K-means clustering of coherent Raman spectra from extracellular vesicles visualized by label-free multiphoton imaging

A Review of Labeling Approaches Used in Small Extracellular Vesicles Tracing and Imaging

Small extracellular vesicles (sEVs), a subset of extracellular vesicles (EVs) originating from the endosomal compartment, are a kind of lipid bilayer vesicles released by almost all types of cells, serving as natural carriers of nucleic acids, proteins, and lipids for intercellular communication and transfer of bioactive molecules. The current findings suggest their vital role in physiological and pathological processes. Various sEVs labeling techniques have been developed for the more advanced study of the function, mode of action, bio-distribution, and related information of sEVs. In this review, we summarize the existing and emerging sEVs labeling techniques, including fluorescent labeling, radioisotope labeling, nanoparticle labeling, chemical contrast agents labeling, and label-free technique. These approaches will pave the way for an in-depth study of sEVs. We present a systematic and comprehensive review of the principles, advantages, disadvantages, and applications of these techniques, to help promote applications of these labeling approaches in future research on sEVs.

Weakly Supervised Identification and Localization of Drug Fingerprints Based on Label-Free Hyperspectral CARS Microscopy

Understanding drug fingerprints in complex biological samples is essential for the development of a drug. Hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) microscopy, a label-free nondestructive chemical imaging technique, can profile biological samples based on their endogenous vibrational contrast. Here, we propose a deep learning-assisted HS-CARS imaging approach for the investigation of drug fingerprints and their localization at single-cell resolution. To identify and localize drug fingerprints in complex biological systems, an attention-based deep neural network, hyperspectral attention net (HAN), was developed. By formulating the task to a multiple instance learning problem, HAN highlights informative regions through the attention mechanism when being trained on whole-image labels. Using the proposed technique, we investigated the drug fingerprints of a hepatitis B virus therapy in murine liver tissues. With the increase in drug dosage, higher classification accuracy was observed, with an average area under the curve (AUC) of 0.942 for the high-dose group. Besides, highly informative tissue structures predicted by HAN demonstrated a high degree of similarity with the drug localization shown by the in situ hybridization staining results. These results demonstrate the potential of the proposed deep learning-assisted optical imaging technique for the label-free profiling, identification, and localization of drug fingerprints in biological samples, which can be extended to nonperturbative investigations of complex biological systems under various biological conditions.

Label-free optical redox ratio from urinary extracellular vesicles as a screening biomarker for bladder cancer

Extracellular vesicles (EVs) have been studied for their potential applications in cancer screening, diagnosis, and treatment monitoring. Most studies have focused on the bulk content of EVs; however, it is also informative to investigate their metabolic status, and changes under different physiological and environmental conditions. In this study, noninvasive, multimodal, label-free nonlinear optical microscopy was used to evaluate the optical redox ratio of large EVs (microvesicles) isolated from the urine of 11 dogs in three cohorts (4 healthy, 4 transitional cell carcinoma (TCC) of the bladder, and 3 prostate cancer). The optical redox ratio is a common metric comparing the autofluorescence intensities of metabolic cofactors FAD and NAD(P)H to characterize the metabolic profile of cells and tissues, and has recently been applied to EVs. The optical redox ratio revealed that dogs with TCC of the bladder had a more than 2-fold increase in NAD(P)H-rich urinary EVs (uEVs) when compared to healthy dogs, whereas dogs with prostate cancer had no significant difference. The optical redox ratio values of uEVs kept at -20°C for 48 hours were significantly different from those of freshly isolated uEVs, indicating that this parameter is more reliable when assessing freshly isolated uEVs. These results suggest that the label-free optical redox ratio of uEVs, indicating relative rates of glycolysis and oxidative phosphorylation of parent cells and tissues, may act as a potential screening biomarker for bladder cancer.

Label-free characterization of single extracellular vesicles using two-photon fluorescence lifetime imaging microscopy of NAD(P)H

The heterogeneous nature of extracellular vesicles (EVs) creates the need for single EV characterization techniques. However, many common biochemical and functional EV analysis techniques lack single EV resolution. Two-photon fluorescence lifetime imaging microscopy (FLIM) is widely used to functionally characterize the reduced form of nicotinamide adenine dinucleotide and nicotinamide adenine dinucleotide phosphate (NAD(P)H) in cells and tissues. Here, we demonstrate that FLIM can also be used to image and characterize NAD(P)H in single isolated EVs. EVs were isolated using standard differential ultracentrifugation techniques from multiple cell lines and imaged using a custom two-photon FLIM system. The presented data show that the NAD(P)H fluorescence lifetimes in isolated cell-derived EVs follow a wide Gaussian distribution, indicating the presence of a range of different protein-bound and free NAD(P)H species. EV NAD(P)H fluorescence lifetime distribution has a larger standard deviation than that of cells and a significantly different fluorescence lifetime distribution than the nuclei, mitochondria, and cytosol of cells. Additionally, changes in the metabolic conditions of cells were reflected in changes in the mean fluorescence lifetime of NAD(P)H in the produced EVs. These data suggest that FLIM of NAD(P)H could be a valuable tool for EV research.