arXiv:2511.00119v1 Announce Type: cross 
Abstract: Spatial transcriptomics (ST) technologies can be used to align transcriptomes with histopathological morphology, presenting exciting new opportunities for biomolecular discovery. Using ST data, we construct a novel framework, GeneFlow, to map transcriptomics onto paired cellular images. By combining an attention-based RNA encoder with a conditional UNet guided by rectified flow, we generate high-resolution images with different staining methods (e.g. H&E, DAPI) to highlight various cellular/tissue structures. Rectified flow with high-order ODE solvers creates a continuous, bijective mapping between transcriptomics and image manifolds, addressing the many-to-one relationship inherent in this problem. Our method enables the generation of realistic cellular morphology features and spatially resolved intercellular interactions from observational gene expression profiles, provides potential to incorporate genetic/chemical perturbations, and enables disease diagnosis by revealing dysregulated patterns in imaging phenotypes. Our rectified flow-based method outperforms diffusion-based baseline method in all experiments. Code can be found at https://github.com/wangmengbo/GeneFlow.

يمثل التطور الأخير لـ GeneFlow تقدمًا كبيرًا في مجال الاكتشافات البيومولكولية من خلال ترجمته الفعالة لبيانات التعبير الجيني على مستوى الخلية الواحدة إلى صور هيستوباثولوجية عالية الدقة. يستخدم هذا الإطار المبتكر تقنيات النسخ الجيني المكاني لمزامنة النسخ الجينية مع الشكل الخلوي، مما يفتح آفاقًا مثيرة للباحثين لاستكشاف الوظائف الخلوية وآليات الأمراض بدقة أكبر. تعزز مجموعة من مشفر RNA المعتمد على الانتباه وUNet الشرطي الموجه بواسطة تدفق مصحح جودة الصور وطرق التلوين، مما يجعله أداة قيمة للعلماء في سعيهم للحصول على رؤى بيولوجية أعمق.

El reciente desarrollo de GeneFlow marca un avance significativo en el campo del descubrimiento biomolecular al traducir eficazmente los datos de expresión génica de una sola célula en imágenes histopatológicas de alta resolución. Este marco innovador utiliza transcriptómica espacial para alinear transcriptomas con la morfología celular, abriendo emocionantes posibilidades para que los investigadores exploren funciones celulares y mecanismos de enfermedades con mayor precisión. La combinación de un codificador de ARN basado en atención y un UNet condicional guiado por flujo rectificado mejora la calidad de las imágenes y los métodos de tinción, convirtiéndolo en una herramienta valiosa para los científicos en su búsqueda de conocimientos biológicos más profundos.

Le développement récent de GeneFlow représente une avancée significative dans le domaine de la découverte biomoléculaire en traduisant efficacement les données d'expression génique à cellule unique en images histopathologiques haute résolution. Ce cadre innovant utilise la transcriptomique spatiale pour aligner les transcriptomes avec la morphologie cellulaire, ouvrant ainsi de nouvelles possibilités passionnantes pour les chercheurs afin d'explorer plus précisément les fonctions cellulaires et les mécanismes de la maladie. La combinaison d'un encodeur d'ARN basé sur l'attention et d'un UNet conditionnel guidé par un flux rectifié améliore la qualité des images et des méthodes de coloration, en faisant un outil précieux pour les scientifiques dans leur quête de connaissances biologiques plus profondes.

The recent development of GeneFlow marks a significant advancement in the field of biomolecular discovery by effectively translating single-cell gene expression data into high-resolution histopathological images. This innovative framework utilizes spatial transcriptomics to align transcriptomes with cellular morphology, opening up exciting possibilities for researchers to explore cellular functions and disease mechanisms more accurately. The combination of an attention-based RNA encoder and a conditional UNet guided by rectified flow enhances image quality and staining methods, making it a valuable tool for scientists in the quest for deeper biological insights.

GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow

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.

GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow

Was this article worth reading? Share it

ZeroGPT.org

SVGenius

Attentive AI

Dynamiq

MixflowAI

Workflow

Ready to build your own newsroom?