C9orf72 hexanucleotide repeat expansions impair microglial response in models of ALS [RNA]
Single-nucleus RNA-seq · human · 30 samples
- What it is
- Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). 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
- 30 samples: 8 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
- C9orf72 hexanucleotide repeat expansions impair microglial response in ALSNature neuroscience · 2025
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 GSE302295_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 PRJNA1289820 and SRA study SRP599966. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 16 samples.
How we read this: 8 replicated groups read from 30 sample titles; they account for 16 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.
- GSM9101330 C9KO_diff1_d32
- GSM9101331 C_line_diff1_d32
- GSM9101332 C9KO_diff2_d25
- GSM9101333 C_line_diff2_d25
- GSM9101334 C9KO_diff3_d32
- GSM9101335 C_line_diff3_d32
- GSM9101336 C9KO_diff4_d32 C9KO_diff4_d
- GSM9101337 C_line_diff4_d32 C_line_diff4_d
- GSM9101338 C9KO_diff4_d25 C9KO_diff4_d
- GSM9101339 C_line_diff4_d25 C_line_diff4_d
- GSM9101340 C9KO_diff5_d25
- GSM9101341 C_line_diff5_d25
- GSM9101342 C9KO_diff6_d32
- GSM9101343 C_line_diff6_d32
- GSM9101344 C9orf72 patient line_diff4_d25 C9orf72 patient line_di…
- GSM9101345 C9orf72 patient line-isogenic_diff4_d25 C9orf72 patient line-is…
- GSM9101346 C9orf72 patient line_diff4_d32 C9orf72 patient line_di…
- GSM9101347 C9orf72 patient line-isogenic_diff4_d32 C9orf72 patient line-is…
- GSM9101348 C9orf72 patient line_diff7_d25 C9orf72 patient line_di…
- GSM9101349 C9orf72 patient line-isogenic_diff7_d25 C9orf72 patient line-is…
- GSM9101350 C9orf72 patient line_diff7_d32 C9orf72 patient line_di…
- GSM9101351 C9orf72 patient line-isogenic_diff7_d32 C9orf72 patient line-is…
- GSM9101352 C9orf72 patient line_diff8_d25
- GSM9101353 C9orf72 patient line-isogenic_diff8_d25
- GSM9101354 C9orf72 patient line_diff9_d25
- GSM9101355 C9orf72 patient line-isogenic_diff9_d25
- GSM9101356 C9orf72 patient line_diff10_d25 C9orf72 patient line_di…
- GSM9101357 C9orf72 patient line-isogenic_diff10_d25 C9orf72 patient line-is…
- GSM9101358 C9orf72 patient line_diff10_d32 C9orf72 patient line_di…
- GSM9101359 C9orf72 patient line-isogenic_diff10_d32 C9orf72 patient line-is…
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares als
- GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares als
- GSE267625 Whole blood transcriptome in long-COVID patients reveals association with lung function and immune response 111 samples shares als
- GSE314342 Genome-scale perturb-seq in primary human CD4+ T cells reveals genes regulating T cell programs and human immune traits. 577 samples shares als
- GSE49711 RNA-Seq reveals an unprecedented complexity of the neuroblastoma transcriptome and is suitable for clinical endpoint prediction [RNA-Seq] 498 samples shares als
- GSE274680 Inflammation and lymphocyte activation during longitudinal progression to rheumatoid arthritis in at-risk individuals 243 samples shares als
- GSE295173 Multi-modal single-cell sequencing reveals network transition in circulating monocytes that aligns with faster recovery in trauma patients and favors a response to M-CSF [scRNA-Seq] 30 samples shares als
- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares als
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.