Analog multiplexing of a laser clock and computational photon counting for fast fluorescence lifetime imaging microscopy.

The dynamic range and fluctuations of fluorescence intensities and lifetimes in biological samples are large, demanding fast, precise, and versatile techniques. Among the high-speed fluorescence lifetime imaging microscopy (FLIM) techniques, directly sampling the output of analog single-photon detectors at GHz rates combined with computational photon counting can handle a larger range of photon rates. Traditionally, the laser clock is not sampled explicitly in fast FLIM; rather the detection is synchronized to the laser clock so that the excitation pulse train can be inferred from the cumulative photon statistics of several pixels. This has two disadvantages for sparse or weakly fluorescent samples: inconsistencies in inferring the laser clock within a frame and inaccuracies in aligning the decay curves from different frames for averaging. The data throughput is also very inefficient in systems with repetition rates much larger than the fluorescence lifetime due to significant silent regions where no photons are expected. We present a method for registering the photon arrival times to the excitation using time-domain multiplexing for fast FLIM. The laser clock is multiplexed with photocurrents into the silent region. Our technique does not add to the existing data bottleneck, has the sub-nanosecond dead time of computational photon counting based fast FLIM, works with various detectors, lasers, and electronics, and eliminates the errors in lifetime estimation in photon-starved conditions. We demonstrate this concept on two multiphoton setups of different laser repetition rates for single and multichannel FLIM multiplexed into a single digitizer channel for real-time imaging of biological samples.

Compact simultaneous label-free autofluorescence multi-harmonic microscopy for user-friendly photodamage-monitored imaging.

Significance: Label-free nonlinear optical microscopy has become a powerful tool for biomedical research. However, the possible photodamage risk hinders further clinical applications. Aim: To reduce these adverse effects, we constructed a new platform of simultaneous label-free autofluorescence multi-harmonic (SLAM) microscopy, featuring four-channel multimodal imaging, inline photodamage monitoring, and pulse repetition-rate tuning. Approach: Using a large-core birefringent photonic crystal fiber for spectral broadening and a prism compressor for pulse pre-chirping, this system allows users to independently adjust pulse width, repetition rate, and energy, which is useful for optimizing imaging conditions towards no/minimal photodamage. Results: It demonstrates label-free multichannel imaging at one excitation pulse per image pixel and thus paves the way for improving the imaging speed by a faster optical scanner with a low risk of nonlinear photodamage. Moreover, the system grants users the flexibility to autonomously fine-tune repetition rate, pulse width, and average power, free from interference, ensuring the discovery of optimal imaging conditions with high SNR and minimal phototoxicity across various applications. Conclusions: The combination of a stable laser source, independently tunable ultrashort pulse, photodamage monitoring features, and a compact design makes this new system a robust, powerful, and user-friendly imaging platform.

Supercontinuum intrinsic fluorescence imaging heralds free view of living systems.

Optimal imaging strategies remain underdeveloped to maximize information for fluorescence microscopy while minimizing the harm to fragile living systems. Taking hint from the supercontinuum generation in ultrafast laser physics, we generated supercontinuum fluorescence from untreated unlabeled live samples before nonlinear photodamage onset. Our imaging achieved high-content cell phenotyping and tissue histology, identified bovine embryo polarization, quantified aging-related stress across cell types and species, demystified embryogenesis before and after implantation, sensed drug cytotoxicity in real-time, scanned brain area for targeted patching, optimized machine learning to track small moving organisms, induced two-photon phototropism of leaf chloroplasts under two-photon photosynthesis, unraveled microscopic origin of autumn colors, and interrogated intestinal microbiome. The results enable a facility-type microscope to freely explore vital molecular biology across life sciences.

Gentle label-free nonlinear optical imaging relaxes linear-absorption-mediated triplet.

Sample health is critical for live-cell fluorescence microscopy and has promoted light-sheet microscopy that restricts its ultraviolet-visible excitation to one plane inside a three-dimensional sample. It is thus intriguing that laser-scanning nonlinear optical microscopy, which similarly restricts its near-infrared excitation, has not broadly enabled gentle label-free molecular imaging. We hypothesize that intense near-infrared excitation induces phototoxicity via linear absorption of intrinsic biomolecules with subsequent triplet buildup, rather than the commonly assumed mechanism of nonlinear absorption. Using a reproducible phototoxicity assay based on the time-lapse elevation of auto-fluorescence (hyper-fluorescence) from a homogeneous tissue model (chicken breast), we provide strong evidence supporting this hypothesis. Our study justifies a simple imaging technique, e.g., rapidly scanned sub-80-fs excitation with full triplet-relaxation, to mitigate this ubiquitous linear-absorption-mediated phototoxicity independent of sample types. The corresponding label-free imaging can track freely moving C. elegans in real-time at an irradiance up to one-half of water optical breakdown.

A deep learning-based stripe self-correction method for stitched microscopic images.

Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers.

Nonlinear optical imaging by detection with optical parametric amplification (invited paper).

Nonlinear optical imaging is a versatile tool that has been proven to be exceptionally useful in various research fields. However, due to the use of photomultiplier tubes (PMTs), the wide application of nonlinear optical imaging is limited by the incapability of imaging under ambient light. In this paper, we propose and demonstrate a new optical imaging detection method based on optical parametric amplification (OPA). As a nonlinear optical process, OPA intrinsically rejects ambient light photons by coherence gating. Periodical poled lithium niobate (PPLN) crystals are used in this study as the media for OPA. Compared to bulk nonlinear optical crystals, PPLN crystals support the generation of OPA signal with lower pump power. Therefore, this characteristic of PPLN crystals is particularly beneficial when using high-repetition-rate lasers, which facilitate high-speed optical signal detection, such as in spectroscopy and imaging. A PPLN-based OPA system was built to amplify the emitted imaging signal from second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy imaging, and the amplified optical signal was strong enough to be detected by a biased photodiode under ordinary room light conditions. With OPA detection, ambient-light-on SHG and CARS imaging becomes possible, and achieves a similar result as PMT detection under strictly dark environments. These results demonstrate that OPA can be used as a substitute for PMTs in nonlinear optical imaging to adapt it to various applications with complex lighting conditions.

Nonlinear Imaging Histopathology: A Pipeline to Correlate Gold-Standard Hematoxylin and Eosin Staining With Modern Nonlinear Microscopy.

Hematoxylin and eosin (H&E) staining, the century-old technique, has been the gold standard tool for pathologists to detect anomalies in tissues and diseases such as cancer. H&E staining is a cumbersome, time-consuming process that delays and wastes precious minutes during an intraoperative diagnosis. However, even in the modern era, real-time label-free imaging techniques such as simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy have delivered several more layers of information to characterize a tissue with high precision. Still, they have yet to translate to the clinic. The slow translation rate can be attributed to the lack of direct comparisons between the old and new techniques. Our approach to solving this problem is to: 1) reduce dimensions by pre-sectioning the tissue in 500 µm slices, and 2) produce fiducial laser markings which appear in both SLAM and histological imaging. High peak-power femtosecond laser pulses enable ablation in a controlled and contained manner. We perform laser marking on a grid of points encompassing the SLAM region of interest. We optimize laser power, numerical aperture, and timing to produce axially extended marking, hence multilayered fiducial markers, with minimal damage to the surrounding tissues. We performed this co-registration over an area of 3 × 3 mm2 of freshly excised mouse kidney and intestine, followed by standard H&E staining. Reduced dimensionality and the use of laser markings provided a comparison of the old and new techniques, giving a wealth of correlative information and elevating the potential of translating nonlinear microscopy to the clinic for rapid pathological assessment.

Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue.

With the latest advancements in optical bioimaging, rich structural and functional information has been generated from biological samples, which calls for capable computational tools to identify patterns and uncover relationships between optical characteristics and various biomedical conditions. Constrained by the existing knowledge of the novel signals obtained by those bioimaging techniques, precise and accurate ground truth annotations can be difficult to obtain. Here we present a weakly supervised deep learning framework for optical signature discovery based on inexact and incomplete supervision. The framework consists of a multiple instance learning-based classifier for the identification of regions of interest in coarsely labeled images and model interpretation techniques for optical signature discovery. We applied this framework to investigate human breast cancer-related optical signatures based on virtual histopathology enabled by simultaneous label-free autofluorescence multiharmonic microscopy (SLAM), with the goal of exploring unconventional cancer-related optical signatures from normal-appearing breast tissues. The framework has achieved an average area under the curve (AUC) of 0.975 on the cancer diagnosis task. In addition to well-known cancer biomarkers, non-obvious cancer-related patterns were revealed by the framework, including NAD(P)H-rich extracellular vesicles observed in normal-appearing breast cancer tissue, which facilitate new insights into the tumor microenvironment and field cancerization. This framework can be further extended to diverse imaging modalities and optical signature discovery tasks.

Combining the guidelines and multiphoton imaging methods to improve the prognostic value of tumor-infiltrating lymphocytes in breast cancer.

Multiphoton microscopy (MPM) was introduced to label-freely obtain tumor-infiltrating lymphocytes (TILs) images from a total of 611 patients, and the prognostic value of TILs in breast cancer was assessed by the MPM method (TILs-MPM) and guidelines method proposed by the International Immuno-Oncology Biomarker Working Group (TILs-WG), respectively. Moreover, the clinical (CLI) model, TILs-WG + TILs-MPM model, and full model (CLI + TILs-WG + TILs-MPM) were developed to investigate the prognostic value of TILs. The results show that TILs-WG performs better in estrogen receptor (ER)-negative subgroup, and TILs-MPM is comparable with TILs-WG in the ER-negative subgroup, but has the best performance in the ER-positive subgroup. Furthermore, the TILs-WG + TILs-MPM model can significantly improve the prognostic power compared with the TILs-WG model, and the full model has excellent performance, with high area under the curve (AUC) and hazard ratio (HR) in both ER-positive, ER-negative subgroups, and the complete cohort. Our results suggest that the combination of TILs-WG with TILs-MPM model can greatly improve the prognostic value of TILs.

Computational Photon Counting Using Multithreshold Peak Detection for Fast Fluorescence Lifetime Imaging Microscopy.

Time-resolved photon counting methods have a finite bandwidth that restricts the acquisition speed of techniques like fluorescence lifetime imaging microscopy (FLIM). To enable faster imaging, computational methods can be employed to count photons when the output of a detector is directly digitized at a high sampling rate. Here, we present computational photon counting using a hybrid photodetector in conjunction with multithreshold peak detection to count instances where one or more photons arrive at the detector within the detector response time. This method can be used to distinguish up to five photon counts per digitized point, whereas previous demonstrations of computational photon counting on data acquired with photomultiplier tubes have only counted one photon at a time. We demonstrate in both freely moving C. elegans and a human breast cancer cell line undergoing apoptosis that this novel multithreshold peak detection method can accurately characterize the intensity and fluorescence lifetime of samples producing photon rates up to 223%, higher than previously demonstrated photon counting FLIM systems.

Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity.

Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.

Label-free metabolic and structural profiling of dynamic biological samples using multimodal optical microscopy with sensorless adaptive optics.

Label-free optical microscopy has matured as a noninvasive tool for biological imaging; yet, it is criticized for its lack of specificity, slow acquisition and processing times, and weak and noisy optical signals that lead to inaccuracies in quantification. We introduce FOCALS (Fast Optical Coherence, Autofluorescence Lifetime imaging, and Second harmonic generation) microscopy capable of generating NAD(P)H fluorescence lifetime, second harmonic generation (SHG), and polarization-sensitive optical coherence microscopy (OCM) images simultaneously. Multimodal imaging generates quantitative metabolic and morphological profiles of biological samples in vitro, ex vivo, and in vivo. Fast analog detection of fluorescence lifetime and real-time processing on a graphical processing unit enables longitudinal imaging of biological dynamics. We detail the effect of optical aberrations on the accuracy of FLIM beyond the context of undistorting image features. To compensate for the sample-induced aberrations, we implemented a closed-loop single-shot sensorless adaptive optics solution, which uses computational adaptive optics of OCM for wavefront estimation within 2 s and improves the quality of quantitative fluorescence imaging in thick tissues. Multimodal imaging with complementary contrasts improves the specificity and enables multidimensional quantification of the optical signatures in vitro, ex vivo, and in vivo, fast acquisition and real-time processing improve imaging speed by 4-40 × while maintaining enough signal for quantitative nonlinear microscopy, and adaptive optics improves the overall versatility, which enable FOCALS microscopy to overcome the limits of traditional label-free imaging techniques.

Single-photon peak event detection (SPEED): a computational method for fast photon counting in fluorescence lifetime imaging microscopy.

Fluorescence lifetime imaging microscopy (FLIM) characterizes samples by examining the temporal properties of fluorescence emission, providing useful contrast within samples based on the local physical and biochemical environment of fluorophores. Despite this, FLIM applications have been limited in scope by either poor accuracy or long acquisition times. Here, we present a method for computational single-photon counting of directly sampled time-domain FLIM data that is capable of accurate fluorescence lifetime and intensity measurements while acquiring over 160 Mega-counts-per-second with sub-nanosecond time resolution between consecutive photon counts. We demonstrate that our novel method of Single-photon PEak Event Detection (SPEED) is more accurate than direct pulse sampling and faster than established photon counting FLIM methods. We further show that SPEED can be implemented for imaging and quantifying samples that benefit from higher -throughput and -dynamic range imaging with real-time GPU-accelerated processing and use this capability to examine the NAD(P)H-related metabolic dynamics of apoptosis in human breast cancer cells. Computational methods for photon counting such as SPEED open up more opportunities for fast and accurate FLIM imaging and additionally provide a basis for future innovation into alternative FLIM techniques.

Real-time pixelwise phasor analysis for video-rate two-photon fluorescence lifetime imaging microscopy.

Two-photon fluorescence lifetime imaging microscopy (FLIM) is a widely used technique in biomedical optical imaging. Presently, many two-photon time-domain FLIM setups are limited by long acquisition and postprocessing times that decrease data throughput and inhibit the ability to image fast sub-second processes. Here, we present a versatile two-photon FLIM setup capable of video-rate (up to 25 fps) imaging with graphics processing unit (GPU)-accelerated pixelwise phasor analysis displayed and saved simultaneously with acquisition. The system uses an analog output photomultiplier tube in conjunction with 12-bit digitization at 3.2 GHz to overcome the limited maximum acceptable photon rate associated with the photon counting electronics in many FLIM systems. This allows for higher throughput FLIM acquisition and analysis, and additionally enables the user to assess sample fluorescence lifetime in real-time. We further explore the capabilities of the system to examine the kinetics of Rhodamine B uptake by human breast cancer cells and characterize the effect of pixel dwell time on the reduced nicotinamide adenine dinucleotide and reduced nicotinamide adenine dinucleotide phosphate (NAD(P)H) autofluorescence lifetime estimation accuracy.

Label-Free Multimodal Multiphoton Intravital Imaging.

Label-free intravital optical imaging is an emergent visualization tool that is not only useful for basic biological research, but also for preclinical research with potential translational clinical applications. The complete absence of exogenous labeling or genetic alterations avoids plausible harmful perturbation to biological processes and the pristine physiological environment, as the endogenous biomolecules enable intrinsic imaging contrasts to interrogate various live multicellular organisms of interest. This tool has evolved from single-modality, single-photon imaging into multimodal multiphoton imaging, in order to gain different contrasts simultaneously during imaging sessions, and permit long-term time-lapse studies that have begun to spawn more diverse applications.

Label-Free Deep Profiling of the Tumor Microenvironment.

Label-free nonlinear microscopy enables nonperturbative visualization of structural and metabolic contrast within living cells in their native tissue microenvironment. Here a computational pipeline was developed to provide a quantitative view of the microenvironmental architecture within cancerous tissue from label-free nonlinear microscopy images. To enable single-cell and single-extracellular vesicle (EV) analysis, individual cells, including tumor cells and various types of stromal cells, and EVs were segmented by a multiclass pixelwise segmentation neural network and subsequently analyzed for their metabolic status and molecular structure in the context of the local cellular neighborhood. By comparing cancer tissue with normal tissue, extensive tissue reorganization and formation of a patterned cell-EV neighborhood was observed in the tumor microenvironment. The proposed analytic pipeline is expected to be useful in a wide range of biomedical tasks that benefit from single-cell, single-EV, and cell-to-EV analysis. SIGNIFICANCE: The proposed computational framework allows label-free microscopic analysis that quantifies the complexity and heterogeneity of the tumor microenvironment and opens possibilities for better characterization and utilization of the evolving cancer landscape.

Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients.

The notion of personalized medicine demands proper prognostic biomarkers to guide the optimal therapy for an invasive breast cancer patient. However, various risk prediction models based on conventional clinicopathological factors and emergent molecular assays have been frequently limited by either a low strength of prognosis or restricted applicability to specific types of patients. Therefore, there is a critical need to develop a strong and general prognosticator. Methods: We observed five large-scale tumor-associated collagen signatures (TACS4-8) obtained by multiphoton microscopy at the invasion front of the breast primary tumor, which contrasted with the three tumor-associated collagen signatures (TACS1-3) discovered by Keely and coworkers at a smaller scale. Highly concordant TACS1-8 classifications were obtained by three independent observers. Using the ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8 and established a risk prediction model based on the TACS-score. In a blind fashion, consistent retrospective prognosis was obtained from 995 breast cancer patients in both a training cohort (n= 431) and an internal validation cohort (n = 300) collected from one clinical center, and in an external validation cohort (n = 264) collected from a different clinical center. Results: TACS1-8 model alone competed favorably with all reported models in predicting disease-free survival (AUC: 0.838, [0.800-0.872]; 0.827, [0.779-0.868]; 0.807, [0.754-0.853] in the three cohorts) and stratifying low- and high-risk patients (HR 7.032, [4.869-10.158]; 6.846, [4.370-10.726], 4.423, [2.917-6.708]). The combination of these factors with the TACS-score into a nomogram model further improved the prognosis (AUC: 0.865, [0.829-0.896]; 0.861, [0.816-0.898]; 0.854, [0.805-0.894]; HR 7.882, [5.487-11.323]; 9.176, [5.683-14.816], and 5.548, [3.705-8.307]). The nomogram identified 72 of 357 (~20%) patients with unsuccessful 5-year disease-free survival that might have been undertreated postoperatively. Conclusions: The risk prediction model based on TACS1-8 considerably outperforms the contextual clinical model and may thus convince pathologists to pursue a TACS-based breast cancer prognosis. Our methodology identifies a significant portion of patients susceptible to undertreatment (high-risk patients), in contrast to the multigene assays that often strive to mitigate overtreatment. The compatibility of our methodology with standard histology using traditional (non-tissue-microarray) formalin-fixed paraffin-embedded (FFPE) tissue sections could simplify subsequent clinical translation.

Simultaneous two-photon activation and imaging of neural activity based on spectral-temporal modulation of supercontinuum light.

SIGNIFICANCE: Recent advances in nonlinear optics in neuroscience have focused on using two ultrafast lasers for activity imaging and optogenetic stimulation. Broadband femtosecond light sources can obviate the need for multiple lasers by spectral separation for chromatically targeted excitation. AIM: We present a photonic crystal fiber (PCF)-based supercontinuum source for spectrally resolved two-photon (2P) imaging and excitation of GCaMP6s and C1V1-mCherry, respectively. APPROACH: A PCF is pumped using a 20-MHz repetition rate femtosecond laser to generate a supercontinuum of light, which is spectrally separated, compressed, and recombined to image GCaMP6s (930 nm excitation) and stimulate the optogenetic protein, C1V1-mCherry (1060 nm excitation). Galvanometric spiral scanning is employed on a single-cell level for multiphoton excitation and high-speed resonant scanning is employed for imaging of calcium activity. RESULTS: Continuous wave lasers were used to verify functionality of optogenetic activation followed by directed 2P excitation. Results from these experiments demonstrate the utility of a supercontinuum light source for simultaneous, single-cell excitation and calcium imaging. CONCLUSIONS: A PCF-based supercontinuum light source was employed for simultaneous imaging and excitation of calcium dynamics in brain tissue. Pumped PCFs can serve as powerful light sources for imaging and activation of neural activity, and overcome the limited spectra and space associated with multilaser approaches.

Real-time three-dimensional histology-like imaging by label-free nonlinear optical microscopy.

BACKGROUND: The current gold-standard formalin-fixed and paraffin-embedded (FFPE) histology typically requires several days for tissue fixing, embedding, sectioning, and staining to provide depth-resolved tissue feature visualization. During these time- and labor- intense processes, the in vivo tissue dynamics and three-dimensional structures undergo inevitable loss and distortion. METHODS: A simultaneous label-free autofluorescence multiharmonic (SLAM) microscope is used to conduct ex vivo and in vivo imaging of fresh human and rat tissues. Four nonlinear optical imaging modalities are integrated into this SLAM microscope, including second harmonic generation (SHG), two-photon fluorescence (2PF), third harmonic generation (THG), and three-photon fluorescence (3PF). By imaging fresh human and rat tissues without any tissue processing or staining, various biological tissue features are effectively visualized by one or multiple imaging modalities of the SLAM microscope. In particular, some of the most essential features in hematoxylin and eosin (H&E)-stained histology, such as collagen fibers and nuclei, are also present in the SLAM microscopy images with good contrast. Because nuclei are evident from negative contrast, the nuclei are segmented from the SLAM images using deep learning. Finally, a color-transforming algorithm is developed to convert the grey-scale images acquired by the SLAM microscope to the virtually H&E-stained histology-like images. The converted histology-like images are later compared with the FFPE histology at the same tissue site. In addition, the nuclear-to-cytoplasmic ratios (N/C ratios) of the cells in the SLAM image are quantified, which has diagnostic relevance for cancer. RESULTS: Various histological correlations are identified with high similarities for the color-converted histology-like SLAM microscopy images. By applying the color transforming algorithm on real-time SLAM image sequences and 3D SLAM image stacks, we report, for the first time and to the best our knowledge, real-time 3D histology-like imaging. Furthermore, the quantified N/C ratio of the cells in the SLAM image are overlaid on the converted histology-like image as a new image contrast. CONCLUSIONS: We demonstrated real-time 3D histology-like imaging and its future potential using SLAM microscopy aided by color remapping and deep-learning-based feature segmentation.

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

Extracellular vesicles (EVs) have emerged as potential biomarkers in cancer research and for clinical diagnosis. Little is known, however, about their spatial distributions in tissue and the different subpopulations that may exist. Here we report the use of label-free nonlinear optical imaging techniques to provide spatially resolved chemical information of EVs within untreated tissues. A multimodal nonlinear optical imaging system incorporating multiphoton autofluorescence and hyperspectral coherent anti-Stokes Raman scattering (CARS) imaging was built to visualize the spatial tissue distribution and probe the spectra of EVs. K-means clustering is performed on the CARS spectra from EVs in rat mammary tissues and human breast tumor tissue to reveal both the spatial distribution of EV clusters and their different chemical signatures. Correlations are identified between EV clusters and metabolic information.