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 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.Impact StatementMore research groups are using cryo-soft-X-ray tomography as a correlative imaging tool to study the ultrastructure of cells and tissues but very few tomograms are segmented with existing segmentation programs. Segmentation is usually a prerequisite for measuring the geometry of features in tomograms but the time- and labour-intensive nature of current segmentation techniques means that such measurements are rarely across a large number of tomograms, as is required for robust statistical analysis. Contour has been designed to facilitate the automation of segmentation and, as a result, reduce manual effort and increase the number of tomograms that can be segmented. Because it requires minimal manual intervention, Contour is not as prone to human error as programs that require the users to trace the edges of cellular features. Geometry measurements of the segmented volumes can be calculated using this program, providing a new platform to quantitate cryoSXT data. Contour also supports quantitation of volumes imported from other segmentation programs. The generation of a large sample of segmented volumes with Contour that can be used as a representative training dataset for machine learning applications is a long-term aspiration of this technique.

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.…”

“…Video 2 ). 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.…”

“…( 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.…”