TECHNOLOGY DEVELOPMENT
Technology innovation in the BPI Lab
One of the greatest challenges in the study of human immunity is the quantitative visualisation of immune cells at the biologically-demanded sensitivity.
Visualising immune cells through a microscope has been a powerful strategy for scientific discovery for many years. However, only the latest revolutionary technologies in advanced live cell microscopy, fluorescence labelling chemistry, and fluidics-perfusion demonstrate now the technological leaps long hoped for to overcome the challenges to image slow or fast and short and/or long-range cell-cell interactions crucial for the communication between immune cells in living tissues at the spatiotemporal sensitivity as demanded by the biology and free of any undesired influence from the microscope observer.
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.
Next, to improve spatio-temporal resolution while enhancing cellular compatibility, minimising phototoxicity and photobleaching, we combine TFM with SIM microscopy in three modalities: 2D-TIRF, 3D, and astigmatism. Combining TFM with TIRF-SIM and employing the high refractive index (nr = 1.49) silicone gel, the 2D TIRF-SIM-TFM technique shows a > 2-fold improvement in the spatial resolution and a > 10-fold improvement in the temporal resolution compared to previous TFM modalities. To extend our probing to mechanical force generated along the axial direction, we next combine TFM with 3D SIM. The 3D SIM-TFM technique offers the same 100 nm lateral spatial resolution obtained with 2D TIRF-SIM-TFM with now an additional axial resolution of 250 nm to capture the movement of beads along the direction perpendicular to the gel surface. Regarding the temporal resolution, 3D SIM-TFM offers 3D force sampling with a speed 1.5 times slower than 2D TIRF-SIM-TFM, because it requires scanning in the axial direction, but much faster compared to any standard raster scanning based microscopy techniques, such as confocal or STED, because of its WF 2D illumination. As a final step, we lift of the limitations in 3D SIM-TFM by combining TFM with astigmatic imaging, improving significantly the temporal resolution for simultaneous lateral and vertical force measurements. The aTFM technique offers 2.5D force acquisition with temporal resolution comparable to 2D TIRF-SIM-TFM, due to its ability to extract both lateral and vertical gel displacement informations within one single 2D image. These three techniques, easily implemented on a single optical set-up, offer a powerful platform which in the future will allow to quantify force generation during immune cell function at unprecedented sensitivity and bio-compatibility.
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.
Further information:
Colin-York H et al, Distinct actin cytoskeleton behaviour in primary and immortalised T-cells, Journal of Cell Science, 2019.
Colin-York H & Javanmardi Y et al, Spatiotemporally Super-resolved Volumetric Traction Force Microscopy, Nano Letters, 2019.
Colin-York H et al, 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.
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.
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 M, Lewalle 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.
Figure taken from Colin-York, Nature Device & Materials Engineering, 2021.
Further information:
Korobchevskaya K, Colin-York H, Barbieri L, Fritzsche M, Extended mechanical force measurements using structured illumination microscopy, Philosophical Transactions Royal Society A, 2021.
Li D*, Colin-York H*, Barbieri L*, Javanmardi Y, Guo T, Korobchevskaya K, Moendarbary E, Li D, Fritzsche M, Astigmatic Traction force
microscopy (aTFM), Nature Communications, 2021 (*co-first authors).
Barbieri L*, Colin-York H*, Korobchevskaya K*, Li D, Karedla N, Schneider F, Dustin M, Li D, Fritzsche M, Two-dimensional TIRF-SIM
Traction Force Microscopy (2D TIRF-SIM-TFM), Nature Communications, 2021 (*co-first authors).
Colin-York H, Quantifying nano-scale cellular force generation, Nature Device & Materials Engineering, 2021.
Colin-York H & Javanmardi Y et al, Spatiotemporally Super-resolved Volumetric Traction Force Microscopy, Nano Letters, 2019.
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 H, Shrestha D, Felce JH, Waithe D, Moeendarbary E, Davis SJ, Eggeling C, and Fritzsche M, Super-resolved Traction Force Microscopy (STFM), Nano Letters, 2016.
Simultaneous Quantification
by AFM–FRAP
Quantifying the adaptive mechanical behaviour of living cells is essential for the understanding of their inner working and function. Yet, despite the establishment of quantitative methodologies correlating independent measurements of cell mechanics and its underlying molecular kinetics, explicit knowledge of the sensitivity of the feedback mechanisms of cells controlling their adaptive mechanics behaviour remains elusive.
To overcome this challenge, we combined atomic force microscopy (AFM) and fluorescence recovery after photobleaching (FRAP) offering simultaneous quantification and of molecule kinetics and mechanics in living cells. Simultaneous quantification of the relationship between molecule kinetics and cell mechanics may open-up unprecedented insights into adaptive mechanobiological mechanisms of cells.
The AFM–FRAP platform offers three major types of investigations, which are not possible in independent FRAP or AFM experiments:
(1) Quantitative measurements of the dynamic range of cellular mechanosensation. (2) Mechanistic study of the origin of cell mechanics: mechanical properties and mechanical force production by living cells can be dynamically interrogated in response to pharmacological and genetic treatments. (3) Mechanistic investigation of the implications of protein kinetics for cell mechanics: dissecting the protein dynamics underlying cell mechanics by dynamically exerting mechanical load onto living cells.
Contact:
Please contact for further information: mskamra@uni-goettingen.de and marco.fritzsche(at)rdm.ox.ac.uk.
Further information:
Skamrahl M et al, Simultaneous Quantification of the Interplay Between Molecular Turnover and Cell Mechanics by AFM–FRAP, Small, 2019.
Systematic application of AFM-FRAP in this way revealed changes in the actin turnover and filament lengths of ventral actin stress fibers in response to constant mechanical force at the apical actin cortex with a dynamic range from 0.1 to 10 nN. This study highlighted a direct relationship of active mechanosensation and adaptation of the cellular actin cytoskeleton.
AFM-FRAP setup quantifying the mechanoadaptation of the actin turnover rate upon constant force application in living cells. First application of this technical combination showed that cells respond to external force by decreasing their actin turnover yielding longer filaments. Figure taken from Skamrahl et al, Small 2019.
