Despite extensive interest, extracellular vesicle (EV) research remains technically challenging. One of the unexplored gaps in EV research has been the inability to characterize the spatially and functionally heterogeneous populations of EVs based on their metabolic profile. In this paper, we utilize the intrinsic optical metabolic and structural contrast of EVs and demonstrate in vivo/in situ characterization of EVs in a variety of unprocessed (pre)clinical samples. With a pixel-level segmentation mask provided by the deep neural network, individual EVs can be analyzed in terms of their optical signature in the context of their spatial distribution. Quantitative analysis of living tumor-bearing animals and fresh excised human breast tissue revealed abundance of NAD(P)H-rich EVs within the tumor, near the tumor boundary, and around vessel structures. Furthermore, the percentage of NAD(P)H-rich EVs is highly correlated with human breast cancer diagnosis, which emphasizes the important role of metabolic imaging for EV characterization as well as its potential for clinical applications. In addition to the characterization of EV properties, we also demonstrate label-free monitoring of EV dynamics (uptake, release, and movement) in live cells and animals. The in situ metabolic profiling capacity of the proposed method together with the finding of increasing NAD(P)H-rich EV subpopulations in breast cancer have the potential for empowering applications in basic science and enhancing our understanding of the active metabolic roles that EVs play in cancer progression.

Recent advances in label-free virtual histology promise a new era for real-time molecular diagnosis in the operating room and during biopsy procedures. To take full advantage of the rich, multidimensional information provided by these technologies, reproducible and reliable computational tools that could facilitate the diagnosis are in great demand. In this study, we developed a deep-learning-based framework to recognize cancer versus normal human breast tissue from real-time label-free virtual histology images, with a tile-level AUC (area under receiver operating curve) of 95% and slide-level AUC of 100% on unseen samples. Furthermore, models trained on a high-quality laboratory-generated dataset can generalize to independent datasets acquired from a portable intraoperative version of the imaging technology with a physics-based adapted design. Classification activation maps and final feature visualization revealed discriminative patterns, such as tumor cells and tumor-associated vesicles, that are highly associated with cancer status. These results demonstrate that through the combination of real-time virtual histopathology and a deep-learning framework, accurate real-time diagnosis could be achieved in point-of-procedure clinical applications.

Cholesterol has been implicated in the clinical progression of breast cancer, a disease that continues to be the most commonly diagnosed cancer in women. Previous work has identified the cholesterol metabolite, 27-hydroxycholesterol (27HC), as a major mediator of the effects of cholesterol on breast tumor growth and progression. 27HC can act as an estrogen receptor (ER) modulator to promote the growth of ERα+ tumors, and a liver x receptor (LXR) ligand in myeloid immune cells to establish an immune-suppressive program. In fact, the metastatic properties of 27HC require the presence of myeloid cells, with neutrophils (PMNs) being essential for the increase in lung metastasis in murine models. In an effort to further elucidate the mechanisms by which 27HC alters breast cancer progression, we made the striking finding that 27HC promoted the secretion of extracellular vesicles (EVs), a diverse assortment of membrane bound particles that include exosomes. The resulting EVs had a size distribution that was skewed slightly larger, compared to EVs generated by treating cells with vehicle. The increase in EV secretion and size was consistent across three different subtypes: primary murine PMNs, RAW264.7 monocytic cells and 4T1 murine mammary cancer cells. Label-free analysis of 27HC-EVs indicated that they had a different metabolite composition to those from vehicle-treated cells. Importantly, 27HC-EVs from primary PMNs promoted tumor growth and metastasis in two different syngeneic models, demonstrating the potential role of 27HC induced EVs in the progression of breast cancer. EVs from PMNs were taken up by cancer cells, macrophages and PMNs, but not T cells. Since EVs did not alter proliferation of cancer cells, it is likely that their pro-tumor effects are mediated through interactions with myeloid cells. Interestingly, RNA-seq analysis of tumors from 27HC-EV treated mice do not display significantly altered transcriptomes, suggesting that the effects of 27HC-EVs occur early on in tumor establishment and growth. Future work will be required to elucidate the mechanisms by which 27HC increases EV secretion, and how these EVs promote breast cancer progression. Collectively however, our data indicate that EV secretion and content can be regulated by a cholesterol metabolite, which may have detrimental effects in terms of disease progression, important findings given the prevalence of both breast cancer and hypercholesterolemia.

Intraoperative imaging in surgical oncology can provide information about the tumor microenvironment as well as information about the tumor margin. Visualizing microstructural features and molecular and functional dynamics may provide important diagnostic and prognostic information, especially when obtained in real-time at the point-of-procedure. A majority of current intraoperative optical techniques are based on the use of the labels, such as fluorescent dyes. However, these exogenous agents disrupt the natural microenvironment, perturb biological processes, and alter the endogenous optical signatures that cells and the microenvironment can provide. Portable nonlinear imaging systems have enabled intraoperative imaging for realtime detection and diagnosis of tissue. We review the development of a label-free multimodal nonlinear optical imaging technique that was adapted into a portable imaging system for intraoperative optical assessment of resected human breast tissue. New developments have applied this technology to assessing needlebiopsy specimens. Needle-biopsy procedures most always precede surgical resection and serve as the first sampling of suspicious masses for diagnosis. We demonstrate the diagnostic feasibility of imaging core needle-biopsy specimens during veterinary cancer surgeries. This intraoperative label-free multimodal nonlinear optical imaging technique can potentially provide a powerful tool to assist in cancer diagnosis at the point-of-procedure.

Significance: Needle biopsy (NB) procedures are important for the initial diagnosis of many types of cancer. However, the possibility of NB specimens being unable to provide diagnostic information, (i.e., non-diagnostic sampling) and the time-consuming histological evaluation process can cause delays in diagnoses that affect patient care. Aim:We aim to demonstrate the advantages of this label-free multimodal nonlinear optical imaging (NLOI) technique as a non-destructive point-of-procedure evaluation method for NB tissue cores, for the visualization and characterization of the tissue microenvironment.Approach: A portable, label-free, multimodal NLOI system combined second-harmonic generation (SHG) and third-harmonic generation and two-and three-photon autofluorescence (2PF, 3PF) microscopy. It was used for intraoperative imaging of fresh NB tissue cores acquired during canine cancer surgeries, which involved liver, lung, and mammary tumors as well as soft-tissue sarcoma; in total, eight canine patients were recruited. An added tissue culture chamber enabled the use of this NLOI system for longitudinal imaging of fresh NB tissue cores taken from an induced rat mammary tumor and healthy mouse livers.Results: The intraoperative NLOI system was used to assess fresh canine NB specimens during veterinary cancer surgeries. Histology-like morphological features were visualized by the combination of four NLOI modalities at the point-of-procedure. The NLOI results provided quantitative information on the tissue microenvironment such as the collagen fiber orientation using Fourier-domain SHG analysis and metabolic profiling by optical redox ratio (ORR) defined by 2PF/(2PF + 3PF). The analyses showed that the canine mammary tumor had more randomly oriented collagen fibers compared to the tumor margin, and hepatocarcinoma had a wider distribution of ORR with a lower mean value compared to the liver fibrosis and the normalappearing liver. Moreover, the loss of metabolic information during tissue degradation of fresh murine NB specimens was shown by overall intensity decreases in all channels and an increase of mean ORR from 0.94 (standard deviation 0.099) to 0.97 (standard deviation 0.077) during 1-h longitudinal imaging of a rat mammary tumor NB specimen. The tissue response to staurosporine (STS), an apoptotic inducer, from fresh murine liver NB specimens was also observed.