Imaging Lab

The imaging lab is working collaboratively with research teams to apply methods that have emerged in recent years, which improve both 3D and 4D image capture and the reconstruction of large-scale tissue areas or whole mount specimens from serial sections.

Project links/resources:
Kan A et al. Quantitative analysis of Plasmodium ookinete motion in three dimensions suggests a critical role for cell shape in the biomechanics of malaria parasite gliding motility. Cell Microbiol. 2014 May;16(5):734-50.

This project aims to increase sample throughput and minimise manual operations by applying new software tools that increase automation. These methods promise to overcome some of the major challenges in microscopy, such as the capture of live specimens with high spatio-temporal resolution.

The application of quantitative methods for the segmentation and extraction of features in digital images is critical for understanding the statistical importance of observations made in biological imaging. We work with a number of software packages to improve the automation of image segmentation and the export of measured parameters. Where possible, we streamline data analysis using open source and free software, to give researchers greater independence and to encourage wider collaboration. We are also working on ways to manage and process large data sets (up to 100s of gigabytes in size) to ensure we can avoid bottlenecks commonly associated with new technologies, which have continually increasing rates of data output.

Image: https://www.wehi.edu.au/sites/default/files/Art-science-retinal-vascilature.png
Image caption: Automated computational analysis image of retinal vasculature.

Project resource:
Sleebs et al. Inhibition of Plasmepsin V activity demonstrates its essential role in protein export, PfEMP1 display, and survival of malaria parasites. PLoS Biol. 2014 Jul 1;12(7):e1001897.