Label-free nonlinear optical signatures of extracellular vesicles in liquid and tissue biopsies of human breast cancer

Abstract

Extracellular vesicles (EVs) have been implicated in metastasis and proposed as cancer biomarkers. However, heterogeneity and small size makes assessments of EVs challenging. Often, EVs are isolated from biofluids, losing spatial and temporal context and thus lacking the ability to access EVs in situ in their native microenvironment. This work examines the capabilities of label-free nonlinear optical microscopy to extract biochemical optical metrics of EVs in ex vivo tissue and EVs isolated from biofluids in cases of human breast cancer, comparing these metrics within and between EV sources. Before surgery, fresh urine and blood serum samples were obtained from human participants scheduled for breast tumor surgery (24 malignant, 6 benign) or healthy participants scheduled for breast reduction surgery (4 control). EVs were directly imaged both in intact ex vivo tissue that was removed during surgery and in samples isolated from biofluids by differential ultracentrifugation. Isolated EVs and freshly excised ex vivo breast tissue samples were imaged with custom nonlinear optical microscopes to extract single-EV optical metabolic signatures of NAD(P)H and FAD autofluorescence. Optical metrics were significantly altered in cases of malignant breast cancer in biofluid-derived EVs and intact tissue EVs compared to control samples. Specifically, urinary isolated EVs showed elevated NAD(P)H fluorescence lifetime in cases of malignant cancer, serum-derived isolated EVs showed decreased optical redox ratio in stage II cancer, but not earlier stages, and ex vivo breast tissue showed an elevated number of EVs in cases of malignant cancer. Results further indicated significant differences in the measured optical metabolic signature based on EV source (urine, serum and tissue) within individuals.

Figure 1

Workflow for characterizing EVs in breast cancer using single-EV capabilities of nonlinear optical microscopy and example dataset from one participant. (a) Study overview including participant cancer status, EV isolation, and tissue extraction, and imaging. (b) Concentration and size of isolated EVs. (c) NAD(P)H single-EV fluorescence lifetime distribution; (d) NAD(P)H single-EV phasor distribution for isolated EVs from urine (yellow) and serum (red) from a single participant with malignant breast cancer. (e) Single-EV FAD autofluorescence intensity distribution. (f) Single-EV NAD(P)H intensity distribution for isolated EVs from urine (yellow), serum (red), ex vivo tumor tissue (green), and normal-appearing ex vivo tissue from the same participant “Tumor adj.” (light blue). Panels (b–f) all show data collected from samples from one single participant with malignant breast cancer, indicating the broad range of different variables that were collected from each participant.

Figure 2

Example images of EVs in situ in tissue and isolated EVs. (a) Example SLAM image of ex vivo tumor tissue with 4-color map displaying each SLAM modality. (b) Example SLAM image of urinary isolated EVs. (c) Example FLIM image of urinary isolated EVs with rainbow colormap displaying mean NAD(P)H fluorescence lifetime. (d–f) Insets corresponding to smaller field of view from (a–c), respectively, showing single-EVs zoomed-in.

Figure 3

Optical metabolic characterization of control, benign, and malignant EVs across different sources. EV source is indicated by column, and patient cancer status is indicated as: normal control (blue), benign (orange), malignant (yellow), and non-tumor tissue from participants with malignant cancer “tumor adjacent” (light blue). Example SLAM images from (c) normal control, (f) benign, and (i) malignant ex vivo tissues are additionally shown using the same color scale. Significance: *p < 0.05; not significant if unmarked; in (e) the s value for benign and malignant is significantly different.

Figure 4

Optical metabolic characterization of EVs by cancer stage for selected metrics. Three metrics showed significant differences based on participant cancer stage: (a) EVs per mm² segmented from ex vivo tissue images, (b) mean EV THG in ex vivo tissue images, and (c) mean EV ORR in EVs isolated from serum. Significance: *p < 0.05, **p < 0.01, ***p < 0.001, not significant if unmarked.

Figure 5

Optical metabolic characterization of EVs from different sources. The mean value of each metric was computed for measured EVs from all sources across all cancer status groups for: (a) mean NAD(P)H fluorescence lifetime from FLIM, (b) g and s phasor lifetime components from FLIM, (c) mean size from NTA, (d) concentration of isolated EVs per mL of initial biofluid from NTA, (e) mean FAD SLAM intensity, (f) mean NAD(P)H SLAM intensity, (g) mean THG SLAM intensity, and (h) mean ORR, calculated from NAD(P)H and FAD intensities from SLAM. Significance using paired Student’s t-test to examine intra-individual changes: ns not significant; *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 6

Linear correlation coefficient examining optical metabolic variables within and between EV source. Correlations within a single-source for: (a) isolated urinary EVs, (b) isolated serum EVs, and (c) ex vivo tissue EVs. Correlations comparing different sources for: (d) isolated urinary EVs vs. ex vivo tissue EVs, (e) isolated serum EVs vs. ex vivo tissue EVs, and (f) isolated urinary EVs vs. isolated serum EVs.