Development of tools to facilitate correlative 2D light microscopy and 3D x-ray histology for biomedical applications

Abstract

Histology is the study of the microscopic structure of biological tissues, where thin (4 Histology is the study of the microscopic structure of biological tissues, where thin (4 μm) sections of tissues such as lung, skin, or brain are examined under the light microscope (LM). These methods have underpinned many discoveries in biology and are widely used in research and clinical diagnoses. However, classical histology only provides a two dimensional (2D) view of tissue features that are inherently three-dimensional (3D). X-ray micro-computed tomography (μCT) generates 3D images of biological tissues in the millimetre down to micrometre (μm) scale. Examples of biological structures in this range include capillaries, lung alveoli, and even whole organs of small animals. As μCT imaging does not destroy the sample, the same specimen can subsequently be imaged with LM, extending current 2D methods with 3D visualisation of tissue microstructure. This project develops a method for correlative LM and μCT of formalin-fixed paraffin-embedded (FFPE) specimens, which is compatible with routine histology workflows. Firstly, protocols for μCT imaging of FFPE tissues without additional sample preparation were developed, providing the equivalent of hundreds of histology slides within hours. An objective and user-friendly image quality measurement tool (GaussQuality) was developed to quantitatively optimise these protocols. Image analysis tools for semi-automatically integrating correlative images were developed using open-source software, saving hours of researcher time. The developed workflow guides the user through co-registering their correlative image datasets to obtain interactive visualisations of their data. This workflow preserves the high spatial resolution of LM, which is not currently possible with equivalent methods in literature. Notably, a tool was developed to transfer expert annotations made on gold standard LM images to the μCT images, bringing 3D context to established methods of visualising biological structures. In the future, these transferred annotations can provide validated training data for 3D segmentation of biological features from μCT images. The developed acquisition and integration workflows were applied to three case studies in collaboration with clinical partners: assessment of COVID-19-related lung damage patterns, classification of lung adenocarcinoma subtypes, and detection of lymph node metastasis in head and neck cancers. Integration of the correlative images for these studies was achieved within hours, compared to the several days it would have taken to manually process these images. This provides proof that correlative LM and μCT has huge potential to facilitate imaging studies that were previously inaccessible, adding a new dimension to our understanding of biological structure. (μm) sections of tissues such as lung, skin, or brain are examined under the light microscope (LM). These methods have underpinned many discoveries in biology and are widely used in research and clinical diagnoses. However, classical histology only provides a twodimensional (2D) view of tissue features that are inherently three-dimensional (3D). X-ray micro-computed tomography (CT) generates 3D images of biological tissues in the millimetre down to micrometre (μm) scale. Examples of biological structures in this range include capillaries, lung alveoli, and even whole organs of small animals. As μCT imaging does not destroy the sample, the same specimen can subsequently be imaged with LM, extending current 2D methods with 3D visualisation of tissue microstructure. This project develops a method for correlative LM and μCT of formalin-fixed paraffin-embedded (FFPE) specimens, which is compatible with routine histology workflows. Firstly, protocols for μCT imaging of FFPE tissues without additional sample preparation were developed, providing the equivalent of hundreds of histology slides within hours. An objective and user-friendly image quality measurement tool (GaussQuality) was developed to quantitatively optimise these protocols. Image analysis tools for semi-automatically integrating correlative images were developed using open-source software, saving hours of researcher time. The developed workflow guides the user through co-registering their correlative image datasets to obtain interactive visualisations of their data. This workflow preserves the high spatial resolution of LM, which is not currently possible with equivalent methods in literature. Notably, a tool was developed to transfer expert annotations made on gold standard LM images to the μCT images, bringing 3D context to established methods of visualising biological structures. In the future, these transferred annotations can provide validated training data for 3D segmentation of biological features from μCT images. The developed acquisition and integration workflows were applied to three case studies in collaboration with clinical partners: assessment of COVID-19-related lung damage patterns, classification of lung adenocarcinoma subtypes, and detection of lymph node metastasis in head and neck cancers. Integration of the correlative images for these studies was achieved within hours, compared to the several days it would have taken to manually process these images. This provides proof that correlative LM and μCT has huge potential to facilitate imaging studies that were previously inaccessible, adding a new dimension to our understanding of biological structure.

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ORCID for Philipp Schneider: orcid.org/0000-0001-7499-3576

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