Deep-STORM: super-resolution single-molecule microscopy by deep learning

We present an ultra-fast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically-blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities, and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking data-set. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data.Comment: 7 pages, added code download reference and DOI for the journal versio

Mutational patterns along different evolution paths of follicular lymphoma

Follicular lymphoma (FL) is an indolent disease, characterized by a median life expectancy of 18-20 years and by intermittent periods of relapse and remission. FL frequently transforms into the more aggressive diffuse large B cell lymphoma (t-FL). In previous studies, the analysis of immunoglobulin heavy chain variable region (IgHV) genes in sequential biopsies from the same patient revealed two different patterns of tumor clonal evolution: direct evolution, through acquisition of additional IgHV mutations over time, or divergent evolution, in which lymphoma clones from serial biopsies independently develop from a less-mutated common progenitor cell (CPC). Our goal in this study was to characterize the somatic hypermutation (SHM) patterns of IgHV genes in sequential FL samples from the same patients, and address the question of whether the mutation mechanisms (SHM targeting, DNA repair or both), or selection forces acting on the tumor clones, were different in FL samples compared to healthy control samples, or in late relapsed/transformed FL samples compared to earlier ones. Our analysis revealed differences in the distribution of mutations from each of the nucleotides when tumor and non-tumor clones were compared, while FL and transformed FL (t-FL) tumor clones displayed similar mutation distributions. Lineage tree measurements suggested that either initial clone affinity or selection thresholds were lower in FL samples compared to controls, but similar between FL and t-FL samples. Finally, we observed that both FL and t-FL tumor clones tend to accumulate larger numbers of potential N-glycosylation sites due to the introduction of new SHM. Taken together, these results suggest that transformation into t-FL, in contrast to initial FL development, is not associated with any major changes in DNA targeting or repair, or the selection threshold of the tumor clone

Emergent nanoscale superparamagnetism at oxide interfaces

Atomically sharp oxide heterostructures exhibit a range of novel physical phenomena that do not occur in the parent bulk compounds. The most prominent example is the appearance of highly conducting and superconducting states at the interface between the band insulators LaAlO3 and SrTiO3. Here we report a new emergent phenomenon at the LaMnO3/SrTiO3 interface in which an antiferromagnetic insulator abruptly transforms into a magnetic state that exhibits unexpected nanoscale superparamagnetic dynamics. Upon increasing the thickness of LaMnO3 above five unit cells, our scanning nanoSQUID-on-tip microscopy shows spontaneous formation of isolated magnetic islands of 10 to 50 nm diameter, which display random moment reversals by thermal activation or in response to an in-plane magnetic field. Our charge reconstruction model of the polar LaMnO3/SrTiO3 heterostructure describes the sharp emergence of thermodynamic phase separation leading to nucleation of metallic ferromagnetic islands in an insulating antiferromagnetic matrix. The model further suggests that the nearby superparamagnetic-ferromagnetic transition can be gate tuned, holding potential for applications in magnetic storage and spintronics

Quantum kinetic approach to the calculation of the Nernst effect

We show that the strong Nernst effect observed recently in amorphous superconducting films far above the critical temperature is caused by the fluctuations of the superconducting order parameter. We employ the quantum kinetic approach for the derivation of the Nernst coefficient. We present here the main steps of the calculation and discuss some subtle issues that we encountered while calculating the Nernst coefficient. In particular, we demonstrate that in the limit T=0 the contribution of the magnetization ensures the vanishing of the Nernst signal in accordance with the third law of thermodynamics. We obtained a striking agreement between our theoretical calculations and the experimental data in a broad region of temperatures and magnetic fields.Comment: 24 pages, 13 figure

Anti-GnRH antibodies can induce castrate levels of testosterone in patients with advanced prostate cancer

D17DT consists of the GnRH decapeptide linked to diphtheria toxoid. The aim of this pilot study was to assess the tolerance of D17DT and the production of anti-GnRH antibodies from two doses, 30 and 100 μg, in patients with locally advanced prostate cancer. Twelve patients with histologically proven prostate cancer in whom hormonal therapy was indicated were recruited. Patients received either 30 or 100 μg given intramuscularly on three separate occasions over six weeks. Patients were followed up and blood was taken for estimation of serum testosterone, PSA and anti-GnRH antibody titre. Overall the drug was well tolerated. In 5 patients a significant reduction in serum testosterone and PSA was seen. Castrate levels of testosterone were achieved in 4 and maintained for up to 9 months. Patients with the highest antibody titre had the best response in terms of testosterone suppression. This study shows that it is possible to immunize a patient with prostate cancer against GnRH to induce castrate levels of testosterone. This state appears to be reversible. This novel form of immunotherapy may have advantages over conventional forms of hormonal therapy and further studies are warranted in order to try and increase the proportion of responders. © 2000 Cancer Research Campaig

DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learning

Localization microscopy is an imaging technique in which the positions of individual nanoscale point emitters (e.g. fluorescent molecules) are determined at high precision from their images. This is the key ingredient in single/multiple-particle-tracking and several super-resolution microscopy approaches. Localization in three-dimensions (3D) can be performed by modifying the image that a point-source creates on the camera, namely, the point-spread function (PSF). The PSF is engineered using additional optical elements to vary distinctively with the depth of the point-source. However, localizing multiple adjacent emitters in 3D poses a significant algorithmic challenge, due to the lateral overlap of their PSFs. Here, we train a neural network to receive an image containing densely overlapping PSFs of multiple emitters over a large axial range and output a list of their 3D positions. Furthermore, we then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach numerically as well as experimentally by 3D STORM imaging of mitochondria, and volumetric imaging of dozens of fluorescently-labeled telomeres occupying a mammalian nucleus in a single snapshot.Comment: main text: 9 pages, 5 figures, supplementary information: 29 pages, 20 figure

Multimodal single-molecule microscopy with continuously controlled spectral resolution

Color is a fundamental contrast mechanism in fluorescence microscopy, providing the basis for numerous imaging and spectroscopy techniques. Building on spectral imaging schemes that encode color into a fixed spatial intensity distribution, here, we introduce continuously controlled spectral-resolution (CoCoS) microscopy, which allows the spectral resolution of the system to be adjusted in real-time. By optimizing the spectral resolution for each experiment, we achieve maximal sensitivity and throughput, allowing for single-frame acquisition of multiple color channels with single-molecule sensitivity and 140-fold larger fields of view compared with previous super-resolution spectral imaging techniques. Here, we demonstrate the utility of CoCoS in three experimental formats, single-molecule spectroscopy, single-molecule Förster resonance energy transfer, and multicolor single-particle tracking in live neurons, using a range of samples and 12 distinct fluorescent markers. A simple add-on allows CoCoS to be integrated into existing fluorescence microscopes, rendering spectral imaging accessible to the wider scientific community