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Chromosome organization is crucial for genome function. Here, we present a method for visualizing chromosomal DNA at super-resolution and then integrating Hi-C data to produce three-dimensional models of chromosome organization. Using the super-resolution microscopy methods of OligoSTORM and OligoDNA-PAINT, we trace 8 megabases of human chromosome 19, visualizing structures ranging in size from a few kilobases to over a megabase. Focusing on chromosomal regions that contribute to compartments, we discover distinct structures that, in spite of considerable variability, can predict whether such regions correspond to active (A-type) or inactive (B-type) compartments. Imaging through the depths of entire nuclei, we capture pairs of homologous regions in diploid cells, obtaining evidence that maternal and paternal homologous regions can be differentially organized. Finally, using restraint-based modeling to integrate imaging and Hi-C data, we implement a method–integrative modeling of genomic regions (IMGR)–to increase the genomic resolution of our traces to 10 kb., Author summary Questions regarding the impact of chromosome structure on genome function are focusing increasingly on the manner in which chromosomes are organized within the nucleus. In fact, studies of processes as diverse as gene activation and repression as well as genome repair and stability are all querying how the 3D organization of chromosomal DNA may be a major player. Here, we apply our strategy for tracing chromosomes at super-resolution, traversing over 8 megabases of human chromosome 19 while visualizing genomic features ranging in size from kilobases to megabases. This technology has enabled exploration of the physical nature of a genomic feature called the compartment; compartments are widely hypothesized to reflect the partitioning of genomes into relatively more and less active regions. Excitingly, we find that compartments are, indeed, physical structures, that they are sometimes distinct and other times entangled. We also find that the maternally-derived and the paternally-derived homologous regions can differ more than would be expected by chance. Finally, by integrating image data with information regarding the frequency with which genomic segments contact each other, we produce a 3D model of how the 8 megabases we have imaged may be organized within a single nucleus, achieving 10 kb genomic resolution.

We report a novel optical single-emitter-localization methodology that uses the phase induced by path length differences in a Mach-Zehnder interferometer to improve localization precision. Using information theory, we demonstrate that the localization capability of a modified Fourier domain signal generated by photon interference enables a more precise localization compared to a standard Gaussian intensity distribution of the corresponding point spread function. The calculations were verified by numerical simulations and an exemplary experiment, where the centers of metal nanoparticles were localized to a precision of 3 nm.

The ability to localize precisely a single optical emitter is important for particle tracking applications and super resolution microscopy. It is known that for a traditional microscope the ability to localize such an emitter is limited by the photon count. Here we analyze the ability to improve such localization by imposing interference fringes. We show here that a simple grating interferometer can introduce such improvement in certain circumstances and analyze what is required to increase the localization precision further.

In this paper, we demonstrate hyper-spectral imaging of fluorescent microspheres in a scanning-confocal-fluorescence microscope by spatially dispersing the spectra using a novel broadband diffractive optic, and applying a nonlinear optimization technique to extract the full-incident spectra. This broadband diffractive optic has a designed optical efficiency of over 90% across the entire visible spectrum. We used this technique to create two-color images of two fluorophores and also extracted their emission spectra with good fidelity. This method can be extended to image both spatially and spectrally overlapping fluorescent samples. Full control in the number of emission spectra and the feasibility of enhanced imaging speed are demonstrated as well.

The ability to track single fluorescent particles within a three dimensional (3D) cellular environment can provide valuable insights into cellular processes. In this paper, we present a modified nonlinear image decomposition technique called K-factor that reshapes the 3D point spread function (PSF) of an XYZ image stack into a narrow Gaussian profile. The method increases localization accuracy by ~60% with compare to regular Gaussian fitting, and improves minimal resolvable distance between overlapping PSFs by ~50%. The algorithm was tested both on simulated data and experimentally. This work presets a novel use of the nonlinear image decomposition technique called K-factor that reshapes the three dimensional (3D) point spread function (PSF) of an XYZ image stack into a narrow Gaussian profile. The experimentally obtained PSF of a Z-stack raw data that is acquired by a widefield microscope has a more elaborate shape that is given by the Gibson and Lanni model. This shape increases the computational complexity associated with the localization routine, when used in localization microscopy techniques. Furthermore, due to its nature, this PSF spreads over a larger volume, making the problem of overlapping emitters detection more pronounced. The ability to use Gaussian fitting with high accuracy on 3D data can facilitate the computational complexity, hence reduce the processing time required for the generation of the 3D superresolved image. In addition it allows the detection of overlapping PSFs and reduces the effects of the penetration of out of focus PSFs into in focused PSFs, therefore enables the increase in the activated fluorophore density by ~50%. The algorithm was tested both on simulated data and experimentally, where it yielded an increase in the localization accuracy by ~60% with compare to regular Gaussian fitting, and improved the minimal resolvable distance between overlapping PSFs by ~50%, making it extremely applicable to the field of 3D biomedical imaging

Two new directions are presented. The first uses modified nonlinear image decomposition to reshapes the 3D point spread function of an XYZ Z-stack and improves the localization accuracy. The second improves the localization accuracy by labeling the specimens with GNPs and their subsequent flickering at different temporal frequencies.

We show that the localization precision of single nanoparticles can be improved by imposing an interference pattern on the Point-Spread-Function. Localization precision of 0.1nm for a single emitter was obtained.

Interference of signal in Fourier space, emitted from single probes, is used to localize it by recording and computing the phase of the fringes. Such system has applications in super resolution localization microscopy.

Localization of a single fluorescent particle with sub-diffraction limit accuracy is a key merit in fluorescence microscopy. Implementation of nonlinear filtering algorithms prior the localization process can improve the localization accuracy of standard existing methods and also enable the localization of overlapping particles, allowing the use of increased fluorophore activation density, and thereby increased data collection speed. In this paper we present the use of an image decomposition algorithm termed K-factor which reduces an image into a nonlinear set of contrast-ordered decompositions whose joint product reassembles the original image. The K-factor technique is implemented on the images, prior to the localization of the fluorescent probes. Numerical simulations of fluorescence data with random probe positions, and especially at high densities of activated fluorophores demonstrated an improvement in the localization precision with compare to single fitting techniques. Implanting the proposed concept also on experimental data of cellular structures yielded the theoretically predicted resolution enhancement.

Optimal four-dimensional imaging requires high spatial resolution in all dimensions, high speed and minimal photobleaching and damage. We developed a dual-view, plane illumination microscope with improved spatiotemporal resolution by switching illumination and detection between two perpendicular objectives in an alternating duty cycle. Computationally fusing the resulting volumetric views provides an isotropic resolution of 330 nm. As the sample is stationary and only two views are required, we achieve an imaging speed of 200 images/s (i.e., 0.5 s for a 50-plane volume). Unlike spinning-disk confocal or Bessel beam methods, which illuminate the sample outside the focal plane, we maintain high spatiotemporal resolution over hundreds of volumes with negligible photobleaching. To illustrate the ability of our method to study biological systems that require high-speed volumetric visualization and/or low photobleaching, we describe microtubule tracking in live cells, nuclear imaging over 14 h during nematode embryogenesis and imaging of neural wiring during Caenorhabditis elegans brain development over 5 h.

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

Localization of a single fluorescent particle with sub-diffraction-limit accuracy is a key merit in localization microscopy. Existing methods such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) achieve localization accuracies of single emitters that can reach an order of magnitude lower than the conventional resolving capabilities of optical microscopy. However, these techniques require a sparse distribution of simultaneously activated fluorophores in the field of view, resulting in larger time needed for the construction of the full image. In this paper we present the use of a nonlinear image decomposition algorithm termed K-factor, which reduces an image into a nonlinear set of contrast-ordered decompositions whose joint product reassembles the original image. The K-factor technique, when implemented on raw data prior to localization, can improve the localization accuracy of standard existing methods, and also enable the localization of overlapping particles, allowing the use of increased fluorophore activation density, and thereby increased data collection speed. Numerical simulations of fluorescence data with random probe positions, and especially at high densities of activated fluorophores, demonstrate an improvement of up to 85% in the localization precision compared to single fitting techniques. Implementing the proposed concept on experimental data of cellular structures yielded a 37% improvement in resolution for the same super-resolution image acquisition time, and a decrease of 42% in the collection time of super-resolution data with the same resolution.

We present a method to improve the localization precision of nanoparticles over Gaussian fitting by imposing an interference pattern on the Point-Spread-Function. Localization precision of 0.1nm for a single emitter was obtained.

Localization precision of single emitters can be fundamentally improved by imposing interference fringes on the Point-Spread-Function. We show that this interference effect can be used for coherent and incoherent signals for sub nanometer localization.

Elucidating protein functions and molecular organisation requires to localise precisely single or aggregated molecules and analyse their spatial distributions. We develop a statistical method SODA (Statistical Object Distance Analysis) that uses either micro- or nanoscopy to significantly improve on standard co-localisation techniques. Our method considers cellular geometry and densities of molecules to provide statistical maps of isolated and associated (coupled) molecules. We use SODA with three-colour structured-illumination microscopy (SIM) images of hippocampal neurons, and statistically characterise spatial organisation of thousands of synapses. We show that presynaptic synapsin is arranged in asymmetric triangle with the 2 postsynaptic markers homer and PSD95, indicating a deeper localisation of homer. We then determine stoichiometry and distance between localisations of two synaptic vesicle proteins with 3D-STORM. These findings give insights into the protein organisation at the synapse, and prove the efficiency of SODA to quantitatively assess the geometry of molecular assemblies., Elucidating molecular organisation requires precise localisation and analysis. Here the authors develop SODA software for automatic and quantitative mapping of statistically coupled molecules, and use it to unravel spatial organisation of thousands of synaptic proteins in SIM and 3DSTORM microscopy.