Joint single-cell profiling of Cas9 edits and transcriptomes reveals on- and off-target effects on gene expression
Single-cell RNA-seq · human · 26 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
- 26 samples: 25 distinct titles, 1 of them repeatedGroups 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
- Joint single-cell profiling of Cas9 edits and transcriptomes reveals widespread off-target events and effects on gene expressionbioRxiv : the preprint server for biology · 2025
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 GSE314191_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 PRJNA1390229 and SRA study SRP655626. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 26 sample titles: 25 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.
- GSM8679741 RNA-seq, Jurkat, CTLA4 RNP, Mock
- GSM8679742 RNA-seq, Jurkat, CTLA4 RNP, IST
- GSM8679743 RNA-seq, Jurkat, Donor + CTLA4 RNP, IST
- GSM8679744 RNA-seq, K562, Donor + B2M RNP, Mock
- GSM8679745 RNA-seq, K562, B2M RNP, IST
- GSM8679746 RNA-seq, K562, Donor + B2M RNP, IST
- GSM8679747 Superb-seq, K562, BAF NuRD complexes, 500 cells
- GSM8679748 Superb-seq, K562, BAF NuRD complexes, 10k cells
- GSM9378414 Superb-seq, 293FT K562 U2OS, benchmarking, 10k cells, rep1
- GSM9378415 Superb-seq, 293FT K562 U2OS, benchmarking, 10k cells, rep2
- GSM9378477 rhAmpSeq, K562, Unedited, 52 mutiplex
- GSM9378478 rhAmpSeq, K562, ARID1A guide 05, 52 multiplex
- GSM9378479 rhAmpSeq, K562, ARID1A guide 06, 52 multiplex
- GSM9378480 rhAmpSeq, K562, SMARCA4 guide 19, 52 multiplex
- GSM9378481 rhAmpSeq, K562, SMARCA4 guide 22, 52 multiplex
- GSM9378482 rhAmpSeq, K562, CHD3 guide 10, 52 multiplex
- GSM9378483 rhAmpSeq, K562, CHD4 guide 12, 52 multiplex
- GSM9378484 rhAmpSeq, K562, CHD4 guide 14, 52 multiplex
- GSM9378485 rhAmpSeq, K562, Unedited, ADSS1 singleplex
- GSM9378486 rhAmpSeq, K562, ARID1A guide 05, ADSS1 singleplex
- GSM9378487 rhAmpSeq, K562, ARID1A guide 06, ADSS1 singleplex
- GSM9378488 rhAmpSeq, K562, SMARCA4 guide 19, ADSS1 singleplex
- GSM9378489 rhAmpSeq, K562, SMARCA4 guide 22, ADSS1 singleplex
- GSM9378490 rhAmpSeq, K562, CHD3 guide 10, ADSS1 singleplex
- GSM9378491 rhAmpSeq, K562, CHD4 guide 12, ADSS1 singleplex
- GSM9378492 rhAmpSeq, K562, CHD4 guide 14, ADSS1 singleplex
- GSE279392 Single-cell RNA and TCR sequencing from skin, blood and cerebral spinal fluid (CSF) samples from Degos disease patients 116 samples shares single-cell
- GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares single-cell
- GSE254176 Dynamic changes of immune cells in systemic lupus erythematosus with nephritis using longitudinal peripheral blood single-cell RNA-seq 57 samples shares single-cell
- GSE332623 Immunological Differences in Atopic Dermatitis Across Age Groups: Insights from Single-Cell Multi-Omics 54 samples shares single-cell
- GSE272005 Genomic and single-cell landscape of transformed cutaneous T-cell lymphoma 32 samples shares single-cell
- GSE253904 High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis 24 samples shares single-cell
- GSE253661 Longitudinal single-cell transcriptomics of monocytes, Tfh and gd Tcells following a control human malaria infection. 16 samples shares single-cell
- GSE287976 Single-Cell Omics Analysis of Human Basophils Reveals Two Transcriptionally Distinct Populations 12 samples shares 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.