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.

Macrocyclic peptides have garnered increasing attention due to their therapeutic potential. However, the low cell membrane permeability of macrocyclic peptides limits their access to intracellular targets. This study aimed to apply live-cell Raman imaging to detect intracellular macrocyclic peptides as a potential therapeutic modality. Alkyne-tags with specific Raman bands were attached to several types of macrocyclic peptides originally designed as HIV protease inhibitors. Membrane permeability was measured by using the traditional penetration test. Variations in permeability were observed among the compounds, with the alkyne group showing a minimal effect on the results. Macrocyclic peptides with an alkyne-tag, which exhibited no permeability in the penetration test, were added to the live cells and evaluated using Raman imaging. Two-dimensional (2D) and three-dimensional (3D) Raman imaging detected the macrocyclic peptides in the intracellular region. However, the alkyne signal was not observed in cells treated with the macrocyclic peptides, showing cell penetration in the penetration test. These results are consistent with the observed pharmacological activities and suggest that the traditional penetration test may be insufficient for effective drug design of macrocyclic peptides. We conclude that combining emerging techniques, such as Raman imaging, with traditional methods can provide a more detailed evaluation of the permeability of macrocyclic peptides.

Lysosomes are important organelles involved in intracellular degradation and nutrition-dependent signal transduction. While existing studies have primarily focused on lysosomal pH changes during processes such as lysosomal membrane damage leading to cell death, investigations into lysosomal pH alterations during cell invasion have been largely overlooked. Here, we present a method to analyze lysosomal pH changes during cancer cell invasion using a microfluidic chip-based surface-enhanced Raman scattering (SERS) technique for real-time imaging. Our custom-designed microfluidic chip simulated the cell invasion process, enabling simultaneous, real-time, and in situ SERS monitoring of cells exhibiting varying degrees of invasion and migration. The synthesized pH nanoprobe is based on 4-MPy-modified gold-silver core-shell nanoparticles, boasting good biocompatibility, a high SERS enhancement effect, and subcellular-level targeted monitoring capabilities. Through our SERS microfluidic chip, we observed a slight decrease in lysosomal pH and a slight increase in extracellular pH, with lysosomes predominantly located in the cell periphery during cell invasion. This study contributes to a deeper understanding of the relationship between lysosomes and cancer metastasis.

Purpose. Chemical Exchange Saturation Transfer (CEST) MRI relies on multiple saturation offsets to correct B₀ inhomogeneity-induced quantification errors at the cost of a prolonged scan time. We previously developed a deep learning-based method for B₀ inhomogeneity correction in Glutamate-weighted CEST (GluCEST) using a parsimonious number of Z-spectrum offset images, which can significantly reduce scan time and provide better correction quality. In this study, we propose a Transformer-based model that achieves B₀ correction using only downfield Z-spectrum offset images, further reducing scan time by about 50%. Methods. B₀ correction was performed separately for the positive and negative sides of the Z-spectrum using reduced saturation offset acquisitions. We constructed distinct Swin transformer networks for each side, training them to learn the nonlinear mapping from a limited number of GluCEST images at various frequencies on the positive side to the specific 3 ppm points where GluCEST peaks. A similar methodology was applied to NOE CEST imaging, optimizing each network to effectively handle the unique characteristics of each spectrum side. Results. The Transformer-based models significantly outperformed the previous deep learning methods both visually and quantitatively. By limiting inputs to only the positive Z-spectrum offsets, we achieved a 50% reduction in data acquisition time compared with previous deep learning approaches while maintaining B₀ inhomogeneity correction accuracy. Conclusion. Efficient B₀ inhomogeneity correction in GluCEST and NOE MRI can be achieved by using a select number of offset images from the downfield Z-spectrum, reducing the acquisition time by over 80%. The proposed transformer-based model demonstrates superior performance over traditional convolutional neural networks, offering a robust and efficient solution for performing CEST MRI in clinical practice.

Endogenous RNAs orchestrate cellular processes through precise spatiotemporal regulation, and live-cell imaging of these molecules is essential for dissecting their roles in gene expression, disease progression, and cellular homeostasis. While traditional methods rely on exogenous labeling or fixation, fluorescent RNA aptamer-based sensors have emerged as transformative tools for visualizing endogenous RNAs in their native context. These sensors allow real-time tracking of mRNA localization, miRNA activity, and RNA-protein interactions with minimal disturbance. However, challenges such as the requirement for exogenous fluorogenic dyes, limited brightness, photostability, and target specificity in complex cellular environments hinder their wider application. This review provides a comprehensive overview of recent advances in fluorescent RNA aptamer-based sensors, discussing the design principles, mechanisms of fluorescence activation, and their application in live-cell RNA imaging. We also address the current limitations and future directions for improving these sensors, highlighting their transformative potential in RNA biology and their implications for diagnostic and therapeutic strategies.