RNA sequencing has become a powerful tool for measuring gene activity, but it has an important limitation. The number of sequencing reads does not always directly reflect the true amount of RNA in a sample. Researchers at Fudan University have developed a new method to make RNA sequencing measurements more accurate and comparable across experiments.

One major challenge in RNA sequencing is bias. Differences between experiments, known as batch effects, can make results difficult to compare. In addition, some genes are more easily detected than others due to their sequence, which can distort measurements. Because of this, scientists often rely on relative changes rather than absolute values, which limits how data can be interpreted across studies.

To solve this problem, the researchers introduced a system called TranScale. This approach uses a set of carefully designed RNA standards with known, precisely measured concentrations. These standards are added to samples and processed alongside them, allowing researchers to directly measure how much bias is present in each experiment.

A metrological framework for absolute and comparable RNA quantification

a Design of biomimetic transcripts of TranScale. The set of 100 transcripts was engineered to mimic human transcriptome complexity (e.g., multi-exon genes, alternative splicing, fusion events) and incorporated mirror sequences to prevent interference with endogenous gene detection. b Assignment of absolute copy numbers with SI traceability. The certified value for each transcript was determined using isotope dilution mass spectrometry (IDMS), a primary reference measurement procedure (see Methods for details), establishing a metrological chain traceable to the SI unit mole. c Experimental design and calibration workflow. Two biological sample sets were spiked-in and sequenced across 12 batches, varying by lab, library preparation protocol, and sequencing platform to generate substantial batch effects. The calibration workflow involves a quality screening of spike-in performance followed by the generation of a library-specific linear regression curve. Technical replicates are libraries (n = 3). d Principal component analysis (PCA) of uncalibrated data. Sample clustering is dominated by technical factors (e.g., lab, protocol) rather than biological identity. e PCA of calibrated data. After calibration, batch effects are removed, and samples cluster correctly according to their true biological groups. f Enabling inter-gene comparison. The framework converts relative expression units (e.g., Fragments Per Kilobase of transcript per Million mapped reads, FPKM) into absolute copy numbers, allowing for the direct quantitative comparison between different genes within a sample.

By using these standards, the team created calibration curves that convert sequencing reads into absolute RNA quantities. This revealed that even when relative fold changes appear consistent, the actual RNA levels can vary significantly due to hidden biases.

Importantly, the method improved consistency between laboratories. It reduced variability and increased the ability to detect true biological signals. This makes RNA sequencing data more reliable and easier to compare across different studies and institutions.

Overall, this work represents an important step toward standardizing RNA sequencing. By linking measurements to international units, researchers can move toward a universal system where gene expression levels can be directly compared across experiments, platforms, and laboratories.

Availability – Source code for gene expression analysis and the TranScale calibration pipeline have been deposited on GitHub, available at https://github.com/zhyu0807/TranScale/tree/main