Table 3_Pilot longitudinal integrated transcriptomic–metabolomic study reveals immune and

Estefanía Espín's dataset contains integrated transcriptomic and metabolomic profiles from a longitudinal cohort of non-hospitalized healthcare workers with long COVID (N=12) and matched controls (N=35). It includes 63 differentially expressed genes and 24 annotated metabolites identified via whole-blood RNA profiling and serum metabolomics. The data was last updated on June 4, 2026, and is shared under a CC-BY-4.0 license.

Use Cases

• Identify candidate biomarkers for long COVID based on differential gene expression and metabolite abundance.
• Analyze coordinated immune-metabolic dysregulation via integrated network and pathway analysis.
• Study longitudinal trajectories of metabolites like oxoglutarate in post-acute infection outcomes.
• Investigate the role of neutrophil-associated inflammatory pathways (e.g., S100A8, LY96) in persistent symptoms.
• Generate hypotheses linking innate immune activation to neurocognitive symptoms such as fatigue and brain fog.

Strengths

• Includes data from a longitudinal cohort of 12 long COVID cases and 35 matched controls.
• Integrates two high-dimensional data types: transcriptomics (63 differentially expressed genes) and metabolomics (24 annotated metabolites).
• Focuses on an underrepresented population: non-hospitalized healthcare workers.

Limitations

• Cohort size is small (12 cases), which the authors note limits generalizability.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Estefanía Espín via figshare.

Collection Method

Whole-blood RNA profiled using NanoString nCounter PanCancer Immune Panel; serum metabolites analyzed via untargeted ultra-high-performance liquid chromatography–mass spectrometry.

Freshness

Last updated 2026-06-04 05:44:39; freshness should be verified.