A Connected Spatial Workflow Reveals Biomarker Context in Glioblastoma
Summary
For teams advancing glioblastoma immunotherapy research, knowing that cytotoxic T cells are present is not enough. The critical question is whether those cells are positioned in the tumor microenvironment to support an effective immune response. Yet fragmented biomarker workflows can make that answer difficult to trust. Separate staining, imaging and analysis steps increase variability, make spatial relationships harder to confirm and leave serial-section comparisons open to interpretation. That uncertainty can push teams to expand panels later, repeat validation work and delay confident target or biomarker decisions.
In this case study, see how a connected spatial biology workflow resolves phenotype and location together within a single tissue context. Using recombinant antibodies, multiplex imaging and AI-enabled image analysis, researchers mapped 20+ biomarkers across one FFPE glioblastoma section while preserving tissue architecture. The workflow identified three spatial candidate biomarkers that fragmented methods might miss, supporting earlier panel prioritization and better target evaluation.
Key takeaways:
- Reduce workflow handoffs by connecting reagents, imaging and analysis, helping teams limit variability and improve reproducibility
- Measure phenotype and spatial context together on one FFPE section, keeping every relationship measurable within the same tissue
- Generate broader biomarker evidence from fewer specimens by mapping 20+ markers without serial sectioning
- Detect negative findings that redirect strategy, including absent PD-1 and PD-L1 expression
- Prioritize panels and targets earlier, supporting more informed go/no-go decisions before costly validation
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https://lifesciences.danaher.com/
https://lifesciences.danaher.com/content/dam/danaher/gated/AACR-Poster-case-study-final.pdf
https://help.salesforce.com/s/articleView?id=sf.mc_es_demanager.htm