Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow

• URL: http://arxiv.org/abs/2511.00977v1
• Date: Sun, 02 Nov 2025 15:41:38 GMT
• Title: Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow
• Authors: Kristiyan Sakalyan, Alessandro Palma, Filippo Guerranti, Fabian J. Theis, Stephan Günnemann,
• Abstract summary: Understanding of cellular microenvironment is essential for deciphering tissue development and disease data.<n>NicheFlow is a flow-based generative model that infers the temporal trajectory of cellular microenvironments across spatial slides.
• Score: 80.00833033784079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Uncovering spatial tissue domains and cell types in spatial omics through cross-scale profiling of cellular and genomic interactions [26.7111709393529]
  We present CellScape, a deep learning framework designed to overcome limitations for high-performance spatial transcriptomics analysis.<n>CellScape models cellular interactions in tissue space and genomic relationships among cells, producing comprehensive representations.<n>This technique uncovers biologically informative patterns that improve spatial domain segmentation.
  arXiv  Detail & Related papers  (2026-02-13T06:22:43Z)
• ITC-RWKV: Interactive Tissue-Cell Modeling with Recurrent Key-Value Aggregation for Histopathological Subtyping [2.99938892718088]
  We propose a dual-stream architecture that models the interplay between macroscale tissue features and aggregated cellular representations.<n>We introduce a bidirectional tissue-cell interaction module to enable mutual attention between localized cellular cues and their surrounding tissue environment.
  arXiv  Detail & Related papers  (2025-10-24T14:03:52Z)
• Learning noisy tissue dynamics across time scales [0.0]
  We introduce a biomimetic machine learning framework capable of inferring noisy multicellular dynamics directly from experimental movies.<n>We show that our model can accurately generate the experimental dynamics of developmental systems, such as the fly wing, and cell signaling processes mediated by ERK waves.
  arXiv  Detail & Related papers  (2025-10-21T21:33:54Z)
• Kuramoto Orientation Diffusion Models [67.0711709825854]
  Orientation-rich images, such as fingerprints and textures, often exhibit coherent angular patterns.<n>Motivated by the role of phase synchronization in biological systems, we propose a score-based generative model.<n>We implement competitive results on general image benchmarks and significantly improves generation quality on orientation-dense datasets like fingerprints and textures.
  arXiv  Detail & Related papers  (2025-09-18T18:18:49Z)
• TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model [32.670806339139034]
  We propose a novel approach that integrates topological constraints into a diffusion model to improve the generation of realistic, contextually accurate cell topologies.<n>Our method refines the simulation of cell distributions and interactions, increasing the precision and interpretability of results in downstream tasks.
  arXiv  Detail & Related papers  (2024-12-08T18:02:22Z)
• Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition [64.56321246196859]
  We propose a novel dyNamically Evolving dUal skeleton-semantic syneRgistic framework.<n>We first construct the spatial-temporal evolving micro-prototypes and integrate dynamic context-aware side information.<n>We introduce the spatial compression and temporal memory mechanisms to guide the growth of spatial-temporal micro-prototypes.
  arXiv  Detail & Related papers  (2024-11-18T05:16:11Z)
• Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold [83.18058549195855]
  We argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities.<n>In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depend on the microenvironment of cells specific to each patient.<n>We propose Meta Flow Matching (MFM), a practical approach to integrate along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations.
  arXiv  Detail & Related papers  (2024-08-26T20:05:31Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• Engineering morphogenesis of cell clusters with differentiable programming [2.0690546196799042]
  We use recent advances in automatic differentiation to discover local interaction rules and genetic networks.<n>We show that one can learn the parameters governing cell interactions in the form of interpretable genetic networks.
  arXiv  Detail & Related papers  (2024-07-08T18:05:11Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.