Dataset Information

Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration.

ABSTRACT: We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes. We attempt to use conservative imaging and labeling parameters, with the goal of establishing a baseline case that points to the potential feasibility of optical circuit reconstruction, leaving open the possibility of higher-performance labeling technologies and algorithms. We find that, even with these conservative assumptions, an all-optical approach to dense neural morphology reconstruction may be possible via the proposed algorithmic framework. Future work should explore both the design-space of chemical labels and barcodes, as well as algorithms, to ultimately enable routine, high-performance optical circuit reconstruction.

SUBMITTER: Yoon YG

PROVIDER: S-EPMC5660712 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration.

Yoon Young-Gyu YG Dai Peilun P Wohlwend Jeremy J Chang Jae-Byum JB Marblestone Adam H AH Boyden Edward S ES

Frontiers in computational neuroscience 20171024

We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and <i>in-situ</i> molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-mergi ...[more]

PMID: 29114215

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Project description:Mitochondria are double membrane bound organelles indispensable for biological processes such as apoptosis, cell signaling, and the production of many important metabolites, which includes ATP that is generated during the process known as oxidative phosphorylation (OXPHOS). The inner membrane contains folds called cristae, which increase the membrane surface and thus the amount of membrane-bound proteins necessary for the OXPHOS. These folds have been of great interest not only because of their importance for energy conversion, but also because changes in morphology have been linked to a broad range of diseases from cancer, diabetes, neurodegenerative diseases, to aging and infection. With a distance between opposing cristae membranes often below 100 nm, conventional fluorescence imaging cannot provide a resolution sufficient for resolving these structures. For this reason, various highly specialized super-resolution methods including dSTORM, PALM, STED, and SIM have been applied for cristae visualization. Expansion Microscopy (ExM) offers the possibility to perform super-resolution microscopy on conventional confocal microscopes by embedding the sample into a swellable hydrogel that is isotropically expanded by a factor of 4-4.5, improving the resolution to 60-70 nm on conventional confocal microscopes, which can be further increased to ∼ 30 nm laterally using SIM. Here, we demonstrate that the expression of the mitochondrial creatine kinase MtCK linked to marker protein GFP (MtCK-GFP), which localizes to the space between the outer and the inner mitochondrial membrane, can be used as a cristae marker. Applying ExM on mitochondria labeled with this construct enables visualization of morphological changes of cristae and localization studies of mitochondrial proteins relative to cristae without the need for specialized setups. For the first time we present the combination of specific mitochondrial intermembrane space labeling and ExM as a tool for studying internal structure of mitochondria.

Dataset Information

Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration.

Publications

Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration.

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