A NanoString Centre of Excellence in Spatial Biology

What is spatial biology?

Systems Biology and Data Science

Systems biology and data science involves the collection and processing of high-quality data from vast biological samples, using automated, robust, high-throughput and efficient procedures. The Systems Biology and Data Science core facility utilizes state-of-the-art technologies and data analytics to make the most of your samples.

Consultation

Submission

Interactive phased work

Outcome

Collaborate:

  • Define desired outcome
  • Realisation
  • Timeline

Project plan:

  • Define experimental and analytical processes

Connect:

  • Experimental work
  • Data analysis
  • Reporting

Conclude:

  • Review summary and determine conclusion

Consultation

The consultation will discuss the scientific boundaries of your project, the desired outcomes and detail special considerations.

Submission

Based on the consultation we will submit a proposed plan for the sample collection, data collation and analysis as per your requirements. This will entail overall analysis goals and will provide an approximate timeline for conclusion.

Phased Work

Through consultation and analysis we will move forward towards generating summary reports, allowing for necessary adjustments to the project strategy. We value your collaboration to ensure project phases are aligned and will have meeting availability as phases progress.

Outcome

Project data and results will be available throughout the process and we will provide a final summary in our outcome meeting.

Your outcome meeting may include:

  • A summary of analysis in your preferred format
  • Relevant data and results in an organised format with a guide to increase ease of access to pertinent information
  • Consultation and collaboration with your project team to review results and chart your future progress

ARTICLES

Allergy

1. Nanostring Immune Gene Expression profiling in Allergic Rhinitis

The Systems Biology and Data Science Shared Research Facility compared Nanostring immune gene expression of nasal lavage and whole blood samples in allergy sufferers and healthy individuals

Viral infections

1. Nanostring diagnosis and typing of influenza

2. Inflammatory responses to a pathogenic West Nile virus strain. ( Huang B, West N, Vider J, Zhang P, Griffiths RE, Wolvetang E, Burtonclay P, Warrilow D.BMC Infect Dis. 2019 Oct 29;19(1):912. doi: 10.1186/s12879-019-4471-8.PMID: 31664929 Free PMC article.)

3. Modulation of Monocyte-Driven Myositis in Alphavirus Infection Reveals a Role for CX3CR1+ Macrophages in Tissue Repair (Zaid A, Tharmarajah K, Mostafavi H, Freitas JR, Sheng KC, Foo SS, Chen W, Vider J, Liu X, West NP, Herrero LJ, Taylor A, Mackay LK, Getts DR, King NJC, Mahalingam S.mBio. 2020 Mar 3;11(2):e03353-19. doi: 10.1128/mBio.03353-19)

4. Key viral immune genes and pathways identify elite athletes with URS. (Colbey C, Drew MK, Cox AJ, Vider J, Pyne DB, Vlahonich N, Hughes D, Waddington G, Appaneal R, Burke LM, Lundy B, Toomey M, Watts D, Lovell G, Praet S, Halson SL, Welvaert M, Zhang P, Cripps AW, West NP.Exerc Immunol Rev. 2020;26:56-78.)

Cancer

1. Comparison of skin biopsy sample processing and storage methods on high dimensional immune gene expression using the Nanostring nCounter system. (Vider J, Croaker A, Cox AJ, Raymond E, Rogers R, Adamson S, Doyle M, O'Brien B, Cripps AW, West NP.Diagn Pathol. 2020 May 15;15(1):57. doi: 10.1186/s13000-020-00974-4.)

Obesity

1. A correlation-based network for biomarker discovery in obesity with metabolic syndrome. (Chen PY, Cripps AW, West NP, Cox AJ, Zhang P.BMC Bioinformatics. 2019 Dec 10;20(Suppl 6):477. doi: 10.1186/s12859-019-3064-2.)

Single cell

“There is no such thing as a cell average”. The Single Cell Facility has the means to sort and isolate single cells, and generate single cell products to use for gene expression analysis or next-generation sequencing.

Gene Expression

With the NanoString nCounter Dx system analyse up to 770 genes in key biological pathways and cell types. Robust for use with FFPE and cell lysate with no amplification. New 3D biology allows for simultaneous RNA, DNA and protein analysis.

Data Analytics

Data Analytics at Menzies Health Institute Queensland provides data analytics solutions for research. With expert data analysis services across molecular and clinical research our aim is to write customised assistance for the analysis and modelling of biological data.

Cytometry

Using state of the art cytometers to phenotype cell subsets. Undertake single cell or simultaneous multi-parametric analysis. Our technology allows simultaneous multiparametric analysis of thousands of cells per second, enabling trained researchers to rapidly analyse complex cell populations.

NanoString

The NanoString nCounter digital analysis system is highly multiplexed to enable rapid, precise, and cost-effective research. NanoString technology uses unique color-coded molecular barcodes that can hybridize directly to nucleic acid molecules, making it ideal for a range of discovery and translational research applications. The NanoString portfolio of application-specific solutions currently includes kits for gene expression, protein (including phosoproteins), copy number variation and miRNA analysis.

Enquire now

To submit an enquiry for your data analytics needs

Flow cytometry

Our technology: BD Calibur Millipore Guava BD LSRII Fortessa BD AriaFusion - cell sorter; single cell sorting capability In Glycomics: Beckman coulter Cyan cytometer MoFlo cell sorter; single cell sorting capability Mass cytometry: Accelerate your immune cell phenotyping using mass cytometry. The MHIQ Systems Biology and Data Science Core collaborates with the Ramaciotti Centre of Systems Biology at Sydney University. Using inductively coupled plasma time-of-flight mass spectrometry (ICP-MS). Mass cytometry is revolutionizing the ability to analyse single cells in unprecedented depth and breadth with minimal sample sizes. More than 40 markers can be analysed simultaneously on a single cell. This allows the capture of a tremendous amount of data with a relative low number of cells.

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