Optimization of the iDISCO protocol for 3D reconstruction of cancer samples using light sheet microscopy.

e15086 Background: Histopathology is widely recognized as the gold standard for cancer grading and staging, forming the cornerstone of many personalized treatment decisions. Despite its critical role, histopathological analysis remains susceptible to significant diagnostic challenges, with up to 10% of samples presenting ambiguous results that require further investigation 1 . Several studies have underlined that a fundamental limit to the accuracy of conventional histopathology stems from the intrinsic 2D nature of this method, which relies on the analysis of several thin slices of tissue rather than the intact 3D volume 2,3 . Emerging evidence highlights the potential of non-destructive 3D pathology methods to improve diagnostic accuracy and sensitivity. These methods have been shown to enhance the detection of metastases, the analysis of biomarkers, the assessment of vascular density and tortuosity, and the identification of basal-cell carcinoma outgrowths 3,4 . Furthermore, 3D histopathology offers a more comprehensive view of the tumor microenvironment (TME), capturing its molecular and morphological heterogeneity, which is critical for accurate diagnosis and prognosis 5 . Methods: In our laboratory, we developed an efficient pipeline for imaging volumetric tissue samples. This pipeline encompasses three key steps: (1) iDISCO tissue clearing and staining, that prevents light scattering and ensures high-quality imaging of the samples; (2) high-resolution light-sheet fluorescence microscopy (LSFM) imaging, a cutting-edge technique that enables rapid 3D reconstruction of macroscopic tissue samples with microscopic resolution; and (3) computational data analysis to extract quantitative information from the large imaging datasets generated 6 . While the imaging and data analysis steps are relatively fast, the tissue clearing process remains a bottleneck, as it can require several days depending on the sample size. This limitation presents a challenge for the clinical application, where rapid turnaround times are essential for timely diagnoses. Results: Here, we present an optimized version of the iDISCO protocol, which allows for the clearing and staining of samples using eosin and Sytox (a fluorescent analog of hematoxylin) in less than a single workday. Our protocol has so far been validated in liver and kidney organs, and we aim to test it on other organs in future studies. Conclusions: This advancement represents a further step towards introducing 3D histopathology into clinical practice, paving the way for more precise and comprehensive oncological diagnoses, ultimately improving patient outcomes and enabling more tailored therapeutic approaches. References: doi: 10.5858/arpa.2020-0458-OA. doi: 10.1117/1.JBO.27.3.036501. doi: 10.1111/cup.12535. doi: 10.1038/s41551-017-0139-0. doi: 10.1158/1078-0432.CCR-21-0529. doi: 10.1364/OE.20.020582.

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Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.e15086
Primary Topic
Cell Image Analysis Techniques
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article
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article

Optimization of the iDISCO protocol for 3D reconstruction of cancer samples using light sheet microscopy.

Alessandra Franceschini, Ludovico Silvestri, Claudia Capitini, Parmida Kouchakan
Journal of Clinical Oncology
Cell Image Analysis Techniques
article

Optimization of the iDISCO protocol for 3D reconstruction of cancer samples using light sheet microscopy.

Alessandra Franceschini, Ludovico Silvestri, Claudia Capitini, Parmida Kouchakan
article en

Abstract

e15086 Background: Histopathology is widely recognized as the gold standard for cancer grading and staging, forming the cornerstone of many personalized treatment decisions. Despite its critical role, histopathological analysis remains susceptible to significant diagnostic challenges, with up to 10% of samples presenting ambiguous results that require further investigation 1 . Several studies have underlined that a fundamental limit to the accuracy of conventional histopathology stems from the intrinsic 2D nature of this method, which relies on the analysis of several thin slices of tissue rather than the intact 3D volume 2,3 . Emerging evidence highlights the potential of non-destructive 3D pathology methods to improve diagnostic accuracy and sensitivity. These methods have been shown to enhance the detection of metastases, the analysis of biomarkers, the assessment of vascular density and tortuosity, and the identification of basal-cell carcinoma outgrowths 3,4 . Furthermore, 3D histopathology offers a more comprehensive view of the tumor microenvironment (TME), capturing its molecular and morphological heterogeneity, which is critical for accurate diagnosis and prognosis 5 . Methods: In our laboratory, we developed an efficient pipeline for imaging volumetric tissue samples. This pipeline encompasses three key steps: (1) iDISCO tissue clearing and staining, that prevents light scattering and ensures high-quality imaging of the samples; (2) high-resolution light-sheet fluorescence microscopy (LSFM) imaging, a cutting-edge technique that enables rapid 3D reconstruction of macroscopic tissue samples with microscopic resolution; and (3) computational data analysis to extract quantitative information from the large imaging datasets generated 6 . While the imaging and data analysis steps are relatively fast, the tissue clearing process remains a bottleneck, as it can require several days depending on the sample size. This limitation presents a challenge for the clinical application, where rapid turnaround times are essential for timely diagnoses. Results: Here, we present an optimized version of the iDISCO protocol, which allows for the clearing and staining of samples using eosin and Sytox (a fluorescent analog of hematoxylin) in less than a single workday. Our protocol has so far been validated in liver and kidney organs, and we aim to test it on other organs in future studies. Conclusions: This advancement represents a further step towards introducing 3D histopathology into clinical practice, paving the way for more precise and comprehensive oncological diagnoses, ultimately improving patient outcomes and enabling more tailored therapeutic approaches. References: doi: 10.5858/arpa.2020-0458-OA. doi: 10.1117/1.JBO.27.3.036501. doi: 10.1111/cup.12535. doi: 10.1038/s41551-017-0139-0. doi: 10.1158/1078-0432.CCR-21-0529. doi: 10.1364/OE.20.020582.

Journal of Clinical OncologyVol. 43(16_suppl)
WWF Italia (IT)
Openalex Percentile: Top 91%
Cell Image Analysis Techniques
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