arXiv:2410.10652v3 Announce Type: replace-cross 
Abstract: Cells in multicellular organisms coordinate to form functional and structural niches. With spatial transcriptomics enabling gene expression profiling in spatial contexts, it has been revealed that spatial niches serve as cohesive and recurrent units in physiological and pathological processes. These observations suggest universal tissue organization principles encoded by conserved niche patterns, and call for a query-based niche analytical paradigm beyond current computational tools. In this work, we defined the Niche Query Task, which is to identify similar niches across ST samples given a niche of interest (NOI). We further developed QueST, a specialized method for solving this task. QueST models each niche as a subgraph, uses contrastive learning to learn discriminative niche embeddings, and incorporates adversarial training to mitigate batch effects. In simulations and benchmark datasets, QueST outperformed existing methods repurposed for niche querying, accurately capturing niche structures in heterogeneous environments and demonstrating strong generalizability across diverse sequencing platforms. Applied to tertiary lymphoid structures in renal and lung cancers, QueST revealed functionally distinct niches associated with patient prognosis and uncovered conserved and divergent spatial architectures across cancer types. These results demonstrate that QueST enables systematic, quantitative profiling of spatial niches across samples, providing a powerful tool to dissect spatial tissue architecture in health and disease.

أظهرت الأبحاث الحديثة حول النسخ الجيني المكاني كيف تتعاون الخلايا في الكائنات متعددة الخلايا لتشكيل مناطق وظيفية وهيكلية. تسلط هذه الدراسة الضوء على أهمية المناطق المكانية في كل من الحالات الصحية والمرضية، مما يشير إلى وجود مبادئ عالمية لتنظيم الأنسجة تنعكس في أنماط المناطق المحفوظة. إن فهم هذه الديناميكيات أمر بالغ الأهمية لتعزيز معرفتنا بالعمليات البيولوجية وقد يكون له آثار على البحث الطبي.

Investigaciones recientes sobre transcriptómica espacial han revelado cómo las células en organismos multicelulares colaboran para formar nichos funcionales y estructurales. Este estudio destaca la importancia de los nichos espaciales tanto en estados saludables como patológicos, sugiriendo que existen principios universales de organización tisular reflejados en patrones de nichos conservados. Comprender estas dinámicas es crucial para avanzar en nuestro conocimiento de los procesos biológicos y podría tener implicaciones para la investigación médica.

Des recherches récentes sur la transcriptomique spatiale ont mis en lumière la manière dont les cellules des organismes multicellulaires collaborent pour créer des niches fonctionnelles et structurelles. Cette étude souligne l'importance des niches spatiales tant dans les états sains que malades, suggérant qu'il existe des principes universels d'organisation tissulaire reflétés dans des motifs de niches conservés. Comprendre ces dynamiques est crucial pour faire progresser nos connaissances sur les processus biologiques et pourrait avoir des implications pour la recherche médicale.

Recent research on spatial transcriptomics has shed light on how cells in multicellular organisms work together to create functional and structural niches. This study highlights the importance of spatial niches in both healthy and diseased states, suggesting that there are universal principles of tissue organization that are reflected in conserved niche patterns. Understanding these dynamics is crucial for advancing our knowledge of biological processes and could have implications for medical research.

Querying functional and structural niches on spatial transcriptomics data

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.

Querying functional and structural niches on spatial transcriptomics data

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