GEO series

Spatial membrane lipid remodeling encodes epithelial lineage and state in human colon cancer

GSE325755 Homo sapiens (human) Other 11 samples Submitted 2026/08/27 Platform GPL24676

At a glance

human · 11 samples

Organism: Homo sapiens (human)
Topic: Colorectal cancerThis series is part of our curated briefing on it.
Samples: 11 samples
Data you can get: A processed table (series matrix) and supplementary files from NCBI.

Summary (written by the submitters)

Cancer progression is driven by profound intratumoral heterogeneity that is spatially organized within tissues, yet remains difficult to resolve at the molecular level. While transcriptomic spatial approaches have advanced rapidly, the spatial organization and functional relevance of tumor lipid metabolism remain poorly understood. Here, we present an integrative spatial multi-omic strategy that anchors high-resolution lipid imaging to transcriptome-defined cellular states.

Read the full summary ↓

Applying this approach to human primary and metastatic colon cancer specimens, we jointly profiled spatial lipid distributions and gene expression across tumor epithelial compartments. This approach revealed specific, spatially restricted epithelial lipid phenotypes defined by membrane phospholipid composition. These lipid phenotypes were tightly associated with transcriptional programs reflecting epithelial lineage, differentiation state, and proliferative activity. In addition, we observed that secretory-associated epithelial states were enriched in metastatic lesions and were associated with increased levels of DHA-containing phospholipids and transcriptional activation of peroxisomal lipid metabolic pathways. Together, these findings demonstrate that membrane phospholipid composition encodes biologically meaningful epithelial cell states within human tumors. More broadly, this work establishes a generalizable strategy for interpreting spatial lipidomic data through biologically informed integration with spatial transcriptomics, enabling cell-state–resolved metabolic phenotyping in cancer.

This dataset

Open GSE325755 in finder → Find more human datasets → Colorectal cancer landscape →

Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE325755_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

Also filed as BioProject PRJNA1441556. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

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+ 11 more — browse all 11 samples with per-sample file links →

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GSE235714 Immune-Epithelial Interactions and Tissue Remodeling in the Inflammatory Respiratory Nasal Tract [NanoString GeoMx spatial transcriptomics] 40 samples shares remodeling, epithelial, spatial
GSE324131 A single-cell and spatial atlas of prostate cancer reveals the combinatorial nature of gene modules underlying lineage plasticity and metastasis 38 samples shares spatial, lineage, state
GSE300809 Enhancer Dynamics and Spatial Organization Drive Anatomically Restricted Cellular States in the Human Spinal Cord [STABseq] 16 samples shares spatial, state
GSE332618 Identification and Characterization of Adrenergic and Mesenchymal Cancer Cell States in Neuroblastoma Using Spatial-Omics [Spatial Transcriptomics] 16 samples shares spatial, state

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