bioRxivPathologyPreprint
Spatial Glyco-Codes Define Human Liver Pathology and Progression
Xiaolong TianiD, Anthony A. FungiD, Xingbo Shang, Dingyao ZhangiD, Binfan Chen, Lei Zhang, K E X I N LiiD, Mei ZhongiD, Yifan DengiD, Mingyu Yang, Yao LuiD, Bo Tao, Fu GaoiD, Alev BaysoyiD, Xiao Louise Lin, Aleksandra IvoviciD, Sidi CheniD, Frank Li, Mina L Xu, Xuchen ZhangiD, Mark GersteiniD, Xiaoyong YangiD, Chen Liu, Rong FaniD
Abstract
Glycosylation is a fundamental process regulating cellular function, tissue organization, and disease progression. However, comprehensive glycan profiling at single-cell spatial resolution remains largely inaccessible, particularly in clinical archival tissues. Here we develop spatial-GPT, a multimodal platform for simultaneous profiling of glycans, proteins, and/or transcripts in archival formalin-fixed paraffin-embedded (FFPE) tissues. Using a panel of 30 DNA-encoded lectins recognizing major mammalian glycan motifs and structural classes, sequencing-based spatial-GPT (DBiT-GPT) mapped the spatial glycome, proteome, and transcriptome across 16 human liver specimens encompassing steatosis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC), leading to identification of spatial glyco-codes - combinatorial glycan states associated with distinct cellular identities, tissue features, and pathological processes. Unexpectedly, glyco-codes alone were sufficient to resolve major cell types, disease states, and HCC subtypes, revealing a previously unappreciated level of biological information encoded within the tissue glycome. Spatial glycomics uncovered tumor-like glyco-codes in premalignant regions, suggesting that glycan reprogramming may precede overt malignant transformation. Using imaging-based single-cell spatial glycan-protein profiling (CODEX-GP), we track glyco-codes across the whole-tissue architecture of 3 representative HCC samples. We further examined the glyco-codes across more than 300 patient specimens and quantified cell-type- and disease-specific glyco-codes as well as glycan-defined immune-evasion, T-cell-exhaustion, and steato-fibrotic niches. Together, these findings establish spatial glyco-codes as a previously unrecognized layer of tissue organization that encodes cellular identity, tissue function, and disease progression. The ability of glyco-codes to distinguish major liver pathologies across independent patient cohorts further highlights their potential as a new class of molecular histopathology biomarkers.
- organism: human
- assay: spatial transcriptomics
- assay: proteomics
- assay: DNA methylation
- research focus: cell type annotation
- research focus: differential expression
- research focus: database / resource
- research focus: benchmarking
- approach: deep learning
- approach: experimental assay
- Citations
- 0
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