Key Systems Microscopy Methods
With biological inputs varying from cell lines to patient-derived circulating tumour cells and tissue samples, an integrated Systems Microscopy pipeline is used to derive mechanistic or diagnostic insights, as well as to enable high-throughput drug / therapy discovery. This approach supports both fundamental and clinical research outcomes through efficient (re)use of a core set of experimental and analytical tools.
Australia’s only dedicated Systems Microscopy pipeline
Our Systems Microscopy approach integrates multidisciplinary methods into a coherent pipeline. Central to this pipeline are automated experimental perturbations using liquid handling systems to enhance throughput and reproducibility. Such perturbations are followed by automated fluorescence imaging using dedicated microscopy hardware (confocal and epifluorescence systems), with “Proteomic Microscopy” a major new capability unlocking the potential of in vivo, spatially resolved systems biology (see ‘1’ below). Subsequent to image acquisition, automated image analysis (see ‘2’ below) is performed using a variety of open source software platforms, as well as tools developed locally with expert collaborators in machine vision. These tools extract objective, high-dimensional quantitative data from biological images, facilitating downstream statistical, machine learning and deep learning analyses (see ‘3’ below). We are developing unique data visualization tools to coherently assess and interpret the resulting wealth of image and quantitative data, using immersive environments, traditional desktop and cutting-edge VR platforms. This work, in collaboration with the Expanded Perception and Interaction Centre (EPICentre, UNSW Art & Design) is providing unique capacity for interpretation and collaboration around cellular data, as well as for clinical analyses of clinical / biomedical images (X-ray, MRI, CT-scan etc).
1) Automated Cellular Imaging including Proteomic Microscopy
Automation of experimental and imaging protocols supports reproducible, objective and high-throughput research methods. In particular, we integrate live and fixed (correlative) imaging to extend molecular analysis of dynamic processes. Moreover, with support from the Ramaciotti Systems Microscopy Unit of the UNSW Biomedical Imaging Facility, we are now deploying highly multiplexed fluorescence imaging of 20+ molecular components in each individual cell. This “Proteomic Microscopy” provides spatially resolved, molecularly detailed insights into the composition of single cells and sub-cellular compartments.
2) Quantitative Image Analysis
A variety of image analysis software tools are used to analysis fixed, dynamic cellular and tissue image data, typically at the single cell and/or sub-cellular object level. Predominantly using open source tools, we also collaborate with experts in machine vision and deep learning to develop cutting edge methods for cell / object detection and classification. Automated image analysis is a pivotal aspect of our work, converting visual information into quantitative data that is amenable to objective statistical and machine learning analysis for the generation of new biologically or clinically significant insights.
3) Statistical Analysis, Machine Learning, AI & Data Visualization
We employ a suite of quantitative data analysis and visualization tools, to translate biological information into insights. In-house, we use Knime as a backbone for rapid prototyping of automated analysis pipelines, typically calling R for more challenging statistical problems involving multivariate statistics, dimension reduction and manifold projection, dynamic lineage inference, information theoretic analyses of causal relationships and regulatory network structure, etc. Machine learning approaches are utilized to guide feature selection (e.g. which biological / image features are important to define a given state or process) and to develop predictive models. We work with experts in AI-driven data analysis and large scale data visualization to support enhanced interpretation of outcomes, including through development of unique immersive- and VR-based data visualization software.
