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The integrated high-resolution reflection-mode photoacoustic and fluorescence confocal microscopy.

ABSTRACT: A dual modality microscopy with the highest imaging resolution reported so far based on reflection-mode photoacoustic and confocal fluorescence is presented in this study. The unique design of the imaging head of the microscope makes it highly convenient for scalable high-resolution imaging by simply switching the optical objectives. The submicron resolution performance of the system is demonstrated via in vivo imaging of zebrafish, normal mouse ear, and a xenograft tumor model inoculated in the mouse ear. The imaging results confirm that the presented dual-modality microscopy imaging system could play a vital role in observing model organism, studying tumor angiogenesis and assessment of antineoplastic drugs.

SUBMITTER: Liu C

PROVIDER: S-EPMC6423349 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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The integrated high-resolution reflection-mode photoacoustic and fluorescence confocal microscopy.

Liu Chengbo C Liao Jiuling J Chen Longchao L Chen Jianhua J Ding Rubo R Gong Xiaojing X Cui Caimei C Pang Zhiqiang Z Zheng Wei W Song Liang L

Photoacoustics 20190228

A dual modality microscopy with the highest imaging resolution reported so far based on reflection-mode photoacoustic and confocal fluorescence is presented in this study. The unique design of the imaging head of the microscope makes it highly convenient for scalable high-resolution imaging by simply switching the optical objectives. The submicron resolution performance of the system is demonstrated via <i>in vivo</i> imaging of zebrafish, normal mouse ear, and a xenograft tumor model inoculated ...[more]

PMID: 30923675

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Project description:Histological visualizations are critical to clinical disease management and are fundamental to biological understanding. However, current approaches that rely on bright-field microscopy require extensive tissue preparation prior to imaging. These processes are both labor intensive and contribute to creating significant delays in clinical feedback for treatment decisions that can extend to 2-3 weeks for standard paraffin-embedded tissue preparation and interpretation, especially if ancillary testing is needed. Here, we present the first comprehensive study on the broad application of a novel label-free reflection-mode imaging modality known as photoacoustic remote sensing (PARS) for visualizing salient subcellular structures from various common histopathological tissue preparations and for use in unprocessed freshly resected tissues. The PARS modality permits non-contact visualizations of intrinsic endogenous optical absorption contrast to be extracted from thick and opaque biological targets with optical resolution. The technique was examined both as a rapid assessment tool that is capable of managing large samples (> 1 cm2) in under 10 min, and as a high contrast imaging modality capable of extracting specific biological contrast to simulate conventional histological stains such as hematoxylin and eosin (H&E). The capabilities of the proposed method are demonstrated in a variety of human tissue preparations including formalin-fixed paraffin-embedded tissue blocks and unstained slides sectioned from these blocks, including normal and neoplastic human brain, and breast epithelium involved with breast cancer. Similarly, PARS images of human skin prepared by frozen section clearly demonstrated basal cell carcinoma and normal human skin tissue. Finally, we imaged unprocessed murine kidney and achieved histologically relevant subcellular morphology in fresh tissue. This represents a vital step towards an effective real-time clinical microscope that overcomes the limitations of standard histopathologic tissue preparations and enables real-time pathology assessment.

Dataset Information

The integrated high-resolution reflection-mode photoacoustic and fluorescence confocal microscopy.

Publications

The integrated high-resolution reflection-mode photoacoustic and fluorescence confocal microscopy.

Similar Datasets

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