Restored glyoxylate metabolism in induced-hepatocytes after CRISPR/Cas-mediated AGXT gene correction and direct cell reprogramming of Primary Hyperoxaluria Type 1 fibroblasts
RNA-seq (gene expression) · human · 39 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)
- Samples
- 39 samples: 37 distinct titles, 2 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
- Restored glyoxylate metabolism after AGXT gene correction and direct reprogramming of primary hyperoxaluria type 1 fibroblastsiScience · 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 GSE226019_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: 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 PRJNA938377 and SRA study SRP424384. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 39 sample titles: 37 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.
- GSM7061670 hFibros WT-1_biol.rep.1
- GSM7061671 hFibros WT-1_biol.rep.2
- GSM7061672 iHeps WT-1 day 8 (exp.5)_biol.rep.1
- GSM7061673 iHeps WT-1 day 8 (exp.5)_biol.rep.2
- GSM7061674 iHeps WT-1 day 13 (exp.1)
- GSM7061675 iHeps WT-1 day 13 (exp.5)
- GSM7061676 iHeps WT-1 day 13 (exp.6)
- GSM7061677 hFibros WT-3
- GSM7061678 iHeps WT-3 day 8 (exp.1)
- GSM7061679 iHeps WT-3 day 8 (exp.5)
- GSM7061680 iHeps WT-3 day 13 (exp.1)
- GSM7061681 iHeps WT-3 day 13 (exp.5)
- GSM7061682 Primary human hepatocytes pool
- GSM7061683 hFibros PH1-1
- GSM7061684 iHeps PH1-1 day 8 (exp.9)
- GSM7061685 iHeps PH1-1 day 8 (exp.10)
- GSM7061686 iHeps PH1-1 day 8 (exp.11)
- GSM7061687 iHeps PH1-1 day 13 (exp.9)
- GSM7061688 iHeps PH1-1 day 13 (exp.10)
- GSM7061689 iHeps PH1-1 day 13 (exp.11)
- GSM7061690 hFibros GC-1A
- GSM7061691 iHeps GC-1A day 8 (exp.9)
- GSM7061692 iHeps GC-1A day 13 (exp.9)
- GSM7061693 hFibros GC-1C
- GSM7061694 iHeps GC-1C day 8 (exp.9)
- GSM7061695 iHeps GC-1C day 13 (exp.9)
- GSM7061696 hFibros pB-1C
- GSM7061697 iHeps pB-1C day 8 (exp.10)
- GSM7061698 iHeps pB-1C day 8 (exp.11)
- GSM7061699 iHeps pB-1C day 13 (exp.10)
- GSM7061700 iHeps pB-1C day 13 (exp.11)
- GSM7061701 hFibros pA-1A
- GSM7061702 iHeps pA-1A day 8 (exp.10)
- GSM7061703 iHeps pA-1A day 8 (exp.11)
- GSM7061704 iHeps pA-1A day 13 (exp.10)
- GSM7061705 iHeps pA-1A day 13 (exp.11)
- GSM7061706 hFibros pD-1A
- GSM7061707 iHeps pD-1A day 8 (exp.11)
- GSM7061708 iHeps pD-1A day 13 (exp.11)
- GSE280564 Reprogramming of normal fibroblasts into ovarian cancer-associated fibroblasts via non-vesicular paracrine signaling induces an activated fibroblast phenotype 12 samples shares reprogramming, fibroblasts
- GSE266618 Systematic discovery of CRISPR-boosted CAR T cell immunotherapies 60 samples shares crispr
- GSE299089 A genome-wide CRISPR screen identifies the TNRC18 gene locus as a novel regulator of inflammatory signaling 54 samples shares crispr
- GSE290621 Quadruple adenine base edited allogeneic CAR T cells outperform CRISPR/Cas9 nuclease engineered T cells [RNA-seq] 33 samples shares crispr
- GSE330227 In vivo genome-wide CRISPR screens in human T cells to enhance T cell therapy for solid tumors 30 samples shares crispr
- GSE337839 CRISPR activation of DLX5 drives neural progenitors to the GnRH cell fate 29 samples shares crispr
- GSE327133 Virus-like particles enable targeted gene engineering and pooled CRISPR screening in primary human myeloid cells [CRISPRoff] 28 samples shares crispr
- GSE288118 An arrayed CRISPR screen identifies knockout combinations improving antibody productivity in HEK293 cells 28 samples shares crispr
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.