A Type I Photosensitizer-Polymersome Boosts Reactive Oxygen Species Generation by Forcing H-Aggregation for Amplifying STING Immunotherapy.

Activation of the innate immune Stimulator of Interferon Genes (STING) pathway potentiates antitumor immunity. However, delivering STING agonists systemically to tumors presents a formidable challenge, and resistance to STING monotherapy has emerged in clinical trials with diminishing natural killer (NK) cell proliferation. Here, we encapsulated the STING agonist diABZI within polymersomes containing a Type I photosensitizer (NBS), creating a nanoagonist (PNBS/diABZI) for highly responsive tumor immunotherapy. This structure promoted H-aggregation and intersystem crossing of NBS, resulting in a ∼ 3-fold amplification in superoxide anion and singlet oxygen generation. The photodynamic therapy directly damaged hypoxia tumor cells and stimulated the proliferation of NK cells and cytotoxic T lymphocytes, thereby sensitizing STING immunotherapy. A single systemic intravenous administration of PNBS/diABZI eradicated orthotopic mammary tumors in murine models, achieving long-term antitumor immune memory to inhibit tumor recurrence and metastasis and significantly improving long-term tumor-free survival. This work provides a design rule for boosting reactive oxygen species production by promoting the intersystem crossing process, highlighting the potential of Type I photosensitizer-polymer vehicles for augmenting STING immunotherapy.

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer.

OBJECTIVES: Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM. METHODS: A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC). RESULTS: The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874-0.978), 0.897 (95% CI: 0.801-0.994), 0.885 (95% CI: 0.795-0.975), and 0.889 (95% CI: 0.823-0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model. CONCLUSIONS: The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans. CRITICAL RELEVANCE STATEMENT: The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans. KEY POINTS: Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients.

Three-dimensional spatio-angular fluorescence microscopy with a polarized dual-view inverted selective-plane illumination microscope (pol-diSPIM).

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the three-dimensional orientations and diffraction-limited positions of ensembles of fluorescent dipoles that label biological structures, and we share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model our samples, their excitation, and their detection using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labelled giant unilamellar vesicles, fast-scarlet-labelled cellulose in xylem cells, and phalloidin-labelled actin in U2OS cells. Additionally, we observe phalloidin-labelled actin in mouse fibroblasts grown on grids of labelled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.

Phase diversity-based wavefront sensing for fluorescence microscopy.

Fluorescence microscopy is an invaluable tool in biology, yet its performance is compromised when the wavefront of light is distorted due to optical imperfections or the refractile nature of the sample. Such optical aberrations can dramatically lower the information content of images by degrading image contrast, resolution, and signal. Adaptive optics (AO) methods can sense and subsequently cancel the aberrated wavefront, but are too complex, inefficient, slow, or expensive for routine adoption by most labs. Here we introduce a rapid, sensitive, and robust wavefront sensing scheme based on phase diversity, a method successfully deployed in astronomy but underused in microscopy. Our method enables accurate wavefront sensing to less than λ/35 root mean square (RMS) error with few measurements, and AO with no additional hardware besides a corrective element. After validating the method with simulations, we demonstrate calibration of a deformable mirror > 100-fold faster than comparable methods (corresponding to wavefront sensing on the ~100 ms scale), and sensing and subsequent correction of severe aberrations (RMS wavefront distortion exceeding λ/2), restoring diffraction-limited imaging on extended biological samples.

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations to show that applying the trained 'de-aberration' networks outperforms alternative methods, and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

3D-printed biomimetic scaffolds with precisely controlled and tunable structures guide cell migration and promote regeneration of osteochondral defect.

Untreated osteochondral defects will develop into osteoarthritis, affecting patients' quality of life. Since articular cartilage and subchondral bone exhibit distinct biological characteristics, repairing osteochondral defects remains a major challenge. Previous studies have tried to fabricate multilayer scaffolds with traditional methods or 3D printing technology. However, the efficacy is unsatisfactory because of poor control over internal structures or a lack of integrity between adjacent layers, severely compromising repair outcomes. Therefore, there is a need for a biomimetic scaffold that can simultaneously boost osteochondral defect regeneration in both structure and function. Herein, an integrated bilayer scaffold with precisely controlled structures is successfully 3D-printed in one step via digital light processing (DLP) technology. The upper layer has both 'lotus- and radial-' distribution pores, and the bottom layer has 'lotus-' pores to guide and facilitate the migration of chondrocytes and bone marrow mesenchymal stem cells, respectively, to the defect area. Tuning pore sizes could modulate the mechanical properties of scaffolds easily. Results show that 3D-printed porous structures allow significantly more cells to infiltrate into the area of 'lotus- and radial-' distribution pores during cell migration assay, subcutaneous implantation, andin situtransplantation, which are essential for osteochondral repair. Transplantation of this 3D-printed bilayer scaffold exhibits a promising osteochondral repair effect in rabbits. Incorporation of Kartogenin into the upper layer of scaffolds further induces better cartilage formation. Combining small molecules/drugs and precisely size-controlled and layer-specific porous structure via DLP technology, this 3D-printed bilayer scaffold is expected to be a potential strategy for osteochondral regeneration.

Augmented Super-Resolution Radial Fluctuations (aSRRF) Pushing the Limits of Structured Illumination Microscopy.

Structured illumination microscopy (SIM) is a versatile super-resolution technique known for its compatibility with a wide range of probes and fast implementation. While 3D SIM is capable of achieving a spatial resolution of ∼120 nm laterally and ∼300 nm axially, attempting to further enhance the resolution through methods such as nonlinear SIM or 4-beam SIM introduces complexities in optical configurations, increased phototoxicity, and reduced temporal resolution. Here, we have developed a novel method that combines SIM with augmented super-resolution radial fluctuations (aSRRF) utilizing a single image through image augmentation. By applying aSRRF reconstruction to SIM images, we can enhance the SIM resolution to ∼50 nm isotopically, without requiring any modifications to the optical system or sample acquisition process. Additionaly, we have incorporated the aSRRF approach into an ImageJ plugin and demonstrated its versatility across various fluorescence microscopy images, showcasing a remarkable two-fold resolution increase.

Three-dimensional structured illumination microscopy with enhanced axial resolution.

The axial resolution of three-dimensional structured illumination microscopy (3D SIM) is limited to ∼300 nm. Here we present two distinct, complementary methods to improve axial resolution in 3D SIM with minimal or no modification to the optical system. We show that placing a mirror directly opposite the sample enables four-beam interference with higher spatial frequency content than 3D SIM illumination, offering near-isotropic imaging with ∼120-nm lateral and 160-nm axial resolution. We also developed a deep learning method achieving ∼120-nm isotropic resolution. This method can be combined with denoising to facilitate volumetric imaging spanning dozens of timepoints. We demonstrate the potential of these advances by imaging a variety of cellular samples, delineating the nanoscale distribution of vimentin and microtubule filaments, observing the relative positions of caveolar coat proteins and lysosomal markers and visualizing cytoskeletal dynamics within T cells in the early stages of immune synapse formation.

Incorporating the image formation process into deep learning improves network performance.

We present Richardson-Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN's performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.

Practical considerations for quantitative light sheet fluorescence microscopy.

Fluorescence microscopy has evolved from a purely observational tool to a platform for quantitative, hypothesis-driven research. As such, the demand for faster and less phototoxic imaging modalities has spurred a rapid growth in light sheet fluorescence microscopy (LSFM). By restricting the excitation to a thin plane, LSFM reduces the overall light dose to a specimen while simultaneously improving image contrast. However, the defining characteristics of light sheet microscopes subsequently warrant unique considerations in their use for quantitative experiments. In this Perspective, we outline many of the pitfalls in LSFM that can compromise analysis and confound interpretation. Moreover, we offer guidance in addressing these caveats when possible. In doing so, we hope to provide a useful resource for life scientists seeking to adopt LSFM to quantitatively address complex biological hypotheses.

Multiscale fluorescence imaging of living samples.

Fluorescence microscopy is a highly effective tool for interrogating biological structure and function, particularly when imaging across multiple spatiotemporal scales. Here we survey recent innovations and applications in the relatively understudied area of multiscale fluorescence imaging of living samples. We discuss fundamental challenges in live multiscale imaging and describe successful examples that highlight the power of this approach. We attempt to synthesize general strategies from these test cases, aiming to help accelerate progress in this exciting area.

The strategy for estrogen receptor mediated-risk assessment in environmental water: A combination of species sensitivity distributions and in silico approaches.

Risk assessment for molecular toxicity endpoints of environmental matrices may be a pressing issue. Here, we combined chemical analysis with species sensitivity distributions (SSD) and in silico docking for multi-species estrogen receptor mediated-risk assessment in water from Dongjiang River, China. The water contains high levels of phenolic endocrine-disrupting chemicals (PEDCs) and phthalic acid esters (PAEs). The concentration of ∑4PEDCs and ∑6PAEs ranged from 2202 to 3404 ng/L and 834-4368 ng/L, with an average of 3241 and 2215 ng/L, respectively. The SSD approach showed that 4-NP, BPA, E2 of PEDCs, and DBP, DOP, and DEHP could severely threaten the aquatic ecosystems, while most other target compounds posed low-to-medium risks. Moreover, binding affinities from molecular docking among PEDCs, PAEs, and estrogen receptors (ERα, Erß, and GPER) were applied as toxic equivalency factors. Estrogen receptor-mediated risk suggested that PEDCs were the main contributors, containing 53.37-69.79% of total risk. They potentially pose more severe estrogen-receptor toxicity to zebrafish, turtles, and frogs. ERß was the major contributor, followed by ERα and GPER. This study is the first attempt to assess the estrogen receptor-mediated risk of river water in multiple aquatic organisms. The in silico simulation approach could complement toxic effect evaluations in molecular endpoints.

Cytonemes coordinate asymmetric signaling and organization in the Drosophila muscle progenitor niche.

Asymmetric signaling and organization in the stem-cell niche determine stem-cell fates. Here, we investigate the basis of asymmetric signaling and stem-cell organization using the Drosophila wing-disc that creates an adult muscle progenitor (AMP) niche. We show that AMPs extend polarized cytonemes to contact the disc epithelial junctions and adhere themselves to the disc/niche. Niche-adhering cytonemes localize FGF-receptor to selectively adhere to the FGF-producing disc and receive FGFs in a contact-dependent manner. Activation of FGF signaling in AMPs, in turn, reinforces disc-specific cytoneme polarity/adhesion, which maintains their disc-proximal positions. Loss of cytoneme-mediated adhesion promotes AMPs to lose niche occupancy and FGF signaling, occupy a disc-distal position, and acquire morphological hallmarks of differentiation. Niche-specific AMP organization and diversification patterns are determined by localized expression and presentation patterns of two different FGFs in the wing-disc and their polarized target-specific distribution through niche-adhering cytonemes. Thus, cytonemes are essential for asymmetric signaling and niche-specific AMP organization.

Ecological and AhR-mediated risk assessment of polycyclic aromatic hydrocarbons and polybrominated diphenyl ethers on multiple aquatic species in river water: A combined chemical analysis and in silico approach.

Assessing the adverse health risks at molecular endpoints to various aquatic organisms could be an urgent issue. In this manuscript, the ecological and AhR-mediated risk of sixteen polycyclic aromatic hydrocarbons (PAHs) and six polybrominated diphenyl ethers (PBDEs) in surface water of Dongjiang River, Southern China was evaluated using chemical analysis and in silico approaches. Average concentrations of ∑16PAHs and ∑6PBDEs were 586.3 ng/L and 2.672 ng/L in the dry season (DS), and 366.8 ng/L and 2.554 ng/L in the wet season (WS). Concentrations of PAHs during the DS were significantly higher than that in the WS, while no obvious seasonal distribution was observed for PBDEs. Only Ant and BaP in all congers of PAHs posed low to medium ecological risks, and PBDEs posed a low ecological risk. Moreover, AhR-mediated risk from PAHs was two orders of magnitude higher that from PBDEs, and the AhR-mediated toxicity on frog and eel were higher than those on other aquatic organisms in Dongjiang River. Phe and BDE209 were the significant contributor to the AhR-mediated risk induced by PAHs and PBDEs, respectively. This study is the first attempt to assess AhR-mediated risk of river water in multiple aquatic organisms.

Multiview confocal super-resolution microscopy.

Confocal microscopy1 remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-dependent degradation in scattering samples and volumetric bleaching2. Here we address these problems, enhancing confocal microscopy performance from the sub-micrometre to millimetre spatial scale and the millisecond to hour temporal scale, improving both lateral and axial resolution more than twofold while simultaneously reducing phototoxicity. We achieve these gains using an integrated, four-pronged approach: (1) developing compact line scanners that enable sensitive, rapid, diffraction-limited imaging over large areas; (2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering; (3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labelled, thick samples; (4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration. We demonstrate these capabilities on more than 20 distinct fixed and live samples, including protein distributions in single cells; nuclei and developing neurons in Caenorhabditis elegans embryos, larvae and adults; myoblasts in imaginal disks of Drosophila wings; and mouse renal, oesophageal, cardiac and brain tissues.

Differential adhesion regulates neurite placement via a retrograde zippering mechanism.

During development, neurites and synapses segregate into specific neighborhoods or layers within nerve bundles. The developmental programs guiding placement of neurites in specific layers, and hence their incorporation into specific circuits, are not well understood. We implement novel imaging methods and quantitative models to document the embryonic development of the C. elegans brain neuropil, and discover that differential adhesion mechanisms control precise placement of single neurites onto specific layers. Differential adhesion is orchestrated via developmentally regulated expression of the IgCAM SYG-1, and its partner ligand SYG-2. Changes in SYG-1 expression across neuropil layers result in changes in adhesive forces, which sort SYG-2-expressing neurons. Sorting to layers occurs, not via outgrowth from the neurite tip, but via an alternate mechanism of retrograde zippering, involving interactions between neurite shafts. Our study indicates that biophysical principles from differential adhesion govern neurite placement and synaptic specificity in vivo in developing neuropil bundles.

Interplay of Forces and the Immune Response for Functional Tendon Regeneration.

Tendon injury commonly occurs during sports activity, which may cause interruption or rapid decline in athletic career. Tensile strength, as one aspect of tendon biomechanical properties, is the main parameter of tendon function. Tendon injury will induce an immune response and cause the loss of tensile strength. Regulation of mechanical forces during tendon healing also changes immune response to improve regeneration. Here, the effects of internal/external forces and immune response on tendon regeneration are reviewed. The interaction between immune response and internal/external forces during tendon regeneration is critically examined and compared, in relation to other tissues. In conclusion, it is essential to maintain a fine balance between internal/external forces and immune response, to optimize tendon functional regeneration.

Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes.

We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance.

Lithography-Based 3D Bioprinting and Bioinks for Bone Repair and Regeneration.

The fabrication of scaffolds that precisely mimic the natural structure and physiochemical properties of bone is still one of the most challenging tasks in bone tissue engineering. 3D printing techniques have drawn increasing attention due to their ability to fabricate scaffolds with complex structures and multiple bioinks. For bone tissue engineering, lithography-based 3D bioprinting is frequently utilized due to its printing speed, mild printing process, and cost-effective benefits. In this review, lithography-based 3D bioprinting technologies including SLA and DLP are introduced; their typical applications in biological system and bioinks are also explored and summarized. Furthermore, we discussed possible evolution of the hardware/software systems and bioinks of lithography-based 3D bioprinting, as well as their future applications.

Structural and developmental principles of neuropil assembly in C. elegans.

Neuropil is a fundamental form of tissue organization within the brain1, in which densely packed neurons synaptically interconnect into precise circuit architecture2,3. However, the structural and developmental principles that govern this nanoscale precision remain largely unknown4,5. Here we use an iterative data coarse-graining algorithm termed 'diffusion condensation'6 to identify nested circuit structures within the Caenorhabditis elegans neuropil, which is known as the nerve ring. We show that the nerve ring neuropil is largely organized into four strata that are composed of related behavioural circuits. The stratified architecture of the neuropil is a geometrical representation of the functional segregation of sensory information and motor outputs, with specific sensory organs and muscle quadrants mapping onto particular neuropil strata. We identify groups of neurons with unique morphologies that integrate information across strata and that create neural structures that cage the strata within the nerve ring. We use high resolution light-sheet microscopy7,8 coupled with lineage-tracing and cell-tracking algorithms9,10 to resolve the developmental sequence and reveal principles of cell position, migration and outgrowth that guide stratified neuropil organization. Our results uncover conserved structural design principles that underlie the architecture and function of the nerve ring neuropil, and reveal a temporal progression of outgrowth-based on pioneer neurons-that guides the hierarchical development of the layered neuropil. Our findings provide a systematic blueprint for using structural and developmental approaches to understand neuropil organization within the brain.