Our Precision Medicine Pipelines
Housed at WIMR within Westmead Research Hub, we have the end-to-end facilitaties to provide for your precision medicine needs. The latest cutting-edge technologies, supported by MRFF Grants, mean that whether you would like to hand your samples to us and then receive insights from your data, or whether you’d prefer to be involved in any or all of the steps along the way, we’re here to support you in bringing precision medicine to clinical care. To achieve this, our researchers follow a structured approach that involves two key pipelines:
a discovery pipeline, where new biomarkers are identified, and
a screening pipeline, where these biomarkers are tested and validated in patient samples.
In PrecisionGO, a series of cutting-edge technologies come together to provide comprehensive, advanced, end-to-end PM pipelines accessible nationwide for discovery and screening. The discovery & screening pipelines work together to bridge the gap between research & real-world clinical applications. Each step leverages advanced expertise to create a detailed understanding of disease mechanisms & enable highly personalised diagnostics & treatments.
Discovery Pipeline: Finding New Biomarkers
The discovery pipeline begins with blood or tissue samples collected from patients, along with their clinical data. These samples contain a mix of different cell types, and to focus on the most relevant cells, researchers use flow cytometry-based cell sorting to isolate specific populations based on unique cell-surface markers. The BD FACSDiscover S8 enables high-speed sorting of rare or functionally distinct cells, while the Cytek Aurora provides deep characterisation of cell types using spectral flow cytometry.
Once the relevant cells are identified and sorted, researchers use genomics and proteomics methods to explore their molecular profiles. This involves whole genome sequencing (WGS) or whole exome sequencing (WES) to study genetic variations, RNA sequencing (RNAseq) and single-cell RNA sequencing (scRNAseq) to understand disease-specific gene expression signatures, and high-parameter proteomics to functionally characterise cells. Technologies like BD Rhapsody, which captures RNA from individual cells, and 10x Genomics Visium CytAssist, which prepares tissue samples for spatial transcriptomics, play key roles in analysing how different cells behave and interact during disease. The 10x Genomics Xenium Analyzer is then used to map gene activity at subcellular resolution, providing insights into how disease-related biomarkers are expressed in specific regions of a tissue sample.
Following this molecular profiling, researchers move to data analysis, where they use bioinformatics tools to identify key genetic and protein markers linked to diseases. These findings undergo validation using additional experimental methods to confirm that the discovered biomarkers are reliable indicators of disease presence or progression. The final outcome of this pipeline is the identification of new biomarkers, which serve as molecular signatures of diseases and can be used for diagnosis, prognosis, or treatment selection.
Screening Pipeline: Applying Biomarkers to Patient Samples
Once biomarkers have been identified in the discovery phase, the screening pipeline is used to test and validate them in a larger number of patient samples. This pipeline starts similarly, with patient samples being collected and stored, sometimes in a biobank for long-term research use. When needed, specific cell types are again sorted using BD FACSDiscover S8 and Cytek Aurora, ensuring that the analysis focuses on the most relevant populations.
Unlike the discovery pipeline, which involves broad and exploratory molecular profiling, the screening pipeline focuses on targeted genomic methods to detect known biomarkers. Techniques such as digital PCR (dPCR), targeted sequencing, and RNAseq are used to confirm the presence of specific genetic mutations or gene expression patterns. At this stage, the QIAGEN QIAcuity One plays a key role by performing highly sensitive digital PCR, allowing researchers to precisely measure biomarker levels in patient samples. This ensures that biomarker detection is accurate and reproducible, which is critical for clinical applications.
Following data analysis and data linkage, where patient genetic data is integrated with clinical records, researchers validate whether the identified biomarkers consistently indicate disease risk, progression, or treatment response. If successful, these biomarkers can be used in diagnostic tests or treatment decisions, marking a crucial step toward personalised medicine.