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• W4312084131 abstract "•DRUM enables label-free histological imaging with the shortest turnaround time•The visibility of cell nuclei by DRUM is close to the degree of labeled images•DRUM reveals diagnostic features comparable with the standard H&E images•DRUM is non-destructive and is compatible with routine pathological examinations Routine examination for intraoperative histopathologic assessment is lengthy and laborious. Here, we present the dark-field reflectance ultraviolet microscopy (DRUM) that enables label-free imaging of unprocessed and thick tissues with subcellular resolution and a high signal-to-background ratio. To the best of our knowledge, DRUM provides image results for pathological assessment with the shortest turnaround time (2-3 min in total from sample preparation to tissue imaging). We also proposed a virtual staining process to convert DRUM images into pseudo-colorized images and enhance the image familiarity of pathologists. By imaging various tissues, we found DRUM can resolve cell nuclei and some extranuclear features, which are comparable to standard H&E images. Furthermore, the essential diagnostic features of intraoperatively excised tumor tissues also can be revealed by DRUM, demonstrating its potential as an additional aid for intraoperative histopathology. Routine examination for intraoperative histopathologic assessment is lengthy and laborious. Here, we present the dark-field reflectance ultraviolet microscopy (DRUM) that enables label-free imaging of unprocessed and thick tissues with subcellular resolution and a high signal-to-background ratio. To the best of our knowledge, DRUM provides image results for pathological assessment with the shortest turnaround time (2-3 min in total from sample preparation to tissue imaging). We also proposed a virtual staining process to convert DRUM images into pseudo-colorized images and enhance the image familiarity of pathologists. By imaging various tissues, we found DRUM can resolve cell nuclei and some extranuclear features, which are comparable to standard H&E images. Furthermore, the essential diagnostic features of intraoperatively excised tumor tissues also can be revealed by DRUM, demonstrating its potential as an additional aid for intraoperative histopathology. Intraoperative histopathology is essential for surgical margin assessment and is used to examine whether the tumor is completely excised.1Somerset H.L. Kleinschmidt-DeMasters B.K. Approach to the intraoperative consultation for neurosurgical specimens.Adv. Anat. Pathol. 2011; 18: 446-449Crossref PubMed Scopus (35) Google Scholar Routine pathological examination was performed by the microscopic examination of tissues that were formalin-fixed and paraffin-embedded (FFPE), thinly sectioned, and stained. This is a lengthy and laborious process that fails to intraoperatively guide surgeons. Preparing frozen tissues is a more rapid alternative, but it still requires a turnaround time of ∼30 min. Furthermore, freezing artifacts caused by edematous and fatty tissues affect histopathological interpretation and diagnostic accuracy.2Preeti A. Sameer G. Kulranjan S. Arun Abhinav S. Preeti R. Sunita Y. Madhu Mati G. Intra-operative frozen sections: experience at A tertiary Care centre.Asian Pac. J. Cancer Prev. 2016; 17: 5057-5061PubMed Google Scholar In addition, because of the destructive nature of FFPE histology and frozen sectioning, a large number of excised tissues may be wasted, compromising their value in downstream molecular and genetic analyses. Therefore, the development of a rapid and nondestructive tissue assessment tool can assist in intraoperative decision-making, minimize physical and mental patient suffering, and reduce surgical risk. Advanced optical imaging techniques have demonstrated significant success in pathological tissue assessment over the past few decades. Replacing the physical section of FFPE or frozen tissues with an optical section can significantly simplify the sample preparation procedure. Microscopy with ultraviolet surface excitation (MUSE) uses the shallow penetration depth of ultraviolet light to achieve moderate optical sectioning.3Fereidouni F. Harmany Z.T. Tian M. Todd A. Kintner J.A. McPherson J.D. Borowsky A.D. Bishop J. Lechpammer M. Demos S.G. Levenson R. Microscopy with ultraviolet surface excitation for rapid slide-free histology.Nat. Biomed. Eng. 2017; 1: 957-966Crossref PubMed Scopus (156) Google Scholar,4Chen Z. Yu W. Wong I.H.M. Wong T.T.W. Deep-learning-assisted microscopy with ultraviolet surface excitation for rapid slide-free histological imaging.Biomed. Opt Express. 2021; 12: 5920-5938Crossref PubMed Scopus (7) Google Scholar,5Yoshitake T. Giacomelli M.G. Quintana L.M. Vardeh H. Cahill L.C. Faulkner-Jones B.E. Connolly J.L. Do D. Fujimoto J.G. Rapid histopathological imaging of skin and breast cancer surgical specimens using immersion microscopy with ultraviolet surface excitation.Sci. Rep. 2018; 8: 4476Crossref PubMed Scopus (52) Google Scholar Structured illumination microscopy (SIM) rejects out-of-focus background digitally by leveraging the fact that only in-focus components can be modulated by structured illumination.6Schlichenmeyer T.C. Wang M. Elfer K.N. Brown J.Q. Video-rate structured illumination microscopy for high-throughput imaging of large tissue areas.Biomed. Opt Express. 2014; 5: 366-377Crossref PubMed Scopus (43) Google Scholar,7Zhang Y. Kang L. Lo C.T.K. Tsang V.T.C. Wong T.T.W. Rapid slide-free and non-destructive histological imaging using wide-field optical-sectioning microscopy.Biomed. Opt Express. 2022; 13: 2782-2796Crossref PubMed Scopus (1) Google Scholar,8Wang M. Kimbrell H.Z. Sholl A.B. Tulman D.B. Elfer K.N. Schlichenmeyer T.C. Lee B.R. Lacey M. Brown J.Q. High-resolution rapid diagnostic imaging of whole prostate biopsies using video-rate fluorescence structured illumination microscopy.Cancer Res. 2015; 75: 4032-4041Crossref PubMed Scopus (55) Google Scholar Light-sheet microscopy (LSM) achieves optical sectioning by a thin “selective” illumination plane and the signal collection from the orthogonal direction.9Glaser A.K. Reder N.P. Chen Y. McCarty E.F. Yin C. Wei L. Wang Y. True L.D. Liu J.T.C. Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens.Nat. Biomed. Eng. 2017; 1: 0084Crossref PubMed Scopus (228) Google Scholar,10Glaser A.K. Reder N.P. Chen Y. Yin C. Wei L. Kang S. Barner L.A. Xie W. McCarty E.F. Mao C. et al.Multi-immersion open-top lightsheet microscope for high-throughput imaging of cleared tissues.Nat. Commun. 2019; 10: 2781-2788Crossref PubMed Scopus (100) Google Scholar,11Xie W. Glaser A.K. Vakar-Lopez F. Wright J.L. Reder N.P. Liu J.T.C. True L.D. Diagnosing 12 prostate needle cores within an hour of biopsy via open-top light-sheet microscopy.J. Biomed. Opt. 2020; 25: 126502Crossref PubMed Scopus (10) Google Scholar,12Chen Y. Xie W. Glaser A.K. Reder N.P. Mao C. Dintzis S.M. Vaughan J.C. Liu J.T.C. Rapid pathology of lumpectomy margins with open-top light-sheet (OTLS) microscopy.Biomed. Opt Express. 2019; 10: 1257-1272Crossref PubMed Scopus (31) Google Scholar Although these technologies can be applied to the pathological assessment of thick tissues and shorten turnaround time, fluorescence labeling is still required to provide image contrast. Fluorescence labeling is challenging to be integrated into the current clinical practice, especially intraoperative pathological examination. Thus, label-free imaging based on endogenous contrast mechanisms is highly desired in modern clinical settings. Reflectance confocal microscopy (RCM) utilizes the refractive properties of various components, including melanin, keratin, and collagen, and exhibits superiority in dermatology.13Que S.K.T. Fraga-Braghiroli N. Grant-Kels J.M. Rabinovitz H.S. Oliviero M. Scope A. Through the looking glass: basics and principles of reflectance confocal microscopy.J. Am. Acad. Dermatol. 2015; 73: 276-284Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar,14Navarrete-Dechent C. Cordova M. Liopyris K. Rishpon A. Aleissa S. Rossi A.M. Lee E. Chen C.C.J. Busam K.J. Marghoob A.A. Nehal K.S. Reflectance confocal microscopy and dermoscopy aid in evaluating repigmentation within or adjacent to lentigo maligna melanoma surgical scars.J. Eur. Acad. Dermatol. Venereol. 2020; 34: 74-81Crossref PubMed Scopus (19) Google Scholar,15Yin C. Wei L. Abeytunge S. Peterson G. Rajadhyaksha M. Liu J. Label-free in vivo pathology of human epithelia with a high-speed handheld dual-axis confocal microscope.J. Biomed. Opt. 2019; 24: 030501Crossref PubMed Scopus (8) Google Scholar The contrast in ultraviolet photoacoustic microscopy (UV-PAM) arises from intrinsic light absorption, which enables high-contrast imaging of cell nuclei.16Wong T.T.W. Zhang R. Hai P. Zhang C. Pleitez M.A. Aft R.L. Novack D.V. Wang L.V. Fast label-free multilayered histology-like imaging of human breast cancer by photoacoustic microscopy.Sci. Adv. 2017; 3: e1602168Crossref PubMed Scopus (172) Google Scholar,17Liu X. Wong T.T.W. Shi J. Ma J. Yang Q. Wang L.V. Label-free cell nuclear imaging by Gruneisen relaxation photoacoustic microscopy.Opt. Lett. 2018; 43: 947-950Crossref PubMed Scopus (19) Google Scholar,18Lai P. Nie L. Wang L. Special issue “Photoacoustic imaging: microscopy, tomography, and their recent applications in biomedicine” in visual computation for industry, biomedicine, and art.Vis. Comput. Ind. Biomed. Art. 2021; 4: 16Crossref PubMed Scopus (4) Google Scholar Nonlinear microscopy, including multiphoton microscopy and stimulated Raman scattering microscopy, can generate optical signals from different sources, such as autofluorescence emission,19Zhang Y. Kang L. Wong I.H.M. Dai W. Li X. Chan R.C.K. Hsin M.K.Y. Wong T.T.W. High-throughput, label-free and slide-free histological imaging by computational microscopy and unsupervised learning.Adv. Sci. 2022; 9: 2102358Crossref Scopus (11) Google Scholar,20Skala M.C. Riching K.M. Gendron-Fitzpatrick A. Eickhoff J. Eliceiri K.W. White J.G. Ramanujam N. In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia.Proc. Natl. Acad. Sci. USA. 2007; 104: 19494-19499Crossref PubMed Scopus (822) Google Scholar,21Li X. Li H. He X. Chen T. Xia X. Yang C. Zheng W. Spectrum- and time-resolved endogenous multiphoton signals reveal quantitative differentiation of premalignant and malignant gastric mucosa.Biomed. Opt Express. 2018; 9: 453-471Crossref PubMed Scopus (25) Google Scholar noncentrosymmetric structures,22Chen X. Nadiarynkh O. Plotnikov S. Campagnola P.J. Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure.Nat. Protoc. 2012; 7: 654-669Crossref PubMed Scopus (631) Google Scholar,23Tao Y.K. Shen D. Sheikine Y. Ahsen O.O. Wang H.H. Schmolze D.B. Johnson N.B. Brooker J.S. Cable A.E. Connolly J.L. Fujimoto J.G. Assessment of breast pathologies using nonlinear microscopy.Proc. Natl. Acad. Sci. USA. 2014; 111: 15304-15309Crossref PubMed Scopus (149) Google Scholar tissue interfaces with a change in refractive indices,24Weigelin B. Bakker G.J. Friedl P. Third harmonic generation microscopy of cells and tissue organization.J. Cell Sci. 2016; 129: 245-255Crossref PubMed Scopus (151) Google Scholar,25Sun Y. You S. Tu H. Spillman Jr., D.R. Chaney E.J. Marjanovic M. Li J. Barkalifa R. Wang J. Higham A.M. et al.Intraoperative visualization of the tumor microenvironment and quantification of extracellular vesicles by label-free nonlinear imaging.Sci. Adv. 2018; 4: eaau5603Crossref PubMed Scopus (59) Google Scholar and molecular vibrations.26Freudiger C.W. Min W. Saar B.G. Lu S. Holtom G.R. He C. Tsai J.C. Kang J.X. Xie X.S. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy.Science. 2008; 322: 1857-1861Crossref PubMed Scopus (1740) Google Scholar,27Liu Z. Su W. Ao J. Wang M. Jiang Q. He J. Gao H. Lei S. Nie J. Yan X. et al.Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology.Nat. Commun. 2022; 13: 4050Crossref PubMed Scopus (41) Google Scholar,28Orringer D.A. Pandian B. Niknafs Y.S. Hollon T.C. Boyle J. Lewis S. Garrard M. Hervey-Jumper S.L. Garton H.J.L. Maher C.O. et al.Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy.Nat. Biomed. Eng. 2017; 1: 0027Crossref PubMed Scopus (312) Google Scholar However, these scanning-based methods have limited imaging throughput and cannot meet the requirements for rapid pathological tissue assessment. Furthermore, the need for bulky and expensive lasers for UV-PAM or NLM increases the total facility budget and is a challenge for the cost-sensitive field of pathology. Full-field optical coherence tomography (FF-OCT) can detect the back-reflected light from structures with different refractive indices and enables parallel detection of 2D label-free images without scanning.29Beaurepaire E. Boccara A.C. Lebec M. Blanchot L. Saint-Jalmes H. Full-field optical coherence microscopy.Opt. Lett. 1998; 23: 244-246Crossref PubMed Scopus (389) Google Scholar,30Dubois A. Vabre L. Boccara A.C. Beaurepaire E. High-resolution full-field optical coherence tomography with a Linnik microscope.Appl. Opt. 2002; 41: 805-812Crossref PubMed Scopus (535) Google Scholar,31Assayag O. Grieve K. Devaux B. Harms F. Pallud J. Chretien F. Boccara C. Varlet P. Imaging of non-tumorous and tumorous human brain tissues with full-field optical coherence tomography.Neuroimage. Clin. 2013; 2: 549-557Crossref PubMed Scopus (141) Google Scholar However, FF-OCT is not typically designed to achieve subcellular resolution and cannot visualize cell nuclei, which are important diagnostic features. Here, we present a rapid, nondestructive, and cost-effective histological imaging method called dark-field reflectance ultraviolet microscopy (DRUM). By using the endogenous mechanisms of both reflectance and absorption, DRUM enables label-free imaging of unprocessed and thick tissues with subcellular resolution and high signal-to-background ratio (SBR). To the best of our knowledge, DRUM can provide the image results for pathological assessment with the shortest turnaround time (2-3 min in total from sample preparation to tissue imaging). Furthermore, a virtual staining process was proposed to convert DRUM images into pseudo-colorized images (pseudo-colorized DRUM) and enhance the image familiarity of the pathologists. The capacity of DRUM was verified by imaging various tissues, including mouse brain and spleen tissues, human brain tumor tissues, and human breast cancer tissues. The results suggest that the proposed method can not only rapidly provide tissue architecture and subcellular features similar to conventional pathological images but also accurately differentiate between normal and tumor tissues. DRUM enables histological imaging by leveraging two endogenous contrast mechanisms. First, nucleic acids have strong absorption characteristics in the deep-ultraviolet (deep-UV) light range, which can be used to provide a negative contrast of cell nuclei.32Kumamoto Y. Taguchi A. Kawata S. Deep-ultraviolet biomolecular imaging and analysis.Adv. Opt. Mater. 2019; 7: 1801099Crossref Scopus (26) Google Scholar Especially, 260-nm deep-UV light corresponds to the absorption peak of nucleic acids32Kumamoto Y. Taguchi A. Kawata S. Deep-ultraviolet biomolecular imaging and analysis.Adv. Opt. Mater. 2019; 7: 1801099Crossref Scopus (26) Google Scholar,33Zeskind B.J. Jordan C.D. Timp W. Trapani L. Waller G. Horodincu V. Ehrlich D.J. Matsudaira P. Nucleic acid and protein mass mapping by live-cell deep-ultraviolet microscopy.Nat. Methods. 2007; 4: 567-569Crossref PubMed Scopus (130) Google Scholar,34Ojaghi A. Carrazana G. Caruso C. Abbas A. Myers D.R. Lam W.A. Robles F.E. Label-free hematology analysis using deep-ultraviolet microscopy.Proc. Natl. Acad. Sci. USA. 2020; 117: 14779-14789Crossref PubMed Scopus (28) Google Scholar and is used in the DRUM system to maximize the negative signal of cell nuclei. Second, some extranuclear components, such as cytoplasm, keratin, and collagen, strongly reflect 260-nm deep-UV light and appear bright in DRUM images. Furthermore, the difference in refractive index (RI) of various extranuclear components causes the recognition of some tissue features.35Giannios P. Toutouzas K.G. Matiatou M. Stasinos K. Konstadoulakis M.M. Zografos G.C. Moutzouris K. Visible to near-infrared refractive properties of freshly-excised human-liver tissues: marking hepatic malignancies.Sci. Rep. 2016; 6: 27910Crossref PubMed Scopus (67) Google Scholar,36Menzel M. Axer M. Amunts K. De Raedt H. Michielsen K. Diattenuation Imaging reveals diferent brain tissue properties.Sci. Rep. 2019; 9: 1939Crossref PubMed Scopus (27) Google Scholar However, these two endogenous signals cannot be directly collected using conventional reflected light microscopy (RLM) with UV excitation. In the RLM, the reflected signals include specular reflection, diffuse reflection from tissues, and scattered light from tissue interfaces (Figure 1A).37van Ginneken B. Stavridi M. Koenderink J.J. Diffuse and specular reflectance from rough surfaces.Appl. Opt. 1998; 37: 130-139Crossref PubMed Scopus (168) Google Scholar,38Wolff L.B. Diffuse-reflectance model for smooth dielectric surfaces.J. Opt. Soc. Am. A. 1994; 11: 2956-2968Crossref Scopus (112) Google Scholar In general, specular reflection is the dominant source in a bright-field configuration, and details from the tissues are almost invisible (Figure 1C). Thus, our DRUM microscope employs dark-field illumination to eliminate the background of specular reflection. However, even in the dark-field configuration, the collected signals primarily originate from the scattered light from the tissue interfaces and cannot reveal the cell nuclei (Figure 1D). To relieve the influence of the scattered light, DRUM further filled the space between the coverslip and tissue interfaces with a liquid to match the RI. In this study, phosphate-buffered saline (PBS) or glycerinum was chosen as the filling liquid, which has an RI similar to that of biological tissues35Giannios P. Toutouzas K.G. Matiatou M. Stasinos K. Konstadoulakis M.M. Zografos G.C. Moutzouris K. Visible to near-infrared refractive properties of freshly-excised human-liver tissues: marking hepatic malignancies.Sci. Rep. 2016; 6: 27910Crossref PubMed Scopus (67) Google Scholar,36Menzel M. Axer M. Amunts K. De Raedt H. Michielsen K. Diattenuation Imaging reveals diferent brain tissue properties.Sci. Rep. 2019; 9: 1939Crossref PubMed Scopus (27) Google Scholar,39Khan R. Gul B. Khan S. Nisar H. Ahmad I. Refractive index of biological tissues: Review, measurement techniques, and applications.Photodiagnosis Photodyn. Ther. 2021; 33: 102192Crossref PubMed Scopus (32) Google Scholar and shows great biocompatibility. In other words, the image contrast of DRUM arises from two aspects: the absorption of cell nuclei towards deep-UV light and diffuse reflection, which has been multiply scattered by extranuclear components within tissues (Figure 1B). By fully utilizing the two label-free contrast mechanisms, DRUM can not only resolve cell nuclei with high visibility but also provide rich information on extranuclear features (Figure 1E). As depicted in Figure 1F, our DRUM microscope uses UV light-emitting diodes (LEDs) and a set of band-pass filters to tune the imaging wavelength. Here, we use three filters with central wavelengths at 260, 357, and 447 nm. The three filters in the collecting path indicate DRUM images (I260), autofluorescence images (I357), and fluorescence images (I447) stained with 4′,6-diamidino-2-phenylindole (DAPI), respectively. It has been reported that the autofluorescence of UV excitation is dominated by tryptophan, which has an emission peak at 357 nm. Other UV fluorescents, such as amino acids, tyrosine, and phenylalanine, are often quenched by tryptophan.40Li C. Pitsillides C. Runnels J.M. Côté D. Lin C.P. Multiphoton microscopy of live tissues with ultraviolet autofluorescence.IEEE J. Sel. Top. Quant. Electron. 2010; 16: 516-523Crossref Scopus (29) Google Scholar In this study, DRUM images only require a frame of I260 without post-processing, whereas both I260 and I357 are used to form a pseudo-colorized DRUM image (Figure 1G, see method details). I447 is an important verification for cell nuclei imaging and provides a comparison with DRUM images. It is noted that, to remove artifacts from non-uniform illumination, the collected images were normalized to a reference background image acquired from a blank area on the sample for each wavelength. In addition, the image acquisition in different filter channels requires slight axial adjustment for re-focusing. Because the DRUM microscope is simple and it has relatively small chromatic aberration. As a result, the collection of both I260 and I357 will not significantly increase the turnaround time for pseudo-colorized DRUM images (see the last section of Results). First, we validated the performance of DRUM by imaging label-free thin mouse brain slices. One excised mouse brain was manually cut into two halves at the coronal plane at a distance of −1.94 mm from bregma. One half was processed into frozen tissues and sectioned at a thickness of ∼10 μm, and the thin frozen slices were imaged using DRUM. Because no exogenous components are introduced during frozen slice preparation, frozen thin slices are suitable for the preliminary validation of label-free DRUM imaging. The other half was histologically processed to obtain the corresponding H&E-stained images for comparison purposes. As shown in Figure 2, cell nuclei distributed in different regions of the mouse brain were revealed with negative contrast in the DRUM images. In particular, the densely packed cell nuclei in CA1 and CA2 of the hippocampus (CA1 and CA2) (Figures 2B and 2C) can be resolved individually. Other anatomical structures, such as the corpus callosum (Figure 2B) and cerebral peduncle (Figure 2D), were also well recognized because of RI differences.41Sun J. Lee S.J. Wu L. Sarntinoranont M. Xie H. Refractive index measurement of acute rat brain tissue slices using optical coherence tomography.Opt Express. 2012; 20: 1084-1095Crossref PubMed Scopus (58) Google Scholar Multiple similarities can be found in the DRUM and H&E-stained images. However, similar to other UV-based imaging methods,16Wong T.T.W. Zhang R. Hai P. Zhang C. Pleitez M.A. Aft R.L. Novack D.V. Wang L.V. Fast label-free multilayered histology-like imaging of human breast cancer by photoacoustic microscopy.Sci. Adv. 2017; 3: e1602168Crossref PubMed Scopus (172) Google Scholar,19Zhang Y. Kang L. Wong I.H.M. Dai W. Li X. Chan R.C.K. Hsin M.K.Y. Wong T.T.W. High-throughput, label-free and slide-free histological imaging by computational microscopy and unsupervised learning.Adv. Sci. 2022; 9: 2102358Crossref Scopus (11) Google Scholar it is still difficult to resolve nucleoli using DRUM. To demonstrate the imaging capacity of thick and label-free tissues by DRUM and pseudo-colorized DRUM, freshly excised mouse brains and spleens were manually sectioned at a thickness of ∼1.5 mm using a scalpel and then imaged by DRUM. In addition, to obtain the pseudo-colorized DRUM, the corresponding autofluorescence images from the same area were captured by changing the filter wheel into a 357-nm filter channel. Subsequently, the tissues were processed using a standard histological procedure to obtain H&E-stained images for comparison. Figure 3A shows the imaging results of one whole coronal plane of the mouse brain, while two zoomed-in DRUM images are shown in Figure 3B. Even in freshly excised, thick, and label-free tissues, cell nuclei and some anatomical structures, such as the caudoputamen, internal capsule, hippocampus, corpus callosum, and ventricle, were resolved. Furthermore, we magnified four ROIs (indicated as blue, yellow, red, and brown solid boxes in Figure 3B) with the corresponding pseudo-colorized DRUM and H&E images in Figures 3C–3F. The pseudo-colorized DRUM images can closely approximate the authentic H&E appearance and thus enhance the image familiarity of the pathologists. Similarly, cell nuclei distributed in the cortex of the mouse spleen were well recognized by DRUM, as shown in Figures 3G–3I. The corresponding pseudo-colorized DRUM and H&E images are also presented, and a similar density degree of cell nuclei is observed. Note that the microtome sectioned FFPE thin slice cannot exactly replicate the surface imaged by DRUM owing to tissue deformation and the difference in imaging thickness. Despite this difference, the structural features were remarkably similar. Furthermore, the turnaround time of the proposed method is only 2-3 min, including 1-2 min for thick-tissues preparation, ∼300 ms for each image acquisition, and ∼0.5 s for virtual staining. Even for extended-FOV imaging, the total turnaround time is still a few minutes, owing to the fast imaging ability of wide-field DRUM configuration. In addition, a cover glass was used to flatten the tissue surface as much as possible, then no re-focusing is needed during the extended-FOV imaging process. The synchronous control of the UV sCMOS camera and the 3-axis motorized stage was also performed to minimize the time cost of extended-FOV imaging. Glioblastoma (GBM) is among the most invasive and lethal cancers, frequently infiltrating the surrounding healthy tissue and resulting in rapid recurrence.42Sanai N. Polley M.Y. McDermott M.W. Parsa A.T. Berger M.S. An extent of resection threshold for newly diagnosed glioblastomas.J. Neurosurg. 2011; 115: 3-8Crossref PubMed Scopus (1134) Google Scholar,43Renner D.N. Jin F. Litterman A.J. Balgeman A.J. Hanson L.M. Gamez J.D. Chae M. Carlson B.L. Sarkaria J.N. Parney I.F. et al.Effective treatment of established GL261 murine gliomas through picornavirus vaccination-enhanced tumor antigen-specific CD8+ T Cell Responses.PLoS One. 2015; 10: e0125565Crossref PubMed Scopus (20) Google Scholar Intraoperative histopathology is essential for the accurate demarcation of glioblastoma. Here, the feasibility of DRUM and pseudo-colorized DRUM in clinical applications was verified by imaging GL261 tumor-bearing mouse brain tissues. Freshly excised and thick tissues were obtained 14 days after tumor cell implantation. After DRUM imaging, the specimens were histologically processed to obtain H&E-stained images for comparison. Figure 4A depicts an extended-FOV DRUM image, whereas two zoomed-in DRUM images with the corresponding H&E images are presented in Figures 4B and 4C. Similar to the H&E image, a cluster of dense cell nuclei was surrounded by bright extranuclear features in the DRUM image (Figure 4B), which was diagnosed as a marginal invasion of the tumor by one pathology (Z. Wen). Furthermore, the tumors were clearly established and highly proliferative in the lower region of Figure 4A, and both the DRUM and H&E images outline a clear boundary between the normal and tumor regions (Figure 4C). The ROIs (indicated as blue and green solid boxes in Figure 4C) from the normal and tumor regions, respectively, are magnified with the corresponding pseudo-colorized DRUM and H&E images in Figures 4D and 4E. Similar to the conventional H&E results, the tumor region in DRUM and pseudo-colorized DRUM showed a high density of cell nuclei and was immediately adjacent to the normal tissue with low cellularity. To analyze the nuclear features, the cross-sectional area and intercellular distance of cell nuclei, which play an important role in tissue phenotyping and histologic tumor grading, were extracted for the quantitative comparison between normal and tumor regions. To calculate these two parameters, DRUM images were segmented using a Fiji plugin44Schindelin J. Arganda-Carreras I. Frise E. Kaynig V. Longair M. Pietzsch T. Preibisch S. Rueden C. Saalfeld S. Schmid B. et al.Fiji: an open-source platform for biological-image analysis.Nat. Methods. 2012; 9: 676-682Crossref PubMed Scopus (32914) Google Scholar (trainable Weka segmentation45Arganda-Carreras I. Kaynig V. Rueden C. Eliceiri K.W. Schindelin J. Cardona A. Sebastian Seung H. Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.Bioinformatics. 2017; 33: 2424-2426Crossref PubMed Scopus (1067) Google Scholar) and subsequently converted to a binary image (see method details). With the localized center positions of the cell nuclei, the intercellular distance is calculated as the shortest adjacent distance to a neighboring cell nucleus. Figures 4F and 4G show the statistical results, which were calculated from 50 cell nuclei in the two regions of Figure 4C. The median values of intercellular distance were 16.97 μm for normal cells and 10.1 μm for tumor cells, while the median values of the cross-sectional area were 39.05 μm2 for normal cells and 90.47 μm2 for tumor cells. This indicates that tumor cells and normal cells can be distinguished based on nuclear features. To further demonstrate the capacity of DRUM and pseudo-colorized DRUM in an intraoperative setting, formalin-fixed and thick human brain tumor tissues were imaged using the corresponding H&E-stained images as a reference. The specimen from the patient was pathologically confirmed as a meningioma. An extended FOV DRUM image with the corresponding H&E image under the same magnification is shown in Figure 5A. Evidently, lobules of tumorous cells are demarcated by collagen-rich bundles, which is typical for meningiomas. One region in Figure 5A (indicated by the green dashed box) is magnified in Figure 5B, where a cluster of tumor cell nuclei can be clearly observed. Furthermore, Figures 5C and 5D show two zoomed-in DRUM images (indicated by r" @default.
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• W4312084131 title "Rapid and label-free histological imaging of unprocessed surgical tissues via dark-field reflectance ultraviolet microscopy" @default.
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