Originally posted on LinkedIn — March 26, 2026.
Tumors are not just a collection of cell types — they are a spatial composition. Knowing which cells are present in a tumor microenvironment is only the first question. The harder one is: how are those cells arranged? Which cell states appear together, which remain separated, and which spatial arrangements are linked to immune function or therapy response?
To answer this we need both what each cell is doing (transcriptomics) and which functional protein states it sits in (proteomics) — and we need them on the same physical slide, so the spatial coordinates line up exactly.
That’s the motivation behind IN-DEPTH (IN-situ DEtailed Phenotyping To High-resolution transcriptomics): a streamlined workflow that uses single-cell spatial-proteomic imaging to guide spatial-transcriptomic capture on the same tissue section, without the RNA signal loss that plagues sequential staining workflows.
Even with paired protein and RNA on a single slide, integrating the two modalities is non-trivial. Off-the-shelf correlation scores collapse rich multi-scale spatial information into a single number, throwing away the structure that makes a tumor microenvironment interesting in the first place.
We borrowed ideas from graph signal processing — specifically the spectral decomposition of signals defined on a tissue graph — and built Spectral Graph Cross-Correlation (SGCC), a framework that resolves spatially coordinated functional state changes across interacting cell populations at multiple scales rather than collapsing them.
In tonsil germinal centers, SGCC recovered the coordinated transition between dark-zone and light-zone architectures, where transcriptional shifts in T, B, and myeloid populations align with the spatial organization of the germinal center.
We then applied IN-DEPTH + SGCC to diffuse large B-cell lymphoma (DLBCL), comparing EBV-positive and EBV-negative tumors.
The results were striking. EBV-positive DLBCL displayed a markedly more immune-suppressive spatial arrangement: reduced HLA-DR protein expression on tumor cells, and increased T-cell dysfunction signatures specifically in regions where macrophages and CD4 T cells co-localize closely with malignant B cells. EBV doesn’t just hide — it reshapes the geometry of the tumor immune compartment.
The full IN-DEPTH workflow and the SGCC analytical framework are publicly available for reuse across tissue types and disease contexts. Same-slide multi-omics is becoming practical, and graph-spectral approaches give us a principled way to read the patterns inside.
For the full story, see the Cancer Discovery paper (2026).
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