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Spatially Informed and Direction Sensitive Representation Learning for Spatial Transcriptomics

Azim Dehghani Amirabad, Ali Khodabandeh Yalabadi, Artem Moskalev, Yunching Chen, Mehdi Pirooznia, Scott Oloff, Rui Liao · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28579 · cited by 0 works (OpenAlex) · open-access PDF

Arabic

يتم تشكيل الحالة الخلفية من خلال برامج التعبير الجيني الأصلية والبيئة الخلفية التي يعيشها الخلايا. يجعل التعبير الجيني هذا السياق قابلًا للتقييم، ولكن تعلم التصوير الفعلي يتطلب تسجيل كل من الإشارات الجزيئية المعروفة في الفضاء والمؤسسة بين الخلايا. وبالتالي فإن الطرق الموجودة تتعامل مع هذه التحديات جزئًا فقط: يمكن اختيار الجينات التقليدية تجاهل الإشارات المعروفة في الفضاء، في حين أن الأساليب لتصفية العلاقات الخلفية غالبا ما تجعله يقلل من بيئة الخلايا الجزيئية إلى الوجه وحده، مما يجعلنا نقدم Tessera، نموذجًا لتعلم التعبير المحلي الذي يغطي كل

Machine translation (Meta M2M100); the English original follows.

Abstract

Cellular states are shaped by both intrinsic gene expression programs and the spatial microenvironment in which cells reside. Spatial transcriptomics makes this context measurable, but effective representation learning requires capturing both spatially informative molecular signals and intercellular organization. Existing approaches address these challenges only partially: conventional gene selection can overlook spatially structured signals, while methods for encoding cell-cell relationships often either reduce a cell's microenvironment to proximity alone, discarding direction, or rely on absolute coordinates tied to an arbitrary tissue frame. Consequently, cells with similar neighbor distances but distinct spatial organization may receive overly similar representations, despite experiencing different biological contexts that shape their cellular states. We introduce Tessera, a spatial representation learning framework that addresses both molecular selection and cellular context. Tessera combines spatially informed gene selection with a geometric inductive bias that contextualizes each cell within its local tissue environment. By modeling relative cell-cell relationships, Tessera is translation-invariant while preserving direction, allowing neighboring cells to contribute differently according to their spatial context and cellular state. Across held-out tissues and technologies, Tessera outperforms existing spatial foundation models in representation quality and generalizes zero-shot to contexts up to $16\times$ larger than those seen during training.

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Topics: Representation (politics), Context (archaeology), Computer science, Selection (genetic algorithm), Artificial intelligence, Spatial contextual awareness

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