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| Item Type: | Article |
|---|---|
| Title: | Open-ST: high-resolution spatial transcriptomics in 3D |
| Creators Name: | Schott, M., León-Periñán, D., Splendiani, E., Strenger, L., Licha, J.R., Pentimalli, T.M., Schallenberg, S., Alles, J., Samut Tagliaferro, S., Boltengagen, A., Ehrig, S., Abbiati, S., Dommerich, S., Pagani, M., Ferretti, E., Macino, G., Karaiskos, N. and Rajewsky, N. |
| Abstract: | Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst. |
| Keywords: | Spatial Transcriptomics, Single Cell, Open Source, Three Dimensional, Cancer, HNSCC, Metastasis, Subcellular, Resource, Animals, Mice |
| Source: | Cell |
| ISSN: | 0092-8674 |
| Publisher: | Elsevier / Cell Press |
| Volume: | 187 |
| Number: | 15 |
| Page Range: | 3953-3972.e26 |
| Date: | 25 July 2024 |
| Official Publication: | https://doi.org/10.1016/j.cell.2024.05.055 |
| PubMed: | View item in PubMed |
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