An integrated approach for analyzing spatially resolved multi-omics datasets from the same tissue section

Thao Tran¹Felicia Wee²Craig Ryan Joseph²Wanqiu Zhang¹Jeffrey Chun Tatt Lim²Zhen Wei Neo²Li Yen Chong²Francis Hong Xin Yap³Nathan Heath Patterson¹Marc Claesen¹Alice Ly^1*Joe Yeong^2,3,4*

• ¹Aspect Analytics NV, C-mine 12, 3600 Genk, Belgium, Germany
• ²Agency of Science, Technology and Research (A*STAR), Republic of Singapore, Institute of Molecular and Cell Biology (A*STAR), Singapore, Singapore
• ³Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore
• ⁴Immunology & Serology Section, Department of Microbiology, Singapore General Hospital, Singapore, Singapore

Recent advances in spatial transcriptomics (ST) and spatial proteomics (SP) technologies have enabled high-dimensional molecular profiling at single-cell resolution, providing deeper insights into the tumour-immune microenvironment. However, these modalities are typically applied to separate tissue sections, limiting direct comparisons across molecular layers. We developed a wet-lab and computational framework to perform and integrate ST and SP from the same tissue section, as demonstrated on human lung cancer samples. Applying ST, SP, and hematoxylin and eosin (H&E) staining from the same section ensured consistency in tissue morphology and spatial context. Computational registration using Weave software allowed accurate alignment and annotation transfer across modalities. This co-registered dataset enabled single-cell level comparisons of RNA and protein expression, revealed segmentation accuracy and transcript-protein correlation analyses within individual cells. Notably, we observed systematic low correlations between transcript and protein levels-consistent with prior findings-now resolved at cellular resolution. Our approach highlights the feasibility and utility of performing spatially-resolved multi-omics analysis on the same section without compromising data quality, facilitating concordance studies and region-specific analysis of immune and tumour markers, and ultimately advancing our understanding of disease heterogeneity at the molecular level.