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<p>RNA-sequencing’s conversion of molecules to reads is inconsistent. Experiment-to-experiment variations (systemic bias) create batch effects, while gene-to-gene variations (sequence-dependent bias) invalidate inter-gene comparisons, precluding a universal scale. This confines analysis to relative fold-changes, a metric unreliable across batches. We introduce TranScale: 100 biomimetic standards with SI-traceable concentrations certified by Isotope Dilution Mass Spectrometry. Co-processed within samples, they empirically characterize systemic and sequence-dependent biases, generating a library-specific calibration curve (R² &gt; 0.97) to convert reads into absolute quantities. This approach reveals that consistent fold-changes can mask severe absolute errors, exposing systemic biases missed by conventional QC. Across laboratories, this calibration reduced median inter-lab CV from &gt;85% to &lt;25% and increased biological signal-to-noise from ~0 to &gt;7.9, outperforming the widely-used tool ComBat. By anchoring RNA-seq to the SI, our work establishes the metrological foundation for data interoperability and universal benchmarks, enabling absolute comparisons of SI-traceable quantities between any two genes.</p>

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A metrological foundation for absolute transcriptomics using International System of Units-anchored calibrators

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月26日

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  • Yu Zhang,
  • Bingwen Yang,
  • Ying Yu,
  • Xia Wang,
  • Chunyan Niu,
  • Yongzhuo Zhang,
  • Yang Liu,
  • Jingshu Li,
  • Caihang Zhang,
  • Jiayi Yang,
  • Jiayu Tian,
  • Zheng Liu,
  • Zhiyu Tang,
  • Yunhua Gao,
  • Yuanting Zheng,
  • Yuqin Liu,
  • Ting Xiao,
  • Rui Zhang,
  • Xiang Fang,
  • Leming Shi,
  • Lianhua Dong

摘要

RNA-sequencing’s conversion of molecules to reads is inconsistent. Experiment-to-experiment variations (systemic bias) create batch effects, while gene-to-gene variations (sequence-dependent bias) invalidate inter-gene comparisons, precluding a universal scale. This confines analysis to relative fold-changes, a metric unreliable across batches. We introduce TranScale: 100 biomimetic standards with SI-traceable concentrations certified by Isotope Dilution Mass Spectrometry. Co-processed within samples, they empirically characterize systemic and sequence-dependent biases, generating a library-specific calibration curve (R² > 0.97) to convert reads into absolute quantities. This approach reveals that consistent fold-changes can mask severe absolute errors, exposing systemic biases missed by conventional QC. Across laboratories, this calibration reduced median inter-lab CV from >85% to <25% and increased biological signal-to-noise from ~0 to >7.9, outperforming the widely-used tool ComBat. By anchoring RNA-seq to the SI, our work establishes the metrological foundation for data interoperability and universal benchmarks, enabling absolute comparisons of SI-traceable quantities between any two genes.