GEO series

Single-cell transcriptomics of non-functioning and functioning PitNET subtypes reveals differences in the tumor microenvironment and tumor-specific gene expression signatures that correlate with aggressive tumor growth

GSE244101 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2024/11/20 Platform GPL30173

At a glance

Single-cell RNA-seq · human · 10 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 10 samples: 7 × gonadotroph PitNET scRNAseq H vs 3 × lactotroph PitNET scRNAseq HGroups 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: Single-cell transcriptomics link gene expression signatures to clinicopathological features of gonadotroph and lactotroph PitNETJournal of translational medicine · 2024

Summary (written by the submitters)

Pituitary neuroendocrine tumors (PitNET) are among the most common intracranial tumors. Despite a frequently benign course, aggressive behavior occurs for unexplained reasons, especially in functional PitNET. Tumor behavior is known to be under the influence of the tumor microenvironment (TME). However, the relationship between TME cells and aggressive tumor behavior has not been adequately explored in PitNET.

Read the full summary ↓

We used single-cell RNA sequencing and other analyses to comparatively characterize the transcriptome of seven gonadotroph and three lactotroph PitNET and correlate it with clinical behavioral patterns. In our lactotroph PitNET samples, we detect a highly proliferative gene profile with significantly increased expression levels in tumors with aggressive behavioral patterns within a bulk RNA-seq cohort of 134 PitNET samples. We also report high intratumoral heterogeneity in both gonadotroph and lactotroph PitNET and detect gene expression programs belonging to epithelial, endocrine and immunological functional networks in both subtypes. Direct comparison of the lymphoid compartment of both subtypes reveals increased CD4+ T cell abundances in gonadotroph and increased CD4-/CD8- double negative T cell and natural killer T cell abundances in lactotroph PitNET. Also notable is the presence of proliferative lymphocytes, whose occurrence in the bulk RNA-seq cohort positively correlates with more aggressive tumor behavior. Increased CD8+ T cell and natural killer (NK) cell abundancies, however, correlate significantly with reduced aggressiveness indicating anti-tumoral effects. Our study expands the knowledge of the differences in cellular composition of functional and non-functional PitNET subtypes and lays the foundation for further studies on the influence of lymphoid cells on the variable aggressive behavior of PitNET. Regarding the treatment of drug-resistant lactotroph PitNET, proliferative lymphocytes, CD8+ T and NK cells could represent potentially valuable targets for the development of new cancer immunotherapies in the future.

Published in

Single-cell transcriptomics link gene expression signatures to clinicopathological features of gonadotroph and lactotroph PitNET

Potthoff TE, Walter C, Jeising D et al. · Journal of translational medicine 2024 · PMID 39548496 · doi:10.1186/s12967-024-05821-4

This dataset

Open GSE244101 in finder → Find more human RNA-seq 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 GSE244101_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 PRJNA1021231 and SRA study SRP463262. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39548496) ↗

Study design

7 × gonadotroph PitNET scRNAseq H vs 3 × lactotroph PitNET scRNAseq H

Supports a between-group comparison across 10 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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

GSM7806648 gonadotroph PitNET scRNAseq H-014 gonadotroph PitNET scRN…
GSM7806649 gonadotroph PitNET scRNAseq H-018 gonadotroph PitNET scRN…
GSM7806650 gonadotroph PitNET scRNAseq H-034 gonadotroph PitNET scRN…
GSM7806651 gonadotroph PitNET scRNAseq H-082 gonadotroph PitNET scRN…
GSM7806652 gonadotroph PitNET scRNAseq H-099 gonadotroph PitNET scRN…
GSM7806653 gonadotroph PitNET scRNAseq H-111 gonadotroph PitNET scRN…
GSM7806655 gonadotroph PitNET scRNAseq H-130 gonadotroph PitNET scRN…
GSM7806656 lactotroph PitNET scRNAseq H-036 lactotroph PitNET scRNA…
GSM7806657 lactotroph PitNET scRNAseq H-129 lactotroph PitNET scRNA…
GSM7806658 lactotroph PitNET scRNAseq H-171 lactotroph PitNET scRNA…

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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.