Abstract
Quantitative analysis of neuronal morphology is critical in cell type classification and for deciphering how structure gives rise to function in the brain. Most current approaches to imaging and tracing neuronal 3D morphology are data intensive. We introduce SmartScope2, the first open source, automated neuron reconstruction machine integrating online image analysis with automated multiphoton imaging. SmartScope2 takes advantage of a neuron’s sparse morphology to improve imaging speed and reduce image data stored, transferred and analyzed. We show that SmartScope2 is able to produce the complex 3D morphology of human and mouse cortical neurons with six-fold reduction in image data requirements and three times the imaging speed compared to conventional methods.
Highlights
The forefront of bioimaging technology is often driven by a desire for faster collection of larger image volumes, with increased spatial and temporal resolution
S2 is currently implemented with a multiphoton microscope designed for imaging of fixed samples up to ~350 μm thick, but the S2 concept could in principle be extended to any imaging modality where data collection can be spatially localized within a sample
acousto-optical deflector (AOD)-based random-access microscopes are natural targets for implementing future versions of S2 because the truly random-access imaging of AODs can allow for fast data acquisition in arbitrary volumes
Summary
The forefront of bioimaging technology is often driven by a desire for faster collection of larger image volumes, with increased spatial and temporal resolution. The reconstructions show tracing of dendrites, including the basal skirt and apical tuft of a deep-layer pyramidal cell, as well as descending axons of genetically-labeled mouse neurons These S2 scans resulted in an average of 4.6-fold reduction in imaged volume compared to a rectangular bounding box surrounding the same structure This reduction in image area can be regarded as a conservative estimation of S2’s ability to reduce imaging load for neuronal reconstruction To define this rectangular bounding box without S2, either a human operator would have to manually follow dendrites from the cell body until they terminate or leave the sample, or some automated method would have to be deployed to identify the ends of all of the connected structures. Using S2 to image multi-neuron samples is appealing for its automatic reconstruction of multiple neurons, but may be limited in improving acquisition speed because some samples with multiple neurons (e.g. S2 scans 5 and 6 in Table S1.) have less dramatic sparsity than individual neurons
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