Life Analysis
ISSN: 3105-9015 (Print)
ISSN: 3105-9023 (Online)
Photocatalytic self-cleaning and recyclable AuNBPs@TiO2 SERS chip for trace detection and in situ monitoring of methylene blue degradation
Recyclable surface-enhanced Raman scattering (SERS) substrates integrating high sensitivity with effective photocatalytic self-cleaning capability are highly desirable for sustainable pollutant analysis, yet maintaining stable SERS activity during repeated photocatalytic regeneration remains challenging. Herein, a photocatalytic self-cleaning AuNBPs@TiO2 SERS chip was constructed by integrating plasmonic gold nanobipyramids with a photocatalytic TiO2 shell. By regulating the hydrolytic coating process, a uniform TiO2 shell with an optimized thickness of approximately 10 nm was obtained, providing a balance between SERS enhancement and photocatalytic activity. Using methylene blue (MB) as a model organic pollutant, the chip exhibited a wide linear detection range from 10 nM to 1 mM with a low limit of detection of 3.3 nM. The intra-chip and inter-batch relative standard deviations were below 10%, indicating good uniformity and reproducibility. Furthermore, time-dependent SERS spectra enabled in situ monitoring of MB photodegradation under xenon lamp irradiation, with degradation efficiencies exceeding 95% within 90 min for MB concentrations of 10⁻4−10⁻6 M. Independent UV-vis measurements further confirmed MB degradation, showing an 81.91% decrease after 90 min for 10⁻5 M MB. After 12 cycles, the chip retained approximately 72% of its initial SERS signal while maintaining a degradation efficiency above 90%. The AuNBPs@TiO2 chip offers a reusable SERS platform for sensitive MB detection and in situ monitoring of photocatalytic degradation, demonstrating the feasibility of integrating trace detection, photocatalytic regeneration, and repeated use within a single analytical platform.
DNA framework-enabled pathogen detection
The rapid and accurate detection of pathogenic microorganisms is essential for controlling infectious diseases and protecting public health. Traditional methods, including microbial culture, immunoassays, and polymerase chain reaction (PCR), are limited by long turnaround times, cross-reactivity, and reliance on complex instrumentation. In recent decades, DNA frameworks have emerged as promising platforms for constructing high-performance biosensors, owing to their programmable configurations, nanoscale addressability, and dynamic responsiveness. This review summarizes the development of structural DNA nanotechnology and the functionalization strategies of DNA frameworks. We categorize major detection methods empowered by DNA frameworks into electrochemical, fluorescence, and point-of-care testing (POCT) approaches. Their practical applications in clinical diagnosis and environmental monitoring are summarized. Finally, we discuss emerging detection methods and the key challenges that remain to be addressed.
Targets and strategies for cancer diagnosis and imaging
Cancer diagnostic imaging is undergoing a fundamental transition from conventional anatomical visualization to molecular-targeted and functionally driven profiling of tumor biology. Next-generation imaging no longer merely detects morphological lesions, but decodes the dynamic biological states underlying tumor initiation, progression, immune remodeling, metabolic reprogramming, and therapeutic response. Imaging targets with high specificity, biological accessibility, and functional relevance are primary drivers of this technological transition. Current cancer imaging targets can be categorized into three spatial layers: extracellular biomarkers, membrane-associated targets, and intracellular biomarkers. Progress in nanotechnology, molecular engineering, bioorthogonal chemistry, and artificial intelligence (AI) has greatly facilitated the construction of multimodal, multi-target, and intelligent imaging systems. Nevertheless, clinical translation still faces prominent obstacles, including tumor heterogeneity, temporal biomarker fluctuation, limited tissue penetration, nonspecific background signals from off-target activation, and translational gaps. This review systematically classifies imaging targets based on their spatial distribution and biological function, summarizes the latest advances in cancer diagnosis and imaging, and discusses emerging research directions and future prospects for next-generation precision oncology.
Next-generation sequencing-based spatiotemporal transcriptomics: the next wave of spatial transcriptomics
Tissues serve as the functional units of multicellular organisms with intricate spatial organizational complexity. Next-generation sequencing (NGS)-based spatially resolved transcriptomics (SRT) delineates in situ gene expression heterogeneities, establishes high-fidelity associations between transcripts and spatial pixels by segmenting a tissue section into spatial pixels, and elucidates the pivotal roles of cellular spatial organization in biological processes and complex pathological mechanisms. The synergistic integration of nucleic acid barcoding and high-throughput sequencing technologies has rapidly advanced the development of these spatial modalities regarding throughput, resolution, cost-effectiveness, and sensitivity. In this review, we summarize the state-of-the-art sequencing-based spatial transcriptomics, with a primary emphasis on the methodologies. We first introduce two major categories of typical nucleic acid barcoding platforms (in situ barcoding-based and barcoding array-based) used for spatial localization and transcriptome profiling. Then, the burgeoning trend of spatial transcriptomics towards spatiotemporal transcriptomics, which integrates SRT with the temporal dimension to provide more holistic landscapes of gene expression networks, is discussed. We also highlight spatiotemporal transcriptomics based on metabolic RNA labeling that provides unprecedented resolution to resolve transcriptome-wide dynamics in space and time. The emerging applications of these technologies in providing mechanistic insights into complex pathological mechanisms are also discussed. Finally, the perspectives on current bottlenecks and future direction of spatiotemporal transcriptomics are provided.
Electrochemiluminescence biosensing of biomarkers and bioimaging of cellular functional molecules
Electrochemiluminescence (ECL) has emerged as a powerful analytical tool for the detection of biomarkers and the imaging of cellular functional molecules, owing to its low background, high sensitivity, and excellent spatiotemporal resolution. This review first summarizes representative classes of ECL luminophores, including organic small molecules, inorganic nanomaterials, and structurally programmable frameworks and polymers, along with their characteristic properties and recent applications. Subsequently, recent advances in ECL biosensing for in vitro detection of biomarkers such as proteins, nucleic acids, and small molecules are discussed, with particular attention to the evolution of target analytes and breakthroughs in achieving ultra-low detection limits. Next, this review focuses on the cutting-edge applications of ECL imaging at single-cell level. By integrating spatial confinement, label-free imaging, and in situ co-reactant generation with diverse signal-amplification strategies, ECL technology enables dynamic, minimally invasive, and high-resolution imaging of single-cell secretions, membrane proteins, and intracellular molecules, underscoring its potential for resolving functional heterogeneity at the single-cell level. Finally, the current challenges and future directions in ECL biosensing and imaging are outlined.
