2. Pipeline

Electron microscopy is used to reveal the structure of brain tissue at the micron and submicron scales. However, the ability to analyze the spatial relationships between various cellular structures as well as the arrangement of organelles within cells is limited in two-dimensional electron microscopy images. Reconstruction from serial section transmission electron microscopy (ssTEM) offers the possibility of recreating neuronal and glial processes and their organelles in spatially realistic three-dimensional models. Our EM data comes from area CA1 of the hippocampus (courtesy of Dr. Kristen Harris).

We outline the overall computational framework that we have been developing for converting imaging data to spatially realistic meshed models above. Our algorithms and processes are developed in our powerful reconstruction and visualization package VolumeRover Neuron. There are four major stages: 2-D image processing (purple box), 2-D geometry processing (blue box), 2-D to 3-D reconstruction (yellow box), and 3-D geometry processing (green box). An additional set of procedures, the reducing procedures (orange box), are used to generate 1-D multi-compartment models suitable for NEURON from 3-D reconstructions. The red boxes in the above figure represent components of ongoing development.

(a) Original ssTEM image slice. (b) Overlap of EM image and contours after tracing. (c) Contours for 2-D geometry processing.
(d) Consecutive contours initially tiled into 3-D.
(e) Meshed dendrite. (f) Example of an initial mesh. Dendrite is white, and axon is green. (g)Example of an improved mesh (viewed from a different angle).
(h) Extracellular space I. (i) Extracellular space II. (j) Extracellular space III.

The images above show several stages of our processing pipeline. The boundaries of all cellular elements (dendrites, axons, and glia) are traced and classified in each 2-D image slice (Fig. a). These contours undergo further processing steps (referred to as 2-D curation procedures) that accomplish such goals as removing boundary overlaps and resampling by spline-fitting. The 2-D contours are tiled into 3-D objects (Fig. d) using our ContourTiler algorithm (implemented in our VolRover software package). This provides us with 3-D spatially realistic reconstructions of dendrites (Fig. e), axons, and glial processes within a reconstructed neuropil. However, the quality of the initial, tiled surface mesh (Fig. f) is not sufficient to be useful for computational simulations. Our set of 3-D curation procedures removes boundary overlaps in 3-D and applies mesh quality improvement algorithms, such as smoothing, decimation (reducing the number of triangles), and fitting by algebraic spline functions, in order to generate a forest of non-intersecting, high quality-meshed neuronal (Fig. g) and glial processes. These reconstructions are then ready for use in simulations of neuronal activity. Once all cellular elements of the neuropil are reconstructed we can invert and crop the mesh to produce models of extracellular space (Figs. h-j).

We use different models to study different quantities of interest. A sampling follows.

Single dendrite spine morphology (spine head surface area, neck shape, volume), action potential propogation, potential distribution, ionic concentration distribution
Dendrites and axons potential propogation, cross-talk (ephaptic communication)
Full neuropil potential propogation, cross-talk
Extracellular space reaction-diffusion simulation, synaptic impedence