Assessment of Spatial Genomics for Oncology Discovery(Dataset ID: STDS0000186)
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Summary:
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 that provide 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.Overall design:
Anna, LyubetskayaTechnology:
10x Visium
Platform:
Illumina NovaSeq 6000, Illumina NovaSeq 6000, Illumina NovaSeq 6000
Species:
Rattus norvegicus(rn6)
Homo sapiens(hg38)
Mus musculus(mm10)
Tissues:
Colon
Organ parts:
Colon, Pancreatic cancer, B16F10 syngeneic tumor, MC38 syngeneic tumor
Submission date: 2022-08-23Update date: 2022-11-29
Sample number: 29Section number: 29
Contributors
Lyubetskaya; Anna; Rabe; Brian; Fisher; Andrew; Lewin; Anne; Neuhaus; Isaac; Brett; Connie; Brett; Todd; Pereira; Ethel; Golhar; Ryan; Kebede; Sami; Font-Tello; Alba; Mosure; Kathy; Van Wittenberghe; Nicholas; Mavrakis; Konstantinos; MacIsaac; Kenzie; Chen; Benjamin; Drokhlyansky; Eugene
Accessions
GEO Series Accessions:
GSE211895
Reference way
Citation: Lyubetskaya, Anna et al. “Assessment of spatial transcriptomics for oncology discovery.” Cell reports methods vol. 2,11 100340. 15 Nov. 2022, doi:10.1016/j.crmeth.2022.100340