Speakers

Associate Prof. Arutha Kulasinghe

Leads the Clinical Omics Lab at the Fraser Institute, University of Queensland, and is founding scientific director at the Queensland Spatial Biology Centre, Wesley Research Institute. Holds the inaugural Brazil Family Chair in Spatial Medicine. A pioneer of spatial transcriptomics, proteomics, and interactomics in the Asia-Pacific region, his research uses an integrative multi-omics approach to understand the pathobiology of disease.

Dr. Marc Claesen

CTO and co-founder of Aspect Analytics. PhD in machine learning from KU Leuven. Co-founded Aspect Analytics to build tools for spatial multi-omics data analysis, applied to key applications in life sciences, pharmaceutical, and biomarker research.

Dr. Alice Ly

Director of Spatial Biology Applications at Aspect Analytics. PhD in visual neuroscience from University of Melbourne, and moderator of this session.

‍

1. Key Takeaways

• Spatial biology has moved from technology development to real-world clinical application: Marc and Arutha agreed that the spatial biology field is at an inflection point. Assay technologies have matured significantly, and with major biopharma investment signaling that the tools are now mature enough to inform real drug discovery and clinical decisions.
• Spatial context explains what single-cell data alone cannot: Arutha noted that spatial organization drives biology, and provided the example that while CD8+ T cells can indicate tumor "hotness" on a UMAP from single-cell analysis, spatial data reveals if those cells have penetrated the tumor or if they're sitting in fibrotic tissue with no real anti-tumor effect. Related to this, Cellular Neighborhood analysis can identify distinct tissue regions - e.g. tumor core or tumor-stroma interface - that share the same cell type but differ in gene expression and receptor-ligand interactions.
• In the Triple Negative Breast Cancer (TNBC) case study, PD-1/PD-L1 hotspots were concentrated in certain stromal and immune-rich Cellular Neighborhoods outside the tumor. This insight emerged directly from Weave’s integrative analysis, illustrating the discovery power of combining multi-omic spatial layers and unlocking new comprehension of the pathology.
• Weave's Stack Fusion pipeline and Cellular Neighborhood analysis: Stack Fusion integrates multiple spatial assays (e.g. transcriptomics, proteomics, H&E) acquired from same or serial sections into one unified, analyzable object — solving a co-registration problem that previously took weeks of manual work. Cellular Neighborhood analysis lets users cluster and analyze tissue architecture across different resolutions, linking the previously separate knowledge silos of molecular biology and tissue architecture from histology.

‍

2. Webinar Content

Part 1 - Why Spatial, Why Now

Prof. Arutha Kulasinghe walked through a head-and-neck cancer case: a patient with a partial response to checkpoint inhibitor therapy, mapped down to four distinct sub-tumor regions with different metabolic and immune profiles. He connected this to the surge of biopharma investment in spatial foundation models over the past 6–9 months.

You're  rewriting the books of biology by using spatial approaches... you're seeing the disease in the context in which it's found in the body without having to  dissociate those cells.
— Prof. Arutha Kulasinghe

Part 2 - The Weave Platform: Multiomics & Cellular Neighborhoods

Marc Claesen introduced Weave as a cloud-based, enterprise-grade platform, and presented two core capabilities: Stack Fusion (integrating multi-assay data) and Cellular Neighborhood analysis(encoding spatial context per cell, on top of molecular profile).

Single-cell technologies teach you what cells are present in a tissue... but ultimately  the way they're spatially organized dictates everything. It's the same as a soccer team — if you put Ronaldo in the goal, he's not gonna be at his best.
— Marc  Claesen

Part 3 -  Case Study

Arutha presented a pilot study applying Weave to a 35-patient TNBC tissue microarray, integrating spatial proteomics, H&E, andPD-1/PD-L1 proximity ligation assay (PLA) data.

The experience on the Weave platform and my team's experience with Aspect Analytics was seamless in that component.
— Arutha  Kulasinghe

‍

3. Case Study: Triple-Negative Breast Cancer

Context

TNBC is among the hardest-to-treat breast cancer subtypes. Arutha's team wanted to understand the tumor microenvironment more deeply, specifically PD-1/PD-L1 immune checkpoint interactions, but manual co-registration of multi-assay data (a process they'd done for years) was slow and error-prone, especially across a tissue microarray (TMA) where individual cores are hard to align.

Study design: 35-patient multi-donor TMA, primary and metastatic tissue (some donor-matched). PhenoCycler-Fusion (Quanterix) spatial proteomics (48-marker panel), same-slide H&E, and a serial-section PD-1/PD-L1 proximity ligation assay (PLA; Navinci).

Results

Using Weave's image co-registration workflow, the team achieved single-cell-accurate alignment of PCF, PLA, and H&E data.

Standard processing (segmentation, QC, Harmony batch correction, clustering, cell typing) identified 22 cell types across the cohort — epithelial E-cadherin+ tumor cells and macrophages were most common. Cellular Neighborhood analysis identified 12 distinct neighborhoods.

Integrating PLA hotspot data onto the Cellular Neighborhoods revealed that one neighborhood (#5 -stromal/immune) contained large PD-1/PD-L1-positive hotspot regions concentrated in CD8+ cytotoxic niches outside the tumor, while neighborhood #4 (mostly tumor/stromal cells) was depleted of these hotspots.

Discussion

This was described as a genuinely new insight — the team hadn't set out to link PLA hotspots to specific neighborhoods, but it surfaced via the Weave platform.

Arutha noted this kind of multi-modal integration currently has no ready alternative platform, and the team is now layering transcriptomic data from the same cohort onto the existing dataset for further resolution.

‍

Contact Us