GEO series

Single cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species

GSE127465 Mus musculus (mouse) Single-cell RNA-seq 40 samples Submitted 2019/04/09 Platform GPL19057Platform GPL18573

At a glance

Single-cell RNA-seq · mouse · 40 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Topic: Lung cancerThis series is part of our curated briefing on it.
Samples: 40 samples
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)

Tumor-infiltrating myeloid cells (TIMs) comprise monocytes, macrophages, dendritic cells and neutrophils, and have emerged as key regulators of cancer growth. These cells can diversify into a spectrum of states, which may promote or limit tumor outgrowth, but remain poorly understood. Here, we used single-cell RNA sequencing to map TIMs in non-small cell lung cancer patients.

Read the full summary ↓

We uncovered 25 TIM states, most of which were reproducibly found across patients. To facilitate translational research of these populations, we also profiled TIMs in mice. In comparing TIMs across species, we identified a near-complete congruence of population structures among dendritic cells and monocytes; conserved neutrophil subsets; and species differences among macrophages. By contrast, myeloid cell population structures in patients’ blood showed limited overlap with those of TIMs. This study determines the lung TIM landscape and sets the stage for future investigations into the potential of TIMs as immunotherapy targets.

This dataset

Open GSE127465 in finder → Find more RNA-seq datasets → Lung cancer 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 GSE127465_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA524857 and SRA study SRP187083. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 30979687) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 40 more — browse all 40 samples with per-sample file links →

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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.