Efficient Labeling Research Articles

Implementation of Fluorescent-Protein-Based Quantification Analysis in L-Form Bacteria.

Cell-wall-less (L-form) bacteria exhibit morphological complexity and heterogeneity, complicating quantitative analysis of them under internal and external stimuli. Stable and efficient labeling is needed for the fluorescence-based quantitative cell analysis of L-forms during growth and proliferation. Here, we evaluated the expression of multiple fluorescent proteins (FPs) under different promoters in the Bacillus subtilis L-form strain LR2 using confocal microscopy and imaging flow cytometry. Among others, Pylb-derived NBP3510 showed a superior performance for inducing several FPs including EGFP and mKO2 in both the wild-type and L-form strains. Moreover, NBP3510 was also active in Escherichia coli and its L-form strain NC-7. Employing these established FP-labeled strains, we demonstrated distinct morphologies in the L-form bacteria in a quantitative manner. Given cell-wall-deficient bacteria are considered protocell and synthetic cell models, the generated cell lines in our work could be valuable for L-form-based research.

A strategy for producing isotopically labeled peptides with antimicrobial activity or with short in vivo lifetime in Escherichia coli

AbstractEngineered Escherichia coli (E. coli) strains have been widely used to produce isotopically labeled peptides for NMR characterization on their structures and interactions. However, production of antimicrobial peptides (AMPs) by E. coli is still challenging, because AMPs are toxic to E. coli host and would lead to cell death after induction. On the other hand, expression of short peptides in E. coli host often encounter problems of the short in vivo lifetime of the peptides, which were rapidly degraded by endogenous enzymes during expression and purification steps. This report presents a practical method for overcoming these bottlenecks to enable E. coli to express AMPs and peptides that have short in vivo lifetime. This design uses the fusion of thioredoxin tags at both the N‐ and C‐termini of the target peptides. The steric effect of the large soluble tags at both ends of the peptide reduces peptide accessibility, thereby enhancing their in vivo stability and eliminating the toxicity associated with AMPs. The approach was validated using an AMP A3K/L7K‐LAH4 (K3K7) and a membrane fusion peptide (FP), which is a segment of the spike protein of SARS‐CoV‐2 and functions in fusing viral membranes and host cell membranes. Fusion expression of K3K7 with a thioredoxin tag only at the N‐terminal resulted in high toxicity to the host cells, leading to impaired cell growth and a failure to obtain expressed fusion protein. In contrast, the fusion proteins from both termini were successfully expressed and purified. In the case of the FP, the fusion of thioredoxin at both termini significantly enhanced its stability, protecting it from enzymatic degradation during expression and purification steps. On the contrary, the FP with thioredoxin fused only at the N‐terminal was found to be unstable in E. coli host strains. As stable isotope labeling on peptide is essentially important in NMR‐based structure and interaction studies, we also demonstrated that the developed approach enables efficient 15N labeling for NMR studies. This strategy may also be extended to produce other challenging peptides.

Activity-based labelling of ammonia- and alkane-oxidizing microorganisms including ammonia-oxidizing archaea.

Recently, an activity-based labelling protocol for the in vivo detection of ammonia- and alkane-oxidizing bacteria became available. This functional tagging technique enabled targeted studies of these environmentally widespread functional groups, but it failed to capture ammonia-oxidizing archaea (AOA). Since their first discovery, AOA have emerged as key players within the biogeochemical nitrogen cycle, but our knowledge regarding their distribution and abundance in natural and engineered ecosystems is mainly derived from PCR-based and metagenomic studies. Furthermore, the archaeal ammonia monooxygenase is distinctly different from its bacterial counterparts and remains poorly understood. Here, we report on the development of an activity-based labelling protocol for the fluorescent detection of all ammonia- and alkane-oxidizing prokaryotes, including AOA. In this protocol, 1,5-hexadiyne is used as inhibitor of ammonia and alkane oxidation and as bifunctional enzyme probe for the fluorescent labelling of cells via the Cu(I)-catalyzed alkyne-azide cycloaddition reaction. Besides efficient activity-based labelling of ammonia- and alkane-oxidizing microorganisms, this method can also be employed in combination with deconvolution microscopy for determining the subcellular localization of their ammonia- and alkane-oxidizing enzyme systems. Labelling of these enzymes in diverse ammonia- and alkane-oxidizing microorganisms allowed their visualization on the cytoplasmic membranes, the intracytoplasmic membrane stacks of ammonia- and methane-oxidizing bacteria, and, fascinatingly, on vesicle-like structures in one AOA species. The development of this novel activity-based labelling method for ammonia- and alkane-oxidizers will be a valuable addition to the expanding molecular toolbox available for research of nitrifying and alkane-oxidizing microorganisms.

Application of a human-centered design for embedded machine learning model to develop data labeling software with nurses: Human-to-Artificial Intelligence (H2AI)

BackgroundNurses are essential for assessing and managing acute pain in hospitalized patients, especially those who are unable to self-report pain. Given their role and subject matter expertise (SME), nurses are also essential for the design and development of a supervised machine learning (ML) model for pain detection and clinical decision support software (CDSS) in a pain recognition automated monitoring system (PRAMS). Our first step for developing PRAMS with nurses was to create SME-friendly data labeling software. PurposeTo develop an intuitive and efficient data labeling software solution, Human-to-Artificial Intelligence (H2AI). MethodThe Human-centered Design for Embedded Machine Learning Solutions (HCDe-MLS) model was used to engage nurses. In this paper, HCDe-MLS will be explained using H2AI and PRAMS as illustrative cases. FindingsUsing HCDe-MLS, H2AI was developed and facilitated labeling of 139 videos (mean = 29.83 min) with 3189 images labeled (mean = 75 s) by 6 nurses. OpenCV was used for video-to-image pre-processing; and MobileFaceNet was used for default landmark placement on images. H2AI randomly assigned videos to nurses for data labeling, tracked labelers’ inter-rater reliability, and stored labeled data to train ML models. ConclusionsNurses’ engagement in CDSS development was critical for ensuring the end-product addressed nurses’ priorities, reflected nurses’ cognitive and decision-making processes, and garnered nurses’ trust for technology adoption.

MaskTrack: Auto-Labeling and Stable Tracking for Video Object Segmentation.

Video object segmentation (VOS) has witnessed notable progress due to the establishment of video training datasets and the introduction of diverse, innovative network architectures. However, video mask annotation is a highly intricate and labor-intensive task, as meticulous frame-by-frame comparisons are needed to ascertain the positions and identities of targets in the subsequent frames. Current VOS benchmarks often annotate only a few instances in each video to save costs, which, however, hinders the model's understanding of the complete context of the video scenes. To simplify video annotation and achieve efficient dense labeling, we introduce a zero-shot auto-labeling strategy based on the segment anything model (SAM), enabling it to densely annotate video instances without access to any manual annotations. Moreover, although existing VOS methods demonstrate improving performance, segmenting long-term and complex video scenes remains challenging due to the difficulties in stably discriminating and tracking instance identities. To this end, we further introduce a new framework, MaskTrack, which excels in long-term VOS and also exhibits significant performance advantages in distinguishing instances in complex videos with densely packed similar objects. We conduct extensive experiments to demonstrate the effectiveness of the proposed method and show that without introducing image datasets for pretraining, it achieves excellent performance on both short-term (86.2% in YouTube-VOS val) and long-term (68.2% in LVOS val) VOS benchmarks. Our method also surprisingly demonstrates strong generalization ability and performs well in visual object tracking (VOT) (65.6% in VOTS2023) and referring VOS (RVOS) (65.2% in Ref YouTube VOS) challenges.

VideoPro: A Visual Analytics Approach for Interactive Video Programming.

Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection. While data programming shows promise in generating labeled data at scale with user-defined labeling functions, the high dimensional and complex temporal information in videos poses additional challenges for effectively composing and evaluating labeling functions. In this paper, we propose VideoPro, a visual analytics approach to support flexible and scalable video data programming for model steering with reduced human effort. We first extract human-understandable events from videos using computer vision techniques and treat them as atomic components of labeling functions. We further propose a two-stage template mining algorithm that characterizes the sequential patterns of these events to serve as labeling function templates for efficient data labeling. The visual interface of VideoPro facilitates multifaceted exploration, examination, and application of the labeling templates, allowing for effective programming of video data at scale. Moreover, users can monitor the impact of programming on model performance and make informed adjustments during the iterative programming process. We demonstrate the efficiency and effectiveness of our approach with two case studies and expert interviews.

Lighting up Mycobacteria with membrane-targeting peptides.

We report a series of fluorescent probes based on mycobacteria membrane-associated disruption peptide, containing either L- or D-amino acids which were originally designed to kill Mycobacterium tuberculosis via membrane disruption. These peptides were decorated with "always on" and environmentally sensitive fluorophores and showed the rapid and efficient labelling of Mycobacterium smegmatis, with labelling of Mycobacterium tuberculosis demonstrated by two of the probes.

Incentivizing Efficient Label Denoising in Federated Learning

Incentivizing Efficient Label Denoising in Federated Learning

Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery.

Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be challenging at the infiltration zone, even by using surgical adjuncts such as fluorescence guidance (e.g., with 5-aminolevulinic acid). Challenging cases in which there is no visible fluorescence include lower-grade gliomas, tumor cells infiltrating beyond the margin as visualized on pre- and/or intraoperative MRI, and even some high-grade tumors. One field of research aiming to address this problem involves inspecting in detail the light emission spectra from different tissues (e.g., tumor vs. normal brain vs. brain parenchyma infiltrated by tumor cells). Hyperspectral imaging measures the emission spectrum at every image pixel level, thus combining spatial and spectral information. Assuming that different tissue types have different "spectral footprints," eventually related to higher or lower abundances of fluorescent dyes or auto-fluorescing molecules, the tissue can then be segmented according to type, providing surgeons a detailed spatial map of what they see. However, processing from raw hyperspectral data cubes to maps or overlays of tissue labels and potentially further molecular information is complex. This chapter will explore some of the classical methods for the various steps of this process and examine how they can be improved with machine learning approaches. While preliminary work on machine learning in hyperspectral imaging has had relatively limited success in brain tumor surgery, more recent research combines this with fluorescence to obtain promising results. In particular, this chapter describes a pipeline that isolates biopsies in ex vivo hyperspectral fluorescence images for efficient labeling, extracts all the relevant emission spectra, preprocesses them to correct for various optical properties, and determines the abundance of fluorophores in each pixel, which correspond directly with the presence of cancerous tissue. Each step contains a combination of classical and deep learning-based methods. Furthermore, the fluorophore abundances are then used in four machine learning models to classify tumor type, WHO grade, margin tissue type, and isocitrate dehydrogenase (IDH) mutation status in brain tumors. The classifiers achieved average test accuracies of 87%, 96.1%, 86%, and 93%, respectively, thus greatly outperforming prior work both with and without fluorescence. This field is new, but these early results show great promise for the feasibility of data-driven hyperspectral imaging for intraoperative classification of brain tumors during fluorescence-guided surgery.

KMTLabeler: An Interactive Knowledge-Assisted Labeling Tool for Medical Text Classification.

The process of labeling medical text plays a crucial role in medical research. Nonetheless, creating accurately labeled medical texts of high quality is often a time-consuming task that requires specialized domain knowledge. Traditional methods for generating labeled data typically rely on rigid rule-based approaches, which may not adapt well to new tasks. While recent machine learning (ML) methodologies have mitigated the manual labeling efforts, configuring models to align with specific research requirements can be challenging for labelers without technical expertise. Moreover, automated labeling techniques, such as transfer learning, face difficulties in in directly incorporating expert input, whereas semi-automated methods, like data programming, allow knowledge integration through rules or knowledge bases but may lack continuous result refinement throughout the entire labeling process. In this study, we present a collaborative human-ML teaming workflow that seamlessly integrates visual cluster analysis and active learning to assist domain experts in labeling medical text with high efficiency. Additionally, we introduce an innovative neural network model called the embedding network, which incorporates expert insights to generate task-specific embeddings for medical texts. We integrate the workflow and embedding network into a visual analytics tool named KMTLabeler, equipped with coordinated multi-level views and interactions. Two illustrative case studies, along with a controlled user study, provide substantial evidence of the effectiveness of KMTLabeler in creating an efficient labeling environment for medical text classification.

Longer fatty acid-protected GalNAz enables efficient labeling of proteins in living cells with minimized S-glyco modification.

Metabolic glycoengineering provides a powerful tool to label proteins with chemical tags for cell imaging and protein enrichment. The structures of per-O-acetylation on unnatural sugars facilitate membrane permeability and increase cellular uptake and are widely used for metabolic glycan labeling. However, unexpected S-glyco modification was discovered via a non-enzymatic reaction with protein cysteines, which was initially conducted with the hydrolysis of anomeric acetate by esterase. Herein, we synthesized a series of GalNAz derivatives that were protected with various lengths of short-chain fatty acid, including acetate, propionate, butyrate, valerate and pivalate, to detect differences in labeling efficiencies and occurrence of S-glyco modification. Our results demonstrate that all the GalNAz derivatives could effectively label proteins in HeLa cells, except the pivalate group. Of note, But4GalNAz exhibited excellent labeling abilities compared with Ac4GalNAz from the results for western blot, flow cytometry and confocal laser scanning microscopy. Moreover, the results for the S-glyco-modification assay by western blot and chemoproteomic analysis indicated that But4GalNAz generated negligible unexpected labeling signals compared to Ac4GalNAz. Our study uncovers the distinct labeling efficiency of different protected groups on unnatural sugars, which provides an alternative strategy to explore novel glycan probes.

Extending the toolbox for RNA biology with SegModTeX: a polymerase-driven method for site-specific and segmental labeling of RNA

RNA performs a wide range of functions regulated by its structure, dynamics, and often post-transcriptional modifications. While NMR is the leading method for understanding RNA structure and dynamics, it is currently limited by the inability to reduce spectral crowding by efficient segmental labeling. Furthermore, because of the challenging nature of RNA chemistry, the tools being developed to introduce site-specific modifications are increasingly complex and laborious. Here we use a previously designed Tgo DNA polymerase mutant to present SegModTeX — a versatile, one-pot, copy-and-paste approach to address these challenges. By precise, stepwise construction of a diverse set of RNA molecules, we demonstrate the technique to be superior to RNA polymerase driven and ligation methods owing to its substantially high yield, fidelity, and selectivity. We also show the technique to be useful for incorporating some fluorescent- and a wide range of other probes, which significantly extends the toolbox of RNA biology in general.

PCNA as Protein-Based Nanoruler for Sub-10nm Fluorescence Imaging.

Super-resolution microscopy has revolutionized biological imaging enabling direct insight into cellular structures and protein arrangements with so far unmatched spatial resolution. Today, refined single-molecule localization microscopy methods achieve spatial resolutions in the one-digit nanometer range. As the race for molecular resolution fluorescence imaging with visible light continues, reliable biologically compatible reference structures will become essential to validate the resolution power. Here, PicoRulers (protein-based imaging calibration optical rulers), multilabeled oligomeric proteins designed as advanced molecular nanorulers for super-resolution fluorescence imaging are introduced. Genetic code expansion (GCE) is used to site-specifically incorporate three noncanonical amino acids (ncAAs) into the homotrimeric proliferating cell nuclear antigen (PCNA) at 6nm distances. Bioorthogonal click labeling with tetrazine-dyes and tetrazine-functionalized oligonucleotides allows efficient labeling of the PicoRuler with minimal linkage error. Time-resolved photoswitching fingerprint analysis is used to demonstrate the successful synthesis and DNA-based points accumulation for imaging in nanoscale topography (DNA-PAINT) is used to resolve 6nm PCNA PicoRulers. Since PicoRulers maintain their structural integrity under cellular conditions they represent ideal molecular nanorulers for benchmarking the performance of super-resolution imaging techniques, particularly in complex biological environments.

Secretory GFP reconstitution labeling of neighboring cells interrogates cell–cell interactions in metastatic niches

Cancer cells inevitably interact with neighboring host tissue-resident cells during the process of metastatic colonization, establishing a metastatic niche to fuel their survival, growth, and invasion. However, the underlying mechanisms in the metastatic niche are yet to be fully elucidated owing to the lack of methodologies for comprehensively studying the mechanisms of cell–cell interactions in the niche. Here, we improve a split green fluorescent protein (GFP)-based genetically encoded system to develop secretory glycosylphosphatidylinositol-anchored reconstitution-activated proteins to highlight intercellular connections (sGRAPHIC) for efficient fluorescent labeling of tissue-resident cells that neighbor on and putatively interact with cancer cells in deep tissues. The sGRAPHIC system enables the isolation of metastatic niche-associated tissue-resident cells for their characterization using a single-cell RNA sequencing platform. We use this sGRAPHIC-leveraged transcriptomic platform to uncover gene expression patterns in metastatic niche-associated hepatocytes in a murine model of liver metastasis. Among the marker genes of metastatic niche-associated hepatocytes, we identify Lgals3, encoding galectin-3, as a potential pro-metastatic factor that accelerates metastatic growth and invasion.

Functional Foods Regulation System: Proposed Regulatory Paradigm by Functional Food Center

In response to the rising demand for functional foods driven by health-conscious consumers, a robust regulatory framework is imperative. The absence of specific guidelines creates uncertainties for both manufacturers and consumers. In response to the rising demand for functional foods driven by health-conscious consumers, a robust regulatory framework is imperative. The absence of specific guidelines creates uncertainties for both manufacturers and consumers. This article discusses the Functional Food Center's pioneering 17-step approval processfor functional foods, emphasizing scientific validation and transparent communication. However, challenges persist within the FDA approval system, including a protracted timeline and the lack of a dedicated category for functional foods. Drawing inspiration from the efficient kosher labeling system, the Functional Food Center could serve as a certification agency. Certified functional foods could display a designated symbol, ensuring credibility and trustworthiness. This approach streamlines the approval process, fostering innovation, ensuring consumer safety, and meeting the evolving health needs of consumers in a transparent, credible, and regulated functional food market.Keywords:Functional Food Classification, Functional Food Regulation, Functional Food Products, Bioactive Compounds, Functional Food Safety, Kosher Labeling Model, Regulatory Paradigm.

Identification of sulfhydryl-containing proteins and further evaluation of the selenium-tagged redox homeostasis-regulating proteins

Chemoproteomic profiling of sulfhydryl-containing proteins has consistently been an attractive research hotspot. However, there remains a dearth of probes that are specifically designed for sulfhydryl-containing proteins, possessing sufficient reactivity, specificity, distinctive isotopic signature, as well as efficient labeling and evaluation capabilities for proteins implicated in the regulation of redox homeostasis. Here, the specific selenium-containing probes (Se-probes) in this work displayed high specificity and reactivity toward cysteine thiols on small molecules, peptides and purified proteins and showed very good competitive effect of proteins labeling in gel-ABPP. We identified more than 6000 candidate proteins. In TOP-ABPP, we investigated the peptide labeled by Se-probes, which revealed a distinct isotopic envelope pattern of selenium in both the primary and secondary mass spectra. This unique pattern can provide compelling evidence for identifying redox regulatory proteins and other target peptides. Furthermore, our examiation of post-translational modification (PTMs) of the cysteine site residues showed that oxidation PTMs was predominantly observed. We anticipate that Se-probes will enable broader and deeper proteome-wide profiling of sulfhydryl-containing proteins, provide an ideal tool for focusing on proteins that regulate redox homeostasis and advance the development of innovative selenium-based pharmaceuticals.

Visualization and Characterization of the Brain Regional Heterogeneity of Astrocyte-Astrocyte Structural Interactions by Using Improved Iontophoresis with Dual-Fluorescent Dyes.

Astrocytes are morphologically intricate cells and actively modulate the function of the brain. Through numerous fine processes, astrocytes come into contact with neurons, blood vessels, and other glia cells. Emerging evidence has shown that astrocytes exhibit brain regional diversity in their morphology, transcriptome, calcium signaling, and functions. However, little is known about the brain regional heterogeneity of astrocyte-astrocyte structural interaction. So far, the visualization and characterization of the morphological features of adjacent astrocytes have been difficult, and as a result, it is still well-accepted that astrocytes in the adult brain share non-overlapped territory. In contrast, employing an approach that combines viral labeling with dual-fluorescent dyes iontophoresis under brightfield and imaging using confocal microscopy allows for the efficient and specific labeling of adjacent astrocytes, enabling a comprehensive visualization of their fine processes and the degree of their territorial overlap. Our study in the hypothalamic regions of the brain revealed a marked spatial overlap among adjacent astrocytes, which differs from the conventional understanding based on more extensively studied regions, like the hippocampus. Additionally, we revealed the heterogeneity of the astrocyte-neuron ratio across brain regions and conducted an assessment of the photostability and labeling efficiency of fluorescent dyes used for labeling adjacent astrocytes. Our study provides new insights for studying the morphological heterogeneity of astrocytes across the central nervous system.

Metal-free click and bioorthogonal reactions of aggregation-induced emission probes for lighting up living systems.

In 2002, two transformative research paradigms emerged: 'click chemistry' and 'aggregation-induced emission (AIE),' both leaving significant impacts on early 21st-century academia. Click chemistry, which describes the straightforward and reliable reactions for linking two building blocks, has simplified complex molecular syntheses and functionalization, propelling advancements in polymer, material, and life science. In particular, nontoxic, metal-free click reactions involving abiotic functional groups have matured into bioorthogonal reactions. These are organic ligations capable of selective and efficient operations even in congested living systems, therefore enabling in vitro to in vivo biomolecular labelling. Concurrently, AIE, a fluorogenic phenomenon of twisted π-conjugated compounds upon aggregation, has offered profound insight into solid-state photophysics and promoted the creation of aggregate materials. The inherent fluorogenicity and aggregate-emission properties of AIE luminogens have found extensive application in biological imaging, characterized by their high-contrast and photostable fluorescent signals. As such, the convergence of these two domains to yield efficient labelling with excellent fluorescence images is an anticipated progression in recent life science research. In this review, we intend to showcase the synergetic applications of AIE probes and metal-free click or bioorthogonal reactions, highlighting both the achievements and the unexplored avenues in this promising field.

Non-rigid-registration-based positioning and labelling of triaxial OPMs on a flexible cap for wearable magnetoencephalography

BackgroundRecent advances in highly sensitive miniaturized optically pumped magnetometers (OPMs) have enabled the development of wearable magnetoencephalography (MEG) offering great flexibility in experimental setting. The OPM array for wearable MEG is typically attached to a flexible cap and exhibits a variable spatial layout across different subjects, which imposes challenges concerning the efficient positioning and labelling of OPMs. New methodA pair of reflective markers are affixed to each triaxial OPM sensor above its cable to determine its location and sensitive axes. A non-rigid registration of optically digitized marker locations with a pre-labelled template of marker locations is performed to map newly digitized markers to OPMs. ResultsThe positioning and labelling of 66 OPM sensors could be completed within 35 s. Across ten experiments, all OPMs were accurately labelled, and the mean test-retest errors were 0.48 mm for sensor locations and 0.20 degree for sensitive axes. By combining six OPMs’ positions with their respective recordings, magnetic dipoles inside a phantom were located with a mean error of 5.5 mm, and the best fitted dipole for MEG with auditory stimuli presented was located on a subject’s primary auditory cortex. Comparison with existing methodsThe proposed method reduces the reliance on error-prone and laborious manual operations inherent in existing methods, therefore significantly improving the efficiency of OPM positioning and labelling on a flexible cap. ConclusionWe developed a method for the precise and rapid positioning and labelling triaxial OPMs on a flexible cap, thereby facilitating the practical implementation of wearable OPM-MEG.

Glycan Metabolic Fluorine Labeling for In Vivo Visualization of Tumor Cells and In Situ Assessment of Glycosylation Variations.

The abnormality in the glycosylation of surface proteins is critical for the growth and metastasis of tumors and their capacity for immunosuppression and drug resistance. This anomaly offers an entry point for real-time analysis on glycosylation fluctuations. In this study, we report a strategy, glycan metabolic fluorine labeling (MEFLA), for selectively tagging glycans of tumor cells. As a proof of concept, we synthesized two fluorinated unnatural monosaccharides with distinctive 19 F chemical shifts (Ac4 ManNTfe and Ac4 GalNTfa). These two probes could undergo selective uptake by tumor cells and subsequent incorporation into surface glycans. This approach enables efficient and specific 19 F labeling of tumor cells, which permits in vivo tracking of tumor cells and in situ assessment of glycosylation changes by 19 F MRI. The efficiency and specificity of our probes for labeling tumor cells were verified in vitro with A549 cells. The feasibility of our method was further validated with in vivo experiments on A549 tumor-bearing mice. Moreover, the capacity of our approach for assessing glycosylation changes of tumor cells was illustrated both in vitro and in vivo. Our studies provide a promising means for visualizing tumor cells in vivo and assessing their glycosylation variations in situ through targeted multiplexed 19 F MRI.