Research topics
Spatial biology in cancer
Reading tumours and their microenvironment without losing track of where each cell sits in the tissue — Spatial Biology in Cancers.
What this research looks at
A cancer is not a list of cells; it is a tissue. Two cells with the same expression profile do different work depending on whether they sit in the middle of the tumour, along the boundary where immune cells press in, or beside a vessel. Spatial biology maps gene expression across a tissue section while keeping the coordinates, restoring the context that dissociation-based assays had no choice but to discard.
In cancer that context is the question itself — are the immune cells inside the tumour or held at its rim, in which region is the resistant clone expanding, what signals cross the tumour–stroma boundary. None of these can be answered without coordinates.
How contextBio approaches it
Processing of the raw sequencing data runs on AURORA's reproducible pipelines. The more a result depends on preprocessing choices — and spatial data depends on them heavily — the more the record of what was run, and how, has to travel with the output.
Where it stands now
The processing foundation is already running as a service. AURORA takes a raw-data folder and carries it through pipeline execution, progress tracking and report delivery on the web, with the heavy computation running on the lab cluster's job scheduler. Small-RNA sequencing was added recently, and the engine that delivers analysis reports has been rebuilt — widening the frame spatial data needs most: one where what was run, and how, stays with the result.
What it leads to
A spatial map of the tumour microenvironment is not the goal but the input to the next step: predicting response to immunotherapy, finding biomarkers region by region, and supplying the "state of a cell in the company of its neighbours" data that a virtual cell model learns from. Omics with coordinates attached is what lets in-silico experiments rise to the level of tissue.
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