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Angstman, P. J.

Publications and source records attributed to Angstman, P. J..

2 recordsLinked to original sources

ClearScope: a fully integrated light sheet theta microscope for sub-cellular resolution imaging without lateral size constraints

Three-dimensional (3D) ex vivo imaging of cleared intact brains of animal models and large human and non-human primate postmortem brain specimens is important for understanding the physiological neural network connectivity patterns and the pathological alterations underlying neuropsychiatric and neurological disorders. Light-sheet microscopy has emerged as a highly effective imaging modality for rapid high-resolution imaging of large cleared samples. However, the orthogonal arrangements of illumination and detection optics in light sheet microscopy limits the size of specimen that can be imaged. Recently developed light sheet theta microscopy (LSTM) technology addressed this by utilizing a unique arrangement of two illumination light paths oblique to the detection light path, while allowing perpendicular arrangement of the detection light path relative to the specimen surface. Here, we report development of a next-generation, fully integrated, and user-friendly LSTM system for rapid sub-cellular resolution imaging uniformly throughout a large specimen without constraining the lateral (XY) size. In addition, we provide a seamlessly integrated workflow for image acquisition, data storage, pre- and post-processing, enhancement, and quantitative analysis. We demonstrate the system performance by high-resolution 3D imaging of intact mouse brains and human brain samples, and complete data analysis including digital neuron tracing, vessel reconstruction and design-based stereological analysis in 3D. This technically enhanced and user-friendly LSTM implementation will enable rapid quantitative mapping of molecular and cellular features of interests in diverse types of very large samples.

neuroscience↗

A comprehensive, FAIR file format for neuroanatomical structure modeling

With advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, Neuromorphological File Specification (4.0), available at www.mbfbioscience.com/filespecification (Angstman et al. 2020). One of such technologies, the format created and maintained by MBF Bioscience is broadly utilized by the neuroscience community. The data formats structure and capabilities have evolved since its inception, with modifications made to keep pace with advancements in microscopy and the scientific questions raised by worldwide experts in the field. More recent modifications to the neuromorphological data format ensure it abides by the Findable, Accessible, Interoperable, and Reusable (FAIR) data standards promoted by the International Neuroinformatics Coordinating Facility (INCF; Wilkinson et al. 2016). The incorporated metadata make it easy to identify and repurpose these data types for downstream application and investigation. This publication describes key elements of the file format and details their relevant structural advantages in an effort to encourage the reuse of these rich data files for alternative analysis or reproduction of derived conclusions.

neuroscience↗