About YuanST Lab

Welcome to the YuanST (Spatial-Temporal) Lab at the Institute of Science and Technology for Brain-inspired Intelligence (ISTBI), Fudan University. We develop computational methods and spatial omics technologies to understand complex biological systems through multimodal data integration and spatiotemporal analysis. We focus on three interconnected areas: optimizing spatial omics experimental design through computational approaches, developing computational methods for integrating multimodal spatiotemporal data to represent tissue microenvironment, and mining these spatiotemporal data to extract meaningful biological insights and biomedical applications.

RESEARCH FOCUS

Optimization of Spatial Omics Experimental Design

Our lab is dedicated to developing innovative computational approaches to optimize spatial omics experimental design. We employ advanced algorithms and mathematical models to enhance the efficiency and accuracy of spatial transcriptomics and other spatial omics technologies. By carefully considering factors such as tissue architecture, cellular heterogeneity, and technical constraints, we design optimal sampling strategies and experimental protocols. Our computational frameworks help researchers maximize the information gained from each experiment while minimizing costs and technical variations. This work is fundamental to advancing the field of spatial omics and ensuring high-quality data generation for downstream analyses.

Computational Methods for Multimodal Spatiotemporal Integration

A core focus of our research is the development of sophisticated computational methods for integrating diverse spatiotemporal data types. We create advanced algorithms and tools to combine multiple layers of biological information, including spatial transcriptomics, proteomics, and imaging data, while preserving their spatial and temporal contexts. Our methods address the unique challenges of data alignment, normalization, and integration across different modalities and scales. We aim to construct comprehensive representations of tissue microenvironments that capture both spatial organization and temporal dynamics. These integrated approaches provide a more complete understanding of cellular interactions and tissue architecture than any single data type alone.

Mining Spatiotemporal Data for Biological Insights

The third pillar of our research focuses on extracting meaningful biological insights and biomedical applications from integrated spatiotemporal data. We develop and apply advanced machine learning and statistical methods to analyze complex spatial patterns, cellular interactions, and temporal dynamics within tissues. Our approaches help identify key biological mechanisms, disease markers, and therapeutic targets. We particularly emphasize the translation of our findings into practical biomedical applications, such as improving disease diagnosis, understanding development and aging processes, and identifying new therapeutic strategies. This work bridges the gap between computational analysis and clinical applications, contributing to advances in precision medicine and therapeutic development.

PRINCIPAL INVESTIGATOR

Zhiyuan Yuan (原致远)

Associate Professor at Fudan University

zhiyuan@fudan.edu.cn

Dr. Zhiyuan Yuan is a Associate Professor and Ph.D. supervisor at the Institute of Science and Technology for Brain-inspired Intelligence (ISTBI) at Fudan University, where he also serves as Deputy Director of the Center for Integrative Spatial-Omics Research. After receiving his bachelor's from Wuhan University and Ph.D. from Tsinghua University (advisor: Prof. Michael Q. Zhang), he was exceptionally recruited as an independent PI at Fudan in 2022 and was fast-tracked to his current position in 2025. His research focuses on genomic large language models and generative AI for single-cell and spatial omics. Dr. Yuan is a recipient of China's national high-level youth talent program and numerous other awards, with first or corresponding author publications in top journals such as Nature Methods, Nature Machine Intelligence, and Nature Communications. He actively leads national and municipal research grants, mentors students who also publish in high-impact journals, and serves on the youth editorial boards and committees of several professional organizations and journals.

原致远，就职于复旦大学类脑智能科学与技术研究院，长聘副教授/研究员，博士生导师，上海市浦东医院双聘研究员，空间组学整合研究中心副主任。本科毕业于武汉大学计算机学院，博士毕业于清华大学自动化系，博士毕业后被破格引进复旦大学任独立PI（2022.9），后破格晋升为长聘副教授/研究员（2025.4）。

入选国家高层次人才特殊支持计划青年项目（2024），上海市各项人才计划（青年东方英才、晨光学者、扬帆计划、明珠菁英人才、上海35U35）及复旦大学卓学人才计划等。第一/通讯作者身份获得世界人工智能大会青年优秀论文奖（10项/年，2023）、中国生物信息学十大进展（10项/年，2022/2024）、周传纪念奖（10项/年，2023）等奖励。

担任腾飞书院及大数据学院本科生班导师。指导本科生一作发表于Nature Machine Intelligence；指导本科生获得“莙政”科研项目立项并优秀结题，其获复旦大学“莙政学者”奖励；指导研究生一作发表于Nature Methods、Nature Protocols、Cell Genomics、Nature Communications等期刊。

主持国家自然科学基金面上、青年项目、上海市计算生物学重点专项、曹娥江基础创新培育项目；连续3年主持腾讯AI Lab/CCF犀牛鸟专项研究基金（2023/2024/2025），并获得年度“优秀奖”；骨干参与国家重点研发计划、上海市重点专项等重大科研任务。

研究方向为基因组大语言模型、生成式及可解释人工智能方法，及在单细胞/空间组学中的应用。学术成果以第一或通讯作者（含共同）发表在 Nature Methods（2021/2023/2024）、 Nature Machine Intelligence（2025）、 Nature Protocols（2024）、 Cell Genomics（2024）、Nature Communications（2022/2024/2025a/2025b）等期刊。

担任 Genom. Proteom. Bioinf.（GPB）、J. Genet. Genom.（JGG）青年编委。担任方法学（Nat. Methods/Nat. Biotechnol./Nat. Mach. Intell./Nat. Comput. Sci.），生物学（Nat. Metab., Nat. Struct. Mol. Biol., Cell Genom., Cell Syst.）及综合性（Nat. Commun., Science Advances, Natl Sci Rev, Cell Rep.）等>30种权威期刊审稿人。中国自动化学会智能健康与生物信息专委会委员、中国计算机学会生物信息专委会委员、中国生物信息学会(筹)生物信息学算法研究专委会委员、上海市生物信息学会委员。

MEMBERS

We are actively looking for postdoc, graduate/undergraduate students, research assistants! Please find the information here.

Post-doctoral Associate

Daoliang Zhang (张道良)

2025 - Now

Education: PhD, Shandong University

GigaScience ‘24, Bioinformatics Advances ’25

Students

Fangyuan Zhao (赵方圆)

2022 - Now

Joint Ph.D. Student

Education: PhD Candidate, Institute of Computing Technology,CAS

Nature Methods ’24

Senlin Lin (林森林)

2022 - Now

Joint Ph.D. Student

Education: PhD Candidate, Institute of Computing Technology,CAS

Nature Protocols ‘24, Cell Genomics ’24, Nature Machine Intelligence ’25

Zhikang Wang (王智康)

2024 - Now

Research Assistant

Education: PhD Candidate, Monash University

Nature Machine Intelligence ’25

Qi Zou (邹麒)

2024 - Now

Joint Ph.D. Student

Education: PhD Candidate, Shandong University

Chuangyi Han (韩创意)

2024 - Now

2025 - Now

Ph.D. Student

Education: PhD Candidate, Fudan University

Jiabei Cheng (程佳贝)

2025 - Now

Joint Ph.D. Student

Education: PhD Candidate, Shanghai Jiao Tong University

Alumni

Yan Cui (崔岩)

2023 - 2024

Research Assistant

Nature Communications ’25

Current position: PhD student, University of Pennsylvania

Na Yu (于娜)

2024 - 2025

Joint Ph.D. Student

GigaScience ‘24

Current position: Post-doctoral Associate, Westlake University

PUBLICATIONS

Y Cui*, Z Yuan#. "Prioritizing perturbation-responsive gene patterns using interpretable deep learning". Nature communications 16 (1), 6095 (2025).

Paper Software Press1 Press2 Press3

S Lin*, Y Cui, F Zhao, Z Yang, J Song, J Yao, Y Zhao, BZ Qian, Y Zhao#, Z Yuan#. "Complete spatially resolved gene expression is not necessary for identifying spatial domains". Cell Genomics 4 (6) (2024).

Paper Software Press1 Featured article

Z Yuan*#. "MENDER: Fast and scalable tissue structure identification in spatial omics data". Nature Communications 15, 207 (2024).

Paper Software Press1 Press2 Press3 Press4 Featured article

Z Yuan*#, F Zhao*, S Lin, Y Zhao, J Yao, Y Cui, XY Zhang, Y Zhao#. "Benchmarking spatial clustering methods with spatially resolved transcriptomics data". Nature Methods 21, 712–722 (2024).

Paper Software Press1 Press2 Press3 Press4 Press5 Press6 Press7

S Lin*, F Zhao, Z Wu, J Yao, Y Zhao# & Z Yuan#. "Streamlining spatial omics data analysis with Pysodb". Nature Protocols 19, 831–895 (2024).

Paper Software Press1 Press2 Press3

Z Yuan*#, W Pan*, X Zhao, F Zhao, Z Xu, X Li, Y Zhao, MQ Zhang# & J Yao#. "SODB facilitates comprehensive exploration of spatial omics data". Nature Methods 20, 387–399 (2023).

Paper Software Database Press1 Press2 Press3 Press4 Press5 Press6 Press7 Press8 Press9 Press10 Honor1 Featured article

Z Yuan*#, Y Li*, M Shi, F Yang, J Gao, J Yao# & MQ Zhang#. "SOTIP is a versatile method for microenvironment modeling with spatial omics data". Nature Communications 13, 7330 (2022).

Paper Software Press1 Press2 Press3 Press4

Z Yuan*, Q Zhou*, L Cai*, L Pan, W Sun, S Qumu, S Yu, J Feng, H Zhao, Y Zheng, M Shi, S Li, Y Chen#, X Zhang# & MQ Zhang#. "SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment". Nature Methods 18, 1223–1232 (2021).

Paper Software Press1 Press2 Press3 Press4 Press5 Press6 Press7 Honor1 Honor2 Featured article

Y Cui*, Z Yuan#. "Prioritizing perturbation-responsive gene patterns using interpretable deep learning". Nature communications 16 (1), 6095 (2025).

Paper Software Press1 Press2 Press3

S Fang*, M Xu*, L Cao*, X Liu*, M Bezulj*, L Tan*, Z Yuan*, Y Li, T Xia, L Guo, V Kovacevic, J Hui, L Guo, C Liu, M Cheng, L Lin, Z Wen, B Josic, N Milicevic, P Qiu, Q Lu, Y Li, L Wang, L Hu, C Zhang, Q Kang, F Chen, Z Deng, J Li, M Li, S Li, Y Zhao#, G Fan#, Y Zhang#, A Chen#, Y Li#, X Xu#. "Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics". Nature Communications 16 (1), 3741 (2025).

Paper Software Press1 Press2 Press3

P Zhang*, C Gao*, Z Zhang, Z Yuan, Q Zhang, P Zhang, S Du, W Zhou, Y Li, S Li#. "Systematic inference of super-resolution cell spatial profiles from histology images". Nature communications 16 (1), 1838 (2025).

Paper Software

D Zhang*, W Li*, X Sui*, N Yu, S Wang, Z Liu, X Wang, Z Yuan#, R Gao#, W Zhang#. "Space: reconciling multiple spatial domain identification algorithms via consensus clustering". Bioinformatics Advances 5 (1), vbaf084 (2025).

Paper Software

X Xie*, W Liu*, Z Yuan#, H Chen#, W Mao#. "Bridging epigenomics and tumor immunometabolism: molecular mechanisms and therapeutic implications". Molecular Cancer 24 (1), 1-24 (2025).

Paper

Y Zhang*, C Yang, X Chen, L Wu, Z Yuan, F Zhang, & BZ Qian#. "Cancer therapy resistance from a spatial-omics perspective.". Clinical and Translational Medicine 15(7), e70396 (2025)

Paper

X Sun*, W Zhang*, W Li, N Yu, D Zhang, Q Zou, Q Dong, X Zhang, Z Liu, Z Yuan#, R Gao#. "SpaGRA: graph augmentation facilitates domain identification for spatially resolved transcriptomics". Journal of Genetics and Genomics (2024).

Paper Software Press1

Y Huo*, J Wang*, C Liu, J Wang, C Wang, W Guo, Z Yuan, T Guo, J Gu, X Li#. "CancerSRT: a spatially resolved transcriptomics database for human cancers". Journal of Genetics and Genomics (2024).

Paper Software Press1

Paper Software Press1 Featured article

S Lin*, F Zhao, Z Wu, J Yao, Y Zhao# & Z Yuan#. "Streamlining spatial omics data analysis with Pysodb". Nature Protocols 19, 831–895 (2024).

Paper Software Press1 Press2 Press3

Z Yuan*#, F Zhao*, S Lin, Y Zhao, J Yao, Y Cui, XY Zhang, Y Zhao#. "Benchmarking spatial clustering methods with spatially resolved transcriptomics data". Nature Methods 21, 712–722 (2024).

Paper Software Press1 Press2 Press3 Press4 Press5 Press6 Press7

Z Yuan*#. "MENDER: Fast and scalable tissue structure identification in spatial omics data". Nature Communications 15, 207 (2024).

Paper Software Press1 Press2 Press3 Press4 Featured article

R Shen , M Cheng, W Wang, Q Fan, H Yan, J Wen, Z Yuan, J Yao#, Y Li#, J Yuan#. "Graph domain adaptation–based framework for gene expression enhancement and cell type identification in large-scale spatially resolved transcriptomics". Briefings in Bioinformatics 25(6) (2024).

Paper Software

D Zhang*, N Yu*, Z Yuan*, W Li, X Sun, Q Zou, X Li, Z Liu, W Zhang#, R Gao#. "stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation". GigaScience 13, giae089 (2024).

Paper Software

L Cao*, Z Wang*, Z Yuan#, Q Luo#. "mFusion: a multiscale fusion method bridging neuroimages to genes through neurotransmissions in mental health disorders". Commun Biol 7, 1699 (2024)

Paper Software

T Guo*, Z Yuan*, Y Pan, J Wang, F Chen, MQ Zhang#, X Li#. "SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies". Genome Biology 24, 241 (2023).

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Z Yuan*#, J Yao#. "Harnessing Computational Spatial Omics to Explore the Spatial Biology Intricacies". Seminars in Cancer Biology (2023).

Paper

Z Yuan*# & MQ Zhang#. "Spatial Omics DataBase (SODB): increasing accessibility to spatial omics data". Nature Methods (Research Briefing) 20, 359–360 (2023).

Paper

Z Yuan*#, W Pan*, X Zhao, F Zhao, Z Xu, X Li, Y Zhao, MQ Zhang# & J Yao#. "SODB facilitates comprehensive exploration of spatial omics data". Nature Methods 20, 387–399 (2023).

Paper Software Database Press1 Press2 Press3 Press4 Press5 Press6 Press7 Press8 Press9 Press10 Honor1 Featured article

*: These authors contributed equally to this study and share the first authorship.

#: Co-corresponding author

TALKS

Invited Talks

2025.07.22 长沙
中国解剖学会组织学与胚胎学分会2025年学术年会 “人工智能赋能的空间多组学” 分会场邀请报告
2025.07.19 东莞
第三届松山湖数学论坛 “人工智能赋能的单细胞时空分析” 分会场邀请报告
2025.06.26 北京
生命科学前沿技术发展战略对策研讨会 “空间多组学数据整合” 分会场邀请报告
2025.06.25 上海
上海交通大学转化医学研究院 “空间多组学数据整合” 邀请报告
2025.05.23 济南
第十一届全国计算生物学与生物信息学学术会议 “空间多组学数据整合” 分会场邀请报告
2025.05.10 北京
第12届“数学、计算机与生命科学交叉研究”青年学者论坛 “计算空间组学” 主会场邀请报告
2025.04.25 上海
第三届 CCF 生物信息学“新未来”青年学者研讨会 “计算空间组学” 邀请报告
2025.04.12 武汉
武汉大学人工智能学院 “计算空间组学” 邀请报告
2025.03.29 深圳
第二届全国基因组信息学大会 “计算空间组学” 分会场邀请报告
2025.01.20 上海
上海交通大学溥渊未来技术学院 “空间组生物信息学” 邀请报告
2025.01.17 上海
“思享前沿”学术研讨会暨2024年上海市计算机学会生物信息学专委会年会 “空间组生物信息学” 邀请报告
2024.12.15 上海
The 2nd Med-X International Conference “AI-aided spatial omics experimental design” 分会场邀请报告
2024.12.06 广州
第六届全国生物医学数据挖掘与计算学术会议 “空间组学数据的智能分析与挖掘” 分会场邀请报告
2024.11.29 沈阳
第二届CCF数字医学大会 “AI辅助空间组学实验设计” 分会场邀请报告
2024.11.27 北京
第三届中国医学基础学科发展大会 “AI辅助空间组学实验设计” 分会场邀请报告
2024.11.23 杭州
第二届人工智能与生物信息学前沿交叉青年论坛 “空间组生物信息学” 邀请报告
2024.10.26 上海
第二十届上海大肠癌论坛 "空间组学解析组织微环境" 大会报告
2024.10.12 海口
第十三届全国生物信息学与系统生物学学术大会 "AI辅助空间组学实验设计" 大会报告
2024.09.14 济南
山东大学 "空间组学生物信息" 邀请报告
2024.08.09 长春
第九届中国计算机学会生物信息学会议 "空间组学生物信息" 分会场邀请报告
2024.06.06 上海
同济大学 "空间组生物信息学解析组织微环境" 邀请报告
2024.05.24 青岛
第二届山东省生物信息学学术大会暨中国生物信息学学会（筹）生物信息学算法研究专委会学术大会 "空间组生物信息学解析组织微环境" 分会场邀请报告
2024.04.18 福州
第三届中国智能健康与生物信息大会 "空间组生物信息学解析组织微环境" 分会场邀请报告
2024.01.19 上海
复旦大学影像和空间组学论坛 "空间生物信息学" 邀请报告
2023.11.14 上海
复旦大学 "Modeling spatial and temporal omics data" 邀请报告
2023.11.08 上海
计算神经科学与类脑智能教育部重点实验室2023年度学术委员会 "空间生物信息学" 邀请报告
2023.11.06 北京
北京大学 "Exploring Tissue Architectures with Computational Spatial Omics" 邀请报告
2023.11.05 北京
中国生物工程学会第十五届学术年会暨2023年全国生物技术大会 "空间生物信息学" 分会场邀请报告
2023.11.03 上海
第四届国际人类表型组研讨会 "Dissecting Tissue Microenvironment with Spatial Bioinformatics" 分会场邀请报告
2023.10.20 无锡
第三届数学生命科学大会 "空间生物信息学解析组织微环境" 分会场邀请报告
2023.08.26 三亚
崖州湾国家实验室 "Exploring Tissue Architectures with Computational Spatial Omics" 邀请报告
2023.08.16 上海
中国科学院营养与健康研究所 "用计算空间组学探索组织结构" 邀请报告
2023.06.09 杭州
之江实验室 "Exploring Tissue Architectures with Computational Spatial Omics" 邀请报告
2023.05.26 杭州
浙江工业大学信息学院 "Exploring Tissue Architectures with Computational Spatial Omics" 邀请报告
2023.05.20 上海
复旦大学庆学术报告“Exploring Tissue Architectures with Computational Spatial Omics”邀请报告
2023.04.20 上海
复旦大学影像和空间组学论坛 "空间组学探索组织微环境" 邀请报告
2023.04.15 线上
"基因组与AI"一作面对面论坛 "SODB facilitates comprehensive exploration of spatial omics data" 邀请报告
2023.04.12 上海
复旦大学生命科学学院 "空间组学数据中微环境的建模与分析方法" 邀请报告

JOIN US

Postdoctoral Positions

Requirements

Ph.D. degree holders capable of engaging in full-time research.
Candidates should be in principle no older than 35 years of age and have obtained or will obtain their Ph.D. degree within the past three years.
Strong background in one or more of the following areas:
- Computational biology
- Bioinformatics
- Machine learning/AI
- Spatial omics technologies
- Statistics and data analysis
- Computer science
- Math
Programming proficiency in Python, R, or similar languages.
At least one first-author publication record in computational biology or related fields.
Excellent English communication skills (written and verbal).
Demonstrated ability to work independently and as part of a collaborative team.
Strong commitment to reproducible research and open science principles.

Responsibilities

Lead research projects in developing computational methods in single cell or spatial omics.
Analyze complex spatiotemporal datasets to extract biological insights.
Write scientific papers and present research findings.
Contribute to grant writing and project planning.
Mentor graduate and undergraduate students.
Collaborate with experimental biology labs and clinical partners.

Required Application Materials

Detailed curriculum vitae.
Research statement (2 pages, highlighting your expertise and research interests in computational biology).
Code portfolio or GitHub repository (if applicable).
Summary of key research achievements (1-2 pages).
At least two academic references.

Graduate Student Positions

We are actively recruiting graduate students interested in computational biology and spatial omics research. Successful candidates will have:

The qualification for postgraduate recommendation exemption, or meet the requirements of "Application-Review" system for doctoral student admission at Fudan University.
Strong quantitative background (computer science, statistics, mathematics, or related fields).
Programming experience.
Interest in biological applications.
Good communication skills.

Students will receive comprehensive training in:

Computational methods for spatial omics.
Machine learning and statistical analysis.
Biological data analysis.
Scientific writing and presentation.

Undergraduate Research Opportunities

We welcome undergraduate students from computer science, mathematics, statistics, and biological sciences departments to join our research team. Opportunities include:

Summer research projects
Independent study
Senior thesis research
Programming and data analysis experience

How to Apply

Please send your application materials to zhiyuan@fudan.edu.cn.

Include "Position Application - [Your Name]" in the subject line.

For undergraduate research opportunities, please include:

Current transcript
Brief statement of interest
Resume/CV
Relevant coursework and technical skills