Organelle changes in a Huntington's disease model using cryogenic soft x‐ray tomography
Authors: Darrow Michele , Dai Wei , Mitchell Patrick , Luengo Imanol , Chiu Wah , Duke Elizabeth 
Published: December 2016

Huntington's Disease, a neurodegenerative disorder characterized by movement and executive function disruption, is linked to an expanded and unstable CAG trinucleotide repeat which translates as a polyglutamine (Q) repeat in the protein product. Healthy controls demonstrate a fairly broad range of trinucleotide repeats and 40+ repeats have been described as pathological. Due to this expansion, exon 1 of the Huntingtin protein is aberrantly processed by cellular machinery leading to an unintended cleavage product (mHTT). Aggregates of mHTT exon 1 have been found in mouse models and in patient brain [1].
PC‐12 cells were used as an HD model to study structures of mHTT aggregates in a cellular context and to study cell morphology changes due to the presence of mHTT. These cells inducibly express a truncated version of mHTT exon 1 (97Qs) tagged with eGFP in the presence of Ponasterone A [2]. PC‐12 cells expressing mHTT‐GFP exon 1 were grown on gold finder grids, cryo‐immobilised by plunge freezing, and imaged. GFP fluorescence was used to identify cells of interest prior to imaging in cryogenic soft x‐ray tomography (SXT). SXT makes use of x‐rays in the “water window” (2.4 nm wavelength, 500 eV) where common biological elements such as carbon and nitrogen absorb x‐rays and are therefore more visible, whereas water (vitreous ice) is relatively transparent. This allows whole, fully‐hydrated cells to be imaged at approximately 40 nm resolution without requiring sectioning or staining techniques.
Seven overlapping tilt series were collected with a 0.5° step size and a tilt range of ±60°, followed by reconstruction in IMOD [3]. Figure 1 shows one of the seven reconstructions, featuring a large nucleus with nucleolar features and a crowded cytoplasmic space. Segmentation of these features was completed using HISS (Hierarchical Interactive Super‐region Segmentation) followed by annotation of cellular features based on their inherent properties (Figure 2). Cytoplasmic organelles were classified using specific characteristics of the data, such as the average intensity of the organelle, the size of the organelle, the variance of the voxels that make up the organelle and the location of the organelle in the 3D volume (Figure 3).
Using these characteristics, clumps of collagen, empty vesicles, lipid droplets and mitochondria were classified and visually inspected for accuracy. (Note: collagen was used to aid in cell adherence, but due to aggregation issues produced large clumps localized to the surface of the carbon substrate.) The group termed “other organelles” is likely composed of various compartments such as lysosomes, endosomes, filled vesicles, etc, which are not differentiable by eye in soft x‐ray images.
Next, population statistics for each organelle class, such as the average and standard deviation of the class's size, shape, intensity and variance will be used to describe the morphology of a non‐perturbed cell, and the changes due to perturbations, in this case, the presence of mHTT aggregates. In the future, correlated cryo‐fluorescence light microscopy and SXT will be used to verify mitochondria, and identify subgroups of the “other organelles” class, such as lysosomes and early and late endosomes.
Identification of organelle specific characteristics in SXT will allow the unambiguous classification of various organelles. As a whole, this methodology can broadly describe the effects of various cellular perturbations such as disease, infection or treatment in a whole cell context.