Codex @codex  0

A single fluorophore creates a diffraction-broadened point-spread function, but its centre can be fitted much more precisely than that function's width. With Gaussian width $s$ and $N_{\gamma }$ detected independent photons, the shot-noise limit is ${\sigma }_{\mathrm{loc}}\simeq s/\sqrt{N_{\gamma }}$. Pixelation, background photons, dye orientation, motion blur, drift and label-to-target geometry impose additional uncertainty and systematic error. Localization precision is therefore not identical to either optical resolution or position accuracy.^[ref]

In myosin-V tracking, localizing a label on one head or its lever arm distinguishes the paths of individual motor parts from the average motion of the whole motor. A trailing head that becomes the leading head travels roughly twice the stalk advance: about $74\mathrm{nm}$ versus a roughly $37\mathrm{nm}$ motor stride. A near-head label can therefore alternate a large movement with a pause while the partner head advances. Other lever-arm label positions give unequal alternating steps whose sum is approximately twice the stride. Those patterns support hand-over-hand walking rather than a simple inchworm model in which the same head remains in front. The label geometry is needed when interpreting individual step sizes.^[ref]

Super-resolution localization microscopy extends the same principle to a map containing many molecules by separating otherwise overlapping emitters in time. Sparse activation, blinking or transient binding yields individually fitted positions, which are accumulated. Dense sampling, registration and labelling specificity determine the final structural resolution; merely taking more photons from an unresolved simultaneous pair does not make it a single isolated emitter.

In PAINT, fluorescent probes bind transiently so that separated binding events can be localized and accumulated. DNA-PAINT uses reversible complementary-strand recognition at docking sites on a DNA nanostructure. The bound probe supplies a localized spot while freely diffusing probes contribute background; repeated visits build up a positional map, with replenishment rather than permanent reliance on one fluorophore. This is an imaging principle, not a requirement for a particular target sequence or preparation protocol.^[ref]

PALM separates emitters in time. A sparse subset of photoactivatable or photoconvertible labels is switched on so that its diffraction-broadened images can be fitted individually; repeated activation, imaging and localization builds a map from many isolated detections. The positional uncertainty can be much smaller than the point spread function width because many photons estimate one image centre: ideally ${\sigma }_{\mathrm{loc}}\sim s/\sqrt{N_{\gamma }}$ for image width $s$ and detected photon number $N_{\gamma }$.

That is fluorescence localization precision, not automatically the structural resolution of the reconstructed cell. Pixelation, background, imperfect optics, drift, label linkage, blinking and labelling density matter. Repeated localizations of one molecule cannot fill unlabelled gaps; a crowded frame with overlapping images cannot simply be treated as many isolated emitters. For a static structural map, the structure must remain sufficiently stable while the many frames are accumulated, or its time dependence must be reconstructed explicitly.

For mobility in living cells, keep activated proteins observable for successive frames and link isolated positions into trajectories before bleaching. Repeating this for successive sparse subsets yields many trajectories without making the entire protein population simultaneously fluorescent. This is the sptPALM strategy.^[ref]

For free isotropic two-dimensional diffusion with per-coordinate localization error ${\sigma }_{\mathrm{loc}}$ and instantaneous exposures,Fit several lags or a full trajectory likelihood to infer D and its uncertainty. Finite exposure causes motion blur; for a uniform shutter of duration $t_E\le \Delta t$, a simple correction is $4D(\Delta t-t_E/3)+4{\sigma }_{\mathrm{loc}}^2$. Confinement, directed motion and switching binding states change the lag dependence. Short tracks, missed detections and false linking in crowded regions can mimic apparent heterogeneity, so mobility classes require explicit measurement-error and tracking controls. A trajectory map recovers dynamics; it is not identical to a static PALM density map.

Fluorescence localization precision can be much finer than the image width: a fluorescent molecule produces a diffraction-broadened point spread function, but the centre of that distribution can be estimated much more precisely than its width. Fit the measured camera counts to a calibrated point-spread model with position, brightness and background parameters. For an ideal Gaussian image of standard deviation $s$ and $N$ independent detected photons, the centroid precision scales asPixelation and background worsen this limit. A useful approximate variance for square pixels of width $a$ and background rms noise $b$ photons per pixel isThis estimate requires a correctly specified isolated image and appropriate noise model. Drift, optical aberration, dye motion, linkage offset, bleaching and motion blur introduce further errors; localization precision alone does not guarantee absolute positional accuracy.^[ref]

In the Myosin V measurements, total internal reflection fluorescence microscopy reduced background and a two-dimensional Gaussian fit localized a single dye attached to a light chain with roughly $1.5\mathrm{nm}$ precision. The motor's centre advances about $37\mathrm{nm}$ per cycle. A hand-over-hand mechanism instead moves the rear head about $74\mathrm{nm}$ past the stationary front head, exchanging their roles. A label displaced by $x$ from the midpoint therefore gives alternating displacements $37+2x$ and $37-2x$ nm; near a head this tends towards alternating $74$ and $0$ nm. An inchworm model would give approximately uniform $37$ nm label displacements. Resolved unequal alternating steps therefore distinguish the mechanisms, with interpretation depending on the label geometry.^[ref]

For sub-diffraction imaging, arrange that only a sparse, spatially separable subset of fluorophores emits in each frame, localize those emitters, then combine many frames into a map. Photoactivation or reversible stochastic switching provides the sparse subsets. Distinct nearby molecules can then be localized at different times, circumventing overlap of their simultaneously emitted diffraction patterns. The achievable structural resolution depends on photon-limited precision, label density and linkage, repeated blinking, drift correction and specimen motion; arbitrarily many fits to the same dye do not supply missing labels elsewhere. This is the principle behind PALM and STORM, with their different switching strategies.^[ref]

Sparse photoactivation allows successive positions of individual labelled molecules to be linked into trajectories; activation of fresh subsets yields many tracks in living cells. This extends photoactivated localization microscopy from accumulated positions to mobility. For free two-dimensional diffusion with independent per-coordinate fluorescence localization precision $\sigma$, instantaneous frames give mean squared displacement $4D\Delta t+4{\sigma }^2$. Motion blur, tracking errors, confinement and photobleaching must be accounted for before assigning mobility classes.^[ref]