Dr Wittaya Suwakulsiri
Dr

Wittaya Suwakulsiri

Email: 

Background

I am a computational biologist specialising in the integration of multi-omics data to study rheumatoid arthritis (RA). My research combines clinical data, serum proteomics, single-cell transcriptomics, and spatial transcriptomics to understand disease progression, patient trajectories, and flare events.

I am particularly interested in the immune landscape of synovial tissue and how spatial organisation of immune and stromal cells contributes to inflammation and remission. Through advanced statistical modelling and machine learning, including clustering and trajectory inference, I aim to identify predictors of flare and uncover mechanisms that drive differences in patient outcomes.

Alongside my work in RA, I also investigate the link between systemic inflammation and cardiovascular disease, applying spatial, single-cell transcriptomics, proteomics and bioinformatics approaches to explore how chronic inflammation contributes to cardiac dysfunction.

The overarching goal of my research is to improve early prediction of disease trajectories, support personalised management strategies, and contribute to the development of targeted therapies for patients with RA.

Availability

Dr Wittaya Suwakulsiri is:
Available for supervision

Fields of research

Autoimmunity Bioinformatics and computational biology Biological Sciences Biomedical and Clinical Sciences Genetic immunology Genetics Genomics and transcriptomics Immunology

Qualifications

  • Doctor of Philosophy, La Trobe University

Research interests

  • Multi-omics and Machine Learning for RA Flare Prediction and RA Patient Clustering Analysis

    I integrate clinical data, serum proteomics, single-cell transcriptomics, and spatial transcriptomics to study rheumatoid arthritis. Using statistical modelling and machine learning approaches, including Louvain clustering, I analyse patient trajectories and identify predictors of flare. This work aims to improve understanding of disease progression and support personalised management strategies.

  • Synovial tissue immune architecture using Spatial Transcriptomics

    I analyse spatial transcriptomics of synovial tissues from a large cohort of rheumatoid arthritis patients. My aim is to characterise the immune landscape of synovial tissue and identify spatial patterns that may help explain disease progression and flare events. This work contributes to understanding rheumatoid arthritis at a tissue level and supports the development of targeted therapies.

  • Inflammation-Driven Cardiovascular Disease in Rheumatoid Arthritis

    I investigate the link between rheumatoid arthritis and cardiovascular disease by integrating cytokine profiling, proteomics, and bioinformatics. Using NULISAseq and other high-sensitivity proteomic platforms, I study how chronic inflammation contributes to cardiac dysfunction in RA. The goal is to identify biomarkers and molecular pathways that explain heightened cardiovascular risk in RA patients and support early detection and prevention strategies.

Research impacts

Rheumatoid arthritis (RA) is a chronic autoimmune disease with unpredictable flare events and highly variable patient outcomes. Current treatment strategies often rely on trial-and-error approaches, which can delay effective management and increase healthcare costs.

My research focuses on using advanced multi-omics approaches, including serum proteomics, single-cell transcriptomics, and spatial transcriptomics to better understand RA disease progression and flare events. By integrating these datasets with clinical information and applying statistical and machine learning methods, I aim to identify biomarkers that predict when patients are likely to flare and to cluster patients into distinct disease trajectories.

The impact of this work lies in its potential to transform RA management. Early identification of flare predictors and patient subgroups will allow clinicians to make more precise treatment decisions, reduce unnecessary treatments, and minimise long-term joint damage. In the long term, this research will contribute to more personalised care pathways, improved patient outcomes, and reduced burden on healthcare systems.

Current funding

  • 2025 - 2026
    Investigating biomarkers of Pelvic Inflammatory Disease using multi-omics data integration
    UQ - Sanofi Translational Science Hub Partnership Scheme
    Open grant

Availability

Dr Wittaya Suwakulsiri is:
Available for supervision

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Supervision history

Current supervision

  • Master Philosophy

    Rheumatoid arthritis synovial tissue and remission

    Associate Advisor

    Other advisors: Professor Ranjeny Thomas

Enquiries

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