arXiv:2511.00977v1 Announce Type: new 
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.

تقدم دراسة جديدة NicheFlow، وهو نهج مبتكر لنمذجة مسارات الميكروبيئات الخلوية باستخدام النسخ الجيني المكاني. تعزز هذه الطريقة فهمنا لكيفية تطور الأنسجة وتقدم الأمراض من خلال التقاط التطور المنسق للحالات الخلوية، بدلاً من التركيز فقط على الخلايا الفردية. هذه الخطوة مهمة لأنها قد تؤدي إلى رؤى أفضل حول تنظيم الأنسجة واستراتيجيات علاجية محتملة.

Un nuevo estudio presenta NicheFlow, un enfoque innovador para modelar las trayectorias de los microambientes celulares utilizando transcriptómica espacial. Este método mejora nuestra comprensión de cómo se desarrollan los tejidos y progresan las enfermedades al capturar la evolución coordinada de los estados celulares, en lugar de centrarse únicamente en las células individuales. Este avance es significativo, ya que podría conducir a mejores perspectivas sobre la organización de los tejidos y estrategias terapéuticas potenciales.

Une nouvelle étude présente NicheFlow, une approche révolutionnaire pour modéliser les trajectoires des microenvironnements cellulaires à l'aide de la transcriptomique spatiale. Cette méthode améliore notre compréhension de la façon dont les tissus se développent et progressent dans les maladies en capturant l'évolution coordonnée des états cellulaires, plutôt qu'en se concentrant uniquement sur les cellules individuelles. Cette avancée est significative car elle pourrait conduire à de meilleures perspectives sur l'organisation des tissus et des stratégies thérapeutiques potentielles.

A new study introduces NicheFlow, a groundbreaking approach to modeling cellular microenvironment trajectories using spatial transcriptomics. This method enhances our understanding of how tissues develop and progress in diseases by capturing the coordinated evolution of cellular states, rather than focusing solely on individual cells. This advancement is significant as it could lead to better insights into tissue organization and potential therapeutic strategies.

Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow

arXiv:2509.09923v2 Announce Type: replace-cross 
Abstract: Differentiating between the two main subtypes of Inflammatory Bowel Disease (IBD): Crohns disease (CD) and ulcerative colitis (UC) is a persistent clinical challenge due to overlapping presentations. This study introduces a novel computational framework that employs spatial transcriptomics (ST) to create an explainable machine learning model for IBD classification. We analyzed ST data from the colonic mucosa of healthy controls (HC), UC, and CD patients. Using Non-negative Matrix Factorization (NMF), we first identified four recurring cellular niches, representing distinct functional microenvironments within the tissue. From these niches, we systematically engineered 44 features capturing three key aspects of tissue pathology: niche composition, neighborhood enrichment, and niche-gene signals. A multilayer perceptron (MLP) classifier trained on these features achieved an accuracy of $0.774 \pm 0.161$ for the more challenging three-class problem (HC, UC, and CD) and $0.916 \pm 0.118$ in the two-class problem of distinguishing IBD from healthy tissue. Crucially, model explainability analysis revealed that disruptions in the spatial organization of niches were the strongest predictors of general inflammation, while the classification between UC and CD relied on specific niche-gene expression signatures. This work provides a robust, proof-of-concept pipeline that transforms descriptive spatial data into an accurate and explainable predictive tool, offering not only a potential new diagnostic paradigm but also deeper insights into the distinct biological mechanisms that drive IBD subtypes.

تم تقديم إطار حسابي جديد لتمييز بين مرض كرون والتهاب القولون التقرحي، وهما النوعان الرئيسيان من مرض الأمعاء الالتهابي (IBD). يستخدم هذا الإطار النسخ المتماثل المكاني لتطوير نموذج تعلم آلي قابل للتفسير، محققًا دقة تصنيف تبلغ 0.774 لمرضى IBD بناءً على تحليل الفجوات الخلوية.

Se ha introducido un nuevo marco computacional para diferenciar entre la enfermedad de Crohn y la colitis ulcerosa, dos subtipos principales de la enfermedad inflamatoria intestinal (EII). Este marco utiliza la transcriptómica espacial para desarrollar un modelo de aprendizaje automático explicable, logrando una precisión de clasificación de 0.774 para pacientes con EII basado en el análisis de nichos celulares.

Un nouveau cadre computationnel a été introduit pour différencier la maladie de Crohn et la colite ulcéreuse, deux sous-types principaux de la maladie inflammatoire de l'intestin (MII). Ce cadre utilise la transcriptomique spatiale pour développer un modèle d'apprentissage automatique explicable, atteignant une précision de classification de 0,774 pour les patients atteints de MII sur la base de l'analyse des niches cellulaires.

A novel computational framework has been introduced to differentiate between Crohn's disease and ulcerative colitis, two main subtypes of Inflammatory Bowel Disease (IBD). This framework utilizes spatial transcriptomics to develop an explainable machine learning model, achieving a classification accuracy of 0.774 for IBD patients based on cellular niche analysis.

Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow

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