GEO series

Clonal lineage tracing of innate immune cells in human cancer

GSE302113 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 38 samples Submitted 2025/09/08 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 38 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Homo sapiens (human)
Samples: 38 samples: 37 × CKDLGroups 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.
Paper: Clonal lineage tracing of innate immune cells in human cancerCancer cell · 2026

Summary (written by the submitters)

Innate immune cells constitute the majority of the tumor microenvironment (TME), where they mediate both natural anti-tumor immunity and immunotherapy responses. While single-cell T- and B-cell receptor sequencing has provided fundamental insights into the clonal dynamics of human adaptive immunity, the lack of appropriate tools has precluded similar analysis of innate immune cells.

Read the full summary ↓

Here, we developed a method leveraging somatic mitochondrial DNA (mtDNA) mutations to reconstruct clonal lineage relationships between single cells across native human tissues. We jointly sequenced single-cell transposase-accessible chromatin and mtDNA to profile 124,958 cells from matched tumor, non-involved lung tissue (NILT) with peripheral blood of early-stage non-small cell lung cancer (NSCLC) patients, as well as 93,757 cells from matched tumor and peripheral blood of ovarian cancer patients. Our single-cell concomitant profiling of lineage and cell states of thousands of immune cells resolved clonality across cell types, tissue sites, and malignancies. By resolving clonal populations across innate immune subtypes, we demonstrated that TME-resident myeloid subsets, including macrophages and type 3 dendritic cells (DC3), are clonally linked to both circulating and tissue-infiltrating monocytes. Further, we identified distinct DC-biased and macrophage-biased myeloid clones, enriched in the tumor and NILT, respectively, and found that their circulating monocyte precursors exhibit distinct epigenetic profiles, suggesting that myeloid differentiation fate may be predetermined before TME infiltration. These results delineate the clonal pathways of intratumoral myeloid cell recruitment and differentiation in human cancer and suggest that remodeling of the tumor myeloid compartment may be peripherally programmed.

Published in

Clonal lineage tracing of innate immune cells in human cancer

Liu V, Sandor K, Yan PK et al. · Cancer cell 2026 · PMID 42242233 · doi:10.1016/j.ccell.2026.05.006

This dataset

Open GSE302113 in finder → Find more human ChIP / ATAC / CUT&Tag datasets →

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 GSE302113_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1288771 and SRA study SRP599432. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42242233) ↗

Study design

37 × CKDL

Supports a between-group comparison across 37 samples.

How we read this: 1 replicated groups read from 38 sample titles; they account for 37 of them. 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

GSM9096478 CKDL210012672 CKDL
GSM9096479 CKDL210012679 CKDL
GSM9096480 CKDL210012684 CKDL
GSM9096481 CKDL210012687-D2
GSM9096482 CKDL210012690 CKDL
GSM9096483 CKDL210012691 CKDL
GSM9096484 CKDL210012692 CKDL
GSM9096485 CKDL210012693 CKDL
GSM9096486 CKDL210012694 CKDL
GSM9096487 CKDL210012695 CKDL
GSM9096488 CKDL210012696 CKDL
GSM9096489 CKDL210028088 CKDL
GSM9096490 CKDL210028089 CKDL
GSM9096491 CKDL210028090 CKDL
GSM9096492 CKDL210028091 CKDL
GSM9096493 CKDL210028092 CKDL
GSM9096494 CKDL210028093 CKDL
GSM9096495 CKDL210028094 CKDL
GSM9096496 CKDL220016656 CKDL
GSM9096497 CKDL220016657 CKDL
GSM9096498 CKDL220016658 CKDL
GSM9096499 CKDL220016659 CKDL
GSM9096500 CKDL220016660 CKDL
GSM9096501 CKDL220016661 CKDL
GSM9096502 CKDL220016662 CKDL
GSM9096503 CKDL220016663 CKDL
GSM9096504 CKDL220016664 CKDL
GSM9096505 CKDL220016665 CKDL
GSM9096506 CKDL220016666 CKDL
GSM9096507 CKDL220016667 CKDL
GSM9096508 CKDL220016668 CKDL
GSM9096509 CKDL220016669 CKDL
GSM9096510 CKDL220016670 CKDL
GSM9096511 CKDL220016671 CKDL
GSM9096512 CKDL220016672 CKDL
GSM9096513 CKDL220016673 CKDL
GSM9096514 CKDL220016674 CKDL
GSM9096515 CKDL220016675 CKDL

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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.