Chemical & Biomedical Imaging最新文献

Transient stimulated Raman scattering (T-SRS), as an emerging time-domain coherent Raman scattering (TD-CRS) technique, possesses unique natural line-width-limit spectral resolution and sub-mM sensitivity, and offers a powerful spectral platform for chemical identification and imaging of biomarkers in biological tissues. However, readers may face difficulties in understanding clear physical pictures of manipulating quantum states of biomolecules by deriving wave packet interference. Here, we reinterpreted T-SRS as Ramsey interferometry driven by two femtosecond half-π operations of the superposition of biomolecules at room-temperature, an analogue to second-scale Ramsey interference of cold atoms at a temperature of ∼1 μK. This perspective contrasts the features of coherent quantum control of Ramsey interference performed in cold atomic and macroscopic biological systems. Both the theoretical reasoning and numeric simulations of quantum evolution are discussed step by step. The interdisciplinary knowledge will foster the advancement of coherent Raman spectroscopy and precision measurements in chemistry and broad biomedical applications.

Molecular biomarkers play an essential role in accurate disease diagnosis and personalized treatment. Dysregulated metabolism is closely associated with disease development and progression. The discovery of metabolic biomarkers could remarkably promote precision diagnosis and personalized treatment. Current metabolomics approaches can profile a large number of metabolites but are primarily destructive and lack sufficient spatial resolution, which hinders quantitative measurements of the highly dynamic and heterogeneous intracellular metabolic processes. This further limits the discovery of metabolic biomarkers in these diseases. Stimulated Raman scattering (SRS) microscopy addresses these gaps by enabling label-free imaging with high sensitivity, molecular specificity, and subcellular resolution. Integrating Raman-active vibrational probes further extends this approach, allowing for real-time tracking of low-abundance biomolecules and metabolic processes. These capabilities have enabled the discovery of biomarkers for disease diagnosis. In this review, we focus on recent advancements in SRS imaging technologies and data analysis methods and their applications in biomarker discovery and precision medicine. Furthermore, future perspectives and emerging trends in this rapidly evolving research area are discussed.

Transrectal ultrasound (TRUS) is widely used for guiding prostate biopsy due to its real-time imaging capabilities. However, ultrasound (US) lacks sensitivity for detecting prostate cancer, necessitating the integration of preoperative magnetic resonance imaging (MRI) to offer superior soft tissue contrast. To enable MRI-ultrasound fusion during interventions, an accurate 3D reconstruction of freehand TRUS is essential. Existing reconstruction methods typically rely on sequentially estimating interframe transformations, resulting in no explainability and accumulated errors and drift over time. In this paper, we present a framework that leverages preoperative MRI and supervised contrastive learning to reconstruct 3D ultrasound volumes directly from 2D frames. By aligning ultrasound images with corresponding MRI slices based on anatomical similarity, our method bypasses sequential estimation, avoids drift, and improves tracking accuracy. The approach was trained and validated on a large clinical data set of over 500 prostate biopsy cases and demonstrated over 50% improvement in drifting errors. By enhancing both precision and interpretability, our algorithm supports more reliable MRI-ultrasound fusion and holds the potential for improving the diagnostic accuracy of prostate cancer interventions.

Lysosomes are organelles responsible for cellular degradation and recycling. The detection of changes in the lysosomal microenvironment, such as viscosity, oxidative stress, and pH value, as well as their interactions among dynamic organelles, remains an intriguing field that contributes to elucidating intracellular homeostasis. Here, we describe the development of a fluorescent probe tool for uniting fluorescence lifetime imaging microscopy (FLIM) and dual-channel near-infrared (NIR) fluorescence signals, which can simultaneously monitor viscosity and reactive oxygen species (ROS) in lysosomes. SiR-Eda exhibits a viscosity-dependent fluorescence lifetime and ROS-sensitive fluorescence emission, allowing for real-time tracking of lysosomal oxidative stress and viscosity within living cells. We demonstrate the utility of SiR-Eda in detecting changes in lysosomal viscosity and ROS in response to various stimuli including oxidative stress and lysosomal dysfunction. Our probe provides a convenient wash-free multifunctional tool for investigating lysosomal biology and has potential applications in the diagnosis and treatment of lysosome-related diseases.

Understanding diffusion in charged and crowded media is crucial for solving a wide range of biological and materials challenges. Classifying diffusion by traditional methods such as mean square displacement in three-dimensional single-particle tracking (3D SPT) is difficult, especially when there are mixed motion types. To address this, we employed machine learning (ML), specifically decision tree algorithms with feature selection, to identify the six most relevant features for accurate characterization of trajectories. This work demonstrates the value of ML in advancing our understanding of heterogeneous transport that occurs in charged and crowded environments, with a broad range of applications.

Contrast-enhanced inner ear magnetic resonance imaging (MRI) provides clinicians with powerful structural and pathological information for the diagnosis of inner ear diseases. However, currently used gadolinium (Gd) chelate-mediated contrast-enhanced MRI conveys insufficient inner ear specificity, and Gd-based contrast agents have a short body retention time and cause severe ototoxicity. Herein, we present the rational design of a sensitive inner ear-specific nanoprobe (I-PUSPIO) for inner ear MRI that is composed of an ultrasmall iron oxide core, the IETP2 peptide, and polyethylene glycol. Such a well-defined nanostructure endows it with blood-labyrinth barrier crossing capacity, leading to a high accumulation rate in the inner ear and prolonged body retention. In vivo I-PUSPIO can enhance high-resolution MRI of cochlear tissue and shows no evidence of toxicity. This study demonstrates the potential of I-PUSPIO as a sensitive contrast agent for inner ear MRI in clinical settings.

Lysosomes, the acidic degradative hubs of cells, are increasingly recognized as critical regulators of calcium signaling, with dysregulation linked to neurodegenerative diseases and lysosomal storage disorders. However, quantifying lysosomal Ca²⁺ remains a formidable challenge due to the limitations of conventional fluorescent indicators, which suffer from pH interference in acidic environments. In this perspective, we critically evaluate emerging strategies for pH-independent Ca²⁺ sensing and advocate for ionophore-based nanosensors as a transformative solution. These nanosensors, featuring high Ca²⁺ selectivity, tunable dynamic range, and intrinsic pH insensitivity, leverage ion-exchange mechanisms coupled with solvatochromism or other transducers. While recent advances have demonstrated their utility for cation sensing in bulk systems, their application to quantitative lysosomal Ca²⁺ mapping remains underexplored. We highlight the unique advantages of these platforms, including endocytic uptake and compatibility with live-cell imaging, while identifying key challenges such as dye leakage, matrix stability under lysosomal conditions, and imaging-specific issues. By bridging gaps between nanosensor design and biological application, this discussion aims to catalyze the development of robust tools for elucidating lysosomal Ca²⁺ roles in health and disease.

Accurate diagnosis is of great importance in all medical fields, and dermatology is no exception. Among the various routes available to clinicians, medical tests based on imaging properties have become a cornerstone for the assessment of skin diseases. Advancements in dermatological imaging techniques, particularly those combining optical and molecular modalities, are revolutionizing our understanding of skin pathologies. These techniques offer a more comprehensive view of skin conditions, as they provide complementary insights at both macroscopic and molecular levels. This review explores the historical and current state of diverse imaging methodologies, both traditional and advanced, employed in dermatology for diagnostic, therapeutic, and research purposes. Key technological advancements in the field are discussed, including their integration with molecular profiling techniques, such as spatial omics. These hybrid approaches allow for the identification of biomarkers, molecular signatures, and deeper disease mechanisms, contributing to the development of precision medicine for skin conditions. The translational potential of these technologies is also highlighted, emphasizing their implementation into clinical workflows and the challenges of standardization and cost-effectiveness. Moreover, we address the current limitations, including access to specialized equipment, expertise, and data interpretation, and propose strategies to overcome these challenges. The paper finally discusses the potential future role of artificial Intelligence in streamlining image analysis and clinical decision-making support. By examining both the present and future perspectives of dermatological imaging, this review aims to provide a holistic overview of the full potential of the available advanced techniques and their applications.