Fig. 1: Nanodomain interactions alter molecular motion on LD surface.
From: Membrane bridges and nanodomain partitioning govern membrane protein targeting to lipid droplets
a,b, MINFLUX single-molecule trajectories of GPAT4 (a) and HSD17B13 (b) overlaid on confocal images of cells stained with BODIPY 493/503 to label LDs. Confocal LD images were used to classify trajectories by subcellular localization. Representative examples of single-molecule tracks on LDs and the ER are shown on the right. Scale bars, 10 µm (left), 1 µm (middle) and 250 nm (right). c, Histograms of the anomalous diffusion exponent (α) for ER-localized (purple) and LD-localized (salmon) tracks of GPAT4 (left) and HSD17B13 (right). α values were obtained by fitting a power-law equation to the full MSD curve of each trajectory. Dotted lines indicate medians. 95% confidence intervals: GPAT4LD (0.30 to 0.45), GPAT4ER (0.55 to 0.72), HSD17B13LD (0.38 to 0.60) and HSD17B13ER (0.64, 0.83). A two-sided Mann–Whitney test was used to assess the statistical significance of the difference between the α values for the trajectories at each compartment. P = 2.3 × 10−4 (GPAT4) and P = 5.2 × 10−4 (HSD17B13). Ntracks-GPAT4 = 149 (LD) and 182 (ER), Ntracks-HSD17B13 = 149 (LD) and 182 (ER). d, KDE analysis of GPAT4 single-molecule trajectories on LDs (left) and ER (right). Tracks were segmented into 100 ms intervals, and localization density was computed using a Gaussian kernel. Dark-red contours indicate regions of elevated localization density, representing putative nanodomains on the LD phospholipid monolayer. e, Areas of individual nanodomains on the ER and LD membranes were quantified and plotted. The box represents the 25th–75th percentiles (IQR) and the centre line indicates the median. A two-sided Mann–Whitney test was used to assess the statistical significance of the difference in nanodomain sizes at the ER and LD membrane. P = 0.015 (GPAT4) and 9.2 × 10−20 (HSD17B13). Nnanodomains = 688 (GPAT4) and 2208 (HSD17B13). f, The distribution of the apparent diffusion coefficient (Dapp) of GPAT4 within (red) or outside (grey) nanodomains observed on LDs (left) and the ER (right). The Dapp was calculated using a 5-ms rolling window MSD analysis and classified on the basis of the molecule’s position relative to nanodomain boundaries. Nsteps = 306,667 (LD) and 246,591 (ER). A two-sided Mann–Whitney test was used to assess the statistical significance of the difference in Dapp values. PLD = 2.6 × 10−163, PER = 9.7 × 10−234. g, The distributions of the fast and slow mobility populations were derived using a Gaussian mixture model from Extended Data Fig. 2a and classified on the basis of whether segments fell inside (left) or outside (right) the nanodomain boundaries. The slow-moving population is predominantly enriched within nanodomains, whereas the fast-moving population is the major species outside of these nano-regions. Median Dapp values of each population are stated on each graph. Mixing weights, πinside = 0.68 (slow) and 0.32 (fast), and πoutside = 0.27 (slow) and 0.73 (fast). σ2inside = 0.001 (slow) and 0.004 (fast), and σ2outside = 0.001 (slow) and 0.007 (fast). h, The distributions of localization clustering within the nanodomains at the ER and LD membrane for GPAT4 and HSD17B13 were plotted on a cumulative distribution function (CDF) graph. The clustering index was calculated by driving the ratio of mean KDE density at each dense spot and total grid-normalized KDE density.
