A Reversible Plasmonic Nanoprobe for Dynamic Imaging of Intracellular pH during Endocytosis

Aggregation-induced color-coded imaging for HOCl detection in living cells with dark-field microscopy

In this work, we develop a nanoprobe for the detection of hypochlorous acid (HOCl), a key reactive oxygen species, utilizing dark-field microscopy (DFM). The nanoprobe is constructed by covalently attaching PEG-SH and an HOCl-sensitive molecule, FD, to gold nanoparticles (GNPs-FD-PEG). This probe detects HOCl by observing a color shift from green to red due to the aggregation of GNPs, which is triggered by HOCl-induced the cleavage of FD molecule. This aggregation occurs primarily due to electrostatic interactions between the differently charged particles following the cleavage. By recording the scattering signal changes of the GNPs-FD-PEG nanoprobe, we have achieved quantitative detection of HOCl, with a linear response range of 0.5-50 μM and a detection limit of 0.16 μM. By integrating the high-resolution capabilities of DFM imaging with the distinct color change due to electrostatic-induced nanoparticle aggregation, the constructed nanoprobe effectively monitors HOCl in live cells.

An Aggregation-Induced Emission-Based Dual Emitting Nanoprobe for Detecting Intracellular pH and Unravelling Metabolic Variations in Differentiating Lymphocytes

Monitoring T lymphocyte differentiation is essential for understanding T cell fate regulation and advancing adoptive T cell immunotherapy. However, current biomarker analysis methods necessitate cell lysis, leading to source depletion. Intracellular pH (pHi) can be affected by the presence of lactic acid (LA), a metabolic mediator of T cell activity such as glycolysis during T cell activation; therefore, it is a potentially a good biomarker of T cell state. In this work, a dual emitting enhancement-based nanoprobe, namely, AIEgen@F127-AptCD8, was developed to accurately detect the pHi of T cells to "read" the T cell differentiation process. The nanocore of this probe comprises a pair of AIE dyes, TPE-AMC (pH-sensitive moiety) and TPE-TCF, that form a donor-acceptor pair for sensitive detection of pHi by dual emitting enhancement analysis. The nanoprobe exhibits a distinctly sensitive narrow range of pHi values (from 6.0 to 7.4) that can precisely distinguish the differentiated lymphocytes from naïve ones based on their distinct pHi profiles. Activated CD8+ T cells demonstrate lower pHi (6.49 ± 0.09) than the naïve cells (7.26 ± 0.11); Jurkat cells exhibit lower pHi (6.43 ± 0.06) compared to that of nonactivated ones (7.29 ± 0.09) on 7 days post-activation. The glycolytic product profiles in T cells strongly correlate with their pHi profiles, ascertaining the reliability of probing pHi for predicting T cell states. The specificity and dynamic detection capabilities of this nanoprobe make it a promising tool for indirectly and noninvasively monitoring T cell activation and differentiation states.

High Confidence Single Particle Analysis with Machine Learning

Single particle analysis can effectively determine the heterogeneity between particles based on the local information on a single particle, which is utilized extensively for monitoring chemical reactions and biological activities. However, the study of obtaining ensemble reaction information at the single particle level, which can obtain both the structural and functional heterogeneity of particles as well as the ensemble reaction information, is challenging because the selection of a single particle mainly depends on experience, which will lead to a certain randomness when analyzing the ensemble reaction with single particles. Using machine learning, it is demonstrated that the proposed intelligent single particle analysis strategy can provide single particle and ensemble analyses with high confidence. Convolutional neural network and Gaussian mixture model were utilized to develop a machine learning model for resonance scattering imaging analysis of plasmonic nanoparticles. It can identify the scattered light of single particles and select representative or diverse particles. When single particle scattering imaging is used to obtain ensemble information on the reaction, the error caused by the selection of individual particles can be significantly reduced by selecting representative particles. In addition, the real situation of the reaction can be better revealed by selecting diverse particles. These results indicate that the intelligent single particle analysis strategy has great potential for imaging analysis and biological sensing.