Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling

Ningfeng Que; Xiaofei Wang; Jingjing Chen; Yixuan Jiang; Chao Li

doi:10.1007/978-3-032-04927-8_5

Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling

Ningfeng Que
, Xiaofei Wang
, Jingjing Chen
, Yixuan Jiang
, Chao Li (Lead / Corresponding author)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Spatial transcriptomics (ST) is a promising technique that characterizes the spatial gene profiling patterns within the tissue context. Comprehensive ST analysis depends on consecutive slices for 3D spatial insights, whereas the missing intermediate tissue sections and high costs limit the practical feasibility of generating multi-slice ST. In this paper, we propose C2-STi, the first attempt for interpolating missing ST slices at arbitrary intermediate positions between adjacent ST slices. Despite intuitive, effective ST interpolation presents significant challenges, including 1) limited continuity across heterogeneous tissue sections, 2) complex intrinsic correlation across genes, and 3) intricate cellular structures and biological semantics within each tissue section. To mitigate these challenges, in C2-STi, we design 1) a distance-aware local structural modulation module to adaptively capture cross-slice deformations and enhance positional correlations between ST slices, 2) a pyramid gene co-expression correlation module to capture multi-scale biological associations among genes, and 3) a cross-modal alignment module that integrates the ST-paired hematoxylin and eosin (H&E)-stained images to filter and align the essential cellular features across ST and H&E images. Extensive experiments on the public dataset demonstrate our superiority over state-of-the-art approaches on both single-slice and multi-slice ST interpolation. Codes are available at https://github.com/XiaofeiWang2018/C2-STi.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, 2025, Proceedings
Editors	James C. Gee, Jaesung Hong, Carole H. Sudre, Polina Golland, Daniel C. Alexander, Juan Eugenio Iglesias, Archana Venkataraman, Jong Hyo Kim
Place of Publication	Cham
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	45-54
Number of pages	10
Volume	15960
ISBN (Electronic)	9783032049278
ISBN (Print)	9783032049261
DOIs	https://doi.org/10.1007/978-3-032-04927-8_5
Publication status	Published - 2026
Event	28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - Daejeon, Korea, Republic of Duration: 23 Sept 2025 → 27 Sept 2025

Publication series

Name	Lecture Notes in Computer Science
Volume	15960 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Country/Territory	Korea, Republic of
City	Daejeon
Period	23/09/25 → 27/09/25

Keywords

Deep learning
Interpolation
Multimodal modeling
Spatial transcriptomics

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-032-04927-8_5

Cite this

Que, N., Wang, X., Chen, J., Jiang, Y., & Li, C. (2026). Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling. In J. C. Gee, J. Hong, C. H. Sudre, P. Golland, D. C. Alexander, J. E. Iglesias, A. Venkataraman, & J. H. Kim (Eds.), Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, 2025, Proceedings (Vol. 15960, pp. 45-54). (Lecture Notes in Computer Science; Vol. 15960 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-04927-8_5

Que, Ningfeng ; Wang, Xiaofei ; Chen, Jingjing et al. / Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling. Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, 2025, Proceedings. editor / James C. Gee ; Jaesung Hong ; Carole H. Sudre ; Polina Golland ; Daniel C. Alexander ; Juan Eugenio Iglesias ; Archana Venkataraman ; Jong Hyo Kim. Vol. 15960 Cham : Springer Science and Business Media Deutschland GmbH, 2026. pp. 45-54 (Lecture Notes in Computer Science).

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title = "Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling",

abstract = "Spatial transcriptomics (ST) is a promising technique that characterizes the spatial gene profiling patterns within the tissue context. Comprehensive ST analysis depends on consecutive slices for 3D spatial insights, whereas the missing intermediate tissue sections and high costs limit the practical feasibility of generating multi-slice ST. In this paper, we propose C2-STi, the first attempt for interpolating missing ST slices at arbitrary intermediate positions between adjacent ST slices. Despite intuitive, effective ST interpolation presents significant challenges, including 1) limited continuity across heterogeneous tissue sections, 2) complex intrinsic correlation across genes, and 3) intricate cellular structures and biological semantics within each tissue section. To mitigate these challenges, in C2-STi, we design 1) a distance-aware local structural modulation module to adaptively capture cross-slice deformations and enhance positional correlations between ST slices, 2) a pyramid gene co-expression correlation module to capture multi-scale biological associations among genes, and 3) a cross-modal alignment module that integrates the ST-paired hematoxylin and eosin (H\&E)-stained images to filter and align the essential cellular features across ST and H\&E images. Extensive experiments on the public dataset demonstrate our superiority over state-of-the-art approaches on both single-slice and multi-slice ST interpolation. Codes are available at https://github.com/XiaofeiWang2018/C2-STi.",

keywords = "Deep learning, Interpolation, Multimodal modeling, Spatial transcriptomics",

author = "Ningfeng Que and Xiaofei Wang and Jingjing Chen and Yixuan Jiang and Chao Li",

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Que, N, Wang, X, Chen, J, Jiang, Y & Li, C 2026, Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling. in JC Gee, J Hong, CH Sudre, P Golland, DC Alexander, JE Iglesias, A Venkataraman & JH Kim (eds), Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, 2025, Proceedings. vol. 15960, Lecture Notes in Computer Science, vol. 15960 LNCS, Springer Science and Business Media Deutschland GmbH, Cham, pp. 45-54, 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, Daejeon, Korea, Republic of, 23/09/25. https://doi.org/10.1007/978-3-032-04927-8_5

Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling. / Que, Ningfeng; Wang, Xiaofei; Chen, Jingjing et al.
Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, 2025, Proceedings. ed. / James C. Gee; Jaesung Hong; Carole H. Sudre; Polina Golland; Daniel C. Alexander; Juan Eugenio Iglesias; Archana Venkataraman; Jong Hyo Kim. Vol. 15960 Cham: Springer Science and Business Media Deutschland GmbH, 2026. p. 45-54 (Lecture Notes in Computer Science; Vol. 15960 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling

AU - Que, Ningfeng

AU - Wang, Xiaofei

AU - Chen, Jingjing

AU - Jiang, Yixuan

AU - Li, Chao

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - Spatial transcriptomics (ST) is a promising technique that characterizes the spatial gene profiling patterns within the tissue context. Comprehensive ST analysis depends on consecutive slices for 3D spatial insights, whereas the missing intermediate tissue sections and high costs limit the practical feasibility of generating multi-slice ST. In this paper, we propose C2-STi, the first attempt for interpolating missing ST slices at arbitrary intermediate positions between adjacent ST slices. Despite intuitive, effective ST interpolation presents significant challenges, including 1) limited continuity across heterogeneous tissue sections, 2) complex intrinsic correlation across genes, and 3) intricate cellular structures and biological semantics within each tissue section. To mitigate these challenges, in C2-STi, we design 1) a distance-aware local structural modulation module to adaptively capture cross-slice deformations and enhance positional correlations between ST slices, 2) a pyramid gene co-expression correlation module to capture multi-scale biological associations among genes, and 3) a cross-modal alignment module that integrates the ST-paired hematoxylin and eosin (H&E)-stained images to filter and align the essential cellular features across ST and H&E images. Extensive experiments on the public dataset demonstrate our superiority over state-of-the-art approaches on both single-slice and multi-slice ST interpolation. Codes are available at https://github.com/XiaofeiWang2018/C2-STi.

AB - Spatial transcriptomics (ST) is a promising technique that characterizes the spatial gene profiling patterns within the tissue context. Comprehensive ST analysis depends on consecutive slices for 3D spatial insights, whereas the missing intermediate tissue sections and high costs limit the practical feasibility of generating multi-slice ST. In this paper, we propose C2-STi, the first attempt for interpolating missing ST slices at arbitrary intermediate positions between adjacent ST slices. Despite intuitive, effective ST interpolation presents significant challenges, including 1) limited continuity across heterogeneous tissue sections, 2) complex intrinsic correlation across genes, and 3) intricate cellular structures and biological semantics within each tissue section. To mitigate these challenges, in C2-STi, we design 1) a distance-aware local structural modulation module to adaptively capture cross-slice deformations and enhance positional correlations between ST slices, 2) a pyramid gene co-expression correlation module to capture multi-scale biological associations among genes, and 3) a cross-modal alignment module that integrates the ST-paired hematoxylin and eosin (H&E)-stained images to filter and align the essential cellular features across ST and H&E images. Extensive experiments on the public dataset demonstrate our superiority over state-of-the-art approaches on both single-slice and multi-slice ST interpolation. Codes are available at https://github.com/XiaofeiWang2018/C2-STi.

KW - Deep learning

KW - Interpolation

KW - Multimodal modeling

KW - Spatial transcriptomics

UR - https://www.scopus.com/pages/publications/105017861135

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DO - 10.1007/978-3-032-04927-8_5

M3 - Conference contribution

AN - SCOPUS:105017861135

SN - 9783032049261

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T3 - Lecture Notes in Computer Science

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BT - Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, 2025, Proceedings

A2 - Gee, James C.

A2 - Hong, Jaesung

A2 - Sudre, Carole H.

A2 - Golland, Polina

A2 - Alexander, Daniel C.

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Que N, Wang X, Chen J, Jiang Y, Li C. Adaptive Spatial Transcriptomics Interpolation via Cross-modal Cross-slice Modeling. In Gee JC, Hong J, Sudre CH, Golland P, Alexander DC, Iglesias JE, Venkataraman A, Kim JH, editors, Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, 2025, Proceedings. Vol. 15960. Cham: Springer Science and Business Media Deutschland GmbH. 2026. p. 45-54. (Lecture Notes in Computer Science). Epub 2025 Sept 21. doi: 10.1007/978-3-032-04927-8_5