5 µm. (d) STICS measured diffusion coefficient as a function of kinetic rates. Blue, green and red symbols represent k14 > , k12 > and 3k14 > . Dashed black line represents data at D = 0.01 µm² s⁻¹.
… 
Simulation of flow of adhesion proteins within mobile adhesions at high particle density. (a) Simulated adhesion images without photo-activation. Magenta circles and cyan oval outline flowing particles and adhesions at (v x , v y ) = (0.02, 0.02) and (0.02, 0.02) µm s⁻¹, respectively. (b) STICS CF of the image series in (a): the cyan oval shaped peak is due to the adhesions while the magenta peak is caused by particles populating adhesions. (c) and (e) Photo-activated microscopy image series for particle positions of the simulated scenario shown in (a). (d) and (f) STICS CFs of image time series in (c) and (e), respectively. (h) Fractions of active particle populations as a function of time for 3 simulated scenarios. Blue line shows the scenario without photo-activation (a) while green and red represent simulations with photo-activation (c) and (e). (i) Fitted positions along the x-axis of STICS CF from (b), (d) and (f) according to equation (3). Blue, green and red symbols represent (a), (c) and (d) scenarios, respectively. The dashed lines are linear fits through the first 15 temporal lags. Scale bars are 1 µm.
Simulation of flow of adhesion proteins within mobile adhesions at high particle density. (a) Simulated adhesion images without photo-activation. Magenta circles and cyan oval outline flowing particles and adhesions at (v x , v y ) = (0.02, 0.02) and (0.02, 0.02) µm s⁻¹, respectively. (b) STICS CF of the image series in (a): the cyan oval shaped peak is due to the adhesions while the magenta peak is caused by particles populating adhesions. (c) and (e) Photo-activated microscopy image series for particle positions of the simulated scenario shown in (a). (d) and (f) STICS CFs of image time series in (c) and (e), respectively. (h) Fractions of active particle populations as a function of time for 3 simulated scenarios. Blue line shows the scenario without photo-activation (a) while green and red represent simulations with photo-activation (c) and (e). (i) Fitted positions along the x-axis of STICS CF from (b), (d) and (f) according to equation (3). Blue, green and red symbols represent (a), (c) and (d) scenarios, respectively. The dashed lines are linear fits through the first 15 temporal lags. Scale bars are 1 µm.
… 
Experimental data for EGFR-PAGFP diffusion in the plasma membrane of live COS-7 cells. (a) TIRF image of EGFR-PAGFP in the basal membrane of COS-7 cells acquired at 30 ms per frame with 405 nm excitation. Green dashed rectangle displays the zoom-in area of the snapshots in frames 1–3. (b) Images of the identical cell area as in (a) but acquired with 488 nm excitation after brief exposure to 405 nm photo-activation. (c) and (d) STICS CF for the scenarios in (a) and (b), respectively. (e) iMSD for scenarios in (a) and (b) extracted from the fit (see equation (3)) of CFs in (c) and (d). Two trials (cells) for each condition are representative of 5 measurements of 1000 frames each on two different cells. iMSD is the mean and standard deviation of the values extracted for each trial. Blue and red symbols display iMSD for data without (405 nm exitation) and with photo-activation, respectively. Scale bar equals 5 µm.
Experimental data for EGFR-PAGFP diffusion in the plasma membrane of live COS-7 cells. (a) TIRF image of EGFR-PAGFP in the basal membrane of COS-7 cells acquired at 30 ms per frame with 405 nm excitation. Green dashed rectangle displays the zoom-in area of the snapshots in frames 1–3. (b) Images of the identical cell area as in (a) but acquired with 488 nm excitation after brief exposure to 405 nm photo-activation. (c) and (d) STICS CF for the scenarios in (a) and (b), respectively. (e) iMSD for scenarios in (a) and (b) extracted from the fit (see equation (3)) of CFs in (c) and (d). Two trials (cells) for each condition are representative of 5 measurements of 1000 frames each on two different cells. iMSD is the mean and standard deviation of the values extracted for each trial. Blue and red symbols display iMSD for data without (405 nm exitation) and with photo-activation, respectively. Scale bar equals 5 µm.
… 
Diffusion maps obtained by time-resolved STICS from photo-activation microscopy of Lck in activated T cells. (a) and (b) STICS diffusion maps for wt Lck (a) and Y505F Lck (b) at 50 s, 200 s and 400 s respectively. Image regions for the STICS analysis were 16 × 16 pixels that were shifted by 4 pixels in x and y directions. Blue circles in the map indicate slower diffusion while red circles indicatethe highest diffusion coefficient (1 µm² s⁻¹), as shown in the colour scale. (b) The open state mutant, Y505F Lck, showed a higher proportion of slow diffusion compared to the wt Lck. Scale bar equals 4 µm.
+1
Diffusion maps obtained by time-resolved STICS from photo-activation microscopy of Lck in activated T cells. (a) and (b) STICS diffusion maps for wt Lck (a) and Y505F Lck (b) at 50 s, 200 s and 400 s respectively. Image regions for the STICS analysis were 16 × 16 pixels that were shifted by 4 pixels in x and y directions. Blue circles in the map indicate slower diffusion while red circles indicatethe highest diffusion coefficient (1 µm² s⁻¹), as shown in the colour scale. (b) The open state mutant, Y505F Lck, showed a higher proportion of slow diffusion compared to the wt Lck. Scale bar equals 4 µm.
… 
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© 2015 IOP Publishing Ltd
received
8 July 2 014
revised
19 October 20 14
accepted for publication
6 November 2014
published
9 March 2 015
Method s Appl. Fluores c. 3 (2015) 0140 06
1. Introduction
Cell signaling relies on complex networks that
involve many components and numerous possible
interactions between components. The transmission
of information within signaling networks however
relies on the interactions between individual molecules.
The probability of interaction is largely defined by
the dynamic behaviour of each component. Hence
measuring the molecular dynamics of signaling
components has become increasingly important. The
advent of photo-activated localization microscopy
(PALM) has made it possible to obtain super-resolved
images of membrane proteins with unprecedented
spatial resolution [1–3]. The precise localization
of signaling proteins enabled the identification of
clusters [4, 5] and has already revealed novel clustering
mechanisms. The extension of single particle tracking
to PALM imaging data (sptPALM) revealed the dynamic
processes and interactions of membrane proteins
during cell adhesion and signaling [6, 7]. Despite
these breakthroughs sptPALM has drawbacks which
limit its applicability. Indeed, the particle localization
algorithms require a reasonably good signal-to-noise
ratio (SNR) in order to detect molecules within a
given frame with nanometer precision. Therefore, the
excitation laser power needs to be increased in order
for a particle to absorb and emit sufficient number of
photons in a short period of time. This in turn leads
to increases in the photobleaching rates and prevents
the recording of long trajectories. Moreover, photo-
activated fluorophores can ‘blink’, i.e. enter a reversible
E Pandžić et al
Tracking molecular dynamics without tracking: image correlation of photo-activation microscopy
Printed in the UK
014006
MAF
© 2015 IOP Publishing Ltd
2015
3
Methods Appl. Fluoresc.
MAF
2050-6120
10.1088//3/1/014006
00
00
Methods and Applications in Fluorescence
IOP
9
March
2015
Tracking molecular dynamics without tracking: image correlation of
photo-activation microscopy
Elvis Pandžić1, Jérémie Rossy1 and Katharina Gaus1,2
1 ARC Centre for Advanced Molecular Imaging, Australian Centre for NanoMedicine University of New South Wales Australia, Sydney,
NSW, Australia
2 Lowy Cancer Research Centre, Centre for Vascular Research Level 3, Kensington, NSW, Australia
E-mail: k.gaus@unsw.edu.au
Keywords: photo-activation microscopy, spatio-temporal image correlation spectroscopy, single-molecule dynamics
Abstract
Measuring protein dynamics in the plasma membrane can provide insights into the mechanisms
of receptor signaling and other cellular functions. To quantify protein dynamics on the single
molecule level over the entire cell surface, sophisticated approaches such as single particle tracking
(SPT), photo-activation localization microscopy (PALM) and fluctuation-based analysis have
been developed. However, analyzing molecular dynamics of fluorescent particles with intermittent
excitation and low signal-to-noise ratio present at high densities has remained a challenge. We
overcame this problem by applying spatio-temporal image correlation spectroscopy (STICS)
analysis to photo-activated (PA) microscopy time series. In order to determine under which imaging
conditions this approach is valid, we simulated PA images of diffusing particles in a homogeneous
environment and varied photo-activation, reversible blinking and irreversible photo-bleaching
rates. Further, we simulated data with high particle densities that populated mobile objects (such
as adhesions and vesicles) that often interfere with STICS and fluctuation-based analysis. We
demonstrated in experimental measurements that the diffusion coefficient of the epidermal
growth factor receptor (EGFR) fused to PAGFP in live COS-7 cells can be determined in the plasma
membrane and revealed differences in the time-dependent diffusion maps between wild-type and
mutant Lck in activated T cells. In summary, we have developed a new analysis approach for live cell
photo-activation microscopy data based on image correlation spectroscopy to quantify the spatio-
temporal dynamics of single proteins.
paper
doi:10.1088/2050-6120/3/1/014 00 6
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... The MSD, usually by its trend versus temporal lag, instructs us on what type of confinement or obstacle caused the anomalous or confined random walk. Extracting the equivalent of MSD from the CF was done in the past by using STICS (25,50) or equivalent imaging correlation approach (51) and even allowed for diffusion coefficient mapping (25,45), as we saw in the theoretic development leading up to Eqs. 10 and 11 and as shown in spreading STICS CF in Figure 14D. ...
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... When imaging in series rather than simultaneously, consideration should be given to the length of time required to collect the two images, as some systems require on the order of seconds to switch between the desired image settings, by which time the imaged populations may not be seen to correlate with each other. For higher resolution correlation spectroscopy, particle ICS (PICS) [104] and time-resolved STICS (trSTICS) [105] give researchers the ability to analyse SPT and SMLMS data respectively and can achieve nanometre and millisecond spatial and temporal resolutions respectively. PICS has the advantage over classic SPT of not breaking down if multiple trajectories overlap. ...
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  • Dec 2012
  • NAT IMMUNOL
Phosphorylation of the T cell antigen receptor (TCR) by the tyrosine kinase Lck is an essential step in the activation of T cells. Because Lck is constitutively active, spatial organization may regulate TCR signaling. Here we found that Lck distributions on the molecular level were controlled by the conformational states of Lck, with the open, active conformation inducing clustering and the closed, inactive conformation preventing clustering. In contrast, association with lipid domains and protein networks were not sufficient or necessary for Lck clustering. Conformation-driven Lck clustering was highly dynamic, so that TCR triggering resulted in Lck clusters that contained phosphorylated TCRs but excluded the phosphatase CD45. Our data suggest that Lck conformational states represent an intrinsic mechanism for the intermolecular organization of early T cell signaling.
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Integrins β 1 and β 3 exhibit distinct dynamic nanoscale organizations inside focal adhesions
Article
Full-text available
  • Sep 2012
  • NAT CELL BIOL
Integrins in focal adhesions (FAs) mediate adhesion and force transmission to extracellular matrices essential for cell motility, proliferation and differentiation. Different fibronectin-binding integrins, simultaneously present in FAs, perform distinct functions. Yet, how integrin dynamics control biochemical and biomechanical processes in FAs is still elusive. Using single-protein tracking and super-resolution imaging we revealed the dynamic nano-organizations of integrins and talin inside FAs. Integrins reside in FAs through free-diffusion and immobilization cycles. Integrin activation promotes immobilization, stabilized in FAs by simultaneous connection to fibronectin and actin-binding proteins. Talin is recruited in FAs directly from the cytosol without membrane free-diffusion, restricting integrin immobilization to FAs. Immobilized β(3)-integrins are enriched and stationary within FAs, whereas immobilized β(1)-integrins are less enriched and exhibit rearward movements. Talin is enriched and mainly stationary, but also exhibited rearward movements in FAs, consistent with stable connections with both β-integrins. Thus, differential transmission of actin motion to fibronectin occurs through specific integrins within FAs.
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Mapping the Energy and Diffusion Landscapes of Membrane Proteins at the Cell Surface Using High-Density Single-Molecule Imaging and Bayesian Inference: Application to the Multiscale Dynamics of Glycine Receptors in the Neuronal Membrane
Article
  • Jan 2014
  • BIOPHYS J
Protein mobility is conventionally analyzed in terms of an effective diffusion. Yet, this description often fails to properly distinguish and evaluate the physical parameters (such as the membrane friction) and the biochemical interactions governing the motion. Here, we present a method combining high-density single-molecule imaging and statistical inference to separately map the diffusion and energy landscapes of membrane proteins across the cell surface at ∼100 nm resolution (with acquisition of a few minutes). Upon applying these analytical tools to glycine neurotransmitter receptors at inhibitory synapses, we find that gephyrin scaffolds act as shallow energy traps (∼3 kBT) for glycine neurotransmitter receptors, with a depth modulated by the biochemical properties of the receptor-gephyrin interaction loop. In turn, the inferred maps can be used to simulate the dynamics of proteins in the membrane, from the level of individual receptors to that of the population, and thereby, to model the stochastic fluctuations of physiological parameters (such as the number of receptors at synapses). Overall, our approach provides a powerful and comprehensive framework with which to analyze biochemical interactions in living cells and to decipher the multiscale dynamics of biomolecules in complex cellular environments.
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Extracting intracellular diffusive states and transition rates from single-molecule tracking data
Article
  • Feb 2013
  • Br J Pharmacol
We provide an analytical tool based on a variational Bayesian treatment of hidden Markov models to combine the information from thousands of short single-molecule trajectories of intracellularly diffusing proteins. The method identifies the number of diffusive states and the state transition rates. Using this method we have created an objective interaction map for Hfq, a protein that mediates interactions between small regulatory RNAs and their mRNA targets.
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STICCS Reveals Matrix-Dependent Adhesion Slipping and Gripping in Migrating Cells
Article
  • Oct 2012
  • BIOPHYS J
Two-color spatio-temporal image cross-correlation spectroscopy (STICCS) is a new, to our knowledge, image analysis method that calculates space-time autocorrelation and cross-correlation functions from fluorescence intensity fluctuations. STICCS generates cellular flow and diffusion maps that reveal interactions and cotransport of two distinct molecular species labeled with different fluorophores. Here we use computer simulations to map the capabilities and limitations of STICCS for measurements in complex heterogeneous environments containing micro- and macrostructures. We then use STICCS to analyze the co-flux of adhesion components in migrating cells imaged using total internal reflection fluorescence microscopy. The data reveal a robust, time-dependent co-fluxing of certain integrins and paxillin in adhesions in protrusions when they pause, and in adhesions that are sliding and disassembling, demonstrating that the molecules in these adhesions move as a complex. In these regions, both α6β1- or αLβ2-integrins, expressed in CHO.B2 cells, co-flux with paxillin; an analogous cotransport was seen for α6β1-integrin and α-actinin in U2OS. This contrasts with the behavior of the α5β1-integrin and paxillin, which do not co-flux. Our results clearly show that integrins can move in complexes with adhesion proteins in protrusions that are retracting.
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