GEO series
Spatial Glyco-Codes Define Human Liver Pathology and Progression
GSE333851
Homo sapiens
Expression profiling by high throughput sequencing; Other
32 samples
Submitted 2026/06/04
Platform GPL24676
Summary
Glycosylation is a relatively underexplored aspect of the central dogma of biology, yet a key determinant of biological function and encodes disease-associated cellular states. However spatial glycomics is limited primarily by workflows that are costly, specialized, or insufficiently informative for conformation-dependent motifs. To address the need for a deep, cost-effective, and portable spatial glycomics technology, we developed spatial-GPT, a multimodal lectin-based platform for the simultaneous profiling of glycans, proteins, and transcripts from same-slide archival FFPE tissues. By combining DBiT-GPT sequencing with CODEX-GP imaging, spatial-GPT provides robust glycan detection in long-stored specimens, cost-effective multiplexing, and ascription of glycan motifs to cellular identity, pathology, and gene-regulatory programs at subcellular spatial resolution. Applied to human liver disease, spatial-GPT resolved glyco-codes of steatosis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC) subtypes, identified tumor-like glycan remodeling in premalignant regions, revealed conserved HCC-associated glycan programs, and uncovered glycan-defined immune and stromal neighborhoods across tissue microarrays. Spatial-GPT offers a practical extension of pathology, unlocking the glycan dimension on the same tissue section to expose a granularity that may be otherwise obscured by morphology, protein markers, or transcriptomics alone.
This dataset
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Direct links to NCBI, no account and no request form: the whole study as GSE333851_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 32 samples. Raw sequencing reads are also available from ENA.
Also filed as BioProject PRJNA1472674 and SRA study SRP704963. Searching any of these in the dataset finder brings you back here.
Study design
32 conditions, each sampled once — no replicated groups
Read from 32 sample titles: 32 distinct titles with little repetition. Check it against the sample list below before relying on it.
Samples in this study
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- GSM9775937 non-MASH-HCC, tumor 1, glycome, proteome
- GSM9775938 non-MASH-HCC, tumor 1, transcriptome
- GSM9775939 non-tumor background, non-tumor 1, glycome, proteome
- GSM9775940 non-tumor background, non-tumor 1, transcriptome
- GSM9775941 non-MASH-HCC, tumor 2, glycome, proteome
- GSM9775942 non-MASH-HCC, tumor 2, transcriptome
- GSM9775943 non-tumor background, non-tumor 2, glycome, proteome
- GSM9775944 non-tumor background, non-tumor 2, transcriptome
- GSM9775945 non-MASH-HCC, tumor 3, glycome, proteome
- GSM9775946 non-MASH-HCC, tumor 3, transcriptome
- GSM9775947 non-MASH-HCC, tumor 4, glycome, proteome
- GSM9775948 non-MASH-HCC, tumor 4, transcriptome
- GSM9775949 non-MASH-HCC, tumor 5, glycome, proteome
- GSM9775950 non-MASH-HCC, tumor 5, transcriptome
- GSM9775951 non-tumor background, non-tumor 5, glycome, proteome
- GSM9775952 non-tumor background, non-tumor 5, transcriptome
- GSM9775953 non-MASH-HCC, tumor 6, glycome, proteome
- GSM9775954 non-MASH-HCC, tumor 6, transcriptome
- GSM9775955 non-tumor background, non-tumor 6, glycome, proteome
- GSM9775956 non-tumor background, non-tumor 6, transcriptome
- GSM9775957 MASH-HCC, tumor 1, glycome, proteome
- GSM9775958 MASH-HCC, tumor 1, transcriptome
- GSM9775959 MASH-HCC, tumor 2, glycome, proteome
- GSM9775960 MASH-HCC, tumor 2, transcriptome
- GSM9775961 MASH-HCC, tumor 3, glycome, proteome
- GSM9775962 MASH-HCC, tumor 3, transcriptome
- GSM9775963 MASH-HCC, tumor 4, glycome, proteome
- GSM9775964 MASH-HCC, tumor 4, transcriptome
- GSM9775965 MASH-HCC, tumor 5, glycome, proteome
- GSM9775966 MASH-HCC, tumor 5, transcriptome
- GSM9775967 MASH-HCC, tumor 6, glycome, proteome
- GSM9775968 MASH-HCC, tumor 6, transcriptome
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