Single-cell RNA Sequencing Data on T Cell Exhaustion in Chromoblastomycosis

Single-cell RNA sequencing data from a patient with chronic chromoblastomycosis reveals an expanded population of exhausted CD4+ T cells. The dataset, uploaded by Kexin Lei on April 17, 2026, includes bioinformatics analyses and multiplex immunofluorescence validation in a mouse model. Findings suggest a differentiation trajectory from naive to exhausted T cells driven by monocytes/macrophages.

Use Cases

• Analyze T cell exhaustion markers based on scRNA-seq data showing high co-expression of PD-1, TIM-3, and LAG-3.
• Investigate cell-cell communication networks based on described interactions like CTLA4-CD80/86 and LGALS9-CD44.
• Validate immunopathology findings in a mouse model based on the described recapitulation of T cell exhaustion.
• Study differentiation trajectories in chronic inflammation based on inferred paths from naive to exhausted states.

Strengths

• Data is validated by multiplex immunofluorescence in both human lesions and a mouse model.
• Includes trajectory inference and cell-cell communication analysis as described in the methods.
• Released under a CC-BY-4.0 license, permitting broad reuse.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is 115.6 KB, indicating a very limited scope, likely a data sheet or summary document.
• Data may reflect bias inherent to a single patient case study as described.

Provenance

Source

figshare, uploaded by author Kexin Lei.

Collection Method

Single-cell RNA sequencing performed on lesional skin from a CBM patient, followed by bioinformatics analyses.

Time Range

The study timeframe is not specified.

Freshness

Last updated 2026-04-17 08:46:24; freshness should be verified.

Geography

The geographic origin of the patient sample is not specified.