Single-cell RNA-sequencing of human bronchial epithelial organoids infected by four different betacoronaviruses; HCoV-OC43, SARS-CoV, MERS-CoV, and SARS-CoV-2 and mock control.
Single-cell RNA-seq · human · 15 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
- 15 samples: 15 samples, each with a distinct title — no replicated groupsGroups 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.
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 GSE262439_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 PRJNA1071273 and SRA study SRP486994. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 15 sample titles: 15 distinct titles with little repetition. 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.
- GSM8169722 22_Control-1
- GSM8169723 23_Control-2
- GSM8169724 24_Control-3
- GSM8169725 19_OC43-1
- GSM8169726 20_OC43-2
- GSM8169727 21_OC43-3
- GSM8169728 10_SARS1-1
- GSM8169729 11_SARS1-2
- GSM8169730 12_SARS1-3
- GSM8169731 4_MERS-1
- GSM8169732 5_MERS-2
- GSM8169733 6_MERS-3
- GSM8169734 13_SARS2-1
- GSM8169735 14_SARS2-2
- GSM8169736 15_SARS2-3
- GSE254266 Single-cell analysis of lung epithelial cells reveals age and cell population-specific responses to SARS-CoV-2 infection in ciliated cells 14 samples shares sars-cov-2, single-cell, epithelial
- GSE271378 Exploring host cellular responses to SARS-CoV-2 variants using RNA sequencing of infected human airway epithelial cells 44 samples shares sars-cov-2, epithelial
- GSE241292 SARS-CoV-2-infected human nasal epithelial air-liquid interface cultures 16 samples shares sars-cov-2, epithelial
- GSE167410 Histone H4 is increased in SARS-CoV-2-infected lung epithelial cells and linked to COVID-19 severity 16 samples shares sars-cov-2, epithelial
- GSE260763 Expansion of memory Vδ2 T cells following SARS-CoV-2 vaccination revealed by temporal single-cell transcriptomics 60 samples shares sars-cov-2, single-cell
- GSE224198 Single-cell multi-omics analysis revealing immune features of inactivated SARS-CoV-2 vaccination in systemic lupus erythematosus patients 26 samples shares sars-cov-2, single-cell
- GSE264680 Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data 167 samples shares rna-sequencing, single-cell
- GSE287301 Single-cell RNA-sequencing of HNSCC-infiltrating T cells 48 samples shares rna-sequencing, single-cell
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.