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Peruzzi, N.

Publications and source records attributed to Peruzzi, N..

2 recordsLinked to original sources

Efficient murine cardiac phenotyping by combining synchrotron-based phase-contrast micro-CT, histology, immunofluorescence and spatial transcriptomics

1.Congenital heart disease is commonly studied using genetically modified mouse models, but characterization of complex three-dimensional (3D) cardiac anomalies remains technically challenging. Histology of fixed paraffin-embedded samples, typically used for structural and molecular analysis, is limited to two dimensions (2D). Synchrotron radiation-based phase-contrast micro-computed tomography (SRPC-{micro}CT) enables rapid, high-resolution, 3D imaging but had yet to be fully integrated with molecular tissue analysis. Importantly, SRPC-{micro}CT is nondestructive and compatible with paraffin-embedded tissue, potentially allowing integration with downstream molecular analyses such as multiplexed spatial transcriptomic profiling on tissue sections. Here, we present a pipeline combining SRPC-{micro}CT with complementary molecular approaches for rapid and accurate phenotyping of mouse hearts and for investigating disease mechanisms. We demonstrate the successful integration of high-resolution 3D imaging with spatial transcriptomics, fluorescent stainings, and histochemistry. Notably, all 2D modalities were applied sequentially to a single tissue section and registered within the 3D volume. This multimodal framework provides a powerful approach for linking structural and molecular information that is broadly applicable across biomedical research.

developmental biology↗

Histology-guided 3D virtual staining of microCT-imaged lung tissue via deep learning

Histologically stained tissue sections are considered the gold standard for studying microscopic anatomy and diagnosing disease in clinical practice. However, the processes of sectioning and staining are laborious, and the overall method relies on two-dimensional (2D) analysis. In contrast, X-ray-based virtual histology offers the advantage of virtual sectioning while retaining the full three-dimensional (3D) volumetric representation of the tissue. Nevertheless, its grayscale nature has prevented it to be readily utilized by pathologists who are accustomed to conventional histological stains. In this work, we present a histology-guided enhancement platform that can integrate the 3D information provided by synchrotron radiation phase-contrast microCT with the rich visual features characteristic of histological stains. We introduce a multi-stage microCT-histology co-registration method combined with a virtual staining deep neural network and demonstrate successful virtual histological staining of microCT human and mouse lung tissue that closely resembles standard histology. We evaluate our strategy on multiple histological stains and apply it to identify 3D collagen-based remodeling of pulmonary arteries in patients with pulmonary hypertension. Overall, this innovative enhancement pipeline has the potential to aid in the incorporation of microCT into clinical practice, and advance non-destructive 3D pathology for improved diagnostic efficiency and accuracy.

pathology↗