Related Experiment Video
Updated: Aug 19, 2025
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
Published on: July 6, 2022
Assessment of spatial transcriptomics for oncology discovery
Anna Lyubetskaya1, Brian Rabe1, Andrew Fisher1
1Research and Early Development, Bristol Myers Squibb Company, 100 Binney Street, Cambridge, MA 02142, USA.
Abstract:
Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.
Insights
Spatial transcriptomics (ST) aids oncology drug discovery by mapping tumor landscapes. This technology reveals key features like hypoxia and cell interactions, enabling new target identification.
Area of Science:
- Oncology
- Genomics
- Biotechnology
Background:
- Tumor heterogeneity presents a significant hurdle in oncology drug discovery and development.
- Understanding the spatial tumor landscape is crucial for identifying novel therapeutic targets and effective preclinical models.
Purpose of the Study:
- To evaluate the utility of spatial transcriptomics (ST) for advancing oncology discovery.
- To profile diverse tissue types and sample formats using ST to capture spatial information.
Main Methods:
- Profiling of 40 tissue sections and 80,024 capture spots using spatial transcriptomics.
- Verification of ST accuracy and fidelity through matched pathology analysis.
- Analysis of spatial data to identify tumor depth features and cell-type locations.
Main Results:
- ST successfully captured key tumor depth features including hypoxia, necrosis, vasculature, and extracellular matrix variations.
- Spatial context analysis revealed anti-correlation between tumor and immune cells in syngeneic cancer models.
- Demonstrated target identification in pancreatic adenocarcinoma, highlighting tumor-intrinsic biomarkers and paracrine signaling.
Conclusions:
- Spatial transcriptomics is a valuable tool for oncology discovery, providing insights into the tumor microenvironment.
- ST facilitates the identification of novel biomarkers and therapeutic targets by analyzing spatial relationships within tumors.
- This approach enhances the understanding of tumor heterogeneity and supports the development of more effective cancer therapies.
