Astrocyte Metabolic Models for Alzheimer's Disease Progression

Transcriptomic profiles from hippocampal CA1 tissue across four clinical conditions (control, early MCI, advanced MCI, and AD) were integrated into a curated human astrocyte genome-scale metabolic model. The resulting condition-specific models, generated by Andrea Angarita-Rodríguez and last updated in June 2026, are used for flux balance and flux variability analysis. The dataset is a 25.9 KB document describing the modeling results.

Use Cases

• Identify stage-specific metabolic vulnerabilities in astrocytes based on inferred transcriptional profiles.
• Analyze flux variability in pathways like glutamate-glutamine cycling and glutathione metabolism based on model predictions.
• Validate predicted metabolic reprogramming against independent single-nucleus RNA-seq datasets mentioned in the description.
• Explore potential therapeutic targets by investigating pathways with reduced metabolic flexibility during disease progression.

Strengths

• Data is derived from a specific transcriptomic dataset (GSE28146) with four defined clinical conditions.
• Analysis integrates multiple methods: unsupervised deconvolution (CDSeq), canonical marker enrichment, and genome-scale metabolic modeling.
• The dataset is openly licensed under CC-BY-4.0.

Limitations

• The primary data file is a 25.9 KB DOCX document, suggesting the dataset is a summary or description rather than raw model outputs.
• Row and column counts for any underlying data tables are unknown.
• Column-level documentation is absent; field semantics must be inferred from the document.

Provenance

Source

Transcriptomic data sourced from the GSE28146 dataset on hippocampal CA1 tissue.

Collection Method

Transcriptomic profiles were analyzed using CDSeq deconvolution and integrated into a curated human astrocyte genome-scale metabolic model for flux analysis.

Freshness

Last updated 2026-06-02 05:45:35