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Spatial Glyco-Codes Define Human Liver Pathology and Progression

GSE333851 Homo sapiens (human) RNA-seq (gene expression) 32 samples Submitted 2026/06/04 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 32 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Topic
Hepatocellular carcinomaThis series is part of our curated briefing on it.
Samples
32 samples: 32 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
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.
Read the full summary ↓
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
Open GSE333851 in finder → Find more human RNA-seq datasets → Hepatocellular carcinoma landscape →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE333851_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1472674 and SRA study SRP704963. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
32 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 32 sample titles: 32 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
More Hepatocellular carcinoma datasets

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.