Spatial omics in the AI era: Technologies, algorithmic ecosystems, biological applications, and large model perspectives

Haoxiu Wang; Xinwang Yang; Siheng Wang; Zhe Yang; Xiuhui Yang; Yutong Yang; Zirong Li; Yuqi Ren; Qianqian Zhang; Bowen Zhao; Jingming Xiao; Yidong Wang; Junhao Dong; Zhenhao Kou; Jie Li; Liqun Yang; Erhu Zhao; Gregory Fonseca; Ruibang Luo; Mingyu Yang; Hongjuan Cui; Gengjie Jia; Dan Wang; Haoyang Li; Jun Ding; Zhiyuan Yuan; Haojing Shao

doi:10.1002/imt2.70146

Spatial omics in the AI era: Technologies, algorithmic ecosystems, biological applications, and large model perspectives

Haoxiu Wang (Co-first Author)
, Xinwang Yang (Co-first Author)
, Siheng Wang (Co-first Author)
, Zhe Yang (Co-first Author)
, Xiuhui Yang (Co-first Author)
, Yutong Yang (Co-first Author)
, Zirong Li (Co-first Author)
, Yuqi Ren (Co-first Author)
, Qianqian Zhang
, Bowen Zhao
, Jingming Xiao
, Yidong Wang
, Junhao Dong
, Zhenhao Kou
, Jie Li
, Liqun Yang
, Erhu Zhao
, Gregory Fonseca
, Ruibang Luo
, Mingyu Yang^*

Hongjuan Cui^*, Gengjie Jia^*, Dan Wang^*, Haoyang Li^*, Jun Ding^*, Zhiyuan Yuan^*, Haojing Shao^*

^*Corresponding author for this work

Department of Biomedical Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

Spatial omics technologys help overcome key limitations of conventional omics approaches that lack spatial information, by providing a panoramic perspective from the molecular level to the microenvironment scale for addressing spatially resolved biological questions in life sciences. With the rapid advancement of this field, there are significant differences among technology platforms, algorithms, and research workflows, which bring three core challenges to interdisciplinary researchers: the detailed explanation of technical principles, the selection of appropriate algorithms, and the future development directions. This review systematically summarizes the technical platforms and analytical algorithms of spatial omics, compares their advantages and disadvantages in the context of specific tasks and presents application cases across multiple biological fields. It also outlines the emerging research directions and advances in large model integration. It ultimately aims to provide a reference for researchers from diverse disciplines to design and implement spatial omics studies. © 2026 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.

Original language	English
Article number	e70146
Journal	iMeta
Volume	5
Issue number	3
DOIs	https://doi.org/10.1002/imt2.70146
Publication status	Published - Jun 2026

Funding

Haojing Shao was supported by the National Natural Science Foundation of China (32200517), and the Science and Technology Program Funding of Shenzhen Municipality(SYSPG20241211173846020). Hongjuan Cui was supported by the Natural Science Foundation of Chongqing (cstc2021jcyj-cxtt0005, CSTB2023TIAD-STX0007), and the pilot program of Southwest University (SWU-XDZD22006). Gengjie Jia was supported by the National Natural Science Foundation of China (92574112, 32470720), Basic Research Center, Innovation Program of Chinese Academy of Agricultural Sciences (CAAS-BRC-FNH-2025-02, CAAS-ASTIP-2021-AGIS), and the Agricultural Science and Technology Innovation Program (ASTIP). Dan Wang was supported in part by Guangdong Higher Education Upgrading Plan with UICR0400007-24 at Beijing Normal-Hong Kong Baptist University, China and also by Guangdong University Featured Innovation Program Project (2025KTSCX183).

Research Keywords

algorithms
biological applications
large models
spatial omics
technologies

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This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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Wang, H., Yang, X., Wang, S., Yang, Z., Yang, X., Yang, Y., Li, Z., Ren, Y., Zhang, Q., Zhao, B., Xiao, J., Wang, Y., Dong, J., Kou, Z., Li, J., Yang, L., Zhao, E., Fonseca, G., Luo, R., ... Shao, H. (2026). Spatial omics in the AI era: Technologies, algorithmic ecosystems, biological applications, and large model perspectives. iMeta, 5(3), Article e70146. https://doi.org/10.1002/imt2.70146

@article{4294fe5de6ad4ffbb364aceb855ef774,

title = "Spatial omics in the AI era: Technologies, algorithmic ecosystems, biological applications, and large model perspectives",

abstract = "Spatial omics technologys help overcome key limitations of conventional omics approaches that lack spatial information, by providing a panoramic perspective from the molecular level to the microenvironment scale for addressing spatially resolved biological questions in life sciences. With the rapid advancement of this field, there are significant differences among technology platforms, algorithms, and research workflows, which bring three core challenges to interdisciplinary researchers: the detailed explanation of technical principles, the selection of appropriate algorithms, and the future development directions. This review systematically summarizes the technical platforms and analytical algorithms of spatial omics, compares their advantages and disadvantages in the context of specific tasks and presents application cases across multiple biological fields. It also outlines the emerging research directions and advances in large model integration. It ultimately aims to provide a reference for researchers from diverse disciplines to design and implement spatial omics studies. {\textcopyright} 2026 The Author(s). iMeta published by John Wiley \& Sons Australia, Ltd on behalf of iMeta Science.",

keywords = "algorithms, biological applications, large models, spatial omics, technologies",

author = "Haoxiu Wang and Xinwang Yang and Siheng Wang and Zhe Yang and Xiuhui Yang and Yutong Yang and Zirong Li and Yuqi Ren and Qianqian Zhang and Bowen Zhao and Jingming Xiao and Yidong Wang and Junhao Dong and Zhenhao Kou and Jie Li and Liqun Yang and Erhu Zhao and Gregory Fonseca and Ruibang Luo and Mingyu Yang and Hongjuan Cui and Gengjie Jia and Dan Wang and Haoyang Li and Jun Ding and Zhiyuan Yuan and Haojing Shao",

year = "2026",

month = jun,

doi = "10.1002/imt2.70146",

language = "English",

volume = "5",

journal = "iMeta",

issn = "2770-5986",

publisher = "John Wiley \& Sons",

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Wang, H, Yang, X, Wang, S, Yang, Z, Yang, X, Yang, Y, Li, Z, Ren, Y, Zhang, Q, Zhao, B, Xiao, J, Wang, Y, Dong, J, Kou, Z, Li, J, Yang, L, Zhao, E, Fonseca, G, Luo, R, Yang, M, Cui, H, Jia, G, Wang, D, Li, H, Ding, J, Yuan, Z & Shao, H 2026, 'Spatial omics in the AI era: Technologies, algorithmic ecosystems, biological applications, and large model perspectives', iMeta, vol. 5, no. 3, e70146. https://doi.org/10.1002/imt2.70146

Spatial omics in the AI era: Technologies, algorithmic ecosystems, biological applications, and large model perspectives. / Wang, Haoxiu (Co-first Author); Yang, Xinwang (Co-first Author); Wang, Siheng (Co-first Author) et al.
In: iMeta, Vol. 5, No. 3, e70146, 06.2026.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

TY - JOUR

T1 - Spatial omics in the AI era

T2 - Technologies, algorithmic ecosystems, biological applications, and large model perspectives

AU - Wang, Haoxiu

AU - Yang, Xinwang

AU - Wang, Siheng

AU - Yang, Zhe

AU - Yang, Xiuhui

AU - Yang, Yutong

AU - Li, Zirong

AU - Ren, Yuqi

AU - Zhang, Qianqian

AU - Zhao, Bowen

AU - Xiao, Jingming

AU - Wang, Yidong

AU - Dong, Junhao

AU - Kou, Zhenhao

AU - Li, Jie

AU - Yang, Liqun

AU - Zhao, Erhu

AU - Fonseca, Gregory

AU - Luo, Ruibang

AU - Yang, Mingyu

AU - Cui, Hongjuan

AU - Jia, Gengjie

AU - Wang, Dan

AU - Li, Haoyang

AU - Ding, Jun

AU - Yuan, Zhiyuan

AU - Shao, Haojing

PY - 2026/6

Y1 - 2026/6

N2 - Spatial omics technologys help overcome key limitations of conventional omics approaches that lack spatial information, by providing a panoramic perspective from the molecular level to the microenvironment scale for addressing spatially resolved biological questions in life sciences. With the rapid advancement of this field, there are significant differences among technology platforms, algorithms, and research workflows, which bring three core challenges to interdisciplinary researchers: the detailed explanation of technical principles, the selection of appropriate algorithms, and the future development directions. This review systematically summarizes the technical platforms and analytical algorithms of spatial omics, compares their advantages and disadvantages in the context of specific tasks and presents application cases across multiple biological fields. It also outlines the emerging research directions and advances in large model integration. It ultimately aims to provide a reference for researchers from diverse disciplines to design and implement spatial omics studies. © 2026 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.

AB - Spatial omics technologys help overcome key limitations of conventional omics approaches that lack spatial information, by providing a panoramic perspective from the molecular level to the microenvironment scale for addressing spatially resolved biological questions in life sciences. With the rapid advancement of this field, there are significant differences among technology platforms, algorithms, and research workflows, which bring three core challenges to interdisciplinary researchers: the detailed explanation of technical principles, the selection of appropriate algorithms, and the future development directions. This review systematically summarizes the technical platforms and analytical algorithms of spatial omics, compares their advantages and disadvantages in the context of specific tasks and presents application cases across multiple biological fields. It also outlines the emerging research directions and advances in large model integration. It ultimately aims to provide a reference for researchers from diverse disciplines to design and implement spatial omics studies. © 2026 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.

KW - algorithms

KW - biological applications

KW - large models

KW - spatial omics

KW - technologies

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-105044035548&origin=recordpage

U2 - 10.1002/imt2.70146

DO - 10.1002/imt2.70146

M3 - RGC 21 - Publication in refereed journal

SN - 2770-5986

VL - 5

JO - iMeta

JF - iMeta

IS - 3

M1 - e70146

ER -

Spatial omics in the AI era: Technologies, algorithmic ecosystems, biological applications, and large model perspectives

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

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