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Bozic I, Behr MR, Brown JQ. Quantitative and comparative assessment of dyes and protocols for rapid ex vivo microscopy of fresh tissues. Sci Rep 2024; 14:21376. [PMID: 39271788 PMCID: PMC11399393 DOI: 10.1038/s41598-024-72213-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 09/04/2024] [Indexed: 09/15/2024] Open
Abstract
Using ex vivo microscopy, virtual pathology can improve histological procedures by providing pathology images in near real-time without tissue destruction. Several emerging and promising approaches leverage fast-acting small-molecule fluorescent stains to replicate traditional pathology structural contrast, combined with rapid optical sectioning microscopes. However, several vital challenges must be addressed to translate virtual pathology into the clinical environment. One such challenge is selecting robust, reliable, and repeatable staining protocols that can be adopted across institutions. In this work, we addressed the effects of dye selection and staining protocol on image quality in rapid point-of-care imaging settings. For this purpose, we used structured illumination microscopy to evaluate fluorescent dyes currently used in the field of ex vivo virtual pathology, in particular, studying the effects of staining protocol and temporal and photostability on image quality. We observed that DRAQ5 and SYBR gold provide higher image quality than TO-PRO3 and RedDot1 in the nuclear channel and Eosin Y515 in the extracellular/cytoplasmic channel than Atto488. Further, we found that TO-PRO3 and Eosin Y515 are less photostable than other dyes. Finally, we identify the optimal staining protocol for each dye and demonstrate pan-species generalizability.
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Affiliation(s)
- Ivan Bozic
- Department of Biomedical Engineering, Tulane University, New Orleans, 70118, USA
| | - Madeline R Behr
- Department of Biomedical Engineering, Tulane University, New Orleans, 70118, USA
| | - J Quincy Brown
- Department of Biomedical Engineering, Tulane University, New Orleans, 70118, USA.
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Hu G, Greene J, Zhu J, Yang Q, Zheng S, Li Y, Alido J, Guo R, Mertz J, Tian L. HiLo microscopy with caustic illumination. BIOMEDICAL OPTICS EXPRESS 2024; 15:4101-4110. [PMID: 39022539 PMCID: PMC11249696 DOI: 10.1364/boe.527264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 05/18/2024] [Accepted: 05/19/2024] [Indexed: 07/20/2024]
Abstract
HiLo microscopy is an optical sectioning structured illumination microscopy technique based on computationally combining two images: one with uniform illumination and the other with structured illumination. The most widely used structured illumination in HiLo microscopy is random speckle patterns, due to their simplicity and resilience to tissue scattering. Here, we present a novel HiLo microscopy strategy based on random caustic patterns. Building on an off-the-shelf diffuser and a low-coherence LED source, we demonstrate that caustic HiLo can achieve 4.5 µm optical sectioning capability with a 20× 0.75 NA objective. In addition, with the distinct intensity statistical properties of caustic patterns, we show that our caustic HiLo outperforms speckle HiLo, achieving enhanced optical sectioning capability and preservation of fine features by imaging scattering fixed brain sections of 100 µm, 300 µm, and 500 µm thicknesses. We anticipate that this new structured illumination technique may find various biomedical imaging applications.
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Affiliation(s)
- Guorong Hu
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
| | - Joseph Greene
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
| | - Jiabei Zhu
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
| | - Qianwan Yang
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
| | - Shuqi Zheng
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
| | - Yunzhe Li
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
- Currently with the Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, California 94720, USA
| | - Jeffrey Alido
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
| | - Ruipeng Guo
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
| | - Jerome Mertz
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
- Department of Biomedical Engineering, Boston University
, Boston, Massachusetts 02215, USA
| | - Lei Tian
- Department of Electrical & Computer Engineering, Boston University
, Boston, Massachusetts 02215, USA
- Department of Biomedical Engineering, Boston University
, Boston, Massachusetts 02215, USA
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Rapid and label-free histological imaging of unprocessed surgical tissues via dark-field reflectance ultraviolet microscopy. iScience 2022; 26:105849. [PMID: 36647380 PMCID: PMC9839964 DOI: 10.1016/j.isci.2022.105849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/04/2022] [Accepted: 12/19/2022] [Indexed: 12/29/2022] Open
Abstract
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.
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