Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912789734359040 |
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| author | Sakalyan, Kristiyan Palma, Alessandro Guerranti, Filippo Theis, Fabian J. Günnemann, Stephan |
| author_facet | Sakalyan, Kristiyan Palma, Alessandro Guerranti, Filippo Theis, Fabian J. Günnemann, Stephan |
| contents | Understanding the evolution of cellular microenvironments in spatiotemporal data is essential for deciphering tissue development and disease progression. While experimental techniques like spatial transcriptomics now enable high-resolution mapping of tissue organization across space and time, current methods that model cellular evolution operate at the single-cell level, overlooking the coordinated development of cellular states in a tissue. We introduce NicheFlow, a flow-based generative model that infers the temporal trajectory of cellular microenvironments across sequential spatial slides. By representing local cell neighborhoods as point clouds, NicheFlow jointly models the evolution of cell states and spatial coordinates using optimal transport and Variational Flow Matching. Our approach successfully recovers both global spatial architecture and local microenvironment composition across diverse spatiotemporal datasets, from embryonic to brain development. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_00977 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow Sakalyan, Kristiyan Palma, Alessandro Guerranti, Filippo Theis, Fabian J. Günnemann, Stephan Machine Learning Quantitative Methods Understanding the evolution of cellular microenvironments in spatiotemporal data is essential for deciphering tissue development and disease progression. While experimental techniques like spatial transcriptomics now enable high-resolution mapping of tissue organization across space and time, current methods that model cellular evolution operate at the single-cell level, overlooking the coordinated development of cellular states in a tissue. We introduce NicheFlow, a flow-based generative model that infers the temporal trajectory of cellular microenvironments across sequential spatial slides. By representing local cell neighborhoods as point clouds, NicheFlow jointly models the evolution of cell states and spatial coordinates using optimal transport and Variational Flow Matching. Our approach successfully recovers both global spatial architecture and local microenvironment composition across diverse spatiotemporal datasets, from embryonic to brain development. |
| title | Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2511.00977 |