Single-Cell RNA-Seq Data from Mouse Stroke Model for White Matter Repair Study

Single-cell RNA sequencing transcriptome data from a mouse model of ischemic stroke, comparing conditional Slc9a1 knockout and wild-type white matter tissues. The data was generated by Shanshan Song and hosted on Harvard Dataverse, with a last update recorded on 2026-06-03. It focuses on microglial and oligodendrocyte subgroups to explore mechanisms of white matter demyelination and remyelination.

Use Cases

• Identify differentially expressed genes in microglia and oligodendrocytes based on Nhe1 knockout status.
• Analyze pathway activation, such as CREB signaling, using ingenuity pathway analysis results mentioned in the description.
• Investigate cell-cell interaction mechanisms for remyelination based on the described pro-phagocytosis and lactate shuffling gene expression.
• Compare acidic intracellular pH (pHi) effects on microglial subgroups between genotypes.

Strengths

• Data is derived from a controlled genetic knockout model (Slc9a1 cKO vs. WT), enabling mechanistic study.
• Analysis includes single-cell resolution transcriptomics, allowing for subgroup identification within cell types.
• Findings are linked to specific biological pathways (CREB signaling) and functional outcomes (remyelination).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale analyses.
• Freshness should be verified as the last update date is in the future (2026-06-03).

Provenance

Source

Harvard Dataverse

Collection Method

Single-cell RNA sequencing of mouse white matter tissues post-stroke.

Time Range

Samples collected at 3 days post-stroke.

Freshness

Last updated 2026-06-03 09:39:55