PURE-seq Integrates FACS and PIP-seq for Single-Cell Genomics of Ultra-rare Cells
Single-cell RNA-seq · human · 17 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
- 17 samples: 3 × LT-HSC, middle-aged, vs 3 × LT-HSC, old, vs 2 × LT-HSC, young, vs 2 × HSC,Groups 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
- PURE-seq integrates FACS and PIP-seq for single-cell genomics of ultra-rare cellsNature communications · 2026
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE273803_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1143372 and SRA study SRP523829. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 10 samples.
How we read this: 4 replicated groups read from 17 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.
- GSM8437192 1k, postsort
- GSM8437193 10k, postsort
- GSM8437194 100k, postsort
- GSM8437195 1M, postsort
- GSM8437196 1k, presort
- GSM8437197 1k, postsort, yield
- GSM8437198 1k, postsort, sc
- GSM8437199 LT-HSC, young, rep1 LT-HSC, young,
- GSM8437200 LT-HSC, young, rep2 LT-HSC, young,
- GSM8437201 LT-HSC, middle-aged, rep1 LT-HSC, middle-aged,
- GSM8437202 LT-HSC, middle-aged, rep2 LT-HSC, middle-aged,
- GSM8437203 LT-HSC, middle-aged, rep3 LT-HSC, middle-aged,
- GSM8437204 LT-HSC, old, rep1 LT-HSC, old,
- GSM8437205 LT-HSC, old, rep2 LT-HSC, old,
- GSM8437206 LT-HSC, old, rep3 LT-HSC, old,
- GSM8437207 HSC, rep1 HSC,
- GSM8437208 HSC, rep2 HSC,
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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.