Large Stokes shift fluorescent RNAs for dual-emission fluorescence and bioluminescence imaging in live cells.

Fluorescent RNAs, aptamers that bind and activate small fluorogenic dyes, have provided a particularly attractive approach to visualizing RNAs in live cells. However, the simultaneous imaging of multiple RNAs remains challenging due to a lack of bright and stable fluorescent RNAs with bio-orthogonality and suitable spectral properties. Here, we develop the Clivias, a series of small, monomeric and stable orange-to-red fluorescent RNAs with large Stokes shifts of up to 108 nm, enabling the simple and robust imaging of RNA with minimal perturbation of the target RNA's localization and functionality. In combination with Pepper fluorescent RNAs, the Clivias enable the single-excitation two-emission dual-color imaging of cellular RNAs and genomic loci. Clivias can also be used to detect RNA-protein interactions by bioluminescent imaging both in live cells and in vivo. We believe that these large Stokes shift fluorescent RNAs will be useful tools for the tracking and quantification of multiple RNAs in diverse biological processes.

Imaging the dynamics of messenger RNA with a bright and stable green fluorescent RNA.

Fluorescent RNAs (FRs) provide an attractive approach to visualizing RNAs in live cells. Although the color palette of FRs has been greatly expanded recently, a green FR with high cellular brightness and photostability is still highly desired. Here we develop a fluorogenic RNA aptamer, termed Okra, that can bind and activate the fluorophore ligand ACE to emit bright green fluorescence. Okra has an order of magnitude enhanced cellular brightness than currently available green FRs, allowing the robust imaging of messenger RNA in both live bacterial and mammalian cells. We further demonstrate the usefulness of Okra for time-resolved measurements of ACTB mRNA trafficking to stress granules, as well as live-cell dual-color superresolution imaging of RNA in combination with Pepper620, revealing nonuniform and distinct distributions of different RNAs throughout the granules. The favorable properties of Okra make it a versatile tool for the study of RNA dynamics and subcellular localization.

Rapid fabrication of physically robust hydrogels.

Hydrogel materials show promise for diverse applications, particular as biocompatible materials due to their high water content. Despite advances in hydrogel technology in recent years, their application is often severely limited by inadequate mechanical properties and time-consuming fabrication processes. Here we report a rapid hydrogel preparation strategy that achieves the simultaneous photo-crosslinking and establishment of biomimetic soft-hard material interface microstructures. These biomimetic interfacial-bonding nanocomposite hydrogels are prepared within seconds and feature clearly separated phases but have a strongly bonded interface. Due to effective interphase load transfer, biomimetic interfacial-bonding nanocomposite gels achieve an ultrahigh toughness (138 MJ m-3) and exceptional tensile strength (15.31 MPa) while maintaining a structural stability that rivals or surpasses that of commonly used elastomer (non-hydrated) materials. Biomimetic interfacial-bonding nanocomposite gels can be fabricated into arbitrarily complex structures via three-dimensional printing with micrometre-level precision. Overall, this work presents a generalizable preparation strategy for hydrogel materials and acrylic elastomers that will foster potential advances in soft materials.

One-Step Manufacturing of Supramolecular Liquid-Crystal Elastomers by Stress-Induced Alignment and Hydrogen Bond Exchange.

Liquid-crystal elastomers (LCEs) capable of performing large and reversible deformation in response to an external stimulus are an important class of soft actuators. However, their manufacturing process typically involves a multistep approach that requires harsh conditions. For the very first time, LCEs with customized geometries that can be manufactured by a rapid one-step approach at room temperature are developed. The LCEs are hydrogen bond (H-bond) crosslinked main chain polymers comprising flexible short side chains. Applying a stretching/shear force to the LCE can simultaneously induce mesogen alignment and H-bond exchange, allowing for the formation of well-aligned LCE networks stabilized by H-bonds. Based on this working principle, soft actuators in fibers and 2D/3D objects can be manufactured by mechanical stretching or melt extrusion within a short time (e.g. <1 min). These actuators can perform reversible macroscopic motions with large, controlled deformations up to 38 %. The dynamic nature of H-bonds also provides the actuators with reprocessability and reprogrammability. Thus, this work opens the way for the one-step and custom manufacturing of soft actuators.

Development of Acrylamide-Based Rapid and Multicolor Fluorogenic Probes for High Signal-to-Noise Live Cell Imaging.

Protein covalent labeling is dramatically useful for studying protein function in living cells and organisms. In this field, the chemical tag technique combined with fluorogenic probes has emerged as a powerful tool. Herein, a series of TMP tag fluorogenic probes have been developed to span the green to full blue spectral range. These probes feature an acrylamide unit that acts as a linker group to conjugate the fluorophore and the ligand as well as a quencher and a covalent reaction group. After the probes bind to eDHFR:L28C, the acrylamide unit specifically reacts with the thiol group of the L28C residue beside the ligand binding pocket, achieving protein-specific labeling without any liberation of leaving groups. With these probes, multicolor and specific protein labeling with a fast reaction rate ( t1/2 = 33 s) and dramatic fluorescence enhancement (4000-fold) were obtained. Furthermore, no-wash protein labeling in both living cells and zebrafish was successfully achieved. We expect it may provide a general and highly effective chemical tool for the study of protein function in living cells and organisms.

Coumarin Photocaging Groups Modified with an Electron-Rich Styryl Moiety at the 3-Position: Long-Wavelength Excitation, Rapid Photolysis, and Photobleaching.

A new class of coumarin photocaging groups modified with an electron-rich styryl moiety at the 3-position was constructed. The large π-conjugated structure and stabilization of the carbocation intermediates by electron donors endowed the new photocaging groups with excellent long-wavelength absorption, large two-photon absorption cross-sections, and high uncaging quantum yields. Moreover, the new photocaging groups displayed unique photobleaching properties after photocleavage as a result of the intramolecular cyclization rearrangement of a carbocation intermediate to form five-membered ring byproducts and block the styryl conjugation at the 3-position. These superior properties of the new photocaging groups are extremely beneficial for high-concentration samples and thick specimens, thus extending the application of photocaging groups in many fields.

Source-Guided Target Feature Reconstruction for Cross-Domain Classification and Detection.

Existing cross-domain classification and detection methods usually apply a consistency constraint between the target sample and its self-augmentation for unsupervised learning without considering the essential source knowledge. In this paper, we propose a Source-guided Target Feature Reconstruction (STFR) module for cross-domain visual tasks, which applies source visual words to reconstruct the target features. Since the reconstructed target features contain the source knowledge, they can be treated as a bridge to connect the source and target domains. Therefore, using them for consistency learning can enhance the target representation and reduce the domain bias. Technically, source visual words are selected and updated according to the source feature distribution, and applied to reconstruct the given target feature via a weighted combination strategy. After that, consistency constraints are built between the reconstructed and original target features for domain alignment. Furthermore, STFR is connected with the optimal transportation algorithm theoretically, which explains the rationality of the proposed module. Extensive experiments on nine benchmarks and two cross-domain visual tasks prove the effectiveness of the proposed STFR module, e.g., 1) cross-domain image classification: obtaining average accuracy of 91.0%, 73.9%, and 87.4% on Office-31, Office-Home, and VisDA-2017, respectively; 2) cross-domain object detection: obtaining mAP of 44.50% on Cityscapes → Foggy Cityscapes, AP on car of 78.10% on Cityscapes → KITTI, MR -2 of 8.63%, 12.27%, 22.10%, and 40.58% on COCOPersons → Caltech, CityPersons → Caltech, COCOPersons → CityPersons, and Caltech → CityPersons, respectively.

Biohybrid Nanorobots Carrying Glycoengineered Extracellular Vesicles Promote Diabetic Wound Repair through Dual-Enhanced Cell and Tissue Penetration.

Considerable progress has been made in the development of drug delivery systems for diabetic wounds. However, underlying drawbacks, such as low delivery efficiency and poor tissue permeability, have rarely been addressed. In this study, a multifunctional biohybrid nanorobot platform comprising an artificial unit and several biological components is constructed. The artificial unit is a magnetically driven nanorobot surface modified with antibacterial 2-hydroxypropyltrimethyl ammonium chloride chitosan, which enables the entire platform to move and has excellent tissue penetration capacity. The biological components are two-step engineered extracellular vesicles that are first loaded with mangiferin, a natural polyphenolic compound with antioxidant properties, and then glycoengineered on the surface to enhance cellular uptake efficiency. As expected, the platform is more easily absorbed by endothelial cells and fibroblasts and exhibits outstanding dermal penetration performance and antioxidant properties. Encouraging results are also observed in infected diabetic wound models, showing improved wound re-epithelialization, collagen deposition, angiogenesis, and accelerated wound healing. Collectively, a biohybrid nanorobot platform that possesses the functionalities of both artificial units and biological components serves as an efficient delivery system to promote diabetic wound repair through dual-enhanced cell and tissue penetration and multistep interventions.

Correction: Reinforced hydrogel network building by a rapid dual-photo-coupling reaction for 3D printing.

Correction for 'Reinforced hydrogel network building by a rapid dual-photo-coupling reaction for 3D printing' by Renjie Zhou et al., Chem. Commun., 2023, 59, 1963-1966, https://doi.org/10.1039/D2CC05677A.

Reinforced hydrogel network building by a rapid dual-photo-coupling reaction for 3D printing.

A facile hydrogel fabrication strategy based on a simultaneous dual-photo-coupling reaction (i.e., photoinduced S-nitrosylation and Schiff base reaction) was reported. This strategy allowed a strengthened three-arm crosslinking network to form in one step and the hydrogels obtained displayed rapid gelation, excellent mechanical strength and biocompatibility for cell encapsulated-3D printing in real time. Our hydrogel fabrication strategy will likely foster advances in biomaterials and the extreme speed and reinforced mechanical strength should significantly benefit 3D printing and related applications.

Microfluidic-templated cell-laden microgels fabricated using phototriggered imine-crosslinking as injectable and adaptable granular gels for bone regeneration.

Injectable granular gels consisting of densely packed microgels serving as scaffolding biomaterial have recently shown great potential for applications in tissue regeneration, which allow administration via minimally invasive surgery, on-target cargo delivery, and high efficiency in nutrient/waste exchange. However, limitations such as insufficient mechanical strength, structural integrity, and uncontrollable differentiation of the encapsulated cells in the scaffolds hamper their further applications in the biomedical field. Herein, we developed a new class of granular gels via bottom-up assembly of cell-laden microgels via photo-triggered imine-crosslinking (PIC) chemistry based on the microfluidic technique. The particulate nature of the granular gels rendered them with shear-thinning and self-healing behavior, thereby functioning as an injectable and adaptable cellularized scaffold for bone tissue regeneration. Specifically, single cell-laden, monodisperse microgels composed of methacrylate- and o-nitrobenzene-functionalized hyaluronic acid and gelatin were prepared using a high-throughput microfluidic technique with a production rate up to 3.7 × 108 microgels/hr, wherein the PIC chemistry alleviated the oxygen inhibition on free-radical polymerization and facilitated enhanced fabrication accuracy, accelerated gelation rate, and improved network strength. Further in vitro and in vivo studies demonstrated that the microgels can serve as carriers to support the activity of the encapsulated mesenchymal stem cells; these cell-laden microgels can also be used as cellularized bone fillers to induce the regeneration of bone tissues as evidenced by the in vivo experiment using the rat femoral condyle defect model. In general, these results represent a significant step toward the precise fabrication of engineered tissue mimics with single-cell resolution and high cell-density and can potentially offer a powerful tool for the design and applications of a next generation of tissue engineering strategy. STATEMENT OF SIGNIFICANCE: Using microfluidic droplet-based technology, we hereby developed a new class of injectable and moldable granular gels via bottom-up assembly of cell-laden microgels as a versatile platform for tissue regeneration. Phototriggered imine-crosslinking chemistry was introduced for microgel cross-linkage, which allowed for the fabrication of microgels with improved matrix homogeneity, accelerated gelation process, and enhanced mechanical strength. We demonstrated that the microgel building blocks within the granular gels facilitated the proliferation and differentiation of the encapsulated mesenchymal stem cells, which can further serve as a cellularized scaffold for the treatment of bone defects.

Design of a palette of SNAP-tag mimics of fluorescent proteins and their use as cell reporters.

Naturally occurring fluorescent proteins (FPs) are the most widely used tools for tracking cellular proteins and sensing cellular events. Here, we chemically evolved the self-labeling SNAP-tag into a palette of SNAP-tag mimics of fluorescent proteins (SmFPs) that possess bright, rapidly inducible fluorescence ranging from cyan to infrared. SmFPs are integral chemical-genetic entities based on the same fluorogenic principle as FPs, i.e., induction of fluorescence of non-emitting molecular rotors by conformational locking. We demonstrate the usefulness of these SmFPs in real-time tracking of protein expression, degradation, binding interactions, trafficking, and assembly, and show that these optimally designed SmFPs outperform FPs like GFP in many important ways. We further show that the fluorescence of circularly permuted SmFPs is sensitive to the conformational changes of their fusion partners, and that these fusion partners can be used for the development of single SmFP-based genetically encoded calcium sensors for live cell imaging.

In situ-formed adhesive hyaluronic acid hydrogel with prolonged amnion-derived conditioned medium release for diabetic wound repair.

Hydrogels have long been used for encapsulating stem cell-derived conditioned mediums to achieve skin regeneration after wounding. However, inappropriate mechanical strength, low adhesion and low elasticity limit their clinical application. To address these challenges, we engineered a hyaluronic acid-based hydrogel grafted with methacrylic anhydride and N-(2-aminoethyl)-4-[4-(hydroxymethyl)-2-methoxy-5-nitrophenoxy]-butanamide (NB) groups to encapsulate a lyophilized amnion-derived conditioned medium (AM-CM). This hydrogel can photopolymerize in situ within 3 s by photo-initiated free-radical crosslinking between methacrylate moieties. Meanwhile, the formed o-nitrosobenzaldehyde groups by photo-irradiation could covalently bond with the amino groups of tissue surface, which allowed strong tissue adhesion. Furthermore, the hydrogel possessed excellent mechanical properties, high elasticity, favorable biocompatibility and prolonged AM-CM release. Our further vitro and in vivo studies showed that the hydrogel significantly accelerated diabetic wound healing by regulating macrophage polarization and promoting angiogenesis. The engineered hydrogel with AM-CM release has high potential to treat chronic wounds in clinics.

Promoting Oral Mucosal Wound Healing with a Hydrogel Adhesive Based on a Phototriggered S-Nitrosylation Coupling Reaction.

The wet and highly dynamic environment of the mouth makes local treatment of oral mucosal diseases challenging. To overcome this, a photo-crosslinking hydrogel adhesive is developed inspired by the success of light-curing techniques in dentistry. The adhesive operates on a fast (within 5 s) phototriggered S-nitrosylation coupling reaction and employs imine anchoring to connect to host tissues. Unlike other often-used clinical agents that adhere weakly and for short durations, this thin, elastic, adhesive, and degradable cyclic o-nitrobenzyl-modified hyaluronic acid gel protects mucosal wounds from disturbance by liquid rinsing, oral movement, and friction for more than 24 h. The results from both rat and pig oral mucosa repair models demonstrate that this new gel adhesive creates a favorable microenvironment for tissue repair and can shorten tissue healing time. This study thus illustrates a therapeutic strategy with the potential to advance the treatment of oral mucosal defects in the clinic.

Hyaluronic acid-based antibacterial hydrogels constructed by a hybrid crosslinking strategy for pacemaker pocket infection prevention.

In this study, we developed an injectable antibacterial hydrogel based on hyaluronic acid (HA) and chlorhexidine (CHX) for cardiovascular implantable electronic device (CIED) infection treatment. To balance stability and moldability, the HA scaffold was pre-crosslinked by 1,4-butanediol diglycidyl ether (BDDE) and then ground to form an HA microgel (CHA). Then, the antibacterial agent CHX was further crosslinked in the CHA microgel through electrostatic interactions between CHA and CHX to obtain hybrid crosslinked hydrogels (CHA/CHX). These hydrogels exhibited shear-thinning/self-recovery behavior, allowing easy injection into the CIED pocket and good matching with the pocket shape without extra space requirements, which represents an improvement on previously reported methods. In vitro and in vivo antibacterial tests showed that the CHA/CHX hydrogels had both good biocompatibility and very effective antibacterial action. The above results indicated that the CHA/CHX hydrogels would be an excellent candidate for CIED pocket infection treatment.

A well defect-suitable and high-strength biomimetic squid type II gelatin hydrogel promoted in situ costal cartilage regeneration via dynamic immunomodulation and direct induction manners.

Reconstructing segmental costal cartilage defects resulting from autologous cartilage grafts in plastic surgery remains a challenge. The present study focused on a biomimetic strategy for in situ costal cartilage regeneration that did not rely on an autogenous/xenogenous tissue graft. A multifunctional biomimetic SGII/HA-DN hydrogel based on a "chemical-curing, shaping, and light-curing" gelation system was developed and evaluated for its mechanical properties, clinical applications and biological functions. This hydrogel showed good suitability to repair defects and a high mechanical support strength (11 MPa, which is close to the natural strength of costal cartilage). Biologically, the hydrogel exhibited dual-immunomodulatory effects on the pro-inflammatory/anti-inflammatory phenotypes of neutrophils and M1/M2 macrophage polarization and subsequently promoted the chondrogenesis of cartilage stem/progenitor cells through both direct induction and indirect stimulation by the M2 macrophage-mediated TGF-ß/Smad pathway. Furthermore, this SGII/HA-DN hydrogel could regulate the local microenvironment, inducing new costal cartilage regeneration in vivo. Our findings demonstrate that the newly developed multifunctional SGII/HA-DN hydrogel provides a strategy with high prospect for the biomimetic repair of segmental costal cartilage defects in clinical practice.

Visualizing RNA dynamics in live cells with bright and stable fluorescent RNAs.

Fluorescent RNAs (FRs), aptamers that bind and activate fluorescent dyes, have been used to image abundant cellular RNA species. However, limitations such as low brightness and limited availability of dye/aptamer combinations with different spectral characteristics have limited use of these tools in live mammalian cells and in vivo. Here, we develop Peppers, a series of monomeric, bright and stable FRs with a broad range of emission maxima spanning from cyan to red. Peppers allow simple and robust imaging of diverse RNA species in live cells with minimal perturbation of the target RNA's transcription, localization and translation. Quantification of the levels of proteins and their messenger RNAs in single cells suggests that translation is governed by normal enzyme kinetics but with marked heterogeneity. We further show that Peppers can be used for imaging genomic loci with CRISPR display, for real-time tracking of protein-RNA tethering, and for super-resolution imaging. We believe these FRs will be useful tools for live imaging of cellular RNAs.

Joint local and global consistency on interdocument and interword relationships for co-clustering.

Co-clustering has recently received a lot of attention due to its effectiveness in simultaneously partitioning words and documents by exploiting the relationships between them. However, most of the existing co-clustering methods neglect or only partially reveal the interword and interdocument relationships. To fully utilize those relationships, the local and global consistencies on both word and document spaces need to be considered, respectively. Local consistency indicates that the label of a word/document can be predicted from its neighbors, while global consistency enforces a smoothness constraint on words/documents labels over the whole data manifold. In this paper, we propose a novel co-clustering method, called co-clustering via local and global consistency, to not only make use of the relationship between word and document, but also jointly explore the local and global consistency on both word and document spaces, respectively. The proposed method has the following characteristics: 1) the word-document relationships is modeled by following information-theoretic co-clustering (ITCC); 2) the local consistency on both interword and interdocument relationships is revealed by a local predictor; and 3) the global consistency on both interword and interdocument relationships is explored by a global smoothness regularization. All the fitting errors from these three-folds are finally integrated together to formulate an objective function, which is iteratively optimized by a convergence provable updating procedure. The extensive experiments on two benchmark document datasets validate the effectiveness of the proposed co-clustering method.

Robust image analysis with sparse representation on quantized visual features.

Recent techniques based on sparse representation (SR) have demonstrated promising performance in high-level visual recognition, exemplified by the highly accurate face recognition under occlusion and other sparse corruptions. Most research in this area has focused on classification algorithms using raw image pixels, and very few have been proposed to utilize the quantized visual features, such as the popular bag-of-words feature abstraction. In such cases, besides the inherent quantization errors, ambiguity associated with visual word assignment and misdetection of feature points, due to factors such as visual occlusions and noises, constitutes the major cause of dense corruptions of the quantized representation. The dense corruptions can jeopardize the decision process by distorting the patterns of the sparse reconstruction coefficients. In this paper, we aim to eliminate the corruptions and achieve robust image analysis with SR. Toward this goal, we introduce two transfer processes (ambiguity transfer and mis-detection transfer) to account for the two major sources of corruption as discussed. By reasonably assuming the rarity of the two kinds of distortion processes, we augment the original SR-based reconstruction objective with l(0) norm regularization on the transfer terms to encourage sparsity and, hence, discourage dense distortion/transfer. Computationally, we relax the nonconvex l(0) norm optimization into a convex l(1) norm optimization problem, and employ the accelerated proximal gradient method to optimize the convergence provable updating procedure. Extensive experiments on four benchmark datasets, Caltech-101, Caltech-256, Corel-5k, and CMU pose, illumination, and expression, manifest the necessity of removing the quantization corruptions and the various advantages of the proposed framework.

Discriminative exemplar coding for sign language recognition with Kinect.

Sign language recognition is a growing research area in the field of computer vision. A challenge within it is to model various signs, varying with time resolution, visual manual appearance, and so on. In this paper, we propose a discriminative exemplar coding (DEC) approach, as well as utilizing Kinect sensor, to model various signs. The proposed DEC method can be summarized as three steps. First, a quantity of class-specific candidate exemplars are learned from sign language videos in each sign category by considering their discrimination. Then, every video of all signs is described as a set of similarities between frames within it and the candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by a set of exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video is treated as a positive (or negative) bag and those frames similar to the given exemplar in Euclidean space as instances. Finally, we formulate the selection of the most discriminative exemplars into a framework and simultaneously produce a sign video classifier to recognize sign. To evaluate our method, we collect an American sign language dataset, which includes approximately 2000 phrases, while each phrase is captured by Kinect sensor with color, depth, and skeleton information. Experimental results on our dataset demonstrate the feasibility and effectiveness of the proposed approach for sign language recognition.