Probing Dynamic Features of Phagosome Maturation in Macrophage using Au@MnOx @SiO2 Nanoparticles as pH-Sensitive Plasmonic Nanoprobes

Gallic acid promotes macrophage phagosome acidification and phagolysosome formation by activating NLRP3/mTOR signaling pathway

INTRODUCTION

Gallic acid (GA) has a good therapeutic effect in bacteriological inhibition and plays a variety of functions in maintaining the stability of the immune system. The aim of the present study was to investigate the effect of GA on the bactericidal activity of macrophages against Vibrio vulnificus (Vv).

METHODS

A cell counting kit-8 (CCK-8) assay was carried out to test the cytotoxicity of GA on J774A.1 cells. Concentration of proinflammatory cytokines in J774A.1 cells were evaluated by ELISA. The internalization and degradation of Vv in the phagosomes were observed by transmission electron microscopy (TEM). The phagosome acidification and phagolysosome formation were detected to evaluate the bacteria-clearing function of J774A.1 cells. The bactericidal activity of GA in vivo was also investigated by collecting the survival time of Vv infected mice and observing the inflammatory infiltration of organs.

RESULTS

Our results demonstrated that GA at 50 μM significantly inhibited the proinflammatory cytokines levels, promoted phagosome acidification and phagolysosome formation in J774A.1 cells with Vv infection. This may be related to the activation of NLRP3/mTOR signaling pathway. Additionally, GA treatment improves the survival and bactericidal activity of mice infected with Vv.

CONCLUSIONS

In summary, GA exerts bactericidal activity against Vv infection by regulating the formation and acidification of phagocytic lysosomes in macrophages.

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.

Optical Microscopy Systems for the Detection of Unlabeled Nanoparticles

Label-free detection of nanoparticles is essential for a thorough evaluation of their cellular effects. In particular, nanoparticles intended for medical applications must be carefully analyzed in terms of their interactions with cells, tissues, and organs. Since the labeling causes a strong change in the physicochemical properties and thus also alters the interactions of the particles with the surrounding tissue, the use of fluorescently labeled particles is inadequate to characterize the effects of unlabeled particles. Further, labeling may affect cellular uptake and biocompatibility of nanoparticles. Thus, label-free techniques have been recently developed and implemented to ensure a reliable characterization of nanoparticles.

This review provides an overview of frequently used label-free visualization techniques and highlights recent studies on the development and usage of microscopy systems based on reflectance, darkfield, differential interference contrast, optical coherence, photothermal, holographic, photoacoustic, total internal reflection, surface plasmon resonance, Rayleigh light scattering, hyperspectral and reflectance structured illumination imaging. Using these imaging modalities, there is a strong enhancement in the reliability of experiments concerning cellular uptake and biocompatibility of nanoparticles, which is crucial for preclinical evaluations and future medical applications.