Intravital microscopy (IVM) emerged and matured as a powerful tool for elucidating pathways in biological processes. Although label-free multiphoton IVM is attractive for its non-perturbative nature, its wide application has been hindered, mostly due to the limited contrast of each imaging modality and the challenge to integrate them. Here we introduce simultaneous label-free autofluorescence-multiharmonic (SLAM) microscopy, a single-excitation source nonlinear imaging platform that uses a custom-designed excitation window at 1110 nm and shaped ultrafast pulses at 10 MHz to enable fast (2-orders-of-magnitude improvement), simultaneous, and efficient acquisition of autofluorescence (FAD and NADH) and second/third harmonic generation from a wide array of cellular and extracellular components (e.g., tumor cells, immune cells, vesicles, and vessels) in living tissue using only 14 mW for extended time-lapse investigations. Our work demonstrates the versatility and efficiency of SLAM microscopy for tracking cellular events in vivo, and is a major enabling advance in label-free IVM.

The preparation, staining, visualization, and interpretation of histological images of tissue is well-accepted as the gold standard process for the diagnosis of disease. These methods were developed historically, and are used ubiquitously in pathology, despite being highly time and labor intensive. Here we introduce a unique optical imaging platform and methodology for label-free multimodal multiphoton microscopy that uses a novel photonic crystal fiber source to generate tailored chemical contrast based on programmable supercontinuum pulses. We demonstrate collection of optical signatures of the tumor microenvironment, including evidence of mesoscopic biological organization, tumor cell migration, and (lymph-)angiogenesis collected directly from fresh ex vivo mammary tissue. Acquisition of these optical signatures and other cellular or extracellular features, which are largely absent from histologically processed and stained tissue, combined with an adaptable platform for optical alignment-free programmable-contrast imaging, offers the potential to translate stain-free molecular histopathology into routine clinical use.

Intraoperative imaging of tumor microenvironment reveals correlations between extracellular vesicle density and breast pathology.

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.

Label-free molecular imaging of the tumor microenvironment interrelates well-known events in carcinogenesis across spatial scales.

The supercontinuum generated exclusively in the normal dispersion regime of a nonlinear fiber is widely believed to possess low optical noise and high spectral coherence. The recent development of flattened all-normal dispersion fibers has been motivated by this belief to construct a general-purpose broadband coherent optical source. Somewhat surprisingly, we identify a large short-term polarization noise in this type of supercontinuum generation that has been masked by the total-intensity measurement in the past, but can be easily detected by filtering the supercontinuum with a linear polarizer. Fortunately, this hidden intrinsic noise and the accompanied spectral decoherence can be effectively suppressed by using a polarization-maintaining all-normal dispersion fiber. A polarization-maintaining coherent supercontinuum laser is thus built with a broad bandwidth (780–1300 nm) and high spectral power (~1 mW/nm).

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.

Histochemistry is a microscopy-based technology widely used to visualize the molecular distribution in biological tissue. Recent developments in label-free optical imaging has demonstrated the potential to replace the conventional histochemical labels/markers (fluorescent antibodies, organic dyes, nucleic acid probes, and other contrast agents) with diverse optical interactions to generate histochemical contrasts, allowing "virtual" histochemistry in three spatial dimensions without preparing a microscope slide (i.e. laborintensive sample preparation). However, the histochemical information in a label-free optical image has often been rather limited due to the difficulty in simultaneously generating multiple histochemical contrasts with strict spatial co-registration. Here, in the first part (Part I) of this two-part series study, we develop a technique of slide-free virtual histochemistry based on label-free multimodal multiphoton microscopy, and simultaneously generate up to four histochemical contrasts from in vivo animal and ex vivo human tissue. To enable this functionality, we construct and demonstrate a robust fiber-based laser source for clinical translation and phenotype a wide variety of vital cells in unperturbed mammary tissue.