Same-Slide Spatial Multi-Omics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment

Stephanie Pei Tung Yiu*, Yuzhou Chang*, Yao Yu Yeo*, Huaying Qiu*, Wenrui Wu*, Hendrik A Michel, Xiaojie Jin, Rongting Huang, Shoko Kure, Lindsay Parmelee, Shuli Luo, Precious Cramer, Jia Le Lee, Yang Wang, Zhangxin Zhao, Jason Yeung, Nourhan El Ahmar, Berkay Simsek, Razan Mohanna, McKayla Van Orden, Wesley S Lu, Kenneth J Livak, Shuqiang Li, Ce Gao, Melinda Burgess, Colm Keane, Jahanbanoo Shahryari, Leandra G Kingsley, Reem N Al-Humadi, Sahar Nasr, Dingani Nkosi, Sam Sadigh, Philip Rock, Leonie Frauenfeld, Louisa Kaufmann, Bokai Zhu, Ankit Basak, Nagendra Dhanikonda, Chi Ngai Chan, Jordan Krull, Ye Won Cho, Chia-Yu Chen, Jonathan Brown, Hongbo Wang, Bo Zhao, Jia-Ying Joey Lee, Lit-Hsin Loo, David M Kim, Vassiliki A Boussiotis, Baochun Zhang, Kevin Wei, Alex K Shalek, Brooke E Howitt, Sabina Signoretti, F Stephen Hodi, W Richard Burack, Scott J Rodig, Qin Ma^#, Sizun Jiang^#

Abstract

Spatial transcriptomics and proteomics have enabled profound insights into tissue organization, yet these technologies remain largely disparate, and emerging same-slide multi-omics approaches are limited in plex, spatial resolution, signal retention, and integrative analytics. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined, resource-efficient, commercially compatible workflow using single-cell spatial proteomics-derived imaging to guide transcriptomic capture on the same slide without RNA signal loss. To integrate modalities beyond niche-level mapping, we developed Spectral Graph Cross-Correlation (SGCC), a proteomic-transcriptomic framework resolving spatially coordinated functional state changes across interacting cell populations. Applied to diffuse large B-cell lymphoma (DLBCL), IN-DEPTH and SGCC enabled stepwise discovery from EBV-positive and EBV-negative tumor comparisons to single-cell resolution, revealing coordinated tumor-macrophage-CD4 T-cell remodeling, immunosuppressive C1Q macrophage enrichment, CD4 T-cell dysfunction, and a candidate IL-27-STAT3 signaling axis. Collectively, IN-DEPTH enables scalable spatial multi-omics to uncover clinically relevant microenvironmental mechanisms and towards robust spatial multi-modal AI models.