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bioRxiv · 10.64898/2026.04.24.720489

A transcriptomic-driven segmentation and cell simulation framework for high-resolution spatial transcriptomics and cell-cell communication

Wanchai, V.·Bustamante-Gomez, N. C.·Kurilung, A.·Beenken, K. E.·Cortes, S.·Smeltzer, M. S.·Leung, Y.-K.·Xiong, J.·Almeida, M.·O'Brien, C. A.·Nookaew, I.
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Abstract

The Visium HD spatial transcriptomics platform enables transcriptome-wide profiling at near-single-cell resolution. However, accurate segmentation of cells to define spatial boundaries relies heavily on histological images. Previous approaches struggle to define cells when the tissues have high cell density, are inflamed, or are mineralized, leading to transcriptomic bleed-through and inaccurate clustering. To address this, we developed TENGU (Transcript-signal Enrichment and Grouping Unit), a comprehensive end-to-end bioinformatic software package. Unlike existing tools, TENGU employs a transcript-first segmentation approach, prioritizing transcript-signal density as the primary modality and utilizing histological images only as a secondary supplement in unresolved regions. These initial boundaries are further optimized through a novel transcriptomic-driven cell simulation algorithm. Iterative refinement of boundaries based on localized gene expression probabilities effectively minimizes spatial scattering and preserves biologically distinct molecular signatures. The pipeline seamlessly integrates tissue segmentation, high-resolution cell-type annotation, and basic spatially aware cell-cell communication (CCC) analysis. We rigorously benchmarked TENGU against the 10X Genomics and Bin2cell pipelines for cell segmentation across diverse and technically challenging microenvironments. TENGU demonstrated superior transcriptomic distinctness in the murine brain, successfully captured matrix-embedded osteocytes, and localized critical osteoimmune CCC networks (Tgfb and Il1a) in a murine model of osteomyelitis. TENGU also resolved species-specific, pro-tumorigenic signaling hubs (MDK-SDC4) within a highly compacted human colorectal cancer xenograft. By mitigating the constraints of traditional image-dependent segmentation, TENGU provides a highly adaptable and robust computational framework that empowers researchers to accurately decode the complex functional micro-anatomy of both healthy and pathological tissues.

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Wanchai, V., Bustamante-Gomez, N. C., Kurilung, A., Beenken, K. E., Cortes, S., Smeltzer, M. S., Leung, Y.-K., Xiong, J., Almeida, M., O'Brien, C. A., Nookaew, I.. 2026-04-28. A transcriptomic-driven segmentation and cell simulation framework for high-resolution spatial transcriptomics and cell-cell communication. https://doi.org/10.64898/2026.04.24.720489

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