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Fluorescence microscopy, with its molecular specificity, is one of the major characterization methods used in the life sciences to understand complex biological systems. Super-resolution approaches 1 – 6 can achieve resolution in cells in the range of 15 to 20 nm, but interactions between individual biomolecules occur at length scales below 10 nm and characterization of intramolecular structure requires Ångström resolution. State-of-the-art super-resolution implementations 7 – 14 have demonstrated spatial resolutions down to 5 nm and localization precisions of 1 nm under certain in vitro conditions. However, such resolutions do not directly translate to experiments in cells, and Ångström resolution has not been demonstrated to date. Here we introdue a DNA-barcoding method, resolution enhancement by sequential imaging (RESI), that improves the resolution of fluorescence microscopy down to the Ångström scale using off-the-shelf fluorescence microscopy hardware and reagents. By sequentially imaging sparse target subsets at moderate spatial resolutions of >15 nm, we demonstrate that single-protein resolution can be achieved for biomolecules in whole intact cells. Furthermore, we experimentally resolve the DNA backbone distance of single bases in DNA origami with Ångström resolution. We use our method in a proof-of-principle demonstration to map the molecular arrangement of the immunotherapy target CD20 in situ in untreated and drug-treated cells, which opens possibilities for assessing the molecular mechanisms of targeted immunotherapy. These observations demonstrate that, by enabling intramolecular imaging under ambient conditions in whole intact cells, RESI closes the gap between super-resolution microscopy and structural biology studies and thus delivers information key to understanding complex biological systems. The authors introduce a single-molecule DNA-barcoding method, resolution enhancement by sequential imaging, that improves the resolution of fluorescence microscopy down to the Ångström scale using off-the-shelf fluorescence microscopy hardware and reagents.

Latest advances in super-resolution microscopy allow the study of subcellular features at the level of single proteins, which could lead to discoveries in fundamental biological processes, specifically in cell signaling mediated by membrane receptors. Despite these advances, accurately extracting quantitative information on molecular arrangements of proteins at the 1–20 nm scale through rigorous image analysis remains a significant challenge. Here, we present SPINNA (Single-Protein Investigation via Nearest-Neighbor Analysis): an analysis framework that compares nearest-neighbor distances from experimental single-protein position data with those obtained from realistic simulations based on a user-defined model of protein oligomerization states. We demonstrate SPINNA in silico, in vitro, and in cells. In particular, we quantitatively assess the oligomerization of the epidermal growth factor receptor (EGFR) upon EGF treatment and investigate the dimerization of CD80 and PD-L1, key surface ligands involved in immune cell signaling. Importantly, we offer an open-source Python implementation and a GUI to facilitate SPINNA’s widespread use in the scientific community. Extracting quantitative information on biomolecular oligomerisation with high resolution remains a significant challenge. Here, the authors propose SPINNA, a framework that compares nearest-neighbour distances from experimental single-protein position data with those obtained from simulations based on a model of protein oligomerisation.

DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution fluorescence microscopy technique that achieves single-molecule ‘blinking’ by transient DNA hybridization. Despite blinking kinetics being largely independent of fluorescent dye choice, the dye employed substantially affects measurement quality. Thus far, there has been no systematic overview of dye performance for DNA-PAINT. Here we defined four key parameters characterizing performance: brightness, signal-to-background ratio, DNA-PAINT docking site damage and off-target signal. We then analyzed 18 fluorescent dyes in three spectral regions and examined them both in DNA origami nanostructures, establishing a reference standard, and in a cellular environment, targeting the nuclear pore complex protein Nup96. Finally, having identified several well-performing dyes for each excitation wavelength, we conducted simultaneous three-color DNA-PAINT combined with Exchange-PAINT to image six protein targets in neurons at ~16 nm resolution in less than 2 h. We thus provide guidelines for DNA-PAINT dye selection and evaluation and an overview of performances of commonly used dyes. The dyes chosen for DNA-PAINT microscopy are pivotal for data quality. This Analysis shows a comprehensive comparison of 18 fluorescent dyes in DNA-PAINT and offers guidance for optimum dye selection in single-color and multiplexed imaging.

State-of-the-art super-resolution microscopy enables nanometer-resolution imaging of proteins, but its multiplexing capacity has been fundamentally constrained. Here, we present Combi-PAINT, a combinatorial DNA barcoding strategy based on DNA-PAINT, that facilitates superlinear scaling of multiplexing with the number of imaging rounds. Instead of assigning one unique docking strand per target, Combi-PAINT encodes targets as combinations of orthogonal sequences, allowing nonlinear scaling of target number with imaging rounds. Using just six imaging rounds, we resolve 41 targets in a field-of-view of 100 x 100 μm2 with ~2.5 nm localization precision and ~90% decoding accuracy in under 30 minutes, representing the fastest sub-10 nm super-resolution microscopy acquisition to date. We benchmark decoding fidelity and demonstrate robust in situ performance in mammalian cells, achieving 97% decoding accuracy. Combi-PAINT is compatible with existing DNA-PAINT workflows and speed-enhancing techniques, offering a scalable, accessible platform for high-content, single-molecule spatial proteomics.Competing Interest StatementThe authors have declared no competing interest.

A large retreat of sea-ice in the ‘stormy’ Atlantic Sector of the Arctic Ocean has become evident through a series of record minima for the winter maximum sea-ice extent since 2015. Results from the Norwegian young sea ICE (N-ICE2015) expedition, a five-month-long (Jan-Jun) drifting ice station in first and second year pack-ice north of Svalbard, showcase how sea-ice in this region is frequently affected by passing winter storms. Here we synthesise the interdisciplinary N-ICE2015 dataset, including independent observations of the atmosphere, snow, sea-ice, ocean, and ecosystem. We build upon recent results and illustrate the different mechanisms through which winter storms impact the coupled Arctic sea-ice system. These short-lived and episodic synoptic-scale events transport pulses of heat and moisture into the Arctic, which temporarily reduce radiative cooling and henceforth ice growth. Cumulative snowfall from each sequential storm deepens the snow pack and insulates the sea-ice, further inhibiting ice growth throughout the remaining winter season. Strong winds fracture the ice cover, enhance ocean-ice-atmosphere heat fluxes, and make the ice more susceptible to lateral melt. In conclusion, the legacy of Arctic winter storms for sea-ice and the ice-associated ecosystem in the Atlantic Sector lasts far beyond their short lifespan.

The Arctic icescape is rapidly transforming from a thicker multiyear ice cover to a thinner and largely seasonal first-year ice cover with significant consequences for Arctic primary production. One critical challenge is to understand how productivity will change within the next decades. Recent studies have reported extensive phytoplankton blooms beneath ponded sea ice during summer, indicating that satellite-based Arctic annual primary production estimates may be significantly underestimated. Here we present a unique time-series of a phytoplankton spring bloom observed beneath snow-covered Arctic pack ice. The bloom, dominated by the haptophyte algae Phaeocystis pouchetii , caused near depletion of the surface nitrate inventory and a decline in dissolved inorganic carbon by 16 ± 6 g C m −2 . Ocean circulation characteristics in the area indicated that the bloom developed in situ despite the snow-covered sea ice. Leads in the dynamic ice cover provided added sunlight necessary to initiate and sustain the bloom. Phytoplankton blooms beneath snow-covered ice might become more common and widespread in the future Arctic Ocean with frequent lead formation due to thinner and more dynamic sea ice despite projected increases in high-Arctic snowfall. This could alter productivity, marine food webs and carbon sequestration in the Arctic Ocean.

Genome-wide association studies are used to identify common genetic variants that affect the structure of selected subcortical regions of the human brain; their identification provides insight into the causes of variability in brain development and may help to determine mechanisms of neuropsychiatric dysfunction. Genetic variants that alter brain development This genome-wide association study of 30,717 individuals identifies common genetic variants that affect the structure of selected subcortical regions of the brain known to be involved in functions associated with movement, learning, memory and motivation. The results provide insight into the causes of variability in human brain development and may help elucidate mechanisms of neuropsychiatric dysfunction. Of particular interest are six novel genetic loci influencing the volumes of the putamen, caudate nucleus and global head size. The highly complex structure of the human brain is strongly shaped by genetic influences 1 . Subcortical brain regions form circuits with cortical areas to coordinate movement 2 , learning, memory 3 and motivation 4 , and altered circuits can lead to abnormal behaviour and disease 2 . To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume 5 and intracranial volume 6 . These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10 −33 ; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

A large retreat of sea-ice in the ‘stormy’ Atlantic Sector of the Arctic Ocean has become evident through a series of record minima for the winter maximum sea-ice extent since 2015. Results from the Norwegian young sea ICE (N-ICE2015) expedition, a five-month-long (Jan-Jun) drifting ice station in first and second year pack-ice north of Svalbard, showcase how sea-ice in this region is frequently affected by passing winter storms. Here we synthesise the interdisciplinary N-ICE2015 dataset, including independent observations of the atmosphere, snow, sea-ice, ocean, and ecosystem. We build upon recent results and illustrate the different mechanisms through which winter storms impact the coupled Arctic sea-ice system. These short-lived and episodic synoptic-scale events transport pulses of heat and moisture into the Arctic, which temporarily reduce radiative cooling and henceforth ice growth. Cumulative snowfall from each sequential storm deepens the snow pack and insulates the sea-ice, further inhibiting ice growth throughout the remaining winter season. Strong winds fracture the ice cover, enhance ocean-ice-atmosphere heat fluxes, and make the ice more susceptible to lateral melt. In conclusion, the legacy of Arctic winter storms for sea-ice and the ice-associated ecosystem in the Atlantic Sector lasts far beyond their short lifespan.

Source at http://dx.doi.org/10.1038/srep40850

The Arctic icescape is rapidly transforming from a thicker multiyear ice cover to a thinner and largely seasonal first-year ice cover with significant consequences for Arctic primary production. One critical challenge is to understand how productivity will change within the next decades. Recent studies have reported extensive phytoplankton blooms beneath ponded sea ice during summer, indicating that satellite-based Arctic annual primary production estimates may be significantly underestimated. Here we present a unique time-series of a phytoplankton spring bloom observed beneath snow-covered Arctic pack ice. The bloom, dominated by the haptophyte algae Phaeocystis pouchetii, caused near depletion of the surface nitrate inventory and a decline in dissolved inorganic carbon by 16 ± 6 g C m−2. Ocean circulation characteristics in the area indicated that the bloom developed in situ despite the snow-covered sea ice. Leads in the dynamic ice cover provided added sunlight necessary to initiate and sustain the bloom. Phytoplankton blooms beneath snow-covered ice might become more common and widespread in the future Arctic Ocean with frequent lead formation due to thinner and more dynamic sea ice despite projected increases in high-Arctic snowfall. This could alter productivity, marine food webs and carbon sequestration in the Arctic Ocean.