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Glucose, as an essential substance directly involved in metabolic processes, is closely related to the occurrence of various diseases such as glucose metabolism disorders and islet cell carcinoma. Therefore, it is crucial to develop sensitive, accurate, rapid, and cost effective methods for frequent and convenient detections of glucose. Microfluidic Paper-based Analytical Devices (μPADs) not only satisfying the above requirements but also occupying the advantages of portability and minimal sample consumption, have exhibited great potential in the field of glucose detection. This article reviews and summarizes the most recent improvements in glucose detection in two aspects of colorimetric and electrochemical μPADs. The progressive techniques for fabricating channels on μPADs are also emphasized in this article. With the growth of diabetes and other glucose indication diseases in the underdeveloped and developing countries, low-cost and reliably commercial μPADs for glucose detection will be in unprecedentedly demand.

Profiling protein expression at single‐cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single‐cell protein analysis methods are developed rapidly, which enable high‐throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single‐cell multi‐omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single‐cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed. In this review, recent progress and applications of single‐cell protein analysis technologies are summarized. The principles, characteristics, and applications of the emerging single‐cell multi‐omics analysis techniques are also introduced. In addition, current limitations and future prospects of single‐cell protein analysis are discussed.

Effective therapeutic strategies for myocardial ischemia/reperfusion (I/R) injury remain elusive. Targeting reactive oxygen species (ROS) provides a practical approach to mitigate myocardial damage following reperfusion. In this study, we synthesize an antioxidant nanozyme, equipped with a single-Platinum (Pt)-atom (PtsaN-C), for protecting against I/R injury. PtsaN-C exhibits multiple enzyme-mimicking activities for ROS scavenging with high efficiency and stability. Mechanistic studies demonstrate that the excellent ROS-elimination performance of the single Pt atom center precedes that of the Pt cluster center, owing to its better synergistic effect and metallic electronic property. Systematic in vitro and in vivo studies confirm that PtsaN-C efficiently counteracts ROS, restores cellular homeostasis and prevents apoptotic progression after I/R injury. PtsaN-C also demonstrates good biocompatibility, making it a promising candidate for clinical applications. Our study expands the scope of single-atom nanozyme in combating ROS-induced damage and offers a promising therapeutic avenue for the treatment of I/R injury. Nanozymes can be used for targeting reactive oxygen species (ROS) to alleviate myocardial ischemia/reperfusion (I/R) injury, but hindered by catalytic performance and toxicity concerns. Here the authors report multienzyme-mimicking and biocompatible Pt-NC single-atom nanozymes as an efficient ROS decomposer for restoring cellular homeostasis and mitigating apoptotic progression after I/R injury.

Background With the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification of meaningful cell populations. Result To address this issue, we compared three classes of performance measures, “precision” as external evaluation, “coherence” as internal evaluation, and stability, of nine methods based on six independent benchmark datasets. Seven unsupervised methods (Accense, Xshift, PhenoGraph, FlowSOM, flowMeans, DEPECHE, and kmeans) and two semi-supervised methods (Automated Cell-type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cytometry datasets. We compute and compare all defined performance measures against random subsampling, varying sample sizes, and the number of clusters for each method. LDA reproduces the manual labels most precisely but does not rank top in internal evaluation. PhenoGraph and FlowSOM perform better than other unsupervised tools in precision, coherence, and stability. PhenoGraph and Xshift are more robust when detecting refined sub-clusters, whereas DEPECHE and FlowSOM tend to group similar clusters into meta-clusters. The performances of PhenoGraph, Xshift, and flowMeans are impacted by increased sample size, but FlowSOM is relatively stable as sample size increases. Conclusion All the evaluations including precision, coherence, stability, and clustering resolution should be taken into synthetic consideration when choosing an appropriate tool for cytometry data analysis. Thus, we provide decision guidelines based on these characteristics for the general reader to more easily choose the most suitable clustering tools.

Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care. Engineering approaches to precision medicine will harness population-wide data to identify individualized treatment strategies.Personalized medicine harnesses a subject’s own data to individualize their own care, from diagnosis through treatment selection and monitoring.Novel clinical trial designs will play a vital role in assessing the efficacy and safety of emerging therapies and diagnostics.Artificial intelligent platforms will globally optimize combination therapy from the preclinical through clinical stages of validation.The widespread deployment of precision and personalized medicine technologies will involve the convergence of several factors ranging from evolving education at the interface of engineering and medicine and policies that support new clinical trial designs, to scaling the use of electronic medical records (EMR) to drive clinical decision support.

Spatial proteomics elucidates cellular biochemical changes with unprecedented topological level. Imaging mass cytometry (IMC) is a high-dimensional single-cell resolution platform for targeted spatial proteomics. However, the precision of subsequent clinical analysis is constrained by imaging noise and resolution. Here, we propose SpiDe-Sr, a super-resolution network embedded with a denoising module for IMC spatial resolution enhancement. SpiDe-Sr effectively resists noise and improves resolution by 4 times. We demonstrate SpiDe-Sr respectively with cells, mouse and human tissues, resulting 18.95%/27.27%/21.16% increase in peak signal-to-noise ratio and 15.95%/31.63%/15.52% increase in cell extraction accuracy. We further apply SpiDe-Sr to study the tumor microenvironment of a 20-patient clinical breast cancer cohort with 269,556 single cells, and discover the invasion of Gram-negative bacteria is positively correlated with carcinogenesis markers and negatively correlated with immunological markers. Additionally, SpiDe-Sr is also compatible with fluorescence microscopy imaging, suggesting SpiDe-Sr an alternative tool for microscopy image super-resolution. Imaging mass cytometry (IMC) is a powerful single-cell resolution platform for targeted spatial proteomics, but it can be constrained by imaging noise and resolution. Here, the authors propose SpiDe-Sr, a super-resolution network embedded with a denoising module for IMC spatial resolution enhancement.

Rapid advances in nanotechnologies are driving the revolution in controlled drug delivery. However, heterogeneous barriers, such as blood circulation and cellular barriers, prevent the drug from reaching the cellular target in complex physiologic environments. In this review, we discuss the precise design of nanotechnologies to enhance the efficacy, quality, and durability of drug delivery. For drug delivery in vivo, drugs loaded in nanoplatforms target particular sites in a spatial- and temporal-dependent manner. Advances in stimuli-responsive nanoparticles and carbon-based drug delivery platforms are summarized. For transdermal drug delivery systems, specific strategies including microneedles and hydrogel lead to a sustained release efficacy. Moreover, we highlight the current limitations of clinical translation and an incentive for the future development of nanotechnology-based drug delivery.

Patients with polycystic kidney disease (PKD) encounter a high risk of clear cell renal cell carcinoma (ccRCC), a malignant tumor with dysregulated lipid metabolism. SET domain–containing 2 (SETD2) has been identified as an important tumor suppressor and an immunosuppressor in ccRCC. However, the role of SETD2 in ccRCC generation in PKD remains largely unexplored. Herein, we perform metabolomics, lipidomics, transcriptomics and proteomics within SETD2 loss induced PKD-ccRCC transition mouse model. Our analyses show that SETD2 loss causes extensive metabolic reprogramming events that eventually results in enhanced sphingomyelin biosynthesis and tumorigenesis. Clinical ccRCC patient specimens further confirm the abnormal metabolic reprogramming and sphingomyelin accumulation. Tumor symptom caused by Setd2 knockout is relieved by myriocin, a selective inhibitor of serine-palmitoyl-transferase and sphingomyelin biosynthesis. Our results reveal that SETD2 deficiency promotes large-scale metabolic reprogramming and sphingomyelin biosynthesis during PKD-ccRCC transition. This study introduces high-quality multi-omics resources and uncovers a regulatory mechanism of SETD2 on lipid metabolism during tumorigenesis. SET domain–containing 2 (SETD2) is reported as an immunosuppressor in clear cell renal cell carcinoma (ccRCC). Here the authors show that SETD2 loss enhances de novo sphingomyelin biosynthesis during the transition from polycystic kidney disease to ccRCC.

Liquid-liquid phase separations (LLPS) are membraneless organelles driven by biomolecule assembly and are implicated in cellular physiological activities. However, spatiotemporal deciphering of the dynamic proteome in living cells during LLPS formation remains challenging. Here, we introduce the C omposition of L LPS proteome A ssembly by P roximity labeling-assisted M ass spectrometry (CLAPM). We demonstrate that CLAPM can instantaneously label and monitor the FUS interactome shifts within intracellular droplets undergoing spatiotemporal LLPS. We report 129, 182 and 822 proteins specifically present in the LLPS droplets of HeLa, HEK 293 T and neuronal cells respectively. CLAPM further categorizes spatiotemporal dynamic proteome in droplets for living neuronal cells and identifies 596 LLPS-aboriginal proteins, 226 LLPS-dependent proteins and 58 LLPS-sensitive proteins. For validation, we uncover 11 previously unknown LLPS proteins in vivo. CLAPM provides a versatile tool to decipher proteins involved in LLPS and enables the accurate characterization of dynamic proteome in living cells. CLAPM uses proximity labeling mass spectrometry to monitor proteomes within phase-separated droplets. This approach identifies condensate-specific proteins to assess proteome dynamics in various cell types and a neurodegenerative disease mouse model.

The early-life organ development and maturation shape the fundamental blueprint for later-life phenotype. However, a multi-organ proteome atlas from infancy to adulthood is currently not available. Herein, we present a comprehensive proteomic analysis of ten mouse organs (brain, heart, lung, liver, kidney, spleen, stomach, intestine, muscle and skin) at three crucial developmental stages (1-, 4- and 8-weeks after birth) acquired using data-independent acquisition mass spectrometry. We detect and quantify 11,533 protein groups across the ten organs and obtain 115 age-related differentially expressed protein groups that are co-expressed in all organs from infancy to adulthood. We find that spliceosome proteins prevalently play crucial regulatory roles in the early-life development of multiple organs, and detect organ-specific expression patterns and sexual dimorphism. This multi-organ proteome atlas provides a fundamental resource for understanding the molecular mechanisms underlying early-life organ development and maturation. Multi-organ proteomic data is needed to understand the complex processes of early-life organ development and maturation. Here, the authors generated a proteomic atlas covering the development of ten mouse organs from infancy to adulthood and report organ- and age-specific signaling pathways and co-expressed proteins.