Pathogenic TSC2 variants alter developmental trajectories of human cortical organoids and perturb cell-cell communication networks [bulk RNA-seq]
Single-cell RNA-seq · human · 80 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Samples
- 80 samples: 80 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.
- Paper
- Longitudinal multi-omics reveals pathogenic TSC2 variants disrupt developmental trajectories of human cortical organoids derived from Tuberous Sclerosis ComplexbioRxiv : the preprint server for biology · 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 GSE247367_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 PRJNA1037171 and SRA study SRP470971. 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 80 sample titles: 80 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.
- GSM7887844 CTRL_Day35_C1_2_35_A
- GSM7887845 CTRL_Day35_C1_2_35_B
- GSM7887846 CTRL_Day35_11C1_35_A
- GSM7887847 CTRL_Day35_11C1_35_B
- GSM7887848 CTRL_Day35_PGP1_35_A
- GSM7887849 CTRL_Day35_PGP1_35_B
- GSM7887850 TSC_Day35_TSC001_35_A
- GSM7887851 TSC_Day35_TSC001_35_B
- GSM7887852 TSC_Day35_TSC007_35_A
- GSM7887853 TSC_Day35_TSC007_35_B
- GSM7887854 TSC_Day35_TSC009_35_A
- GSM7887855 TSC_Day35_TSC009_35_B
- GSM7887856 TSC_Day35_TSC009Homo_35_A
- GSM7887857 TSC_Day35_TSC009Homo_35_B
- GSM7887858 TSC_Day35_TSC010_35_A
- GSM7887859 TSC_Day35_TSC010_35_B
- GSM7887860 TSC_Day35_TSC010Homo_35_A
- GSM7887861 TSC_Day35_TSC010Homo_35_B
- GSM7887862 TSC_Day35_TSC010Het_35_A
- GSM7887863 TSC_Day35_TSC010Het_35_B
- GSM7887864 CTRL_Day56_C1_2_56_A
- GSM7887865 CTRL_Day56_C1_2_56_B
- GSM7887866 CTRL_Day56_11C1_56_A
- GSM7887867 CTRL_Day56_11C1_56_B
- GSM7887868 CTRL_Day56_PGP1_56_A
- GSM7887869 CTRL_Day56_PGP1_56_B
- GSM7887870 TSC_Day56_TSC001_56_A
- GSM7887871 TSC_Day56_TSC001_56_B
- GSM7887872 TSC_Day56_TSC007_56_A
- GSM7887873 TSC_Day56_TSC007_56_B
- GSM7887874 TSC_Day56_TSC009_56_A
- GSM7887875 TSC_Day56_TSC009_56_B
- GSM7887876 TSC_Day56_TSC009Homo_56_A
- GSM7887877 TSC_Day56_TSC009Homo_56_B
- GSM7887878 TSC_Day56_TSC010_56_A
- GSM7887879 TSC_Day56_TSC010_56_B
- GSM7887880 TSC_Day56_TSC010Homo_56_A
- GSM7887881 TSC_Day56_TSC010Homo_56_B
- GSM7887882 TSC_Day56_TSC010Het_56_A
- GSM7887883 TSC_Day56_TSC010Het_56_B
+ 40 more — browse all 80 samples with per-sample file links →
- GSE247369 Pathogenic TSC2 variants alter developmental trajectories of human cortical organoids and perturb cell-cell communication networks 92 samples shares tsc2, communication, trajectories, pathogenic, organoids, cell-cell, variants, cortical
- GSE247368 Pathogenic TSC2 variants alter developmental trajectories of human cortical organoids and perturb cell-cell communication networks [scRNA-seq] 12 samples shares tsc2, communication, trajectories, pathogenic, organoids, cell-cell, variants, cortical
- GSE296243 Genomic and Immune Landscape of Pancreatic Ductal Adenocarcinoma Associated with Germline Pathogenic Variants in ATM 24 samples shares pathogenic, variants
- GSE244189 Transcriptomic analysis of ASD and IDD associated pathogenic MYT1L S707Q mutation in human cortical interneurons 24 samples shares pathogenic, cortical
- GSE242346 Human stem cells with in vivo high plasticity generated by cell-cell communication 22 samples shares communication, cell-cell
- GSE301142 Multiple mechanisms lead to differential loss-of-function effects of pathogenic SARS2 variants: implications for genotype-phenotype analyses 18 samples shares pathogenic, variants
- GSE266958 Pathogenic PPP2R5D variants alters the transcriptome of patient iPSC-derived glutamatergic neurons 18 samples shares pathogenic, variants
- GSE288121 Pathogenic variants in the splicing factor SF1 lead to a new neurodevelopmental disorder. 12 samples shares pathogenic, variants
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.