T2D Research

Tkachenko et al. 2025 Cross-Study Blood Transcriptomes in Type 2 Diabetes

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tagsreference, type-2-diabetes, transcriptomics, immunology, pbmc
aliasesTkachenko et al. 2025 Cross-Study Blood Transcriptomes in Type 2 Diabetes

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Tkachenko et al. 2025 Cross-Study Blood Transcriptomes in Type 2 Diabetes

Tkachenko et al. performed bulk RNA-seq on whole blood from 8 T2D patients and 8 controls, then meta-analyzed those data with seven public blood RNA-seq datasets to study blood transcriptomic changes in type 2 diabetes.

Citation

  • Tkachenko, A.A. et al. 2025. “Cross-Study Meta-Analysis of Blood Transcriptomes in Type 2 Diabetes.” International Journal of Molecular Sciences 26, 12046. DOI: 10.3390/ijms262412046.
  • Source path: _raw/Tkachenko2025crosstudy.pdf.
  • Data generated by the study are deposited under GSE280402.
  • Code is reported at https://github.com/castrofiber/T2D_blood_meta.

Study Design

  • The authors analyzed eight blood RNA-seq datasets comparing healthy controls and T2D cases: six whole-blood datasets and two PBMC datasets.
  • The starting sample set contained 1054 samples and was reduced to 915 samples after outlier filtering.
  • Differential expression was run separately within each dataset using dream from variancePartition, with sex and available study-specific covariates included in the models.
  • Meta-analysis combined raw p-values with the inverse normal method through metaRNAseq and used a custom homogeneity score to summarize log-fold-change direction and concordance.

Key Findings

  • Individual T2D blood RNA-seq studies showed low concordance in diabetes-associated effect sizes, and four of eight datasets produced zero DEGs at FDR 0.05.
  • Only five DEGs shared direction across the three individual datasets with substantial DEG counts: FBLN2, TPCN1, PC, SHANK1, and PLD4.
  • Cross-study meta-analysis identified 2065 DEGs, including 713 integration-driven discoveries not differentially expressed in any single source dataset.
  • Seven genes had the same direction of effect across all eight datasets: LOC112268118 and MPO had positive logFC, while IL18BP, DELE1, TSPAN32, ITFG2, and NISCH had negative logFC.
  • Enrichment analysis of meta-analysis DEGs implicated neutrophil degranulation, p53-mediated transcriptional regulation, growth-factor receptor signaling, TNF-alpha signaling, oxidative phosphorylation, electron transport, mTOR signaling, cell division, and chromatin organization.
  • The integration-driven discovery genes were enriched for cellular-component terms related to vacuolar membrane, lytic vacuole membrane, and azurophil granule membrane, which the authors interpret as consistent with neutrophil effector functions.

Paper-Relevant Interpretation

  • This paper is a strong cautionary source for interpreting single-study blood or PBMC transcriptomic results in T2D because it shows that individual-study DEG lists can be unstable across cohorts.
  • For the ancestry paper, it supports claim discipline around cohort composition, site, batch, sample type, infection status, tuberculosis status, and technical protocol differences as potential confounders of T2D blood immune signatures.
  • The inclusion of both whole blood and PBMC datasets makes the findings relevant to PBMC immune changes in T2D, but whole-blood and PBMC biology should not be treated as identical.

Limitations

  • The meta-analysis includes both whole-blood and PBMC datasets, so some signals may reflect sample-type differences rather than a single unified blood-cell compartment.
  • The paper emphasizes technical and biological heterogeneity, including globin transcript abundance, protocol differences, cell-composition variation, infection status, and inter-individual variability.
  • The study does not perform ancestry-stratified analysis, so it should support general T2D blood transcriptomics and confounding arguments rather than direct ancestry claims.

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