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TECHNOLOGY DEVELOPMENT

 

Live-cell superresolution eTIRF-SIM platform

The Live-cell superresolution eTIRF-SIM (extended Total Internal Reflection Fluorescence  Structured Illumination Microscopy) platform was established within the framework of an international collaboration between their inventors Profs. Dong Li (Beijing, China) and Eric Betzig (Noble-Laureate-2014, HHMI Janelia Research Campus, USA) and the Fritzsche laboratory funded by Micron Advanced Imaging Unit, the Kennedy Institute, the MRC HIU at the University of Oxford.

The eTIRF-SIM is a state of the art super-resolution microscope. It takes advantage of combining two imaging modalities TIRF and SIM in the same system, which allows achieving up to 90 nm lateral and 150 axial resolutions at high frame acquisition rates – up to 40 ms per colour per frame over a total number of 300-500 frames defined by the general loss of fluorescence due to imaging. The TIRF illumination helps to reduce such phototoxicity and photodamage as the sample is only illuminated at the basal plane, which makes TIRF-SIM an ideal candidate for high resolution live-cell fluorescence imaging.

TIRF-SIM specification:

Excitation laser lines: 488 nm, 560 nm and 640 n.

TIRF depth: 100 - 300 nm

Objective: Olympus UApoN100× NA 1.49 and NA1.7

Contact: 

Please contact for further information: 

kseniya.korobchevskaya(at)kennedy.ox.ac.uk and marco.fritzsche (at) rdm.ox.ac.uk.

 

Statistical Analysis of Scanning Fluorescence Correlation Spectroscopy

Cells rely on versatile diffusion dynamics in their plasma membrane. Quantification of this often heterogeneous diffusion is essential to the understanding of cell regulation and function. Yet such measurements remain a major challenge in cell biology, usually due to low sampling throughput, a necessity for dedicated equipment, sophisticated fluorescent label strategies, and limited sensitivity.

To overcome these challenges, we developed a new pipeline for the statistical analysis of sFCS (scanning Fluorescence Correlation Spectroscopy) data recorded by confocal microscopy, yielding the systematic and robust quantification of diffusion modes in living cells. The protocol allows a systematic mathematical characterisation of the distribution of sFCS diffusion data, involving fitting analysis and the calculation of the biological-relevant parameters, and a quantitative evaluation of the results using weighted fitting residuals and maximum likelihood estimations. Such quantification of sub-diffusion modes were previously only possible using for instance the super-resolution modality STED, which requires specialised fluorescent labels.

Applying this framework to the free and non-Brownian diffusion dynamics of lipids and lycosylphosphatidylinositol (GPI)- anchored proteins demonstrates the power of the method and allows a comprehensive characterization of well-known molecular particle diffusion in computer-simulated data sets, supported lipid bilayers (SLBs), and the plasma membrane of living cells. Combining this analysis pipeline with conventional confocal sFCS experiments may offer alternative avenues for systematic studies of the mechanisms by which living cells adjust their molecular membrane dynamics to their physiological needs.

 

Fluorescence Recovery After Photobleaching

 Proteins within the cell continuously turnover. Understanding how organelles and cytoskeletal structures are assembled and regulated from a theoretical point of view requires knowledge of the kinetic turnover rate constants associated with each of the binding events that are involved in forming the structure of interest. These kinetics have generally been investigated using Fluorescence recovery after photo-bleaching experiments (FRAP) where a small region of interest is bleached by exposure to laser light and fluorescence recovery is monitored (Fig. 1 a). The results of such experiments are quantified by fitting fluorescence recovery curves with exponential recovery functions to measure the half-time of fluorescence recovery (Fig. 1 b). This is a crude measure that does not give any information on the biochemical processes that underlie turnover. One alternative has been to determine protein association/dissociation kinetics in vitro. While this offers a well-controlled environment in which to effect precise measurements, it is becoming increasingly clear that protein association/ dissociation kinetics in the complex intracellular environment differ markedly from those measured in vitro. Other approaches involve complex simulations of protein interaction networks to interpret FRAP data. However, these necessitate many assumptions that cannot easily be verified experimentally. Thus, the ideal method would measure protein association/dissociation kinetics in cells using minimal specialized equipment and minimally complex fitting procedures.

To fill this technical gap, we reasoned that the turnover kinetics of a given protein should depend on the association/dissociation kinetics of its subdomains. We verified this experimentally and showed that the fluorescence recovery of most proteins consists of multiple first-order exponential recovery processes rather than just one. Having determined how many first-order processes participate in recovery, the next challenge is to identify what molecular and biophysical processes underlie each of these. By analyzing the recovery kinetics of different domain-deletion mutants of the protein of interest, we have shown that the recovery of the full-length protein is a convolution of the recovery kinetics of each of its subdomains measured individually.

 

Super-Resolved Traction Force Microscopy

Animal cells continuously sense and respond to mechanical force. Quantifying these forces remains a major challenge across biomedical disciplines; yet such measurements are essential for the understanding of cellular function. 

Traction force microscopy is one of the most successful and broadly-used force probing technologies, chosen for the simplicity of its implementation, flexibility to mimic cellular conditions, and well-established analysis pipe-line. We improve the spatial resolution and accuracy of TFM using STED microscopy. The increased spatial resolution of STED-TFM (STFM) allows a greater than 5-fold higher sampling of the forces generated by the cell than conventional TFM, accessing the nano instead of the micron scale. This improvement is highlighted by computer simulations and an activating RBL cell model system.

In light of the increasing discovery of the importance of mechanobiology in cell physiology, we envisage traction force microscopy to remain a major player for quantifying mechanical forces in living cells. 

Contact: Please contact for further information: 

huw.colinyork (at) pmb.ox.ac.uk and marco.fritzsche (at) rdm.ox.ac.uk.

 

 
Figure shows a comparison between conventional TIRF and eTIRF-SIM modalities. HT1080 cell cultured on a gelatin-coated cover glass ( MT1-MMP red, Beta 1 Integrin blue, F-actin green) sample courtesy Valentina GIfford.

Figure shows a comparison between conventional TIRF and eTIRF-SIM modalities. HT1080 cell cultured on a gelatin-coated cover glass ( MT1-MMP red, Beta 1 Integrin blue, F-actin green) sample courtesy Valentina GIfford.

Further information:

Colin-York H, Cytoskeletal actin patterns shape mast cell activation , Nature Communications Biology, 2019.

Fritzsche M at al, , Self-organizing actin patterns shape membrane architecture but not cell mechanics, Nature Communications, 2017.

Li D et al, Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics, Science, 2015.

 
Schematic of the experiment principle. (Left) sFCS data are recorded by scanning the laser (green dots) along a micrometer line in the membrane (lipids with red headgroups and gray tails), thereby creating (through temporal correlations) FCS data (decaying curves G(τ) from red to blue against correlation time τ) for each pixel along the line (space). (Right) All FCS data are fitted to obtain values of transit times through the observation spot, in which histograms (blue, probability distributions; right, cumulative; top right, logarithmic values; bottom right, linear representation) are fitted by the LogNorm (red line), with weighted residuals in the respective bottom panels.

Schematic of the experiment principle. (Left) sFCS data are recorded by scanning the laser (green dots) along a micrometer line in the membrane (lipids with red headgroups and gray tails), thereby creating (through temporal correlations) FCS data (decaying curves G(τ) from red to blue against correlation time τ) for each pixel along the line (space). (Right) All FCS data are fitted to obtain values of transit times through the observation spot, in which histograms (blue, probability distributions; right, cumulative; top right, logarithmic values; bottom right, linear representation) are fitted by the LogNorm (red line), with weighted residuals in the respective bottom panels.

Contact: 

Please contact for further information: falk.schneider(at)rdm.ox.ac.uk and marco.fritzsche (at) rdm.ox.ac.uk.

Further information:

Schneider F et al, Statistical Analysis of Scanning Fluorescence Correlation Spectroscopy Data Differentiates Free from Hindered Diffusion, ACS Nano, 2018.

 
Figure taken from Fritzsche et al, Nature Protocols 2015.

Figure taken from Fritzsche et al, Nature Protocols 2015.

Analyzing FRAP recovery curves in terms of multi-exponential recovery processes and by measuring the individual recovery rates of the protein’s subdomains, one can gain detailed insight into how each subdomain contributes to turnover as well as the relative importance of each molecular process for overall recovery. Hence, our analysis provides a level of characterization far greater than previous methods. Indeed, changes in the half-time of recovery generally reported in FRAP experiments can result from changes in the number of first-order processes participating in recovery, changes in the rates of some or all of the processes, changes in relative importance of some or all of the processes, or a combination of all of these factors. Our analysis and process identification strategy enable determination of all of these changes, thereby providing valuable quantitative data for systems biology approaches measured in physiologically relevant conditions.

Contact: 

Please contact for further information: marco.fritzsche (at) rdm.ox.ac.uk.

Further information:

Fritzsche M & Charras GT, Dissecting protein reaction dynamics in living cells by fluorescence recovery after photobleaching., Nature Protocols, 2015.

Fritzsche M, Thorogate R, and Charras GT, Analysis of Ezrin turnover dynamics at the submembranous actin cortex., Biophysical Journal, 2014.

 Fritzsche MLewalle A, Duke T, and Charras GT. Analysis of turnover dynamics of the submembranous actin cortex., Molecular Biology of the Cell, 2013 (Faculty 1000).

 

 

Figure taken from Colin-York et al, Nature Protocols 2017.

Further information:

Colin-York H et al, Cytoskeletal Control of Antigen-Dependent T Cell Activation, Cell Reports, 2019.

Colin-York H, Fritzsche M, The future of traction force microscopy, Current Opinion in Biomedical Engineering, 2017.

Colin-York H, Eggeling C, Fritzsche M, Dissection of mechanical force in living cells by super-resolved traction force microscopy, Nature Protocols, 2017.

Colin-York HShrestha D, Felce JH, Waithe D, Moeendarbary E, Davis SJ, Eggeling Cand Fritzsche M, Super-resolved Traction Force Microscopy (STFM)., Nano Letters, 2016.

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