Table 8_Pilot longitudinal integrated transcriptomic–metabolomic study reveals immune and

12 non-hospitalized healthcare workers with long COVID and 35 matched controls were profiled longitudinally using transcriptomic and metabolomic assays. The dataset includes results identifying 63 differentially expressed genes and 24 annotated metabolites, with integrated network analysis revealing pathway-level convergence. It was authored by Estefanía Espín and shared under a CC-BY-4.0 license on figshare in June 2026.

Use Cases

• Identify candidate biomarkers for long COVID based on differential gene expression and metabolite abundance.
• Investigate coordinated immune-metabolic dysregulation through integrated network and pathway analysis.
• Study longitudinal trajectories of biological signatures like oxoglutarate in post-acute infection sequelae.
• Compare biological profiles between non-hospitalized long COVID cases and recovered controls.
• Generate hypotheses linking persistent innate immune activation to neurocognitive symptoms like fatigue and brain fog.

Strengths

• Integrated multi-omics data combining transcriptomic (63 genes) and metabolomic (24 metabolites) profiles.
• Longitudinal design tracks changes pre- and post-infection across cases and controls.
• Focus on an underrepresented population: non-hospitalized healthcare workers.

Limitations

• Small cohort size (12 cases, 35 controls) limits statistical power and generalizability.
• Column-level documentation is absent; field semantics must be inferred after download.
• Platform-specific constraints (NanoString nCounter PanCancer Immune Panel, untargeted UHPLC-MS) may affect cross-study comparability.

Provenance

Source

Estefanía Espín via figshare

Collection Method

Longitudinal cohort study with whole-blood RNA profiling using NanoString nCounter and serum metabolite analysis via untargeted UHPLC-MS.

Freshness

Last updated 2026-06-04 05:42:32; freshness should be verified.