Step 1: Cell morphology considerations – At first, working with adherent cell lines such as fibroblasts allows easy recording on planar lamellipodia. This avoids having to consider 3D geometries. In practice, this can be best achieved with subconfluent, highly spread cells. Moreover, having either too spare or almost confluent cells, due to inadequate initial density and time between spreading and experiment, may impact on cell physiology (i.e. adhesion, metabolism) and experimental reproducibility.
MTT analysis is however not restricted to fibroblast cells.
Step 2: Qdot valence issue – In the absence of uncoupled biotin, commercially available Qdots, which are bound to 4-10 tetravalent streptavidins, can putatively bind up to tens of antibodies (i.e. theoretically between 16 and 40 binding sites per Qdot). These complexes can potentially induce an aggregation of membrane receptors, resulting in possible physiological bias such as artefactual stimulation induced by receptor capping18. Adding a 100× molar excess of biotin minimizes this multivalence and ensures that most of the Qdots are coupled to only one antibody (or none, but uncoupled Qdots will be ultimately washed out).
Importantly, the biotinylated antibody and biotin should be first mixed together, prior to mixing with streptavidin-Qdots, in order to avoid preferential binding of one of the two species to streptavidin.
Nota: in our study of EGFR, we used a biotinylated anti-EGFR (Ab-3, Lab Vision). Although being bivalent, this antibody is non-activating (not shown). However, one should consider, when it is possible, monovalent labeling strategies such as employing F(ab) fragments or monovalent streptavidin against directly targeted biotin19. In our case, we observed that labeling EGFR with Qdots coupled to F(ab) together with an excess of biotin, lead to fully comparable results.
Qdot and antibody concentrations will directly determine the final labeling density. For the MTT approach, this density must be sufficient to spatially sample the cellular dynamic properties.
Step 9: Acquisition parameters – For single-molecule measurements, all acquisition parameters should be optimized, notably the light intensity, filter sets and other optics transmission efficiency, as well as the camera settings. In our experimental conditions, the amplification gain was set at 3850 to avoid saturation.
Moreover, the magnification should ensure that the resulting pixel size verifies the Shannon-Nyquist sampling theorem: the fluorescence peak diameter (according to the Rayleigh criterion and including motion blurring) should be at least twice the pixel size to warrant proper localization.
Step 11: SNR versus acquisition frequency – Signal-to-noise ratio and acquisition speed are critical issues in SPT. Since they are intimately related, an affordable tradeoff must be carefully determined when choosing the time-lag.
More precisely, the time-lag used for recording will directly affect the dynamic range and sensibility. It should be both low enough to ensure a good dynamic (allowing access to fast motions) and high enough to provide a sufficient signal to noise ratio. Noticeably, working with the 512 BFT Cascade camera allowed us to successfully investigate EGFR dynamics at frequencies up to 200 frames/s (5 ms time-lag). New cameras, such as the 128 BFT Cascade, even allow measurements at 1,000 frames/s (1 ms time-lag), with still an acceptable SNR.
Step 15: Input parameters for reconnecting trajectories – Dmax provides an upper limit for reconnecting trajectories which has to be converted in pixels2/lag. This cutoff results from a tradeoff allowing efficient reconnection of fast movements without leading to erroneous reconnections of neighboring blinking molecules for instance. If this input value cannot be straightforwardly estimated, a range of values can be tested using a representative dataset to generate a plot of output versus input diffusion values. Too small input values should generate aborted, short range trajectories, while too high input values should generate artefactual, long range connections, resulting in under or overestimate of diffusion, respectively. MTT should run with an intermediate value, corresponding to an intermediate stable regime, for which the estimated output diffusion is no longer affected by small variations of Dmax.
Step 17B: Input parameters for cartography – The detection index relies on three parameters, Lmin, tmin and wconf, stored in proba_conf_params_varM.dat, in the carto folder. Each parameter may need to be optimized for a given problem. See Sergé et al, Nat. Methods 2008 (DOI 10.1038/nmeth.1233) and refs 14-17 for further information.