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Intraoperative visualization of the tumor microenvironment and quantification of extracellular vesicles by label-free nonlinear imaging by Yi Sun, Sixian.

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Presentation on theme: "Intraoperative visualization of the tumor microenvironment and quantification of extracellular vesicles by label-free nonlinear imaging by Yi Sun, Sixian."— Presentation transcript:

1 Intraoperative visualization of the tumor microenvironment and quantification of extracellular vesicles by label-free nonlinear imaging by Yi Sun, Sixian You, Haohua Tu, Darold R. Spillman, Eric J. Chaney, Marina Marjanovic, Joanne Li, Ronit Barkalifa, Jianfeng Wang, Anna M. Higham, Natasha N. Luckey, Kimberly A. Cradock, Z. George Liu, and Stephen A. Boppart Science Volume 4(12):eaau5603 December 19, 2018 Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

2 Fig. 1 Multimodal intraoperative label-free nonlinear optical images of human breast tissues and the corresponding histology. Multimodal intraoperative label-free nonlinear optical images of human breast tissues and the corresponding histology. Multimodal label-free nonlinear optical image (left) and the colocated histology (right) of (A) invasive ductal carcinoma (IDC) with an overall orientation of collagen alignment and tumor cell infiltration (red dashed arrows), (B) adipocytes (red dashed arrows) and blood vessel (red solid arrows), (C) adipocytes (red dashed arrows) and mammary duct (red solid arrows), and (D) healthy breast tissue from breast reduction surgery. Scale bars, 100 μm. Yi Sun et al. Sci Adv 2018;4:eaau5603 Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

3 Fig. 2 EV enrichment in the tumor microenvironment.
EV enrichment in the tumor microenvironment. (A) Multimodal label-free nonlinear optical image of a tissue site with ductal carcinoma in situ (DCIS) (boundary marked by red dashed line). (B) Colocated H&E histology. (C) THG-contrast image visualizing the DCIS boundary and EVs. (D) Binary image of EVs segmented from (C). Scale bars, 100 μm. Yi Sun et al. Sci Adv 2018;4:eaau5603 Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

4 Fig. 3 Quantification and pathological correlations of EVs.
Quantification and pathological correlations of EVs. (A) Flowchart of EV segmentation and quantification algorithm. (B) Representative THG-contrast image acquired from the tumor microenvironment and processed binary image, highlighting the presence of EVs within the tumor microenvironment. (C) Comparison of EV density from breast cancer cases versus healthy breast reduction cases. The average EV density is 142 ± 55 nl−1 for the cancer cases, while it is only 23 ± 8 nl−1 for the healthy breast reduction cases. ****P < (one-sided Student’s t test). (D) EV density data from each case are registered by the distance from tumor to closest surgical margin and the cancer invasiveness grade. An overall decreasing trend of EV density is identified with increasing tumor-to-margin distance. Data points are divided into three groups (shaded areas) representing different histologic grades of IDC. (E) Relationship between EV density and IDC histologic grade. To minimize the effect of spatial heterogeneity, EV data were chosen from cases within a small range of margin distances (0 to 8 mm). Sample size of each IDC grade is indicated above each bar. ***P < 0.001, **P < 0.01, *P < 0.1 (multiway ANOVA test, multiple comparison test). Yi Sun et al. Sci Adv 2018;4:eaau5603 Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

5 Fig. 4 Determination of phase of tumor cell invasion around desmoplasia by EV distribution.
Determination of phase of tumor cell invasion around desmoplasia by EV distribution. (A) Multimodal label-free nonlinear image of desmoplasia at an early phase. There is an interface (red dashed line) between the tumor and the dense collagen fibers of desmoplasia, and the tumor cells are identified only in the tumor region (white arrows). (B) Colocated histology image of the early-phase desmoplasia. (C) Binary image of segmented EVs from the THG channel of (A). The average (AVG) EV density is quantified to be 144 nl−1, and there is no major difference (113 nl−1 versus 163 nl−1) between the EV counts from each side of the interface. (D) Multimodal nonlinear optical image of desmoplasia at a late phase. The interface between dense collagen fibers and the tumor is marked by a red dashed line, with infiltrating tumor cells being identified within the collagen region (white arrows). (E) Colocated histology image of this late-phase desmoplastic reaction. (F) Binary image of segmented EVs from the THG channel (A). The average EV density of the entire FOV is 575 nl−1, but the EV density within the dense collagen fibers (938 nl−1) is much higher than the EV density within the tumor (188 nl−1). Scale bars, 100 μm. Yi Sun et al. Sci Adv 2018;4:eaau5603 Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

6 Fig. 5 Intraoperative label-free multimodal imaging system.
Intraoperative label-free multimodal imaging system. (A) A photograph of the compact and portable intraoperative label-free multimodal nonlinear imaging system. (B) Software interface of the imaging system. (C) System schematic. (D) Spectral range and display color of the four nonlinear optical imaging modalities. L, lens; GM, galvanometer-scanning mirror; DM, dichroic mirror; OBJ, objective; FW, filter wheel; PMT, photomultiplier tube. (Photo credit: Yi Sun, Biophotonics Imaging Laboratory, University of Illinois at Urbana-Champaign.)‏ Yi Sun et al. Sci Adv 2018;4:eaau5603 Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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