Labeling Strategy Research Articles

Improvement of Electric Fish Optimization Algorithm for Standstill Label Combined with Levy Flight Strategy

The Electric Fish Optimization (EFO) algorithm is inspired by the predation behavior and communication of weak electric fish. It is a novel meta-heuristic algorithm that attracts researchers because it has few tunable parameters, high robustness, and strong global search capabilities. Nevertheless, when operating in complex environments, the EFO algorithm encounters several challenges including premature convergence, susceptibility to local optima, and issues related to passive electric field localization stagnation. To address these challenges, this study introduces Adaptive Electric Fish Optimization Algorithm Based on Standstill Label and Level Flight (SLLF-EFO). This hybrid approach incorporates the Golden Sine Algorithm and good point set theory to augment the EFO algorithm’s capabilities, employs a variable-step-size Levy flight strategy to efficiently address passive electric field localization stagnation problems, and utilizes a standstill label strategy to mitigate the algorithm’s tendency to fall into local optima during the iterative process. By leveraging multiple solutions to optimize the EFO algorithm, this framework enhances its adaptability in complex environments. Experimental results from benchmark functions reveal that the proposed SLLF-EFO algorithm exhibits improved performance in complex settings, demonstrating enhanced search speed and optimization accuracy. This comprehensive optimization not only enhances the robustness and reliability of the EFO algorithm but also provides valuable insights for its future applications.

Selective Methods Promote Protein Solid-State NMR.

Solid-state nuclear magnetic resonance (ssNMR) is indispensable for studying the structures, dynamics, and interactions of insoluble proteins in native or native-like environments. While ssNMR includes numerous nonselective techniques for general analysis, it also provides various selective methods that allow for the extraction of precise details about proteins. This perspective highlights three key aspects of selective methods: selective signals of protein segments, selective recoupling, and site-specific insights into proteins. These methods leverage protein topology, labeling strategies, and the tailored manipulation of spin interactions through radio frequency (RF) pulses, significantly promoting the field of protein ssNMR. With ongoing advancements in higher magnetic fields and faster magic angle spinning (MAS), there remains an ongoing need to enhance the selectivity and efficiency of selective ssNMR methods, facilitating deeper atomic-level insights into complex biological systems.

Transition Metal Catalyzed Deuteration via Hydrogen Isotope Exchange

Direct hydrogen isotope exchange represents a distinctive strategy for deuterium labelling, where the protium is directly replaced by deuterium. In this graphic review, we summarized the progress in deuteration via transition metal catalyzed hydrogen isotope exchange. The review was organized according to the mechanism of C-H bond activation in the homogeneous catalysis, and the heterogeneous catalysis was also discussed according to the catalyst type. Representative mechanistic processes were depicted, and proven cases for tritiation were also highlighted.

Tracking Small Extracellular Vesicles Using a Minimally Invasive PicoGreen Labeling Strategy.

Extracellular vesicles (EVs) are cell-secreted lipid bilayer delimited particles that mediate cellular communication. These tiny sacs of cellular information play an important role in cell communication and alter the physiological process under both normal and pathological conditions. As such, tracking EVs can provide valuable information regarding the basic understanding of cell communication, the onset of early malignancy, and biomarker discovery. Most of the current EV-tracking strategies are invasive, altering the natural characteristics of EVs by modifying the lipid bilayer with lipophilic dyes or surface proteins with fluorescent reporters. The invasive labeling strategies could alter the natural processes of EVs and thereby have major limitations for functional studies. Here, we report an alternative minimally invasive EV labeling strategy using PicoGreen (PG), a small molecule that fluoresces at 520 nm when bound to dsDNA. We show that PG binds to dsDNA associated with small EVs (50-200 nm), forming a stable and highly fluorescent PG-DNA complex in EVs (PG-EVs). In both 2D cell culture and 3D organoid models, PG-EV showed efficient tracking properties, including a high signal-to-noise ratio, time- and concentration-dependent uptake, and the ability to traverse a 3D environment. We further validated PG-EV tracking using dual-labeled EVs following two orthogonal labeling strategies: (1) Bioconjugation via surface amine labeling and (2) donor cell engineering via endogenously expressing mCherry-tetraspanin (CD9/CD63/CD81) reporter proteins. Our study has shown the feasibility of using PG-EV as an effective EV tracking strategy that can be applied for studying the functional role of EVs across multiple model systems.

Dual-Fluorescent Quantum Dot Nanobead-Based Lateral Flow Immunoassay for Simultaneous Detection of C-Reactive Protein and Procalcitonin.

Simultaneous detection of C-reactive protein (CRP) and procalcitonin (PCT) at the point of care is crucial for the management of infections in patients with inflammation and in critical care settings. The challenge of detecting high concentrations of CRP alongside low concentrations of PCT in plasma from inflammatory patients has limited the clinical application of multiplexed immunoassays. Herein, we developed a lateral flow immunoassay (LFIA) that employs quantum dot nanobeads (QDNBs) of varying sizes and colors to enable the simultaneous quantification of PCT and CRP in human plasma. To extend the dynamic range of CRP detection, we combined QDNBs with smaller particle sizes with the CRP detection antibodies, thereby increasing the assay's dynamic range and reducing the hook effect. At the same time, the stronger fluorescence emitted by these larger QDNBs, in conjugation with the PCT detection antibodies, allows for the detection of PCT at the nanogram level, meeting the demand for high sensitivity. The results show that this method can detect CRP concentrations from 0.1 to 3 mg/L and PCT with a detection limit of 0.09 ng/mL, which is on par with clinically used methods. By employing this dual-color and dual-size QDNB labeling strategy, we successfully achieved simultaneous detection of CRP with a broad dynamic range and PCT with high sensitivity in a one-step point-of-care rapid test.

Fluorine Labeling and 19F NMR Spectroscopy to Study Biological Molecules and Molecular Complexes

High‐resolution nuclear magnetic resonance (NMR) spectroscopy represents a key methodology for studying biomolecules and their interplay with other molecules. Recent developments in labeling strategies have made it possible to incorporate fluorine into proteins and peptides reliably, with manageable efforts and, importantly, in a highly site‐specific manner. Paired with its excellent NMR spectroscopic properties and absence in most biological systems, fluorine has enabled scientists to investigate a rather wide range of scientific objectives, including protein folding, protein dynamics and drug discovery. Furthermore, NMR spectroscopic experiments can be conducted in complex environments, such as cell lysate or directly inside living cells. This review presents selected studies demonstrating how 19F NMR spectroscopic approaches enable to contribute to the understanding of biomolecular processes. Thereby the focus has been set to labeling strategies available and specific NMR experiments performed to answer the underlying scientific objective.

Achieving enhanced diagnostic precision in endometrial lesion analysis through a data enhancement framework.

The aim of this study was to enhance the precision of categorization of endometrial lesions in ultrasound images via a data enhancement framework based on deep learning (DL), through addressing diagnostic accuracy challenges, contributing to future research. Ultrasound image datasets from 734 patients across six hospitals were collected. A data enhancement framework, including image features cleaning and soften label, was devised and validated across multiple DL models, including ResNet50, DenseNet169, DenseNet201, and ViT-B. A hybrid model, integrating convolutional neural network and transformer architectures for optimal performance, to predict lesion types was developed. Implementation of our novel strategies resulted in a substantial enhancement in model accuracy. The ensemble model achieved accuracy and macro-area under the receiver operating characteristic curve values of 0.809 of 0.911, respectively, underscoring the potential for use of DL in endometrial lesion ultrasound image classification. We successfully developed a data enhancement framework to accurately classify endometrial lesions in ultrasound images. Integration of anomaly detection, data cleaning, and soften label strategies enhanced the comprehension of lesion image features by the model, thereby boosting its classification capacity. Our research offers valuable insights for future studies and lays the foundation for creation of more precise diagnostic tools.

Enhancing De Novo Protein Sequencing through the C-Terminal Labeling Strategy: Resolving Isobaric Ambiguities by Electron-Transfer/Higher Energy Collision Dissociation (EThcD).

De novo protein sequencing via a bottom-up approach requires various proteases to produce overlapping peptides. However, peptides generated by proteases other than trypsin, LysC, and ArgC often yield C-terminal fragments with suboptimal ionization in positive mode mass spectrometry (MS). This study introduces a novel peptide labeling strategy that involves modifying peptides at the C-terminal and at the carboxyl groups of Aspartic and Glutamic acid with arginine methyl ester (R-met) to improve peptide fragmentation and resolve isobaric ambiguities encountered during sequencing. An amidation reaction is used with coupling reagents to conjugate R-met to the peptide's C-terminal end, introducing a functional group that enhances the detectability of C-terminal peptide fragment ions by mass spectrometry. Subsequently, selecting a charge state of +2 or higher can facilitate optimal fragmentation of the derivatized peptides using electron-transfer/higher energy collision dissociation (EThcD), thereby generating essential w-ions to resolve common isobaric ambiguities. Demonstrating this strategy across diverse protein types, including albumin and antibodies and using different proteases for digestion, highlights the unique characteristics of combining the proposed amidation reaction with the specific proteases tested.

Clean label extraction of bioactive compounds from Chenopodium album and their role in the characterization and stability of ostrich meat

The demand for clean-label products continues to rise, as consumers increasingly prioritize natural and transparent ingredient lists. Natural substances are generally deemed safe for consumption by consumers. This study was focused on the development of clean-label ostrich meat patties with Chenopodium album extract and their storage stability. To prepare C. album extract, microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE) were employed. Ostrich meat patties were prepared using diverse combinations of extract, including 1% UAE, 2% UAE, 1% MAE, 2% MAE, 0.5% UAE+0.5% MAE, and 1% UAE + 1% MAE. The highest pH was observed for MAE in T3 on the 14th day (6.19 ± 0.03). The L* value was observed between 39.12 ± 1.09 and 44.00 ± 1.1. As storage intervals passed, the a* and b* values of ostrich meat patties decreased. After the 14th day of storage, the best results were obtained from 2% UAE (T2), with the lowest TBRAS, Peroxide value (POV), and Total of volatile basic nitrogen (TVBN) readings recorded for T2 (0.74 ± 0.02 MDA/kg, 0.56 ± 0.01 meq peroxide/kg, and 6.28 ± 0.40 mg/100 mL, respectively). At the end of the storage study, the lowest Total Microbial Count (TMC) and coliform count were recorded for T2 (8.08 ± 0.03 and 4.97 ± 0.05 cfu/mL, respectively). At the end of the storage study, T2 exhibited the highest values for total phenolic content (TPC), diphenyl-1-picrylhydrazyl (DPPH), Ferric reducing antioxidant power (FRAP), and azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assay, namely of T2 122 ± 0.28 mg GAE/100 g, 73 ± 0.27%, 5.9 ± 0.01 mmol FSE/100 g, and 83 ± 0.08 mmol/L, respectively. The current study concludes that the UAE extract of C. album incorporated into ostrich meat patties manifests improved safety, quality, and storage stability. The implementation of clean label strategies can facilitate food manufacturers to align with consumer preferences for product transparency and sustainability while ensuring product safety and quality.

Quantitative proteomics and phosphoproteomics profiling of meiotic divisions in the fission yeast Schizosaccharomyces pombe

In eukaryotes, chromosomal DNA is equally distributed to daughter cells during mitosis, whereas the number of chromosomes is halved during meiosis. Despite considerable progress in understanding the molecular mechanisms that regulate mitosis, there is currently a lack of complete understanding of the molecular mechanisms regulating meiosis. Here, we took advantage of the fission yeast Schizosaccharomyces pombe, for which highly synchronous meiosis can be induced, and performed quantitative proteomics and phosphoproteomics analyses to track changes in protein expression and phosphorylation during meiotic divisions. We compared the proteomes and phosphoproteomes of exponentially growing mitotic cells with cells harvested around meiosis I, or meiosis II in strains bearing either the temperature-sensitive pat1-114 allele or conditional ATP analog-sensitive pat1-as2 allele of the Pat1 kinase. Comparing pat1-114 with pat1-as2 also allowed us to investigate the impact of elevated temperature (25 °C versus 34 °C) on meiosis, an issue that sexually reproducing organisms face due to climate change. Using TMTpro 18plex labeling and phosphopeptide enrichment strategies, we performed quantification of a total of 4673 proteins and 7172 phosphosites in S. pombe. We found that the protein level of 2680 proteins and the rate of phosphorylation of 4005 phosphosites significantly changed during progression of S. pombe cells through meiosis. The proteins exhibiting changes in expression and phosphorylation during meiotic divisions were represented mainly by those involved in the meiotic cell cycle, meiotic recombination, meiotic nuclear division, meiosis I, centromere clustering, microtubule cytoskeleton organization, ascospore formation, organonitrogen compound biosynthetic process, carboxylic acid metabolic process, gene expression, and ncRNA processing, among others. In summary, our findings provide global overview of changes in the levels and phosphorylation of proteins during progression of S. pombe cells through meiosis at normal and elevated temperatures, laying the groundwork for further elucidation of the functions and importance of specific proteins and their phosphorylation in regulating meiotic divisions in this yeast.

Impact of site-specific conjugation strategies on the pharmacokinetics of antibody conjugated radiotherapeutics

Antibody radionuclide conjugates are an emerging modality for targeted imaging and potent therapy of disseminated disease. Coupling of radionuclides to monoclonal antibodies (mAbs) is typically achieved by applying non-site-specific labelling techniques. With the ambition of reducing variability, increasing labelling efficacy and stability, several site-specific conjugation strategies have been developed in recent years for toxin- and fluorophore-mAb conjugates. In this study, we studied two site-specific labelling strategies for the conjugation of the macrocyclic chelating agent, DOTA, to the anti-Leucine Rich Repeat Containing 15 (LRRC15) mAb DUNP19. Specifically, one approach utilized a DOTA-bearing peptide (FcIII) with a strong affinity for the fragment crystallizable (Fc) domain of the human IgG1 of DUNP19 (DUNP19LF-FcIII-DOTASS), while the other leveraged a chemo-enzymatic technique to substitute the N-linked bi-antennary oligosaccharides in the human IgG1 Fc domain with DOTA (DUNP19LF-gly-DOTASS). To assess if these methods impact the antibody's binding properties and targeting efficacy, comparative in vitro and in vivo studies of the generated DUNP19-conjugates were performed. While the LRRC15 binding of both radioimmunoconjugates remained intact, the conjugation methods had different impacts on their abilities to interact with FcRn and FcγRs. In vitro assessments of DUNP19LF-FcIII-DOTASS and DUNP19LF-gly-DOTASS demonstrated markedly decreased affinity for FcRn and FcγRIIIa (CD16), respectively. DUNP19LF-FcIII-DOTASS demonstrated increased blood and tissue kinetics in vivo, confirming loss of FcRn binding. While the ablated FcγR interaction of DUNP19LF-gly-DOTASS had no immediate impact on in vivo biodistribution, reduced immunotherapeutic effect can be expected in future studies as a result of reduced NK-cells interaction. In conclusion, our findings underscore the necessity for meticulous consideration and evaluation of mAb labelling strategies, extending beyond mere conjugation efficiency and radiolabeling yields. Notably, site-specific labelling methods were found to significantly influence the immunological impact of Fc interactions. Therefore, it is of paramount importance to consider the intended diagnostic or therapeutic application of the construct and to adopt conjugation strategies that ensure the preservation of critical pharmacological properties and functionality of the antibody in use.

A Fluorogenic Chemogenetic pH Sensor for Imaging Protein Exocytosis.

Fluorescent protein-based pH biosensors enable the tracking of pH changes during protein trafficking and, in particular, exocytosis. The recent development of chemogenetic reporters combining synthetic fluorophores with self-labeling protein tags offers a versatile alternative to fluorescent proteins that combines the diversity of chemical probes and indicators with the selectivity of the genetic encoding. However, this hybrid protein labeling strategy does not avoid common drawbacks of organic fluorophores such as the risk of off-target signal due to unbound molecules. Here, we describe a novel fluorogenic and chemogenetic pH sensor based on a cell-permeable molecular pH indicator called pHluo-Halo-1, whose fluorescence can be locally activated in cells by reaction with HaloTag, ensuring excellent signal selectivity in wash-free imaging experiments. pHluo-Halo-1 was selected out of a series of four fluorogenic molecular rotor structures based on protein chromophore analogues. It displays good pH sensitivity with a pKa of 6.3 well-suited to monitor pH variations during exocytosis and an excellent labeling selectivity in cells. It was applied to follow the secretion of CD63-HaloTag fusion proteins using TIRF microscopy. We anticipate that this strategy based on the combination of a tunable and chemically accessible fluorogenic probe with a well-established protein tag will open new possibilities for the development of versatile alternatives to fluorescent proteins for elucidating the dynamics and regulatory mechanisms of proteins in living cells.

Site-Specific Location of Black Phosphorus Quantum Dot Cluster-Based Nanocomplexes for Synergistic Ion Channel Therapy and Hypoxic Microenvironment Activated Chemotherapy.

The spatiotemporal regulation of ion transport in living cell membrane channels has immense potential for providing novel therapeutic approaches for the treatment of currently intractable diseases. So far, most strategies suffer from uncontrolled ion transport and limited tumor therapy effects. On the premise of low toxicity to healthy tissues, enhancing the degree of ion overloading and the effect of tumor treatment still remains a challenging concern. Herein, an innovative strategy for synergistic ion channel therapy and hypoxic microenvironment activated chemotherapy is proposed. Biocompatible AQ4N/black phosphorus quantum dot clusters@liposomes (AQ4N/BPCs@Lip) nanocomplexes are site-specifically immobilized on the living cell membrane by a metabolic labeling strategy, eliminating the need for modifying or genetically encoding channel structures. Ascribing to the localized temperature increase of BPCs under NIR light irradiation, Ca2+ overinflux can be remotely controlled and the overloading degree was increased; moreover, the local released AQ4N can only be activated in the tumor cell, while it has no toxicity to normal cells. Compared with single intracellular Ca2+ overloading, the tumor cell viabilities decrease 2-fold with synergetic Ca2+ overloading-induced ion channel therapy and hypoxic microenvironment activated chemotherapeutics. Our study demonstrates the example of a remote-controlled ion influx and drug delivery system for tumor therapy.

Optimizing Object Detection Algorithms for Congenital Heart Diseases in Echocardiography: Exploring Bounding Box Sizes and Data Augmentation Techniques.

Congenital heart diseases (CHDs), particularly atrial and ventricular septal defects, pose significant health risks and common challenges in detection via echocardiography. Doctors often employ the cardiac structural information during the diagnostic process. However, prior CHD research has not determined the influence of including cardiac structural information during the labeling process and the application of data augmentation techniques. This study utilizes advanced artificial intelligence (AI)-driven object detection frameworks, specifically You Look Only Once (YOLO)v5, YOLOv7, and YOLOv9, to assess the impact of including cardiac structural information and data augmentation techniques on the identification of septal defects in echocardiographic images. The experimental results reveal that different labeling strategies substantially affect the performance of the detection models. Notably, adjustments in bounding box dimensions and the inclusion of cardiac structural details in the annotations are key factors influencing the accuracy of the model. The application of deep learning techniques in echocardiography enhances the precision of detecting septal heart defects. This study confirms that careful annotation of imaging data is crucial for optimizing the performance of object detection algorithms in medical imaging. These findings suggest potential pathways for refining AI applications in diagnostic cardiology studies.

Machine Learning for Actionable Warning Identification: A Comprehensive Survey

Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static code analyzers. With recent advances in Machine Learning (ML), various approaches have been proposed to incorporate ML techniques into AWI. These ML-based AWI approaches, benefiting from ML’s strong ability to learn subtle and previously unseen patterns from historical data, have demonstrated superior performance. However, a comprehensive overview of these approaches is missing, which could hinder researchers and practitioners from understanding the current process and discovering potential for future improvement in the ML-based AWI community. In this paper, we systematically review the state-of-the-art ML-based AWI approaches. First, we employ a meticulous survey methodology and gather 51 primary studies from 2000/01/01 to 2023/09/01. Then, we outline a typical ML-based AWI workflow, including warning dataset preparation, preprocessing, AWI model construction, and evaluation stages. In such a workflow, we categorize ML-based AWI approaches based on the warning output format. Besides, we analyze the key techniques used in each stage, along with their strengths, weaknesses, and distribution. Finally, we provide practical research directions for future ML-based AWI approaches, focusing on aspects like data improvement (e.g., enhancing the warning labeling strategy) and model exploration (e.g., exploring large language models for AWI).

Charting the Dynamic Trophoblast Plasma Membrane Identifies LYN As a Functional Regulator of Syncytialization.

The differentiation of placental cytotrophoblasts (CTBs) into the syncytiotrophoblast (STB) layer results in a significant remodeling of the plasma membrane proteome. Here, we use a peroxidase-catalyzed proximity labeling strategy to map the dynamic plasma membrane proteomes of CTBs and STBs. Coupled with mass-spectrometry-based proteomics, we identify hundreds of plasma membrane proteins and observe relative changes in protein abundance throughout differentiation, including the upregulation of the plasma-membrane-localized nonreceptor tyrosine kinase LYN. We show that both siRNA-mediated knockdown and small molecule inhibition of LYN kinase function impairs CTB fusion and reduces the expression of syncytialization markers, presenting a function for LYN outside of its canonical role in immunological signaling. Our results demonstrate the use of the proximity labeling platform to discover functional regulators within the plasma membrane and provide new avenues to regulate trophoblast differentiation.

Reducing Food Waste Through Choice Architecture: Insights From Denmark, the UK, and San Francisco

Food waste poses significant environmental, economic, and social challenges, with approximately 1.05 billion tonnes wasted globally in 2022, accounting for 19% of available consumable food. This paper explores the effectiveness of choice architecture in reducing food waste, highlighting successful initiatives in Denmark, the U.K., and San Francisco. Denmark’s “Stop Wasting Food” initiative achieved a 25% reduction in food waste through clear labeling and pricing strategies. The U.K.’s “Love Food Hate Waste” campaign reduced household food waste by 21% by providing practical tools and educational resources. San Francisco’s comprehensive waste management program, including mandatory composting and recycling laws, diverted 80% of waste from landfills. The paper suggests that governments and organizations can implement similar strategies, such as regulatory frameworks, public-private partnerships, and consumer education, to reduce food waste. It also identifies areas for further research, including the long-term sustainability of these interventions and their adaptability to different cultural and social contexts. These findings emphasize the importance of innovative approaches through choice architecture to reduce food waste for environmental sustainability and economic efficiency.

Viral capsid-mediated efficient cell labeling of micron-sized magnetic particles for highly sensitive long-term in vivo tumor cells MRI tracking

As a contrast agent, micron-sized iron oxide particles (MPIO) offer major advantages for monitoring tumor cells in vivo—applications crucial for studies of tumorigenesis and metastasis. However, labeling tumor cells with MPIO is often dependent on phagocytosis, which limits the labeling efficiency of the cells and thus affects in vivo tumor cell tracking. To address this issue, we prepared a virus-like particle (VLP) consisting of the SV40 VP1 protein modified on the surface of a MPIO (vMPIO). The vMPIO can enter cells via a pathway similar to that of viral infection (caveolae-dependent endocytosis pathway). This approach increased the number of vMPIO in individual labeled cells, which in turn enhances magnetic resonance imaging (MRI) signal intensity and the number of vMPIO in daughter cells. These vMPIO-labeled cells can be detected by MRI in vitro and in vivo even in very small numbers (≤10 cells). Furthermore, vMPIO-labeled cells were successfully applied to long-term tracing of tumor tissue and detection of early metastatic lesions. This VLP-mediated MPIO labeling strategy (vML) not only enables highly sensitive in vivo MRI but also introduces a useful technical tool for investigating tumor invasion and early metastasis.

The impact of plant-based product denomination on consumer expectations and sensory perception: A study with vegan chocolate dessert

In recent years, there has been a growing demand for plant-based products from omnivorous consumers seeking a healthier and more sustainable diet, but sensory issues can still impact the consumption experience. Since food denomination and labeling can play a crucial role in consumer expectation and perception, investigating these interactions is essential. Thus, this study investigated the influence of plant-based product denominations on consumer expectations and sensory perceptions. Using descriptive and affective sensory analysis methods, the response of 300 untrained consumers to three variations of product denomination for the same sample of vegan chocolate dessert was evaluated: Vegan Chocolate Mousse (VCM), Chocolate Mousse (CM), and Creamy Coconut Dessert with Cacao (CCDC). The results indicated that the attributes expected by consumers for each denomination did not always correspond to the actual perceptions, particularly in terms of taste and aroma. The attributes highlighted in the consumption expectation varied among the sales denominations, although the perceived attributes did not significantly differ between the names. Furthermore, the addition of the term “vegan” in the name contributed to a greater correspondence between expectation and consumption reality, although the acceptance score did not significantly differ between the denominations. It was also noted that the term “mousse” did not adequately reflect the texture of the products, prompting consumers to suggest replacing the term with “cream.” The study reinforces the importance of transparent and informative labeling strategies to promote the acceptance of plant-based foods, thereby encouraging increased consumption frequency of plant-based analogs by non-restricted diet consumers, resulting in a better consumption experience.

Visualizing highly bright and uniform cellular ultrastructure by expansion-microscopy with tetrahedral DNA nanostructures

Expansion Microscopy (ExM) is a widely used super-resolution technique that enables imaging of structures beyond the diffraction limit of light. However, ExM suffers from weak labeling signals and expansion distortions, limiting its applicability. Here, we present an innovative approach called Tetrahedral DNA nanostructure Expansion Microscopy (TDN-ExM), addressing these limitations by using tetrahedral DNA nanostructures (TDNs) for fluorescence labeling. Our approach demonstrates a 3- to 10-fold signal amplification due to the multivertex nature of TDNs, allowing the modification of multiple dyes. Previous studies have confirmed minimal distortion on a large scale, and our strategy can reduce the distortion at the ultrastructural level in samples because it does not rely on anchoring agents and is not affected by digestion. This results in a brighter fluorescence, better uniformity, and compatibility with different labeling strategies and optical super-resolution technologies. We validated the utility of TDN-ExM by imaging various biological structures with improved resolutions and signal-to-noise ratios.