Super-resolved 3D tracking of cargo transport through nuclear pore complexes

Abstract

Nuclear pore complexes (NPCs) embedded within the nuclear envelope mediate rapid, selective and bidirectional traffic between the cytoplasm and the nucleoplasm. Deciphering the mechanism and dynamics of this process is challenged by the need for high spatial and temporal resolution. We report here a multicolour imaging approach that enables direct three-dimensional visualization of cargo transport trajectories relative to a super-resolved octagonal double-ring structure of the NPC scaffold. The success of this approach is enabled by the high positional stability of NPCs within permeabilized cells, as verified by a combined experimental and simulation analysis. Hourglass-shaped translocation conduits for two cargo complexes representing different nuclear transport receptor pathways indicate rapid migration through the permeability barrier on or near the NPC scaffold. Binding sites for cargo complexes extend more than 100 nm from the pore openings, which is consistent with a wide distribution of the phenylalanine-glycine polypeptides that bind nuclear transport receptors.

Extended Data Fig. 1. Nanobody Specificity and Fluorescence Background

a, Specificity of the LaG-9 anti-GFP nanobody. Alexa568-labeled nanobody (100 nM) was added to permeabilized cells, incubated for 3 min and washed twice with IB + PVP. Cells were imaged at the nuclear equator. The top row shows the strong GFP fluorescence (λ_ex = 488 nm) obtained from the mEGFP-tagged NPCs of U-2 OS NUP96-mEGFP cells. The Alexa568 fluorescence (λ_ex = 561 nm) from the tagged nanobody matches the GFP fluorescence. In the bottom row, the wild type U-2 OS cells exhibited no fluorescence in the GFP channel and no nanobody binding. The range of the fluorescence images in the bottom row is 10 times smaller than in the top row, emphasizing the very low signal. These data indicate the high specificity of the nanobody for the GFP domain. Similar results were observed for N = 20 cells over 4 independent experiments. b, Fluorescence background and photobleaching of mEGFP. For all images, permeabilized U-2 OS NUP96-mEGFP cells labeled with HMSiR-labeled nanobodies were excited with λ_ex = 532 nm and emission was collected by a dual bandpass (545-623 nm, 656-763 nm). The mEGFP fluorescence (left) was photobleached after ~2 s of illumination, allowing diffusing M9-βGal(Atto542) cargo complexes to be readily visualized (top right). The HMSiR dye was not detectable (bottom right). Similar results were observed for N = 20 cells over 4 independent experiments.

Extended Data Fig. 2. Workflow for NPC Cluster Selection

a, Initial image obtained from ThunderSTORM. b, Image after photon filtering (≥ 3000 photons) and z filtering (0±300 nm) (corresponds to Fig. 2e). c, Clusters remaining after applying a rough diameter threshold (59-153 nm) and with ≥ 10 localizations/cluster. d, Clusters remaining after the double-circle fit and subsequent curating (fit diameter = 80-135 nm; distance between the rings = 40-65 nm; z-centroid = 0±200 nm). These remaining clusters are considered well-localized NPCs (image corresponds to Fig. 2h), and were used to construct the composite images in Figs. 2i–l. Dye localizations are color-coded based on z-height. e, Distribution of the number of localizations per cluster for the experiment in Fig. 2. The number of localizations per cluster followed an approximately exponential distribution (decay constant = 2.9) with an average of 13.2 following the filtering described in (a-c) and with a z-centroid = 0±200 nm (N = 2362 total localizations obtained from 142 clusters from 10 nuclei; each nucleus was an independent biological replicate). f, Photon frequency histograms. The HMSiR-labeled nanobody was bound to NPCs (Fig. 2) and the Atto542-labeled M9-βGal and Imp α were undergoing transport (Figs. 4 and 5). The log-normal fit parameters were used for the simulations in Supplementary Data 1_Software File 1 and Supplementary Data 2_Software File 2. Source numerical data are provided in source data.

Extended Data Fig. 3. Rotation of Individual NPC Clusters

a, Estimating the rotational phase angle (φ). The angles of individual localizations relative to the double-circle centroid of well-localized NPC clusters (Extended Data Fig. 2d) were estimated and binned (5° bins, θ = 0-45°). Four examples are shown here for a range of localizations/cluster (the number of localizations/cluster, n, is given in each figure panel). Distributions were fit to y = 1/9 + (1/20.6)*cos[8(θ-φ)]. The 1/9 term reflects the 9 bins, and the cosine scaling factor is a reasonable average based on simulations. This amplitude scaling factor is insensitive to the goodness-of-fit due to orthogonality – the phase angle shifts the curve laterally, and identical phase angles are obtained by the fitting routine regardless of the scaling factor. The fixed scaling factor enables rapid convergence of the fit. The phase angles from these fits were used to rotate pore clusters before aligning them based on their centroids. b, Angular distribution of localizations in the initially aligned pore clusters (Fig. 2i). Inset: Fig. 2i, bar: 50 nm. c, Angular distribution of rotationally-corrected localizations (Fig. 2k). Inset: Fig. 2k, bar: 50 nm. Pore clusters were rotated based on the phase angle as determined in (a) before aligning. The distribution was fit to y = y₀ + c₁*sin(8(x-ϕ)), where y₀, c₁ and ϕ are fit parameters. The minima define the angles of the dashed spokes in Fig. 2m. Source numerical data are provided in source data.

Extended Data Fig. 4. Nuclear Accumulation of M9-βGal and Imp α in Permeabilized U-2 OS NUP96-mEGFP Cells

a, Nuclear import of M9-βGal(Atto542). The M9-βGal cargo is an ~500 kDa tetramer with four M9 nuclear localization sequences (NLSs) that are recognized by the transportin NTR. Transport reactions were monitored using wide-field fluorescence (λ_ex = 532 nm) in the presence or absence of transportin or RanGTP (RanGDP + GTP) as indicated. The ‘transport mix’ was flowed in at t = 0.5 min. Representative images from four time points are shown (N = 20 cells from 4 independent experiments). [M9-βGal] = 0.25 μM, [transportin] = 0.25 or 1.0 μM, [RanGDP] = 0.5 μM, [NTF2] = 1 μM, [GTP] = 1 mM. b, Kinetics of nuclear import of M9-βGal. Average nuclear fluorescence (±SD) was quantified over time for N = 15 (-transportin), 17 (-RanGTP), or 20 (high and low transportin) cells from 4 independent experiments). Background refers to an area far from the cells. c, Cargo-dependent nuclear uptake of Imp α. Robust accumulation of Imp α(Atto542) into the nucleus (λ_ex = 532 nm) occurs in the presence of the NLS-2xBFP cargo (top row) but not in its absence (bottom row). The range of the fluorescence images in the bottom row is 5 times smaller than in the top row, emphasizing the very low nuclear accumulation (N = 18 cells over 4 independent experiments). [Imp α(Atto542)] = 0.5 μM, [Imp β] = 0.5 μM, [NLS-2xBFP] = 0.5 μM, [RanGDP] = 1.5 μM, [NTF2] = 1 μM and [GTP] = 1 mM. Source numerical data are provided in source data.

Extended Data Fig. 5. Localizations of M9-βGal and NLS-2xBFP Complexes

a,b, Brief M9-βGal complex appearances. Shown are the localizations from Fig. 4a that remained visible for one (a) or two (b) 3 ms frames. c,d, Brief NLS-2xBFP complex appearances. Shown are the localizations from the experiment in Fig. 5 that remained visible for one (c) or two (d) 2 ms frames. Localizations shown in (a-d) appear randomly distributed, consistent with particles that are largely diffusing and not interacting with the NPC. e, Simulated distribution of particles penetrating a barrier. In this simulation (Supplementary Data 2_Software File 2), particles appeared randomly within two compartments separated by a 50 nm thick barrier with a 100 nm diameter pore. This distribution models the M9-βGal complex localizations in Fig. 4a. The localizations appearing within the barrier result from the precision error, indicating that the localizations observed within the NE region in Fig. 4a are likely a consequence of localization error. For a-e, the N values are the number of localizations from 142 NPCs from 10 nuclei; each nucleus was an independent biological replicate. f,g, Localizations along the transport axis. Distribution of z values for M9-βGal complexes (f; from Figs. 4e,f) and NLS-2xBFP complexes (g; from Figs. 5e,f) undergoing import or abortive import. Distributions are fit to a double Gaussian function yielding mean values (±SD) of −100±48 nm and 68±56 nm for M9-βGal complexes, and −104±82 nm and 122±47 nm for NLS-2xBFP complexes. Source numerical data are provided in source data.

Extended Data Fig. 6. Cargo Complex Diffusion Constants

a, 3D step-size histogram for M9-βGal complexes. Step sizes of M9-βGal complexes were calculated from consecutive localizations in the trajectories of Fig. 4c (N = 824 total jump distances from 10 nuclei; each nucleus was an independent biological replicate). Data were fit using Eq. 4, yielding three distinct diffusion constants of 0.2 (18%), 0.8 (50%) and 2.6 (32%) μm²/s, or a weighted average diffusion constant of 1.3 μm²/s. b, 3D step-size histogram for NLS-2xBFP complexes. Step sizes of NLS-2xBFP complexes were calculated from consecutive localizations in the trajectories of Fig. 5c (N = 660 total jump distances from 10 nuclei; each nucleus was an independent biological replicate). Fitting the histogram yielded two distinct diffusion constants of 0.6 (25%) and 2.7 (75%) μm²/s, or a weighted average diffusion constant of 2.2 μm²/s. Due to the highly confined, irrregular volume sampled by the cargo complexes, these average diffusion constant estimates are considered both approximate and an underestimate. For comparison, the diffusion constant of the M9-βGal and NLS-2xBFP cargo complexes in aqueous buffer are approximated as ~34 and 55 μm²/s, respectively, using the Stokes-Einstein equation and a protein density of ~1.35 g/cm³. Source numerical data are provided in source data.

Figure 1.. 3D Super-resolution Microscope and Experimental Workflow.

a, The 3D microscope setup used to obtain super-resolved positional information for single dyes on the NPC scaffold and for individual diffusing cargo complexes. Fluorescence was collected by an EMCCD camera after passing through a home-built adaptive optics (AO) system. The deformable mirror (DM) was used to first correct optical aberrations via a feedback algorithm with the wavefront sensor (WFS), and then introduce the astigmatism needed for 3D imaging. A self-configured TIRF-lock system ensured a z-stability of < 3 nm. Lenses (L), mirrors (M), and dichroic mirrors (Di) are indicated. b, Imaging plane. The bottom surface of nuclei contained NPCs within a nearly flat NE that was perpendicular to the narrow-field illumination beam. The image shows the fluorescence from single HMSiR dye molecules on NPCs (see Fig. 2) using astigmatism imaging, in which z-information is retrieved from the elongation of the spot in x or y. Bar = 1 μm. c, Experimental workflow. The total time for sample preparation and data acquisition was ~20 min after cell permeabilization. Data collection at room temperature required ~10 min via Approach 1 (cargo complexes tracked first, NPCs localized second) or Approach 2 (reverse order). d-f, Microscope z-calibration and precision using a 60 nm root mean square (rms) astigmatism introduced with the DM. The z-dependence of x- and y-spot widths (d) was obtained from z-stack images (100 ms/frame, 41 steps, step size = 25 nm) of five different 0.1 μm beads embedded in 2% agarose (λ_ex = 647 nm; ~2500-3500 photons/spot). The difference between the x- and y-widths was approximately linearly dependent on z (e, dashed black line). The fits in d,e were described earlier, although the data obtained for NPCs used the improved algorithm implemented by ThunderStorm. The localization errors (precisions) were defined as the standard deviation of the position in x, y, and z over 100 images of 0.1 μm beads embedded in 2% agarose (f). Precision values obtained at various excitation intensities (~1500-4500 photons/spot; 50 ms/frame), and z positions were scaled to the 3000 photon level by multiplication by (N_p/3000)^1/2, where N_p = the number of photons, and were fit with Eqs. 5, 6, and 7b. These data illustrate that the three spatial measurement precisions for each fluorescent spot depends on the photons collected and the position in z. See Online Methods for additional details. Source numerical data are provided in source data.

Figure 2.. Localization of the NPC Scaffold.

a, Schematic of the NUP96 distribution within NPCs. The maroon and orange dots (cytoplasmic (C) and nucleoplasmic (N) rings, respectively) indicate the 3D position of the C-termini of the 32 NUP96 molecules within the EM density map (EMD-3103). b, The HMSiR blinking dye labeling approach of the mEGFP domain on NUP96 (not to scale). c,d, Wide-field fluorescence emission of mEGFP from the bottom NE of a permeabilized U-2 OS-CRISPR-NUP96-mEGFP cell (λ_ex = 488 nm). Spots (green) are individual NPCs. Similar results were observed for N = 100 cells. (d) represents an expanded view of the square region identified in (c). e, First pass images of individual super-resolved NPCs. These dye clusters remained after applying photon and z filters. The color bar represents z-height. The total acquisition time per nucleus was ~8 min (50 ms/frame, λ_ex = 647 nm, 3 kW/cm²). f,g, Representative double-circle fit for determining NPC centroids. h, 3D-super-resolution images of individual well-localized NPCs. These clusters survived the double-circle fitting routine, and were selected for alignment into the NPC scaffold. See Extended Data Fig. 2 for selection workflow. i,j, Composite NPC images from aligned but unrotated clusters (1872 localizations obtained from 142 clusters from 10 nuclei; each nucleus was an independent biological replicate). k,l, Composite NPC images from rotated clusters revealing the 8-fold rotational symmetry. These images were constructed from a single dataset. Images from a duplicate dataset are shown in Fig. 5a,b and a quantitative comparison is provided in Table 1. See Supplementary Video 2 for rotated views. m, Localizations from all NPCs separated into 8 segments for each ring based on an angular global fit (Extended Data Fig. 3). The angular centroid within each segment was calculated from a sinusoidal fit to the angle distribution. The difference between the angular centroids of the two rings for the 8 segments is 0.9±3.8°. n, The distribution of z values from all NPCs. The symmetric double Gaussian fit (centered at 0) indicates that the distance between the two rings is 51.2 nm. Simulated distributions and errors are modeled in Fig. 3. Source numerical data are provided in source data.

Figure 3.. Effect of Tilt and Jiggle on Resolving the NPC Scaffold.

a, An expanded version of the data set used to construct Figs. 2k,l. For comparison with the simulations, these data underwent less selection before rotationally-corrected alignment (corresponding to the clusters in Extended Data Fig. 3c without filtering the diameter and the distance between the rings, but with a z-centroid = 0±200 nm), and thus, there are significantly more localizations (N = 2362 localizations from 181 clusters; the distribution of localizations/cluster is given in Extended Data Fig. 2e). The radial width (σ_rw) and z width (σ_zw) are key parameters for comparing distributions, and reflect standard deviations of Gaussian and double Gaussian fits, respectively. b,c, Effect of tilt and jiggle. Average (±SD) radial (b) and z (c) widths from 20 independent simulations. The horizontal black dashed lines indicate the experimental radial and z widths from (a), and the red lines indicate these values for the localization data used to make Figs. 2k,l. The data in (c) indicate that the tilt is ≤ ~10°, and the total jiggle in z is < ~6 nm. While the x, y, and z jiggles were identical in (b,c), they largely independently influence radial- or z-width. With this in mind, the jiggles in x and y are 8-9 nm (b). d,e,f, Simulated distributions based on various jiggle and tilt values. As an initial reference, a simulated distribution with no tilt or jiggle (d) reveals the data scatter expected based on the current localization precision. Considering the results in (b,c), a minimal non-zero z jiggle of 3 nm was asssumed, which suggested a maximum reasonable tilt of 9° and x and y jiggles of 8 nm (e). Alternatively, considering that the z jiggle is potentially larger, a more moderate tilt (5°) suggests a z jiggle of 5 nm and x and y jiggles of 9 nm (f). Both (e) and (f) reasonably reproduced the experimental data. The simulations reported here do not include centroid localization errors – these would be identical in effect to the jiggles. By applying the two-circle fit algorithm to simulated data, the centroid localization errors were estimated as 4-5 nm in x, y, and z. See Online Methods for additional details. Source numerical data are provided in source data.

Figure 4.. 3D Tracking of M9-βGal Cargo Complexes Interacting with and Transiting Through NPCs.

a,b, All M9-βGal(Atto542) localizations (N = 2665 localizations from 142 NPCs from 10 nuclei; each nucleus was an independent biological replicate, green) within a 400 nm cube centered on NPC clusters (from Fig. 2, red), shown from the side (a) and from the cytoplasm (b). Total acquisition time per nucleus for cargo localizations was ~2 min (3 ms/frame, λ_ex = 532 nm, 50 kW/cm²). The two horizontal dashed lines (0±20 nm) in a indicate the approximate location of the NE membranes. [M9-βGal(Atto542)] = 1 nM, [transportin] = 1.0 μM, [RanGDP] = 0.5 μM, [NTF2] = 1 μM, [GTP] = 1 mM. c,d, Localizations for cargos detected in three or more consecutive frames (N = 1077 localizations selected from the data in a,b) in localization (c) and Gaussian rendered (d) maps. Eliminated localizations appear randomly distributed (Extended Data Figs. 5a,b,e). e-h, Representative trajectories. Select trajectories (from N = 239 trajectories obtained from the data in a,b) separately identified as (e) Import (39%), (f) Abortive Import (27%), (g) Export (18%), and (h) Abortive Export (16%). i,j, Distribution map of localizations from import and abortive import trajectories within the NPC scaffold (z = 0±20 nm), shown from the side (i) and from the cytoplasm (j). k, The z-dependent radial distribution of the localizations from import and abortive import trajectories within the central pore region. Values are duplicated by reflection through r = 0 to generate an image representative of a pore cross-section, thus illustrating the hourglass shape of the translocation conduit. Source numerical data are provided in source data.

Figure 5.. 3D Tracking of NLS-2xBFP Cargo Complexes Interacting with and Transiting Through NPCs.

a,b, Composite NPC images, shown from the cytoplasm (a) and from the side (b). Visual comparison with Fig. 2k,l reveals the structural reproducibility. A quantitative comparison is given in Table 1. c,d, Localizations for NLS-2xBFP/Imp α/Imp β cargo complexes detected in three or more consecutive frames (N = 882 localizations from 115 NPCs from 10 nuclei; each nucleus was an independent biological replicate) in localization (c) and Gaussian rendered (d) maps. Total acquisition time per nucleus for cargo complex localizations was ~2 min (2 ms/frame, λ_ex = 532 nm, 100 kW/cm²). Eliminated localizations (particles visible for 1 or 2 frames) appear randomly distributed (Extended Data Figs. 5c,d). The two horizontal dashed lines (0±20 nm) in c indicate the approximate location of the nuclear envelope membranes. [Imp α(Atto542)] = 1 nM, [Imp β] = 0.5 μM, [NLS-2xBFP] = 0.5 μM, [RanGDP] = 1.5 μM, [NTF2] = 1 μM, [GTP] = 1 mM. e-h, Representative trajectories. Select trajectories (from N = 196 trajectories a selected from the data in c,d) separately identified as (e) Import (34%), (f) Abortive Import (36%), (g) Export (13%), and (h) Abortive Export (17%). i,j, Distribution map of localizations from import and abortive import trajectories within the NPC scaffold (z = 0±20 nm), shown from the side (i) and from the cytoplasm (j). k, The z-dependent radial distribution of the localizations from import and abortive import trajectories within the central pore region (compare with Fig. 4k). Source numerical data are provided in source data.

Figure 6.. Overlap of the Transportin and Imp α/Imp β Import Pathways.

a, Overlap of z-dependent radial distribution maps. Data are from Fig. 4k (black, M9-βGal import complexes) and Fig. 5k (red, NLS-2xBFP import complexes). b, Volume-corrected heat maps for the transportin and Imp α/Imp β import pathways. The particle distributions in a were volume corrected by calculating the particle densities within annular cylinders. At each (r,z) coordinate, the number of localizations within an annular cylinder (r = ±10 nm and z = ±10 nm) were determined, and divided by the total volume of the region to yield the density. The color scale represents the particle density (number of localizations per volume in nm³ x 10⁶). c,d, Radial density distributions at different z-heights. Data from b were plotted for the indicated z-values and fit with a Gaussian function. While a few localizations were observed at small radii, individual localizations in such locations had large effects on density values due to the small volumes; these values were deemed unreliable, and therefore were eliminated from the analysis. For both cargo complexes, the peak density for z = 0±20 nm is at r ≈ 32 nm. e,f, Number of localizations in different z-sections. The total number of localizations obtained within 20 nm thick z-sections (over all radii) are indicated. These data indicate that cargo complexes that abort transport are largely rejected before the middle of the pore (z = 0). Source numerical data are provided in source data.