GEO series

Spatial biology reveals macrophage dysfunction in immunosuppressed non-melanoma skin cancer [spatial_ATAC]

GSE306129 Homo sapiens (human) Other 28 samples Submitted 2026/08/07 Platform GPL34284Platform GPL24676

At a glance

human · 28 samples

Organism: Homo sapiens (human)
Samples: 28 samples: 28 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.
Paper: Spatial biology reveals altered macrophage states in immunosuppressed non-melanoma skin cancerCell · 2026

Summary (written by the submitters)

The rising incidence of non-melanoma skin cancer (NMSC), including basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), particularly among immunosuppressed individuals, underscores the need for a deeper understanding of the tumor microenvironment (TME) to develop effective and safe immunotherapies. This study employed single-cell multi-omic analyses to investigate the spatial distribution and transcriptional profiles of immune cells in NMSC from both immunocompetent and immunosuppressed patients.

Read the full summary ↓

Our results challenge the prevailing notion that immunosuppression is primarily driven by reduced immune cell abundance or lymphocytic dysfunction. Instead, we identified a critical deficiency in antigen presentation by macrophages, leading to impaired T-cell diversity and clonal expansion. Additionally, a spatial epigenomic atlas of the NMSC TME revealed distinct spatial niches and epigenetic programs associated with immunosuppression. These findings highlight the central role of innate immunity in shaping anti-tumor responses and offer potential therapeutic targets to enhance immunotherapy in this vulnerable patient population.

Published in

Spatial biology reveals altered macrophage states in immunosuppressed non-melanoma skin cancer

Naara S, Kochat V, Rao X et al. · Cell 2026 · PMID 42556334 · doi:10.1016/j.cell.2026.07.031

This dataset

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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 (GSE306129_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 PRJNA1309491. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42556334) ↗

Study design

28 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 28 sample titles: 28 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

GSM9193766 D01127_BCC_IS_Cohort 1
GSM9193767 D01128_BCC_IC_Cohort 1
GSM9193768 D01130_SCC_IC_Cohort 1
GSM9193769 D01131_SCC_IS_Cohort 1
GSM9193770 D01132_SCC_IC_Cohort 1
GSM9193771 D01133_BCC_IS_Cohort 1
GSM9193772 D01134_BCC_IS_Cohort 1
GSM9193773 D01136_BCC_IC_Cohort 1
GSM9193774 D01143_BCC_IS_Cohort 1
GSM9193775 D01145_BCC_IC_Cohort 1
GSM9193776 D01146_BCC_IS_Cohort 1
GSM9193777 D01147_BCC_IC_Cohort 1
GSM9193778 D01152_BCC_IC_Cohort 1
GSM9193779 D01154_SCC_IS_Cohort 1
GSM9193780 D01138_BCC_IC_Cohort 1
GSM9193781 D01139_BCC_IC_Cohort 1
GSM9193782 D01954_SCC_IS_Cohort 2
GSM9193783 D01955_SCC_IS_Cohort 2
GSM9193784 D01956_SCC_IS_Cohort 2
GSM9193785 D01957_BCC_IS_Cohort 2
GSM9193786 D01958_BCC_IS_Cohort 2
GSM9193787 D02035_SCC_IS_Cohort 2
GSM9193788 D02036_SCC_IS_Cohort 2
GSM9193789 D02037_BCC_IS_Cohort 2
GSM9193790 D02044_SCC_IC_Cohort 2
GSM9193791 D02045_BCC_IC_Cohort 2
GSM9193792 D02064_SCC_IC_Cohort 2
GSM9193793 D02066_SCC_IS_Cohort 2

Datasets on a similar topic

GSE316922 Spatial biology reveals macrophage dysfunction in immunosuppressed non-melanoma skin cancer 36 samples shares immunosuppressed, non-melanoma, dysfunction, macrophage, spatial, biology, skin
GSE308361 Spatial biology reveals macrophage dysfunction in immunosuppressed non-melanoma skin cancer 16 samples shares immunosuppressed, non-melanoma, dysfunction, macrophage, spatial, biology, skin
GSE288843 Multiomic assessments of LNCaP and derived cell strains reveal determinants of prostate cancer pathobiology (ATAC-Seq) 30 samples shares atac, biology
GSE286415 Spatial multi-omics defines a shared glioblastoma infiltrative signature at the resection margin [snATAC-seq] 12 samples shares atac, spatial
GSE323981 MEF2C controls lysosomal and lipid clearance programs linked to Alzheimer’s disease risk in macrophages [ATAC-seq] 8 samples shares atac, macrophage
GSE316079 SLF2 and SMC5 dysfunction drives HSC aging and predisposes to MDS, defining a new inherited bone marrow failure syndrome [ATAC-seq] 6 samples shares atac, dysfunction
GSE255312 Spatial transcriptomics landscape for liver from patients with metabolic dysfunction-associated steatohepatitis (MASH) 72 samples shares dysfunction, spatial
GSE274114 Metabolic dysfunction-associated steatohepatitis reduces interferon and macrophage liver gene signatures in chronic HBV patients 39 samples shares dysfunction, macrophage

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