Precise immuno-fluorescence canceling enables highly multiplexed imaging [scRNA-seq]
Single-cell RNA-seq · human · 12 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
- 12 samples: 6 conditions, mostly in duplicateGroups 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
- Precise immunofluorescence canceling for highly multiplexed imaging to capture specific cell statesNature communications · 2024
Read the full summary ↓
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 GSE242814_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 PRJNA1014896 and SRA study SRP459522. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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.
- GSM7771377 A549 cells_0h_01 A549 cells_0h
- GSM7771378 A549 cells_0h_02 A549 cells_0h
- GSM7771379 A549 cells_0.5h_01 A549 cells_0.5h
- GSM7771380 A549 cells_0.5h_02 A549 cells_0.5h
- GSM7771381 A549 cells_12h_01 A549 cells_12h
- GSM7771382 A549 cells_12h_02 A549 cells_12h
- GSM7771383 A549 cells_24h_01 A549 cells_24h
- GSM7771384 A549 cells_24h_02 A549 cells_24h
- GSM7771385 A549 cells_48h_01 A549 cells_48h
- GSM7771386 A549 cells_48h_02 A549 cells_48h
- GSM7771387 A549 cells_96h_01 A549 cells_96h
- GSM7771388 A549 cells_96h_02 A549 cells_96h
- GSE280639 Multichrome encoding-based multiplexed, spatially resolved imaging reveals single-cell RNA epigenetic modifications heterogeneity 21 samples shares multiplexed, imaging
- GSE269596 Multiplexed single-cell characterization of alternative polyadenylation regulators (scRNA-Seq) 12 samples shares multiplexed, scrna-seq
- GSE268580 Highly multiplexed single cell chromatin regulator and accessibility profiling elucidates chromatin dynamics in human B cell development and oncogenesis, and reveals an adverse prognostic gene expression signature in B lymphoblastic leukemias 15 samples shares multiplexed, highly
- GSE309595 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [scRNA-seq] 270 samples shares scrna-seq
- GSE234181 Long-term multi-modal recording reveals epigenetic adaptation routes in dormant breast cancer cells [scRNA-Seq] 184 samples shares scrna-seq
- GSE287541 Transcriptomic and proteomic characterization of cell and protein biomarkers of checkpoint inhibitor-induced liver injury [scRNA-seq] 92 samples shares scrna-seq
- GSE269826 Conserved patterns of transcriptional dysregulation, heterogeneity and cell states in clear cell kidney cancer [scRNA-seq] 79 samples shares scrna-seq
- GSE270680 A spatially resolved atlas of gastric cancer characterises a lymphocyte aggregated region [scRNA-seq] 77 samples shares scrna-seq
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.