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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

Korean

세포 컨텍스트 상태는 내재적인 유전자 표현 프로그램과 세포가 거주하는 공간 미세 환경 모두에 의해 형성됩니다. 공간 트랜스크립토믹스는이 컨텍스트를 측정 할 수 있지만 효과적인 표현 학습은 공간 정보 분자 신호와 세포 간 조직 모두를 캡처해야합니다. 기존의 접근 방식은 이러한 도전을 부분적으로 해결합니다 : 전통적인 유전자 선택은 공간 구조 신호를 무시할 수 있지만, 세포 간 관계를 인코딩하는 방법은 종종 세포의 미세 환경을 가까이로 줄이거나 방향을 제거하거나 자발적인 조직 프레임에 연결된 절대적 조정에 의존합니다. 결과적으로, 유전자 선택은 비슷한 이웃 거리이지만 구별된 공간 조직이있는 세포는 세포 상태를 형성하는 다른 생물학

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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