Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2508.10232 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912537331630080 |
|---|---|
| author | Acosta, Paul H. Chen, Pingjun Castillo, Simon P. Salvatierra, Maria Esther Yuan, Yinyin Pan, Xiaoxi |
| author_facet | Acosta, Paul H. Chen, Pingjun Castillo, Simon P. Salvatierra, Maria Esther Yuan, Yinyin Pan, Xiaoxi |
| contents | Xenium, a new spatial transcriptomics platform, enables subcellular-resolution profiling of complex tumor tissues. Despite the rich morphological information in histology images, extracting robust cell-level features and integrating them with spatial transcriptomics data remains a critical challenge. We introduce CellSymphony, a flexible multimodal framework that leverages foundation model-derived embeddings from both Xenium transcriptomic profiles and histology images at true single-cell resolution. By learning joint representations that fuse spatial gene expression with morphological context, CellSymphony achieves accurate cell type annotation and uncovers distinct microenvironmental niches across three cancer types. This work highlights the potential of foundation models and multimodal fusion for deciphering the physiological and phenotypic orchestration of cells within complex tissue ecosystems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CellSymphony: Deciphering the molecular and phenotypic orchestration of cells with single-cell pathomics Acosta, Paul H. Chen, Pingjun Castillo, Simon P. Salvatierra, Maria Esther Yuan, Yinyin Pan, Xiaoxi Computer Vision and Pattern Recognition Xenium, a new spatial transcriptomics platform, enables subcellular-resolution profiling of complex tumor tissues. Despite the rich morphological information in histology images, extracting robust cell-level features and integrating them with spatial transcriptomics data remains a critical challenge. We introduce CellSymphony, a flexible multimodal framework that leverages foundation model-derived embeddings from both Xenium transcriptomic profiles and histology images at true single-cell resolution. By learning joint representations that fuse spatial gene expression with morphological context, CellSymphony achieves accurate cell type annotation and uncovers distinct microenvironmental niches across three cancer types. This work highlights the potential of foundation models and multimodal fusion for deciphering the physiological and phenotypic orchestration of cells within complex tissue ecosystems. |
| title | CellSymphony: Deciphering the molecular and phenotypic orchestration of cells with single-cell pathomics |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.10232 |