Trends in Analytical Chemistry最新文献

{"title":"Harnessing CRISPR-Cas systems for point-of-care diagnostic applications","authors":"Ahmed Ghouneimy ,&nbsp;Rashid Aman ,&nbsp;Wenjun Jiang ,&nbsp;Zahir Ali,&nbsp;Magdy Mahfouz","doi":"10.1016/j.trac.2025.118417","DOIUrl":"10.1016/j.trac.2025.118417","url":null,"abstract":"<div><div>CRISPR systems have revolutionized gene editing and therapy. CRISPR enzymes have been repurposed for molecular diagnostics of diverse analytes, including pathogens and disease markers. CRISPR systems offer speed, specificity, programmability, and multiplex detection. However, their translational applications to commercial assays for POC or at-home end users face key challenges such as sample processing, reagent stability, and cost-effectiveness. Here, we highlight the significant advancements in the CRISPR-dx field, focusing on key challenges such as sample preparation, stability, multiplexing capabilities, amplification-free detection, signal interpretation, process automation, and detection of non-nucleic acid analytes. Additionally, we explore potential solutions to transform CRISPR-based diagnostics into complete sample-to-answer systems suitable for POC and at-home applications.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"193 ","pages":"Article 118417"},"PeriodicalIF":12.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Printed potentiometric sensors","authors":"Dean Song ,&nbsp;Canwei Mao ,&nbsp;Chenyu Jiang ,&nbsp;Rongning Liang ,&nbsp;Grzegorz Lisak","doi":"10.1016/j.trac.2025.118420","DOIUrl":"10.1016/j.trac.2025.118420","url":null,"abstract":"<div><div>From environmental monitoring, industrial process control, to point-of-care biofluid analysis, ion sensing has always been a pivotal topic. As the most suitable tool for ion sensing, potentiometric ion sensors are routinely used in these areas. Today, over a billion measurements with potentiometric sensors are preformed globally and hundreds of companies sell ready to use blood-gas analyzers with potentiometric sensors. Tremendous advances over the past decades in the printing technology have opened a new paradigm for low-cost, large-scale and robust fabrication of a new generation of future potentiometric ion sensors for wearable devices and electronic skin. Unfortunately, up to now, the fully printed potentiometric ion sensor has not yet been truly realized. Hence, it is highly desired to address the sensor fabrication challenges, and further extend application scenarios of potentiometric ion sensors. Herein, up-to-date potentiometric sensors fabricated via the printing technology including 2D and 3D printing are highlighted. The application scenarios of these printed sensor are also discussed. Furthermore, the existing challenges and future prospects are provided. We hope that this review will shed new lights on the understanding of printed potentiometric sensors and pave the way for the widespread applications of these chemical sensors.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"192 ","pages":"Article 118420"},"PeriodicalIF":12.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineering of DNA nanostructure-based artificial cells for biosensing applications","authors":"Yuping Cheng,&nbsp;Zhaoxin Li,&nbsp;Chunjuan Zhang,&nbsp;Xinyan He,&nbsp;Jing Zheng,&nbsp;Jianbo Liu","doi":"10.1016/j.trac.2025.118419","DOIUrl":"10.1016/j.trac.2025.118419","url":null,"abstract":"<div><div>With its unique binding specificity and thermodynamic characteristics, DNA nanostructures have been proposed as building blocks and functional elements for artificial cells, thereby empowering them with advanced functions in biosensing. This review focuses on the development of artificial cells based on DNA nanostructures and their applications in biosensing. The precise DNA self-assembly capability enables the DNA nanostructure-based artificial cells with accurate recognition of complex biomolecules, while the dynamic response design can realize targets monitoring and outputs signals in real time. The good biocompatibility and bionic membrane structure make the DNA nanostructure-based artificial cells suitable for living environments, providing a sensitive and intelligent new sensing platform. We expect that the related technological advances will significantly expand the scope of the application of DNA artificial cells in the biosensing field, enabling them to meet the needs of life health and social challenges with greater precision and efficiency.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"192 ","pages":"Article 118419"},"PeriodicalIF":12.0,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High resolution single-cell transcriptomics towards precision profiling across multi-omics and spatial dimensions","authors":"Liyong He ,&nbsp;Wenjia Wang ,&nbsp;Kaitong Dang,&nbsp;Wenyi Zhang,&nbsp;Kaiqiang Ye,&nbsp;Handong Wang,&nbsp;Yan Huang,&nbsp;Xiangwei Zhao","doi":"10.1016/j.trac.2025.118418","DOIUrl":"10.1016/j.trac.2025.118418","url":null,"abstract":"<div><div>Cellular heterogeneity poses major researchers challenges. Tag-based single-cell RNA sequencing (scRNA-seq) methods enables cell-type classification and population diversity in biological systems but lacks resolution for splicing regulation, allele expression, isoform diversity, transcriptional mutation, and the identification of comprehensive RNA types. High-resolution full-length scRNA-seq offers significant advantages in achieving molecular resolution for transcript-level quantification, structural variations detection, and the identification of comprehensive RNA types. It has been widely applied in refines cell fate determination models. Additionally, integrating full-length scRNA-seq technology with multi-omics and spatially resolved sequencing will decipher cellular interactions and their cross-modality molecular mechanisms more precisely. This review focuses on the latest iterative technology of high resolution scRNA-seq spanning poly(A+) mRNA to total RNA full-length capture systems, precision profiling methods across multi-omics and spatial dimensions, and their applications in tumor biology, reproductive medicine, immunology, and developmental biology. Finally, current challenges and prospectives are discussed.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"192 ","pages":"Article 118418"},"PeriodicalIF":12.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current developments in niobium carbide (Nb2C) MXenes: Synthesis, properties, electrochemical and optical biosensing application","authors":"Sachin Ganpat Chavan,&nbsp;Pooja Ramrao Rathod,&nbsp;Aneesh Koyappayil,&nbsp;Gopi Karuppaiah,&nbsp;Anna Go,&nbsp;Min-Ho Lee","doi":"10.1016/j.trac.2025.118415","DOIUrl":"10.1016/j.trac.2025.118415","url":null,"abstract":"<div><div>Since its discovery in 2011, MXene has been the most studied candidate in the 2D material family. Experimentally, over 30 different kinds of MXene have been discovered so far. Their distinct physicochemical properties have made them useful in a large domain, such as photocatalysis, electrochemical and optical biosensing, energy generation, electrochemical energy storage, and catalysis. Among all MXenes, Niobium carbide (Nb₂C) is the most recent MXene member to find suitable applications due to its unique properties, which include being highly compatible, having a wide range of functional groups that can be easily combined, having excellent electrical properties with small or nonexistent band gaps, and having a very efficient in vivo biosensing window. Scientists and researchers are particularly interested in the Nb₂C-MXene because it is one of several non-titanium-based MXenes that are now available. Herein, we summarise the modern progress in the structural properties of Nb₂C-MXene, the accessible synthesis methodologies, and the latest biological sensing applications. However, insufficient research provides a comprehensive understanding of the unique properties and significant biological uses of this innovative 2D material. In light of this, we have compiled a summary of current research on the synthesis and potential future uses of Nb₂C-MXene materials, especially on recent electrochemical, optical, photothermal, gas and humidity biosensors. Last but not least, we summarise the findings and discuss potential future prospects for progressing the efficiency of Nb₂C-MXenes in a range of real-world contexts, highlighting the obstacles along the way.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"192 ","pages":"Article 118415"},"PeriodicalIF":12.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical strategies in early breast cancer diagnostic biomarker discovery by machine learning methods: Promises, advances and outlooks","authors":"Seyed Morteza Naghib ,&nbsp;Mohammad Ali Khorasani ,&nbsp;Fariborz Sharifianjazi ,&nbsp;Ketevan Tavamaishvili","doi":"10.1016/j.trac.2025.118412","DOIUrl":"10.1016/j.trac.2025.118412","url":null,"abstract":"<div><div>Breast Cancer (BC) remains one of the leading causes of cancer-related mortality worldwide, with early detection playing a pivotal role in improving patient survival and treatment outcomes. Biomarkers serve as critical molecular indicators that facilitate the early diagnosis of breast cancer, allowing for timely intervention before the disease progresses to more advanced stages. Traditional methods for biomarker discovery, including immunohistochemistry, polymerase chain reaction (PCR), and enzyme-linked immunosorbent assays (ELISA), have been instrumental in identifying breast cancer markers. However, these approaches often require extensive validation, are time-consuming, and may lack the ability to effectively analyze high-dimensional datasets. The rapid advancements in machine learning (ML) have transformed biomarker discovery by enabling the analysis of complex multi-omics data, integrating genomic, proteomic, and imaging datasets to identify novel biomarkers with enhanced accuracy. This study focuses on the application of ML in detecting key biomarkers COL11A1, TOP2A, MMP1, and EZH2 which are associated with tumor invasiveness, proliferation, and metastatic potential in early-stage breast cancer. These biomarkers were identified through ML-based predictive models such as Random Forest (RF), Support Vector Machines (SVMs), XGBoost, and Deep Neural Networks, which have demonstrated superior performance in distinguishing malignant from benign cases. Our findings highlight the potential of ML-driven biomarker discovery in revolutionizing breast cancer diagnostics by improving risk stratification, enhancing predictive accuracy, and facilitating personalized treatment approaches. By leveraging AI-powered methodologies, clinicians can move toward a data-driven, precision medicine approach, ultimately reducing the burden of late-stage breast cancer diagnoses and mortality rates. However, integrating ML models into routine clinical practice requires addressing key challenges, such as data standardization, model interpretability, and validation through large-scale prospective studies. Future advancements in deep learning (DL), federated learning, and explainable AI (XAI) are expected to further refine these models, ensuring their reliability and applicability in clinical settings.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"192 ","pages":"Article 118412"},"PeriodicalIF":12.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disposable analytical Chemistry: Design, application & sustainability","authors":"Chaudhery Mustansar Hussain ,&nbsp;Ghazanfar Hussain ,&nbsp;Rüstem Keçili","doi":"10.1016/j.trac.2025.118414","DOIUrl":"10.1016/j.trac.2025.118414","url":null,"abstract":"<div><div>Disposable analytical chemistry is a rapidly developing field that focuses on the development and application of one-time-use analytical tools, offering significant advantages in efficiency, affordability and ease of use. These platforms are gaining popularity due to their ability to provide quick, accurate results without the need for extensive preparation making them ideal for applications in medical diagnostics, environmental monitoring and food safety. Rapid scientific and technological progresses in microfluidics, sensors and material science enhanced the capabilities of disposable analytical devices enabling them to detect trace chemical concentrations with high sensitivity. Environmental concerns related to disposable platforms are addressed through the development of eco-friendly materials such as biodegradable polymers and sustainable manufacturing techniques. The shift from traditional methods to disposable devices has revolutionized various industries enhancing sustainability. This review explores the design principles and future potential of disposable analytical chemistry highlighting its growing role in improving healthcare, food and environmental applications.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"192 ","pages":"Article 118414"},"PeriodicalIF":12.0,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent progress in 3D and 4D printing in analytical chemistry with a focus on sample preparation","authors":"Helena Hrušková,&nbsp;Roman Řemínek,&nbsp;František Foret","doi":"10.1016/j.trac.2025.118413","DOIUrl":"10.1016/j.trac.2025.118413","url":null,"abstract":"<div><div>Additive manufacturing, also known as 3D printing, is an emerging fabrication technique that allows the creation of 3D structures of various shapes. Users can benefit from the variety of appropriate materials, from polymeric plastics to concrete or metal. That further predetermines this technology for application in the whole spectrum of scientific fields, including analytical chemistry. An integral part of the analytical process is sample preparation, which mainly enhances the overall analytical system's selectivity and/or sensitivity. Unfortunately, the preparative step is still considered a bottleneck of the analytical procedure. That further shows the necessity of developing new preparative approaches that alleviate the extreme consumption of the experimental time, facilitate the operational demands, and decrease the number of operational steps. 3D printing can contribute significantly to developing simplified sample preparation systems, lab-on-a-chips, and new materials. This review summarized the progress between 2020 and 2024 in 3D printing applied in analytical sample preparation.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"192 ","pages":"Article 118413"},"PeriodicalIF":12.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate calibration of non-destructive spectral sensors with a particular focus on food applications: Validation issues and guidelines","authors":"Tom Fearn ,&nbsp;Claudia Beleites ,&nbsp;Juan Antonio Fernández Pierna ,&nbsp;Vincent Baeten ,&nbsp;Martin Lagerholm ,&nbsp;Jean-Michel Roger ,&nbsp;Anastasios Koidis","doi":"10.1016/j.trac.2025.118410","DOIUrl":"10.1016/j.trac.2025.118410","url":null,"abstract":"<div><div>Multivariate calibration methods have enabled the use of non-destructive spectral sensors in a wide range of applications but carry a risk of overfitting to the available training samples. For this reason, the prediction of unseen samples plays a vital role both in tuning the prediction algorithm and in assessing its performance, two activities that need to be carefully distinguished. Methods employed include data-splitting, cross-validation, and the use of genuinely independent sets of data. These approaches are described and some common issues with them are identified. The focus is on food applications but the methods discussed are widely used in other areas.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"192 ","pages":"Article 118410"},"PeriodicalIF":12.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tailored NIR-II luminescence probes for in situ capture of spatiotemporal physiological information in living organisms","authors":"Shengchun Sun ,&nbsp;Hao Yuan ,&nbsp;Hong Hu ,&nbsp;Jianxing Feng ,&nbsp;Ning Shi ,&nbsp;Yixian Wang","doi":"10.1016/j.trac.2025.118409","DOIUrl":"10.1016/j.trac.2025.118409","url":null,"abstract":"<div><div>Fluctuations in physiological information within organisms serve as crucial indicators of various diseases and stresses. Significant advancements have been made in the development of luminescence probes that operate in the Near-Infrared II (NIR-II) spectral range, enabling in situ sensing of physiological information in living organisms. These innovations have facilitated the study of physiological changes with increased penetration depth for tissue, improved signal-to-background ratios, and enhanced optical imaging resolution. To address the need for in situ sensing within living organisms, key concepts in probe design–such as luminescence intensity, response sites, and localization–have been summarized. Following a review of NIR-II luminescence probe design, we provide a comprehensive overview of their applications in sensing diverse physiological parameters in living organisms, considering the differences in the metabolic mechanisms between humans and plants. Our objective is to highlight the latest progress in NIR-II luminescence probes, offer valuable insights into their molecular design and synthesis, and accelerate the development of novel NIR-II luminescence probes suitable for biomedical and agricultural applications.</div></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"192 ","pages":"Article 118409"},"PeriodicalIF":12.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}