Dynamic and Label-Free Sensing of Cardiomyocyte Responses to Nanosized Vesicles for Cardiac Oxidative Stress Injury Therapy

A dual-mode, image-enhanced, miniaturized microscopy system for incubator-compatible monitoring of live cells

Imaging live cells under stable culture conditions is essential to investigate cell physiological activities and proliferation. To achieve this goal, typically, a specialized incubation chamber that creates desired culture conditions needs to be incorporated into a microscopy system to perform cell monitoring. However, such imaging systems are generally large and costly, hampering their wide applications. Recent advances in the field of miniaturized microscopy systems have enabled incubator cell monitoring, providing a hospitable environment for live cells. Although these systems are more cost-effective, they are usually limited in imaging modalities and spatial temporal resolution. Here, we present a dual-mode, image-enhanced, miniaturized microscopy system (termed MiniCube) for direct monitoring of live cells inside incubators. MiniCube enables both bright field imaging and fluorescence imaging with single-cell spatial resolution and sub-second temporal resolution. Moreover, this system can also perform cell monitoring inside the incubator with tunable time scales ranging from a few seconds to days. Meanwhile, automatic cell segmentation and image enhancement are realized by the proposed data analysis pipeline of this system, and the signal-to-noise ratio (SNR) of acquired data is significantly improved using a deep learning based image denoising algorithm. Image data can be acquired with 5 times lower light exposure while maintaining comparable SNR. The versatility of this miniaturized microscopy system lends itself to various applications in biology studies, providing a practical platform and method for studying live cell dynamics within the incubator.

Exosomes based strategies for cardiovascular diseases: Opportunities and challenges

Exosomes, as nanoscale extracellular vesicles (EVs), are secreted by all types of cells to facilitate intercellular communication in living organisms. After being taken up by neighboring or distant cells, exosomes can alter the expression levels of target genes in recipient cells and thereby affect their pathophysiological outcomes depending on payloads encapsulated therein. The functions and mechanisms of exosomes in cardiovascular diseases have attracted much attention in recent years and are thought to have cardioprotective and regenerative potential. This review summarizes the biogenesis and molecular contents of exosomes and details the roles played by exosomes released from various cells in the progression and recovery of cardiovascular disease. The review also discusses the current status of traditional exosomes in cardiovascular tissue engineering and regenerative medicine, pointing out several limitations in their application. It emphasizes that some of the existing emerging industrial or bioengineering technologies are promising to compensate for these shortcomings, and the combined application of exosomes and biomaterials provides an opportunity for mutual enhancement of their performance. The integration of exosome-based cell-free diagnostic and therapeutic options will contribute to the further development of cardiovascular regenerative medicine.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.