GEO GSE261658: TAF1-dependent transcriptional dysregulation underlies multiple sclerosis [RNA-Seq]
Study Design & Technical Specifications
Sample Size / Cohort
10 Samples
Subject / Study Model
CNS Experimental / Clinical Model
Analytical Modalities
Primary Abstract & Repository Description
Source: NCBI GEO (Omics Archive)Editorial Critique, Methodological Value & Reusability Guide
1. Scientific Context & Premise
Primary Premise (NCBI GEO (Omics Archive)): "Multiple sclerosis (MS) is characterized by central nervous system (CNS) neuroinflammation and demyelination, leading to disablility." Within Multiple Sclerosis (MS), this work examines NCBI GEO, GSE261658, RNA-seq, Transcriptomics, Homo sapiens in relation to paramagnetic rim lesions (PRLs), T2/FLAIR lesion volume, oligoclonal bands, EBV titers, and EDSS progression metrics. Authored by NCBI GEO Contributors.
2. Study Architecture & Findings
Study Architecture: Experimental / Clinical CNS Design (10 Samples) utilizing RNA-seq, Transcriptomics. Key reported conclusion: "This transcriptional profile is accompanied by CNS-resident inflammation, r..."
3. Secondary Analysis & Reusability
Secondary Analysis Applications: (1) Cross-referencing RNA-seq, Transcriptomics variables (NCBI GEO, GSE261658, RNA-seq, Transcriptomics, Homo sapiens) against open repositories in Multiple Sclerosis (MS); (2) Benchmarking feature distributions and effect sizes reported in "GEO GSE261658: TAF1-dependent transcriptional dysregulation underlies multiple s"; (3) Informing cohort stratification and endpoint selection.
4. Methodological Caveats & Governance
Methodological Note: When integrating these RNA-seq, Transcriptomics findings, account for cohort-specific inclusion criteria (10 Samples), potential site/instrument batch effects, and repository license terms (NCBI Public Domain / Open Access Omics).
Data Access, Reproducibility & Analysis Starter Guide
Single-Cell & Multi-Omics Matrix Standard (AnnData .h5ad / Loom)
Quick Data Access & Structured Export
Open the primary repository directly or download structured metadata (JSON / CSV) in one click.
This molecular neuroscience resource provides high-dimensional transcriptomic, epigenomic, or proteomic expression matrices accompanied by cell-level annotations (obs), gene/protein metadata (var), and spatial or dimensionality-reduced coordinates (obsm).
Programmatic Data Retrieval (Python Starter Code & Recommended SDKs)
Python Quickstart: Single-Cell / Multi-Omics Analysis with Scanpy
# 1. Install scverse / Scanpy analysis stack
# pip install scanpy anndata leidenalg matplotlib
import scanpy as sc
import pandas as pd
# Load AnnData (.h5ad) or expression matrix from repository download
# Source: NCBI GEO (Omics Archive) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE261658)
adata = sc.read_h5ad("./data/cns_omics_dataset.h5ad")
# Compute mitochondrial QC metrics & filter low-quality nuclei
adata.var["mt"] = adata.var_names.str.startswith("MT-")
sc.pp.calculate_qc_metrics(adata, qc_vars=["mt"], inplace=True)
adata = adata[(adata.obs.n_genes_by_counts > 300) & (adata.obs.pct_counts_mt < 5.0), :].copy()
# Normalize, log-transform, and compute UMAP manifold
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key="donor_id")
sc.tl.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)Standardized 4-Step Workflow & Governance Checklist
Standardized Acquisition & Preprocessing Workflow
1. Load Count Matrix & Cell Metadata (AnnData / Seurat)
Import raw UMI counts or normalized expression tables alongside donor clinical metadata (Braak stage, APOE genotype, PMI).
2. Quality Control & Doublet / Ambient RNA Filtering
Filter low-quality nuclei/cells based on mitochondrial read fraction, minimum detected genes, and Scrublet/CellBender scores.
3. Batch Integration & Cell-Type Taxonomy Mapping
Correct for donor and sequencing batch effects using Harmony or scVI, then map clusters to Allen Brain Map reference taxonomies.
4. Pseudobulk Differential Expression & Pathway Enrichment
Aggregate counts at the donor-celltype level (Pseudobulk DESeq2/edgeR) to avoid pseudoreplication inflation in disease vs. control comparisons.
Reproducibility & Governance Checklist
- Perform donor-level pseudobulk aggregation rather than treating thousands of cells from one donor as independent samples.
- Include post-mortem interval (PMI), RIN (RNA Integrity Number), age, and sex as covariates in linear mixed models.
- Comply with repository Data Use Agreements (e.g., Synapse/Agora controlled-access terms) if linking human genotypes.
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Academic Research & Medical Information Disclaimer
This page provides structured scientific metadata, methodological critiques, and open-data reusability guidelines intended strictly for neuroscience researchers and data scientists. It does not constitute medical advice, clinical diagnosis, or treatment recommendations.
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