Intraoperative visualization of the tumor microenvironment and quantification of extracellular vesicles by label-free nonlinear imaging

Improving nanotherapy delivery and action through image-guided systems pharmacology

Despite recent advances in the translation of therapeutic nanoparticles (TNPs) into the clinic, the field continues to face challenges in predictably and selectively delivering nanomaterials for the treatment of solid cancers. The concept of enhanced permeability and retention (EPR) has been coined as a convenient but simplistic descriptor of high TNP accumulation in some tumors. However, in practice EPR represents a number of physiological variables rather than a single one (including dysfunctional vasculature, compromised lymphatics and recruited host cells, among other aspects of the tumor microenvironment) — each of which can be highly heterogenous within a given tumor, patient and across patients. Therefore, a clear need exists to dissect the specific biophysical factors underlying the EPR effect, to formulate better TNP designs, and to identify patients with high-EPR tumors who are likely to respond to TNP. The overall pharmacology of TNP is governed by an interconnected set of spatially defined and dynamic processes that benefit from a systems-level quantitative approach, and insights into the physiology have profited from the marriage between in vivo imaging and quantitative systems pharmacology (QSP) methodologies. In this article, we review recent developments pertinent to image-guided systems pharmacology of nanomedicines in oncology. We first discuss recent developments of quantitative imaging technologies that enable analysis of nanomaterial pharmacology at multiple spatiotemporal scales, and then examine reports that have adopted these imaging technologies to guide QSP approaches. In particular, we focus on studies that have integrated multi-scale imaging with computational modeling to derive insights about the EPR effect, as well as studies that have used modeling to guide the manipulation of the EPR effect and other aspects of the tumor microenvironment for improving TNP action. We anticipate that the synergistic combination of imaging with systems-level computational methods for effective clinical translation of TNPs will only grow in relevance as technologies increase in resolution, multiplexing capability, and in the ability to examine heterogeneous behaviors at the single-cell level.

Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology

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.

Label-free visualization and characterization of extracellular vesicles in breast cancer

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.

Simultaneous label-free autofluorescence-multiharmonic microscopy and beyond

Without sophisticated data inversion algorithms, nonlinear optical microscopy can acquire images at subcellular resolution and relatively large depth, with plausible endogenous contrasts indicative of authentic biological and pathological states. Although independent contrasts have been derived by sequentially imaging the same sample plane or volume under different and often optimized excitation conditions, new laser source engineering with inputs from key biomolecules surprisingly enable real-time simultaneous acquisition of multiple endogenous molecular contrasts to segment a rich set of cellular and extracellular components. Since this development allows simple single-beam single-shot excitation and simultaneous multicontrast epidirected signal detection, the resulting platform avoids perturbative sample pretreatments such as fluorescent labeling, mechanical sectioning, scarce or interdependent contrast generation, constraints to the sample or imaging geometry, and intraimaging motion artifacts that have limited in vivo nonlinear optical molecular imaging.

Optical Molecular Imaging of Inflammatory Cells in Interventional Medicine-An Emerging Strategy

The optical molecular imaging of inflammation is an emerging strategy for interventional medicine and diagnostics. The host's inflammatory response and adaptation to acute and chronic diseases provides unique signatures that have the potential to guide interventions. Thus, there are emerging a suite of molecular imaging and sensing approaches for a variety of targets in this area. This review will focus on two key cellular orchestrators that dominate this area, neutrophils and macrophages, with recent developments in molecular probes and approaches discussed.

Detection of weak near-infrared optical imaging signals under ambient light by optical parametric amplification

We present a detection method based on optical parametric amplification to amplify and detect near-infrared (NIR) optical imaging signals. A periodically poled lithium niobate crystal is employed as an optical parametric amplifier (OPA), which provides excellent quasi-phase-matching conditions for the optical parametric amplification process. A weak reflectance imaging signal at 1465 nm is amplified by the OPA with a high gain of up to 92 dB, and the amplified optical signal is detected with a low-cost photodetector under ambient light conditions. Such a high gain leads to a detection limit of 23 pW under a 5 MHz detection bandwidth, which is remarkably lower than the theoretical value of a NIR photomultiplier tube (PMT). By exploiting the advantages of the OPA, the incident power needed for microscopy or imaging is reduced by 40-60 dB. The high imaging gain of the OPA also significantly enhances the imaging penetration depth by selectively detecting the weak signal reflected from deep tissue structures. The successful implementation of the OPA enables a robust and sensitive detection method that offers the potential to replace PMTs in imaging applications within the NIR spectral range.

Volumetric stimulated Raman scattering imaging of cleared tissues towards three-dimensional chemical histopathology

Thin tissue slice based histology has been used as a gold standard for disease diagnosis since over a hundred years ago. However, histopathological evaluation on two-dimensional slides suffers from large variations due to limited sampling. To improve the diagnostic accuracy, three-dimensional (3D) histology is performed through serial sectioning, staining, imaging and reconstruction of individual slices, which is highly time-consuming and labor intensive. We developed a volumetric stimulated Raman scattering (SRS) imaging method, which provides histology-like information in 3D context without the need for staining with dyes. Using a small molecule clearing agent, formamide, we performed tissue clearing within 30 min and achieved an imaging depth up to 500 µm in highly scattered tissues, including brain, kidney, liver and lung. Through a two-color SRS imaging scheme, we obtained histology-like images in cleared brain tissue slices. Our method has the potential for 3D tissue histopathology to improve the accuracy of histopathological examination.

High-resolution microscopy for imaging cancer pathobiology

PURPOSE OF REVIEW

Light microscopy plays an essential role in clinical diagnosis and understanding the pathogenesis of cancer. Conventional bright-field microscope is used to visualize abnormality in tissue architecture and nuclear morphology, but often suffers from many limitations. This review focuses on the potential of new imaging techniques to improve basic and clinical research in pathobiology.

RECENT FINDINGS

Light microscopy has significantly expanded its ability in resolution, imaging volume, speed and contrast. It now allows 3D high-resolution volumetric imaging of tissue architecture from large tissue and molecular structures at nanometer resolution.

SUMMARY

Pathologists and researchers now have access to various imaging tools to study cancer pathobiology in both breadth and depth. Although clinical adoption of a new technique is slow, the new imaging tools will provide significant new insights and open new avenues for improving early cancer detection, personalized risk assessment and identifying the best treatment strategies.

Label-free molecular profiling for identification of biomarkers in carcinogenesis using multimodal multiphoton imaging

Background

Label-free molecular profiling, imaging, and analysis are of particular interest in cancer biology for detecting subtle biochemical changes during cancer progression and potentially during cancer treatment. Multimodal, multiphoton imaging that combines diverse molecular contrasts derived from different physical mechanisms can improve our understanding of the tumor microenvironment.

Methods

A label-free optical molecular profiling technique has been developed based on penta-modal multiphoton imaging to investigate mammary tumor progression in a pre-clinical rat model. Pulses from a coherent supercontinuum were tailored for two-photon (2PF) and three-photon fluorescence (3PF), second (SHG) and third harmonic generation (THG), and hyperspectral coherent anti-Stokes Raman scattering (CARS)-based imaging. A graphic multiphoton molecular profiling model was constructed to intuitively combine the co-registered quantitative, chemical, functional, and structural tissue information, enabling longitudinal in situ biomolecular analysis.

Results

Over a 9-week period of tumor progression, and even before the formation of solid tumor, we observed lipid-protein transitions, microenvironmental reorganization, and a shift from FAD to NAD(P)H fluorescence, which reflects the reprogramming of cellular metabolism in carcinogenesis.

Conclusions

Multimodal multiphoton imaging reveals and interrelates diverse carcinogenic signatures, identifying biomarkers that could serve as early molecular indicators for breast cancer diagnosis. This quantitative multimodal imaging methodology for molecular profiling of associated cancer biomarkers may have a broader impact in fundamental cancer research and future clinical applications.