GEO series

Reprogramming of Tumor-infiltrating Immune Cells in Early Stage of NSCLC

GSE117570 Homo sapiens (human) Single-cell RNA-seq 8 samples Submitted 2019/04/30 Platform GPL18573

At a glance

Single-cell RNA-seq · human · 8 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Topic: Lung cancerThis series is part of our curated briefing on it.
Samples: 8 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)

Comparing the relative proportions of immune cells in tumor and adjacent normal tissue from NSCLC patients demonstrates the early changes of tumor immunity and provides insights to guide immunotherapy design. We mapped the immune ecosystem using computational deconvolution of bulk transcriptome data from the Cancer Genome Atlas (TCGA) and single cell RNA sequencing (scRNA-seq) data of dissociated tumors from early-stage non-small cell lung cancer (NSCLC) to investigate early immune landscape changes occurring during tumorigenesis.

Read the full summary ↓

Computational deconvolution of immune infiltrates in 44 NSCLC and matching adjacent normal samples from TCGA showed heterogeneous patterns of alterations in immune cells. The scRNA-seq analyses of 11,485 cells from 4 treatment-naïve NSCLC patients comparing tumor to adjacent normal tissues showed diverse changes of immune cell compositions. Notably, CD8+ T cells and NK cells are present at low levels in adjacent normal tissues, and are further decreased within tumors. Myeloid cells exhibited marked dynamic reprogramming activities, which were delineated with differentiation paths through trajectory analysis. A common differentiation path from CD14+ monocytes to M2 macrophages was identified among the 4 cases, accompanied by up-regulated genes (e.g. ALCAM/CD166, CD59, IL13RA1, IL7R) with enriched functions (adipogenesis, lysosome), and down-regulated genes (e.g. CXCL2, IL1B, IL6R) with enriched functions (TNFa signaling via NF-kB, inflammatory response). Computational deconvolution and single cell sequencing analyses have revealed a highly dynamic immune reprogramming that occurs in early stage NSCLC development, suggesting that normalizing both immune compartments may represent a viable strategy for treatment of early stage cancer and prevention of progression.

This dataset

Open GSE117570 in finder → Find more human 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 GSE117570_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 PRJNA482529 and SRA study SRP154973. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 31033233) ↗

Samples in this study

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

+ 8 more — browse all 8 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.