Utilising capillary interactions between oligomers in polydimethylsiloxane, an elastomeric biocompatible substrate, and Ga allows for the formation of Ga nanodroplets with sub-10nm gaps between them in a single physical vapour deposition step. These gaps enable the excitation of gap plasmon resonances, which can be tuned by changing either the oligomer content in the substrate or by mechanical stretching. By a simple and effective method of fabrication, structural colors on large areas can be fabricated endowed with the property of mechanoresponsiveness, which can be used in applications such as strain sensors, bio-mechanics, soft-robotics, and prosthetics.
Macromolecules, such as proteins and nucleic acids, play essential roles in cellular functions through dynamic structural changes. Understanding these functions requires detailed characterization of structural dynamics. While techniques like X-ray crystallography and cryo-EM resolve high-resolution static structures, they struggle to capture low-resolution, flexible structures and the full distribution of conformations during chemical reactions. These limitations arise from averaging processes that enhance signal-to-noise ratio (SNR) but exclude flexible regions, distort resolution, and miss rare high-energy states. To address this, we developed individual-particle electron tomography (IPET), a method for determining 3D structures of single particles at low-to-intermediate resolution (up to 2 nm) without averaging. IPET reconstructs a detailed 3D density map by capturing images at multiple tilt angles, facilitating flexible model fitting and revealing unique particle structures. This method reveals unbiased structural distributions, enhancing the study of molecular dynamics, phase transitions, and structural alterations during chemical reactions and self-folding.
Conventional frame-based cameras often struggle with limited dynamic range, leading to saturation and loss of detail when capturing scenes with significant brightness variations. Neuromorphic cameras, inspired by human retina, offer a solution by providing an inherently high dynamic range. This capability enables them to capture both bright and faint celestial objects without saturation effects, preserving details across a wide range of luminosities. This paper investigates the application of neuromorphic imaging technology for capturing celestial bodies across a wide range of flux levels. Its advantages are demonstrated through examples such as the bright planet Saturn with its faint moons and the bright star Sirius A alongside its faint companion, Sirius B.
Early plants are thought to have grown by equal dichotomous branching of the stem, forming iterative self-similar structures that filled the three-dimensional space. Lack of equality between the two branches resulted in one dominating main growth axis with indeterminate growth and another subsidiary axillary growth axis that displayed limited branching potential. Recruitment of differentiation factors to the axillary branch system may have eventually converted the axillary branch to a determinate organ such as leaves with specific function. Recent molecular genetic analysis has identified some of these differentiation molecules, inactivation of which converts the simple leaves to the indeterminate branching system displayed by stems.
Light microscopy has been used to characterize microbes and host-microbes interaction for a long time. On the other hand, the cryo-electron microscope revolutionized structural biology, where we can demonstrate the atomic-level structure of small molecules in a human cell. Imaging using microscopes is a powerful tool to characterize host-pathogen interaction. Most pathogenic microbes translocate various effector proteins and toxins through their secretion system, which damages the host cell plasma membrane and initiates the exchange with the host cell. In this study, we characterize the structure of pore-forming toxins from pathogenic bacteria, Staphylococcus aureus, and its interaction with HEK 293T cells at high resolution using TEM, and cryo-EM.
Although the origin of disease and drug targets are primarily at intracellular space, such targeting is not achievable in currently available drugs. We and others recently show that molecular drugs can be transformed into nanodrug for better subcellular targeting with the enhanced therapeutic performance. This can be achieved via appropriate size and surface chemistry of colloidal nanodrug to control or bypass the endocytic uptake and intracellular trafficking processes. This approach can be adapted for enhanced drug performance with lower side effects.
In spite of research over the past decades, robust, replicable, and clinically translatable markers to objectively diagnose psychiatric disorders are yet to be ascertained. Several factors such as biological heterogeneity (partly due to the complex genetic basis that is further compounded by environmental interactions), the potential mismatch between contemporary diagnostic criteria / clinical symptom scores and findings that emanate from cutting-edge neuroscience observations, and similar others have made the identification of biomarkers a daunting challenge. This challenge becomes much harder to solve in the context of disorders of childhood onset such as, autism spectrum disorders (ASD) especially because of the additional complexity of examining the developing brain.
Single molecule localization microscopy (SMLM) recently became more and more popular for studying the synaptic architecture, providing substantial advances in modern neuroscience. Recently developed methods based on DNA origami calibration transformed SML into an effective quantitative tool able to estimate the oligomeric states of macromolecular complexes. In this work, we apply a recently developed quantitative method based on stochastic optical reconstruction microscopy (qSTORM) to study the distribution of the synaptic proteins Homer in hippocampal neurons. Our experiments prove qSTORM as a suitable tool for novel quantitative insights into the nanoscale organization of excitatory synapses.
Deep learning models have advanced many branches of science. However, these models have not been adequately developed for neuroimaging applications mainly because of the non-availability of large labelled datasets. In this study, we present an explainable deep learning approach to investigate the neurobiology of the autism spectrum disorder (ASD), which is one of the most prevalent neurodevelopmental disorders. Our approach achieved state of the art classification accuracy and identified brain features in discriminating ASDs from the typical subjects and finally identified features that predicted the severity of the symptoms.
Seldom, do we come across a technology that advances multiple research disciplines across science and engineering. One such technology is light sheet that promises to take scientific investigation to the next level. The existing technology, predominantly based on point-focusing has reached a saturation limit, in terms of speed, limited field-of-view and lack of biophysical parameter estimation. Moreover, current technology is complex and needs human intervention. Light sheet techniques based on sheet-illumination expand our abilities for high throughput interrogation of a large pool of live biological specimens with near diffraction-limited resolution and an order increase in field-of-view. The outlook of research community has changed dramatically over the last decade that has seen an increased use of light sheet technology. Light sheet technique has penetrated both biological and physical sciences with its impact on microscopy, cytometry, nanolithography, beam-shaping, plasma physics and optical manipulation. Eventually, the technique will influence other disciplines and may give rise to new research fields.
In an organism, different organ systems are highly specialised for performing dedicated functions. However, it is increasingly becoming clear that the organ systems do not function in isolation but are rather extensively dependent on each other. This phenomenon is known as inter-organ communication and is a novel paradigm of exocrine signaling. In this minireview, we discuss the theoretical implications of this kind of crosstalk and the resources available for practical demonstration of the same. We focus on the fruit fly, Drosophila melanogaster, and the zebrafish, Danio rerio. Both the model organisms are amenable to genetic manipulation and have been largely used to address many pending questions in all the fields of biology using cutting-edge cellular, molecular, and imaging techniques and tools. Both the organisms also offer the advantages of having organ systems functionally equivalent to those of humans to dissect how the development and functions of organs are established in dialogue with others.
Single-pixel imaging geometries for wide-field multiphoton microscopy (SPx-MPM) have emerged as a contender to conventional point-scanning multiphoton systems (PS-MPM) for deep tissue imaging. These systems are thought to be faster due to their multiplexed imaging capabilities with higher photon throughput. In this study we numerically compare the signal to noise metrics of the SPx-MPM to the PS-MPM systems. Our results suggest that PS-MPM systems outperform SPx-MPM systems, despite their higher photon throughput.
Coronavirus Disease 19 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), a highly transmissible and pathogenic coronavirus, has spread at an alarming rate throughout the world since Dec. 2019, claiming 2.196 million deaths globally, as per 30th Jan. 2021, and the count is on. The reason behind this ongoing rapid transmission and global spread of SARS-CoV-2 is that the virus is evolving to become more transmissible as it spreads across the world due to its fast host migration. The virus is rapidly evolving to adapt to the different geo‑climate environments, diverse host immune systems, and other protective counter-measures (such as prolong host survival) by accumulating adaptive mutations, deletions, and recombination. This note focuses on those mutations and the prominent viral strains that are noteworthy for epidemiological and biological reasons.
Understanding stochastic events that control the molecular events leading to the onset of neurodegenerative diseases such as Alzheimer's Disease (AD) is not well understood. Though the bulk of the attention is attributed to the increased burden of detrimental proteoforms generated by the processing of Amyloid Precursor Protein, there lacks a clear consensus on how the molecular events that control the localization and trafficking contribute to the onset. Here, we discuss emerging evidence that indicate the role of nanoscale compositionality of the membrane and random diffusion at the millisecond time scale that contribute to the onset of AD. We believe that intuitive knowledge of nanobiology controlling the local rates of product formation holds the clue for next-generation therapeutics that might delay or halt the onset of AD.
Mitochondrial membrane organization is important for many biological functions, and is implicated in a number of diseases, but conventional microscopy has insufficient resolution to image biologically relevant structures. We present methods to quantify nanoscale membrane curvature using three-dimensional localization-based super-resolution microscopy. Localizations are analyzed using a cluster algorithm followed by principal component analysis to determine local membrane curvature. Results are shown for mitochondria in C2C12 mouse myotubes labeled with Tom20-Dendra2.
The pancreas is a vital organ of the human body that plays a dual role as both endocrine and exocrine organ. The endocrine part of the pancreas is responsible for maintaining blood glucose homeostasis and the exocrine part aids in the digestion of food. The exocrine and the endocrine pancreas mediate their functions by secreting digestive enzymes and islet hormones respectively. It is possible to assess abnormalities in the function of pancreas with assays that detect exocrine and endocrine secretions to assist in predicting pancreatic dysfunction. Although individual assays exist for each of the hormones, it was not the same for the endocrine hormone somatostatin. We have developed a new ELISA assay for the quantification of somatostatin, a pancreatic islet hormone that can be used along with other hormonal assays to examine the pancreatic health. Abnormalities in pancreatic health can be predicted early especially through genetic risk factors, some of the genetic risk factors like Cyclin Dependent Kinase Inhibitor 2A (CDKN2A) are reported to be linked with both diabetes and Pancreatic Ductal Adenocarcinoma (PDAC). Therefore, evaluating the secretory profile of the pancreas in individuals with these genetic risk factors becomes essential to detect initial signs of disorders like diabetes and pancreatic cancer. Exploiting the secretion assays combined with screening for genetic risk factors might help in understanding the intricate interplay between PDAC and diabetes. Overall, this would help in early detection of pancreatic abnormalities enabling intervention to mitigate risk of diabetes and pancreatic cancer.
