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.

Expansion microscopy: principles and uses in biological research

“…Of particular interest is the idea that iExM may enable, in the future, detailed reconstruction of dense brain circuitry (see ref. 47 for a theoretical study of this possibility). Although the spatial resolution of iExM does not yet approach that of electron microscopy, the inherent multicolor nature of optical microscopy can allow for multiple kinds of tags, each labeled with different colors, to be used in an intact tissue nanoscopy context.…”

“…Although the spatial resolution of iExM does not yet approach that of electron microscopy, the inherent multicolor nature of optical microscopy can allow for multiple kinds of tags, each labeled with different colors, to be used in an intact tissue nanoscopy context. Thus information represented by one color could, if insufficient for tracing a neural circuit, be error corrected by the information represented by a second color 47 . Preliminary studies using Brainbow-labeled mouse cortex suggests that iExM can support the visualization of spines and other compartments along neural processes over extended 3D volumes (Fig.…”

Cortical Column and Whole Brain Imaging of Neural Circuits with Molecular Contrast and Nanoscale Resolution

“…FOVs, rates up to ~10 8 µm 3 /hr may be achievable, or ~12 min/fly brain at 4× expansion. Assuming the future development of: a) robust, isotropic expansion at 10× or greater; b) longer working distance high NA water immersion objectives or lossless sectioning (98) of expanded samples; and c) a ubiquitous, dense, and cell-permeable fluorescent membrane stain analogous to heavy metal stains in EM, even densely innervated circuits might be traced, particularly when imaged in conjunction with cell-type specific or stochastically expressed multicolor labels for error checking (99). With such a pipeline in place, ~10-100 specimens might be imaged in a single day at 4-10× expansion, enabling statistically rich, brain-wide studies with protein-specific contrast and nanoscale resolution of neural development, sexual dimorphism, degree of stereotypy, and structure/function or structure/behavior correlations, particularly under genetic or pharmacological perturbation.…”

Scalable approximate Bayesian inference for particle tracking data

“…this corruption. A (highly non-exhaustive) list of recent examples includes: (Parthasarathy et al, 2017), which applies this idea to approximate Bayesian decoding of neuronal spike train data; (Yoon et al, 2017), to segmentation of threedimensional neuronal images; and (Weigert et al, 2017), to denoising of microscopy images.…”