Contour: A semi-automated segmentation and quantitation tool for cryo-soft-X-ray tomography

Abstract

Cryo-soft-X-ray tomography is being increasingly used in biological research to study the morphology of cellular compartments and how they change in response to different stimuli, such as viral infections. Segmentation of these compartments is limited by time-consuming manual tools or machine learning algorithms that require extensive time and effort to train. Here we describe Contour, a new, easy-to-use, highly automated segmentation tool that enables accelerated segmentation of tomograms to delineate distinct cellular compartments. Using Contour, cellular structures can be segmented based on their projection intensity and geometrical width by applying a threshold range to the image and excluding noise smaller in width than the cellular compartments of interest. This method is less laborious and less prone to errors from human judgement than current tools that require features to be manually traced, and it does not require training datasets as would machine-learning driven segmentation. We show that high-contrast compartments such as mitochondria, lipid droplets, and features at the cell surface can be easily segmented with this technique in the context of investigating herpes simplex virus 1 infection. Contour can extract geometric measurements from 3D segmented volumes, providing a new method to quantitate cryo-soft-X-ray tomography data. Contour can be freely downloaded at github.com/kamallouisnahas/Contour.

Near-native state imaging by cryo-soft-X-ray tomography reveals remodelling of multiple cellular organelles during HSV-1 infection

“…Tomograms are reconstructed from these projections using IMOD to compare intracellular morphology between uninfected cells and those at early- or late-stages of infection. Segmentation with tools like Contour [ 60 ] facilitates quantitation and visualization in three dimensions of the observed cellular structures.…”

“…Infected cells had a greater number of vesicles in juxtanuclear regions when compared with uninfected cells ( Fig 4A and S2 Video ). To determine if there was a difference in the size of vesicles between uninfected cells and those at early- or late-stages of infection we developed Contour , a program to segment and quantitate cellular features in 3D volumes [ 60 ]. The widest point of each vesicle in three dimensions from 4 tomograms for each condition was measured ( Fig 4B ).…”

“…( A ) A higher concentration of vesicles is observed at the juxtanuclear compartment in cells at early- or late-stages of infection compared with uninfected cells. ( B ) The maximum width of each vesicle in three-dimensions was measured in Contour [ 60 ]. Width was measured instead of volume because the segmented vesicles were open-ended owing to reduced contrast in the tomograms of membranes normal to the incident X-ray beam.…”

“…Error bars show overall mean ± SD. ( C ) Lipid droplets were segmented and measured using Contour [ 60 ] and their distributions were plotted on a logarithmic scale. Median volumes ± SD (hollow circles plus error bars) are shown for each group because median values are less affected than mean values by non-normal distributions.…”

“…Lipid droplets are carbon-dense organelles that produce high contrast in cryoSXT and were clearly visible as dark solid spheres in the tomograms. These lipid droplets could be readily segmented using Contour [ 60 ], allowing measurement of their volumes. We observed an increase in the volume of these droplets in cells during the early stage of infection when compared with uninfected cells or to cells at the late stage of infection ( Fig 4C ).…”