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BMD Charges and Collaborative Engagement

The Biomarker Discovery Node operates through a collaborative engagement model, supporting projects from early experimental planning through to data generation, analysis and reporting. BMD projects typically involve consultation around cohort design, workflow selection, and experimental design to ensure each study is matched to an appropriate discovery strategy.

Project charges are structured across four core workflow stages: sample preparation, mass spectrometry analysis, database searching, and data analysis/reporting (Figure 1).

Sample preparation costs reflect the workflow-specific reagents, consumables and staff time required to prepare samples for LC-MS. Neat plasma preparation using S-Trap is the base workflow, while depletion and enrichment approaches involve additional technology-specific costs.
Mass spectrometry analysis costs reflect the instrument platform, method length and number of injections required, including standard LC-MS consumables such as solvents, analytical columns and related platform requirements.
Database searching converts raw MS data into peptide and protein-level identifications and quantitative outputs, including protein inference and generation of analysis-ready proteomics datasets. We have all major software platforms available, including license and subscription-based software.
Data analysis and reporting includes quality-control evaluation, assessment of experimental performance, statistical analysis and preparation of a written project report. Reports include methodological details, QC metrics, experiment-level summaries and statistical outputs, with full results tables provided alongside.

Figure 1. BMD project workflow and collaborative service model: Biomarker Discovery Node projects are supported from clinical or experimental planning through to sample processing, MS analysis, database searching, data analysis and reporting. Project charges are structured across the core platform workflow stages, with additional collaborative support available for curated analyses, data handover, publication preparation and manuscript-related outputs.

Clinical/Experimental Planning collaboratively with MPMP

Project pre-planning is one of the most important steps in a successful biomarker discovery study. Early discussions with MPMP staff are provided without charge and form part of the collaborative engagement model that underpins the Biomarker Discovery Node. This early planning helps ensure the selected technology, sample design and analysis strategy are aligned before samples enter the platform workflow.

During pre-planning, we work with collaborators to consider cohort selection, workflow selection, quality control, controls, technical replication and sample collection. These decisions can strongly influence the quality and interpretability of deep plasma proteomics data, particularly in clinical cohorts where biological variation, collection procedures and clinical metadata all shape the final dataset.

Our team can advise on cohort design, including case-control matching, longitudinal or paired sampling, clinical metadata and potential confounders, as well as sample collection SOPs such as anticoagulant choice, processing timelines and storage. We also implement structured QC designs incorporating appropriate controls and technical replicates to monitor technical variation and support robust downstream statistical analysis.

What support is available after data handover?

We work closely with the Monash Genomics and Bioinformatics Platform (MGBP) to support additional, curated and custom bioinformatic analyses for clinical proteomics. MPMP also curates and stores project information, methods and raw data generated through the platform. Additional support for data upload, manuscript methods, figures, review responses or publication preparation are included as part of the collaborative engagement.

The Biomarker Discovery Node operates through a collaborative engagement model that recognises the specialist scientific and technical expertise required to deliver high-quality biomarker discovery studies. Unlike fee-for-service, BMD projects commonly involve substantial intellectual input from platform staff, including consultation on study design, technology selection, method development or optimisation, data analysis, interpretation, and scientific framing of results. Where data generated through a BMD collaborative engagement contribute to publications, grant outcomes or other research outputs, appropriate recognition of platform staff contribution, including co-authorship where warranted, is expected in line with standard authorship and research integrity guidelines.
As early as possible. Early engagement allows BMD staff to advise on study design, workflow compatibility and sample collection requirements before protocols are locked in. Technical variation can be introduced at many stages of clinical sampling, including patient and control selection, anticoagulant choice, centrifugation conditions, single- versus multi-site collection, cross-sectional versus longitudinal design, and sample storage. Addressing these factors early helps minimise avoidable technical effects.
BMD staff can support ethics and clinical trial submissions, including providing internally vetted biofluid collection protocols designed to maximise compatibility across the widest range of plasma proteomics workflows. Early consultation is particularly important if you are interested in a specific technology.
All BMD workflows include standard platform quality-control measures required to monitor sample preparation, LC-MS performance, database searching and data quality. We run instrument suitability and QC every 24 hours on all our mass spectrometers in an automated pipeline. These built-in QC steps are part of routine workflow delivery and are included in standard project charges.
For medium to large clinical cohorts, additional QC samples are required to support robust experimental design and downstream statistical analysis. These may include pooled reference samples, technical replicates, process controls or other study-specific QC materials selected to monitor expected sources of technical variation, such as batch, plate, site or longitudinal sampling effects. Where additional QC samples require sample preparation, instrument time or data analysis, they are charged as additional samples to reflect the associated reagent, consumable and MS analysis costs. For most medium to large clinical cohorts, collaborators should expect additional QC samples in the final study design.
Yes, our staff can be named CI and AI on grants, and often their input and inclusion can significantly strengthen grants given their track records and significant specialised experience. Even when not named on the grant, we can provide letters of support and cost estimates for grant applications to ensure accurate budgeting for planned work.

Monash Proteomics & Metabolomics Platform

Monash Proteomics & Metabolomics Platform

BMD Charges and Collaborative Engagement

Clinical/Experimental Planning collaboratively with MPMP

What support is available after data handover?