Visualising cancer in 3D: 3-Dimensional Tissue Imaging for management of cutaneous basal cell carcinoma.

Surgical management of basal cell carcinoma (BCC) typically involves surgical excision with post-operative margin assessment using the bread-loafing technique; or gold-standard Mohs micrographic surgery (MMS), where margins are iteratively examined for residual cancer after tumour removal, with additional excisions performed upon detecting residual tumour at margins. There is limited sampling of resection margins with bread loafing, with detection of positive margins 44% of the time using 2 mm intervals. To resolve this, we have developed three-dimensional (3D) Tissue Imaging for: (1) complete examination of cancer margins and (2) detection of tumour proximity to nerves and blood vessels. 3D Tissue optical clearing with a light sheet imaging protocol was developed for margin assessment in two datasets assessed by two independent evaluators: (1) 48 samples from 29 patients with varied BCC subtypes, sizes and pigmentation levels; (2) 32 samples with matching Mohs' surgeon reading of tumour margins using two-dimensional haematoxylin & eosin-stained sections. The 3D Tissue Imaging protocol permits a complete examination of deeper and peripheral margins. Two independent evaluators achieved negative predictive values of 92.3% and 88.24% with 3D Tissue Imaging. Images obtained from 3D Tissue Imaging recapitulates histological features of BCC, such as nuclear crowding, palisading and retraction clefting and provides a 3D context for recognising normal skin adnexal structures. Concurrent immunofluorescence labelling of nerves and blood vessels allows visualisation of structures closer to tumour-positive regions, which may have a higher risk for neural and vascular infiltration. Together, this method provides more information in a 3D spatial context, enabling better cancer management by clinicians.

MarShie: a clearing protocol for 3D analysis of single cells throughout the bone marrow at subcellular resolution.

Analyzing immune cell interactions in the bone marrow is vital for understanding hematopoiesis and bone homeostasis. Three-dimensional analysis of the complete, intact bone marrow within the cortex of whole long bones remains a challenge, especially at subcellular resolution. We present a method that stabilizes the marrow and provides subcellular resolution of fluorescent signals throughout the murine femur, enabling identification and spatial characterization of hematopoietic and stromal cell subsets. By combining a pre-processing algorithm for stripe artifact removal with a machine-learning approach, we demonstrate reliable cell segmentation down to the deepest bone marrow regions. This reveals age-related changes in the marrow. It highlights the interaction between CX3CR1+ cells and the vascular system in homeostasis, in contrast to other myeloid cell types, and reveals their spatial characteristics after injury. The broad applicability of this method will contribute to a better understanding of bone marrow biology.

High-resolution three-dimensional imaging for precise staging in melanoma.

INTRODUCTION: Many cancer guidelines include sentinel lymph node (SLN) staging to identify microscopic metastatic disease. Current SLN analysis of melanoma patients is effective but has the substantial drawback that only a small representative portion of the node is sampled, whereas most of the tissue is discarded. This might explain the high clinical false-negative rate of current SLN diagnosis in melanoma. Furthermore, the quantitative assessment of metastatic load and microanatomical localisation might yield prognosis with higher precision. Thus, methods to analyse entire SLNs with cellular resolution apart from tedious sequential physical sectioning are required. PATIENTS AND METHODS: Eleven melanoma patients eligible to undergo SLN biopsy were included in this prospective study. SLNs were fixed, optically cleared, whole-mount stained and imaged using light sheet fluorescence microscopy (LSFM). Subsequently, compatible and unbiased gold standard histopathological assessment allowed regular patient staging. This enabled intrasample comparison of LSFM and histological findings. In addition, the development of an algorithm, RAYhance, enabled easy-to-handle display of LSFM data in a browsable histologic slide-like fashion. RESULTS: We comprehensively quantify total tumour volume while simultaneously visualising cellular and anatomical hallmarks of the associated SLN architecture. In a first-in-human study of 21 SLN of melanoma patients, LSFM not only confirmed all metastases identified by routine histopathological assessment but also additionally revealed metastases not detected by routine histology alone. This already led to additional therapeutic options for one patient. CONCLUSION: Our three-dimensional digital pathology approach can increase sensitivity and accuracy of SLN metastasis detection and potentially alleviate the need for conventional histopathological assessment in the future. GERMAN CLINICAL TRIALS REGISTER: (DRKS00015737).