Publications & technical resources

Optical diffraction limits resolution in visible spectrum to 200 nm in the lateral dimension (x-y) and 500 nm in axial dimension (z). Recent advances in engineering properties of fluorescent proteins and dyes have enabled nanometer scale visualization by localizing sparse ensembles of photoswitchable/photoactivatable molecules through many frames. A final image is formed by combining locations of all the molecules to form a “super-resolution image”. The family of techniques is known as single-molecule localization microscopy (SMLM). Although SMLM enables high precision imaging of 10-20 nm in the lateral dimension, it lacks axial (z) resolution, especially near focus. The Double-Helix Point Spread function (DH-PSF) offers a solution to this problem by enabling high-depth and high-precision 3D imaging.

An outstanding challenge in single-molecule localization microscopy is the accurate and precise localization of individual point emitters in three dimensions in densely labeled samples. One established approach for three-dimensional single-molecule localization is point-spread-function (PSF) engineering, in which the PSF is engineered to vary distinctively with emitter depth using additional optical elements. However, images of dense emitters, which are desirable for improving temporal resolution, pose a challenge for algorithmic localization of engineered PSFs, due to lateral overlap of the emitter PSFs. Here we train a neural network to localize multiple emitters with densely overlapping Tetrapod PSFs over a large axial range. We then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach experimentally with super-resolution reconstructions of mitochondria and volumetric imaging of fluorescently labeled telomeres in cells. Our approach, DeepSTORM3D, enables the study of biological processes in whole cells at timescales that are rarely explored in localization microscopy.

Primary cilia in many cell types contain a periaxonemal subcompartment called the inversin compartment. Four proteins have been found to assemble within the inversin compartment: INVS, ANKS6, NEK8, and NPHP3. The function of the inversin compartment is unknown, but it appears to be critical for normal development, including left–right asymmetry and renal tissue homeostasis. Here we combine superresolution imaging of human RPE1 cells, a classic model for studying primary cilia in vitro, with a genetic dissection of the protein–protein binding relationships that organize compartment assembly to develop a new structural model. We observe that INVS is the core structural determinant of a compartment composed of novel fibril-like substructures, which we identify here by three-dimensional single-molecule superresolution imaging. We find that NEK8 and ANKS6 depend on INVS for localization to these fibrillar assemblies and that ANKS6-NEK8 density within the compartment is regulated by NEK8. Together, NEK8 and ANKS6 are required downstream of INVS to localize and concentrate NPHP3 within the compartment. In the absence of these upstream components, NPHP3 is redistributed within cilia. These results provide a more detailed structure for the inversin compartment and introduce a new example of a membraneless compartment organized by protein–protein interactions.

Translocation from one cavity to another through a narrow constriction (i.e., a “hole”) represents the fundamental elementary process underlying hindered mass transport of nanoparticles and macromolecules within many natural and synthetic porous materials, including intracellular environments. This process is complex and may be influenced by long-range (e.g., electrostatic) particle–wall interactions, transient adsorption/desorption, surface diffusion, and hydrodynamic effects. Here, we used a three-dimensional (3D) tracking method to explicitly visualize the process of nanoparticle diffusion within periodic porous nanostructures, where electrostatic interactions were mediated via ionic strength. The effects of electrostatic interactions on nanoparticle transport were surprisingly large. For example, an increase in the Debye length of only a few nanometers (in a material with a hole diameter of ∼100 nm) increased the mean cavity escape time 3-fold. A combination of computational and experimental analyses indicated that this hindered cavity escape was due to an electrostatic energy barrier in the region of the hole, which was quantitatively explained using DLVO theory. These findings explicitly demonstrate that the cavity escape process was barrier-limited and dominated by electrostatic effects.

Selective recruitment and concentration of signalling proteins within membraneless compartments is a ubiquitous mechanism for subcellular organization. The dynamic flow of molecules into and out of these compartments occurs on faster timescales than for membrane-enclosed organelles, presenting a possible mechanism to control spatial patterning within cells. Here, we combine single-molecule tracking and super-resolution microscopy, light-induced subcellular localization, reaction-diffusion modelling and a spatially resolved promoter activation assay to study signal exchange in and out of the 200 nm cytoplasmic pole-organizing protein popZ (PopZ) microdomain at the cell pole of the asymmetrically dividing bacterium Caulobacter crescentus. Two phospho-signalling proteins, the transmembrane histidine kinase CckA and the cytoplasmic phosphotransferase ChpT, provide the only phosphate source for the cell fate-determining transcription factor CtrA. We find that all three proteins exhibit restricted rates of entry into and escape from the microdomain as well as enhanced phospho-signalling within, leading to a submicron gradient of activated CtrA-P that is stable and sublinear. Entry into the microdomain is selective for cytosolic proteins and requires a binding pathway to PopZ. Our work demonstrates how nanoscale protein assemblies can modulate signal propagation with fine spatial resolution, and that in Caulobacter, this modulation serves to reinforce asymmetry and differential cell fate of the two daughter cells. and differential cell fate of the two daughter cells.

We demonstrate that a deep neural network can be trained to virtually refocus a two-dimensional fluorescence image onto user-defined three-dimensional (3D) surfaces within the sample. Using this method, termed Deep-Z, we imaged the neuronal activity of a Caenorhabditis elegans worm in 3D using a time sequence of fluorescence images acquired at a single focal plane, digitally increasing the depth-of-field by 20-fold without any axial scanning, additional hardware or a trade-off of imaging resolution and speed. Furthermore, we demonstrate that this approach can correct for sample drift, tilt and other aberrations, all digitally performed after the acquisition of a single fluorescence image. This framework also cross-connects different imaging modalities to each other, enabling 3D refocusing of a single wide-field fluorescence image to match confocal microscopy images acquired at different sample planes. Deep-Z has the potential to improve volumetric imaging speed while reducing challenges relating to sample drift, aberration and defocusing that are associated with standard 3D fluorescence microscopy.

Solid-liquid interfaces play an important role for functional devices. Hence, a detailed understanding of the interaction of soft matter objects with solid supports and of the often concomitant structural deformations is of great importance. We address this topic in a combined experimental and simulation approach. We investigated thermoresponsive poly(N-isopropylmethacrylamide) microgels (μGs) at different surfaces in an aqueous environment. As super-resolution fluorescence imaging method, three-dimensional direct stochastical optical reconstruction microscopy (dSTORM) allowed for visualizing μGs in their three-dimensional (3D) shape, for example, in a “fried-egg” conformation depending on the hydrophilicity of the surface (strength of adsorption). The 3D shape, as defined by point clouds obtained from single-molecule localizations, was analyzed. A new fitting algorithm yielded an isosurface of constant density which defines the deformation of μGs at the different surfaces. The presented methodology quantifies deformation of objects with fuzzy surfaces and allows for comparison of their structures, whereby it is completely independent from the data acquisition method. Finally, the experimental data are complemented with mesoscopic computer simulations in order to (i) rationalize the experimental results and (ii) to track the evolution of the shape with changing surface hydrophilicity; a good correlation of the shapes obtained experimentally and with computer simulations was found.

Narrow escape from confinement through a nanochannel is the critical step of complex transport processes including size-exclusion-based separations, oil and gas extraction from the microporous subsurface environment, and ribonucleic acid translocation through nuclear pore complex channels. While narrow escape has been studied using theoretical and computational methods, experimental quantification is rare because of the difficulty in confining a particle into a microscopic space through a nanoscale hole. Here, we studied narrow escape in the context of continuous nanoparticle diffusion within the liquid-filled void space of an ordered porous material. Specifically, we quantified the spatial dependence of nanoparticle motion and the sojourn times of individual particles in the interconnected confined cavities of a liquid-filled inverse opal film. We found that nanoparticle motion was inhibited near cavity walls and cavity escape was slower than predicted by existing theories and random-walk simulations. A combined computational-experimental analysis indicated that translocation through a nanochannel is barrier controlled rather than diffusion controlled.

Single-molecule localization microscopy probes the position and motions of individual molecules in living cells with tens of nanometer spatial and millisecond temporal resolution. These capabilities make single-molecule localization microscopy ideally suited to study molecular level biological functions in physiologically relevant environments. Here, we demonstrate an integrated protocol for both acquisition and processing-analysis of single-molecule tracking data to extract the different diffusive states a protein of interest may exhibit. This information can be used to quantify molecular complex formation in living cells. We provide a detailed description of a camera-based 3D single-molecule localization experiment, as well as the subsequent data processing steps that yield the trajectories of individual molecules. These trajectories are then analyzed using a numerical analysis framework to extract the prevalent diffusive states of the fluorescently labeled molecules and the relative abundance of these states. The analysis framework is based on stochastic simulations of intracellular Brownian diffusion trajectories that are spatially confined by an arbitrary cell geometry. Based on the simulated trajectories, raw single-molecule images are generated and analyzed in the same way as experimental images. In this way, experimental precision and accuracy limitations, which are difficult to calibrate experimentally, are explicitly incorporated into the analysis workflow. The diffusion coefficient and relative population fractions of the prevalent diffusive states are determined by fitting the distributions of experimental values using linear combinations of simulated distributions. We demonstrate the utility of our protocol by resolving the diffusive states of a protein that exhibits different diffusive states upon forming homo- and hetero-oligomeric complexes in the cytosol of a bacterial pathogen.