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Sphingosine-1 phosphate receptor 1 (S1PR1) is crucial for maintaining the stem cell properties of acute myeloid leukemia stem cells

GSE230423 Homo sapiens (human) RNA-seq (gene expression) 60 samples Submitted 2024/08/09 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 60 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Topic
Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples
60 samples: 12 conditions, mostly in triplicateGroups 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.
Summary (written by the submitters)
To identify the most important gene(s) for maintaining the stem cell properties of leukemia stem cells (LSCs), we analyzed the transcriptional profiles of LSC and non-LSC subsets from acute leukemia patients and identified S1PR1 as a novel biomarker of LSCs.
Read the full summary ↓
Then to understand the underlying mechanisms of S1PR1-dependent stemness maintenance, we performed gene expression profiling analyses comparing CD34+S1PR1+ and CD34+S1PR1- cell fractions from AML patients and healthy donors; we also determined the global transcriptional response of KG1 cells to an inhibitor of S1PR1 (FTY720) at two concentrations.
This dataset
Open GSE230423 in finder → Find more human RNA-seq datasets → Acute myeloid leukemia 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 GSE230423_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA961116 and SRA study SRP434182. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly in triplicate
M1_LSC ×4 M1_nonLSC ×4 M2_LSC ×3 M2_nonLSC ×3 M3_LSC ×3 M3_nonLSC ×3 L1_LSC ×3 L1_nonLSC ×3 +26 more

Supports a between-group comparison across 38 samples.

How we read this: 12 replicated groups read from 60 sample titles; they account for 38 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

+ 20 more — browse all 60 samples with per-sample file links →

More Acute myeloid leukemia datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.