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

Domain: biology
Published: 2026-03-26
Editorial outcome: major_revision

Abstract
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.

Introduction
RNA sequencing (RNA-seq) is a core methodology in biomedical research, pivotal for discovering biomarkers and elucidating disease mechanisms1. Its profound impact, however, is challenged by a significant challenge: sequencing reads are not a direct measure of molecular abundance but are instead surrogates for expression2. This is because two distinct categories of bias distort the measurement process, making the conversion of reads to absolute molecular counts impossible without proper calibration3. First, systemic biases for the same gene or transcript inherent in library preparation and sequencing protocols create severe batch effects that compromise reproducibility4, with inter-laboratory coefficients of variation (CVs) reported to reach 85.1%5. This inconsistency fundamentally hampers biomarker validation6 and the establishment of universal clinical decision thresholds7. Second, and more fundamentally, sequence-dependent biases tied to transcript features like GC-content and secondary structure render direct comparisons of gene expression levels between different genes scientifically invalid, even within a single sample. Together, these limitations confine the entire field to analyzing relative fold-changes—a metric that is itself unreliable across different batches.

The community has pursued two distinct strategies to address this. One approach relies on post-hoc computational tools like ComBat8, which, while valuable, correct statistical discrepancies without addressing the underlying measurement uncertainty, risking the conflation of biological heterogeneity with technical artifacts9. The other, more fundamental strategy has focused on physical reference materials. Early internal standards like ERCC10 were critical for assessing technical performance but, being non-biomimetic, fail to model the behavior of endogenous transcripts and thus cannot correct for sequence-specific biases11. Later, biomimetic standards like Sequins12 improved biological fidelity but, like ERCC, lacked SI-traceable value assignment, preventing them from serving as true absolute calibrators. Concurrently, landmark consortia like the MAQC/SEQC4 consortia and the Quartet project13 defined the state-of-the-art for reproducible relative quantification, reinforcing the boundaries of the existing analytical approach rather than advancing beyond them.

Thus, despite these sophisticated efforts, a universal solution for true absolute quantification across the transcriptome remained unmet. This requires a direct, metrologically traceable link between sequencing reads and molecular counts—a principle whose importance for quantitative biology is increasingly recognized14. The absence of this link is a critical barrier preventing RNA-seq from becoming a fully quantitative discipline, limiting its use where accuracy is paramount, such as defining universal diagnostic cutoffs15 or enabling systems-level stoichiometric modeling of cellular networks. While niche applications have demonstrated absolute quantification for specific RNA classes, such as AQRNA-seq for microRNAs16, their specialized designs underscore that a universal, transcriptome-wide solution has not yet been achieved.

Here, we present TranScale, a set of biomimetic RNA standards with SI-traceable certified values, and demonstrate its use within a comprehensive framework for both diagnostic validation and absolute calibration of RNA-seq workflows. Their efficacy stems from a distinct integration of three synergistic principles that directly address the aforementioned biases. By being co-processed with samples, they physically experience and thus correct for the systemic biases that cause batch effects. Their biomimetic design emulates endogenous transcripts, allowing them to accurately model and correct for the sequence-dependent biases that invalidate inter-gene comparisons. Finally, their absolute quantities, certified with SI-traceability via isotope dilution mass spectrometry (IDMS)17,18, provide the invariable anchor required to convert the entire measurement to an absolute scale. This establishes a clear metrological chain from the sequencing reads back to the mole19, contributing to the broader goal of integrating measurement science into biology14. We demonstrate that this framework not only substantially reduces inter-laboratory CV but also enables the absolute comparison of different genes across the transcriptome. This work thus provides a framework and a set of tools to address a long-standing issue in the field, facilitating the advancement of RNA-seq from a comparative towards a more quantitative discipline.

Results
A metrological framework for absolute and comparable RNA quantification
To address the major hurdle of conventional RNA sequencing, we developed a comprehensive metrological framework designed to achieve both data harmonization across samples and absolute comparability between genes (Fig. 1). The foundation of this framework is a set of 100 biomimetic RNA spike-in transcripts, engineered to mirror the complexity of the human transcriptome and designed to be non-interfering, named TranScale (Fig. 1a). Crucially, each transcript was assigned a certified copy number concentration with SI traceability using a primary reference method, isotope dilution mass spectrometry (IDMS), thus anchoring all subsequent measurements to a stable, absolute scale (Fig. 1b).

To rigorously test this framework, we designed a multi-laboratory study intended to generate substantial batch effects (Fig. 1c). As anticipated, principal component analysis (PCA) of the uncalibrated data from two distinct biological sample sets confirmed that technical variability effectively obscured the underlying biological differences, with samples clustering by lab and library preparation method (Fig. 1d). This result exemplifies the core challenge of data comparability in large-scale transcriptomics.

Our framework introduces a library-specific calibration strategy that leverages TranScale as SI-traceable spike-ins to address these challenges. As a proof-of-concept, we demonstrate that this calibration accomplishes the two primary goals of quantitative transcriptomics. First, it effectively removes confounding batch effects, restoring the correct clustering of samples based on their biological identity and thus enabling robust inter-sample comparability (Fig. 1e). Second, it transforms relative expression units into absolute copy numbers, permitting direct and meaningful inter-gene comparisons within any given sample (Fig. 1f). Having established the framework’s capability to address these core challenges, we proceeded with a rigorous metrological evaluation of its performance and a thorough validation of its application to endogenous human transcripts in the subsequent sections.

Design and metrological characterization of the TranScale
The foundation of our calibration strategy is a purpose-built TranScale designed for both biological relevance and metrological rigor (Fig. 2). To achieve this, we designed a library of 100 transcripts to be both biomimetic and computationally orthogonal. The biomimetic properties are detailed in Fig. 2a. Specifically, the library was designed not only with wild-type sequences but also to include clinically relevant transcript variants such as alternative splicing isoforms, single-nucleotide variants, and fusion genes, thus reflecting the complexity of biological samples. Furthermore, the 100 transcripts cover a wide length distribution (500–3800 nt) (Supplementary Table 1), and their proportional representation across different length bins was designed to be comparable to that of the human transcriptome. Similarly, the GC content of the spike-ins (40–50%) spans the range typically observed in human genes. All transcripts were confirmed to have high purity (Supplementary Table 2, Supplementary Fig. 1). Critically, we utilized inverted mirror sequences of human genes. This design ensures that the spike-ins behave similarly to endogenous transcripts during the physical steps of library preparation and sequencing, while remaining computationally distinct, thus preventing analytical ambiguity.

Next, to establish a metrological anchor for absolute quantification, we first assigned certified values with SI traceability to the individual stock solutions of each of the 100 transcripts using isotope dilution mass spectrometry (IDMS), a gold-standard reference measurement procedure (Supplementary Fig. 2, Supplementary Table 3). To ensure this traceability, the absolute concentration of each transcript was determined against primary standards from the National Institute of Metrology (NIM) (Supplementary Table 4), and the procedure demonstrated high consistency across all measurands (Supplementary Table 5). The copy number concentration of each of the 100 transcript stocks was determined by averaging 12 measurement results derived from four repeated digestions across two independent vials. This rigorous characterization process yielded high precision, with relative standard deviations (RSDs) for all transcripts ranging from 1.06% to 9.04% (all well below 10%) (Supplementary Data 1). Following the theoretical design matrix (Fig. 2b, heatmap), these certified stocks were then used to prepare two master mixes (Mix1 and Mix2) via precision gravimetry.

The final certified concentrations in these mixes were derived from the certified stock values and high-precision gravimetric data. The certified absolute concentrations for all 100 transcripts in both Mix1 and Mix2 of TranScale are presented (Fig. 2b, top and middle right plots; Supplementary Data 2). These values span the intended 10⁵-fold dynamic range, making them suitable for calibrating a wide spectrum of transcript abundances. Crucially, a comprehensive uncertainty budget was established for each transcript to define the final certified values and their associated expanded uncertainties (k=2). This evaluation systematically combined the uncertainty contributions from the initial characterization (u char, incorporating uncertainties from both IDMS and gravimetric preparation), between-vial homogeneity (u bb), and long-term stability (u lts), as detailed in Supplementary Data 3. The final expanded relative uncertainties for the absolute copy number concentrations were confirmed to be ≤16% for Mix1 and ≤18% for Mix2. The resulting uncertainty for the critical Mix1-to-Mix2 expression ratios was confirmed to be ≤23%. Furthermore, the log₂ expression ratios between the two mixes were certified to establish the definitive metrological anchor for relative quantification. While the design targeted a nominal 256-fold range (Log₂ from −4 to +4), the certified values defined an actual 378-fold dynamic range (Log₂ from −3.84 to +4.73) (Fig. 2b, bottom right plot).

To independently confirm the accuracy of the IDMS-certified values of TranScale, we performed orthogonal validation using one-step reverse transcription digital PCR (RT-dPCR) (Supplementary Table 6, Supplementary Data 4, Supplementary Fig. 3-7), and their reverse transcription efficiencies were validated (Supplementary Table 7). For a representative subset of 20 transcripts spanning the full concentration range, the copy numbers determined by RT-dPCR showed a high degree of concordance with the IDMS-assigned values (Pearson’s r > 0.99), verifying the ground-truth accuracy of our RM (Fig. 2c, Supplementary Table 8). Furthermore, TranScale successfully met the stringent ISO criteria for homogeneity and stability (Supplementary Table 9, Supplementary Data 5–8), ensuring its reliability for widespread distribution and use.

Finally, we confirmed the sequencing orthogonality of the spike-in RM. We sequenced TranScale alone using multiple library preparations (poly(A) selection, rRNA depletion) and sequencing platforms (Illumina NovaSeq, MGI DNBSEQ-T7). In all cases, reads aligned with high efficiency (>99.1%) to the TranScale reference sequences, while a negligible fraction (<0.0015%) misaligned to the human genome (hg38) (Fig. 2d). This result confirms that TranScale is analytically “clean” and can be confidently co-processed with biological samples to enable absolute quantification without introducing cross-contamination or analytical artifacts.

Metrological diagnosis of systemic and sequence-dependent measurement errors
To directly assess the accuracy of uncalibrated RNA-seq data, we established a metrological diagnostic framework based on TranScale. A direct comparison of FPKM values against their SI-traceable reference values revealed significant transcript-specific scatter within each batch, alongside unpredictable global offsets between batches (Supplementary Fig. 8). To deconstruct these errors, we developed a dual-metric system. The first metric, Absolute ME, isolates and quantifies the transcript-specific, sequence-dependent bias for each calibrator by comparing its mean-normalized FPKM to its certified SI-traceable value. The wide dispersion of Absolute ME values observed within every batch (Fig. 3a) confirm that individual transcripts are quantified with vastly different efficiencies, providing direct evidence that uncalibrated inter-gene comparisons are fundamentally unreliable.

To test the bedrock assumption of transcriptomics—the accuracy of relative quantification—we introduced a second metric, Ratio ME. This metric directly quantifies the error in the measured expression ratio between pairs of calibrators against their true SI-traceable ratio (Fig. 3b). This dual-metric diagnosis led to a crucial, paradoxical finding. The L2_DNB_P batch, despite exhibiting the most chaotic sequence-dependent biases (the largest Absolute ME dispersion), preserved relative expression ratios with high precision. Conversely, the L2_DNB_R batch, while showing high internal consistency (a narrow Absolute ME distribution), severely distorted the true quantitative ratios.

These contrasting batches provide definitive, orthogonal evidence that high relative precision can mask severe absolute measurement errors, and vice versa. This dismantles the foundational assumption that relative precision is a sufficient proxy for data quality. The magnitude of the observed Ratio ME dwarfs the certified uncertainty of TranScale (<23%), unequivocally attributing the overwhelming majority of quantification error to the RNA-seq workflow itself.

Finally, the performance hierarchy across batches identified by our dual-metric framework was independently corroborated by standard indicators like Root Mean Square Error (RMSE) and Spearman’s Correlation (RC). However, while metrics like RC assess precision, only our metrological approach measures trueness—the deviation from a known standard. This analysis also confirmed that quantification accuracy degrades for low-abundance transcripts, as seen with Mix2 (Fig. 3c, d). Collectively, these findings establish that a dual-metric, metrological evaluation is critical to fully characterize workflow performance and expose hidden vulnerabilities that conventional, relative-based QC metrics would miss.

A library-specific strategy for measurement bias correction
To correct the observed quantification errors, we implemented a library-specific calibration approach based on creating a custom calibration curve for each library. This approach first identifies a set of high-confidence internal calibrators among TranScale by filtering for spike-in transcripts with a measurement error (ME) within a predefined accuracy threshold (e.g., ±5% in log₂ scale). The number of these reliable calibrators varied with batch quality, ranging from over 75 in the highest-quality batch to fewer than 40 in lower-quality batches (Fig. 4a).

Plotting the certified input copy numbers of these selected calibrators against their measured FPKM values (in a log₂ scale) revealed a highly linear relationship (R² > 0.97) in every batch, irrespective of its overall accuracy (Fig. 4b). This stable, library-specific linear relationship forms the basis of our calibration method. It implies that within a given library preparation, the combined effects of systemic (batch-level) and sequence-dependent (transcript-level) biases manifest as a consistent linear transformation of the true quantities. Therefore, a library-specific linear model is sufficient to reverse this transformation and restore absolute accuracy.

The calibration model, derived from a subset of high-confidence calibrators, demonstrated robust generalization across the entire spike-in panel. Post-correction, the ratio of measured-to-certified copy numbers (in a log₂ scale) for all 100 spike-ins converged tightly around 1.0 across every batch, including for transcripts initially measured with high error (Fig. 4c, d, Supplementary Fig. 9). The framework’s robustness was compellingly demonstrated by its successful normalization of the two most challenging batches, each representing a distinct failure mode identified in our diagnostic phase. It corrected the L2_DNB_P batch, which harbored large absolute errors masked by apparent relative precision, as well as the L2_DNB_R batch, which was defined by severe sequence-dependent biases (high Absolute ME dispersion). The ability of a single linear model to reduce both systemic and sequence-dependent biases validates the core theoretical premise of our metrological approach.

Notably, the process also identified a consistent subset of “recalcitrant” transcripts that resisted correction across all batches (Supplementary Data 9). These transcripts were designed with features known to challenge short-read quantification, including complex alternative splicing and high sequence homology arising from repetitive elements, fusion/wild-type pairs, and single nucleotide variants (SNVs). For some transcripts, such as R17 and R42, these challenges were compounded by their low abundance, where stochastic noise amplifies quantification errors. The failure to calibrate these challenging sequences indicates a breakdown in the upstream bioinformatic workflow. This highlights the framework’s utility not only for correction but also for diagnosing the inherent limitations of standard RNA-seq data processing pipelines, a finding that is further explored in the Discussion.

To quantify the impact of this harmonization on data comparability, we calculated the coefficient of variation (CV) for absolute quantity measurements across different sources of technical variability. Across various conditions—including different protocols (Fig. 4e), platforms (Fig. 4f), and combinations thereof (Fig. 4g)—calibration consistently reduced the median CV. Even in the most challenging cross-study comparison (different labs, platforms, and protocols), the median CV for absolute copy numbers was reduced from over 85% to below 15% post-calibration (Fig. 4h).

Harmonization of absolute quantification across batches
To ensure the robustness of our calibration framework, we first established an optimized input ratio for the TranScale calibrators. Our standard protocol uses 1 ng of TranScale per μg of total RNA, which resulted in TranScale-derived reads constituting approximately 1.40% - 4.57% of the total library (Supplementary Data 10). Through an in silico downsampling analysis (Supplementary Fig. 10a), we confirmed that this input amount provides a safe margin above the minimum requirement for robust model fitting across different platforms and library preparation methods. Guided by this validated and stringent QC process (Supplementary Fig. 10b), we then applied the calibration framework to resolve inter-laboratory batch effects for endogenous genes. Before correction, absolute quantities (FPKM in a log₂ scale) of endogenous genes in the Quartet reference materials (RMD5 and D6) exhibited high cross-batch variability (Fig. 5a, b, Supplementary Fig 11a–c), particularly across labs, platforms, and protocols where median CVs exceeded 85%. Our physical calibration framework significantly improved data concordance, reducing median CVs to consistently below 25% across all comparison strata (lab, platform, and protocol) (Fig. 5a, b).

This improvement in precision was critical for resolving biological signals. As expected, principal component analysis (PCA) of uncorrected absolute data showed samples clustering entirely by experimental batch, yielding a signal-to-noise ratio (SNR) of approximately zero (Fig. 5c). We then compared our physical calibration against conventional computational and relative normalization strategies. Widely-used computational tools offered limited improvements, with SVA failing to separate the groups (SNR = 1.2, Fig. 5d) and ComBat achieving partial separation (SNR = 5.9, Fig. 5e). In contrast, our physical calibration segregated the D5 and D6 biological groups from technical artifacts, achieving a strong SNR of 7.9, with 75.4% of the total variance explained by PC1 and PC2 (Fig. 5f). This result demonstrates that prospectively correcting the measurement data is more effective than retrospectively modeling statistical artifacts.

To further characterize our calibrator’s utility, we examined two additional strategies also enabled by TranScale. A purely ratio-based normalization, which is inherently designed to maximize relative separation, achieved a higher SNR of 9.0 (Supplementary Fig. 11d). Notably, a hybrid approach—applying a ratio-based transformation to our already calibrated absolute data—yielded the highest signal separation of all methods (SNR = 11.1, Supplementary Fig. 11e). While these ratio-based transformations excel at maximizing visual separation for discovery, our primary physical calibration is distinct in providing the foundational, absolute measurement results with SI traceability that are essential for establishing universal, quantitative benchmarks.

We validated this performance hierarchy in a clinically relevant context using paired lung cancer (LCA) and normal (LCN) cell line samples. In the uncorrected data, strong batch effects partially masked the cancer phenotype (SNR = 5.6, Fig. 5g). Applying our primary absolute calibration method eliminated this noise, resolving the tumor and normal samples and increasing the SNR to 11.8 (Fig. 5h).

Validation of inter-gene comparability and absolute accuracy
To validate the absolute accuracy and inter-gene comparability for endogenous transcripts, we upgraded the Quartet RNA reference materials from a relative ratio-based standard to one with SI-traceable absolute quantities. We defined a set of conserved, cross-batch detected genes by retaining transcripts detected in all eight batches, with an FPKM > 0.1 in all three technical replicates within each qualifying batch. High-quality batches were screened based on absolute quantitative performance evaluation and inter-batch statistical consistency. This process generated a new, dual-function reference dataset containing absolute copy numbers for 13,483 genes in RMD5 and 13,862 genes in RMD6 (Fig. 6a, Supplementary Fig. 12, Supplementary Data 12). The metrological quality of this dataset was high, with over 84% of the assigned absolute values having a relative standard uncertainty (u char) below 20%.

To rigorously validate this new absolute reference dataset, we performed extensive orthogonal and external benchmarking. First, absolute copy numbers measured by our calibrated RNA-seq showed strong concordance with quantities measured by a reference method, RT-dPCR, for 46 target genes in both D5 (Fig. 6b, Pearson’s r = 0.89) and D6 (Fig. 6c, Pearson’s r = 0.89). The expression ratios (D5/D6) derived from these absolute values were also highly concordant with those from RT-dPCR (Fig. 6d, Pearson’s r = 0.88).

With this internally validated absolute dataset of endogenous genes in hand, we could test the central premise of our framework: whether the diagnostic paradigm developed using spike-ins holds true for the endogenous transcriptome. We applied the same dual-metric analysis to our dPCR-validated dataset, calculating the Absolute and Ratio Measurement Error (ME) distributions for thousands of endogenous genes across the eight batches (Fig. 6e–g). The results provided a powerful, transcriptome-wide validation of the two distinct and non-intuitive failure modes first identified using our calibrators. The L2_DNB_P batch, for instance, unequivocally confirmed the “subtle paradox.” Mirroring its behavior with the spike-ins, this workflow exhibited highly dispersed Absolute MEs for endogenous genes, indicating a chaotic internal measurement process. Critically, this severe flaw was again masked by a deceptively precise Ratio ME distribution (Fig. 6g). This provides definitive proof that the danger of high relative precision masking poor absolute accuracy is a real-world risk for endogenous gene quantification. Likewise, the L2_DNB_R batch confirmed the second, equally critical paradox. Just as with the calibrators, this workflow displayed a remarkably consistent internal process for endogenous genes, evidenced by its tightly compacted Absolute ME distribution (Fig. 6e, f). Yet, it again failed to preserve relative quantification, revealed by its dispersed Ratio ME distribution (Fig. 6g).

By demonstrating that the exact same paradoxical behaviors discovered in our controlled system are replicated at the transcriptome scale, we confirm that these vulnerabilities are not theoretical but are inherent risks in the measurement of endogenous genes. This establishes that a dual-metric system is not merely an improvement but a fundamental necessity for robust quality control in any RNA-seq experiment. As a final validation, we benchmarked our data against the original Quartet “ground truth” ratios13. The calibration significantly reduced the ME and tightened the distribution of measured ratios compared to the uncorrected data (Fig. 6g, h). This demonstrates that our absolute correction also improves the accuracy of relative quantification against an established external standard.

Having validated the framework, we applied it to reveal the true quantitative landscape of endogenous genes. We observed that conventional FPKM values present a compressed and distorted view of housekeeping gene expression. In contrast, our calibration transforms the data to absolute molecular counts, restoring the full dynamic range and revealing a well-defined quantitative structure (Fig. 6i). This fundamental restoration of the data landscape enables direct and meaningful comparisons between the absolute expression levels of different genes (Fig. 6j).

This new capability for absolute quantification provides novel biological insights by enabling the quantitative dissection of distinct biological relationships. First, we examined the link between a master regulator and its direct target. In the constructed absolute quantification dataset D5, our data reveal that the absolute transcript level of the MET proto-oncogene is about 150-fold lower than that of its key downstream adaptor, the GRB2, and that is highly consistent with the results verified by RT-dPCR (log₂ copy number ratio of GRB2/MET ≈ 7.3 vs. ≈ 7.7, Supplementary Fig 13). Our study enabled the expression profile quantification within any given sample, and also validated the absolute quantification of this pair of genes in dataset D6. Likewise, the copy number ratio of these two genes across both samples was consistent with the RT-dPCR results (Fig. 6b–d, Supplementary Fig 13). This observation moves beyond simple correlation to provide, using a generalized method, an estimate of the transcript abundance ratio within a critical therapeutic pathway. In a second example, our framework provides a quantitative link between genomic structure and transcriptional output for tumor samples. We observed that the absolute transcript levels of ERBB2 and GRB7, two genes known to be co-amplified on chromosome 17q12 in tumor tissue, such as breast cancer20. We verified that the basal expression level of GRB7 is low in normal sample (11.78 log₂ copies per μg of total RNA for D5), but is elevated by over 400-fold (20.42 log₂ copies per μg of total RNA) in the tumor sample LCA (Supplementary Fig. 13a). Furthermore, the expression levels of GRB7 and ERBB2 are approximately equal, as an excellent concordance (20.1 vs. 20.4 log₂ copies per μg of total RNA) between TranScale-calibrated RNA-seq and dPCR absolute measurements (Supplementary Fig. 13a), this co-overexpression provides biological insight into the enhancement of oncogenic signaling, and quantitative evidence that a genomic co-amplification event is translated into a near 1:1 ratio of transcript output—a precise mapping from genome to transcriptome that was previously difficult to ascertain with confidence (Fig. 6i). Collectively, these results, validated by both an orthogonal method and an external reference standard, demonstrate that our application framework successfully mitigates sequence-dependent biases for endogenous transcripts. This enables robust, accurate, and direct comparisons of absolute expression levels between different genes, resolving a fundamental limitation in transcriptomics.

Demonstration of robust diagnostic classification
Finally, to illustrate the potential of TranScale in identifying biological insights and supporting clinical decision-making, we conducted a diagnostic simulation targeting the ERBB2 (HER2) oncogene across 12 independent sequencing libraries. We established a “ground truth” diagnostic cutoff (18.5 of log₂ copies/µg total RNA) based on reference dPCR data, which clearly distinguished the tumor model (LCA) from normal controls (Quartet D5/D6). We observed that technical batch effects in uncalibrated relative quantification (FPKM) frequently masked the overexpression phenotype of the tumor samples, leading to inconsistent classifications. In contrast, TranScale calibration effectively neutralized these variations, recovering the true absolute abundance of ERBB2 and ensuring 100% concordance with the dPCR-defined diagnostic classification across all platforms and laboratories (Supplementary Fig. 14). These results highlight the capability of SI-anchored absolute quantification to reveal biological signals that may be obscured by technical noise in multi-center studies.

Methods
Design and generation of TranScale RNA transcripts
The TranScale panel was meticulously developed through a three-stage process to create a high-fidelity biomimetic standard for RNA sequencing.

Biomimetic design and sequence selection. Our design process targeted a panel of approximately 100 transcripts that collectively mirror the human transcriptome. The selection criteria were multifaceted:
(i) Biomimetic sequence strategy: To preserve structural characteristics while eliminating sequence homology, each transcript was designed by inverting a selected human coding sequence (CDS). This strategy maintains native sequence complexity, such as a GC content similar to the hg38 genome, while ensuring that TranScale reads do not misalign during bioinformatic analysis.
(ii) Functional diversity: The selected sequences represented a range of biological features, including wild-type genes, clinically relevant fusion genes, and alternative splicing isoforms.
(iii) Physicochemical range: The initial design aimed for a broad length distribution from ~500 nt to over 4000 nt to cover the majority of the human mRNA length spectrum.

Production, optimization, and quality control. This stage focused on translating the computational design into physical RNA molecules of the highest metrological quality.
(i) Template generation: The full DNA sequence for each calibrator—comprising a 5’ BamHI site, a T7 promoter, the inverted CDS, a 3’ 28bp poly(A) tail, a T7 terminator, and a final EcoRI site—was synthesized and cloned into a pMV vector (BGI Genomics). Following sequence verification (Supplementary Table 1), recombinant vectors were transformed into E. coli DH5α. Template DNA for in vitro transcription (IVT) was obtained by BamHI/EcoRI digestion and purified with the Universal DNA Purification Kit (Tiangen).
(ii) In vitro transcription and pragmatic optimization: IVT was performed using the T7 High Yield RNA Synthesis Kit (Yeasen). A 20 µL reaction containing 1 µg of template DNA, 10 mM of each NTP, 1× reaction buffer, and 2 µL T7 RNA Polymerase Mix was incubated at 37 °C for 4 hours, followed by DNase I treatment. At this critical stage, we observed that some of the longer target transcripts (>3800 nt) yielded impure products due to premature termination. Prioritizing analytical validity, we systematically shortened these sequences to a maximal length that permitted consistent, high-purity synthesis.
(iii) Purification and verification: The resulting full-length RNA was purified using the MEGAclear Kit (Thermo Fisher), and its concentration was estimated with a Nanodrop. The integrity and purity of each of the 100 final transcripts were rigorously verified as a single, sharp peak on an Agilent 2100 Bioanalyzer (RNA 6000 Nano kit).

Final panel composition and sufficiency. The final TranScale panel consists of 100 well-defined, high-purity RNA transcripts. Despite the necessary length adjustments for quality control, the panel spans a significant range of lengths (500–3800 nt). Importantly, the utility of the panel is not contingent on the detection of all 100 calibrators. Our operational analyses show that robust calibration models (R² > 0.95) can be reliably constructed using as few as 20-30 well-distributed calibrators, confirming the sufficiency of the final panel for its intended application (Supplementary Fig. 10).

Isotope Dilution Mass Spectrometry (IDMS) for TranScale
The absolute concentration (copy number) of each of the 100 purified RNA transcripts of TranScale RM was certified using Isotope Dilution Mass Spectrometry (IDMS), a primary reference measurement procedure recognized by the Joint Committee for Traceability in Laboratory Medicine (JCTLM). The certification workflow was as follows:

NMP standards preparation. As standards, the certified RMs of adenosine 5′-monophosphate (AMP), guanosine 5′-monophosphate disodium salt (GMP), cytidine 5′-monophosphate (CMP), and uridine 5′-monophosphate disodium salt (UMP) were obtained from the National Institute of Metrology, China (NIM) (NMPs). The corresponding isotope-labeled (13C, 15N) nucleotide monophosphates (LNMPs) (Silantes, Germany) were used as internal controls.

Quantification of NMPs standards. For quantification of transcripts, NMPs in the digested samples were separated completely using an SB-AQ C18 column (Agilent, USA), 0.1% formic acid (v/v) was used as mobile phase in a flow rate of 0.2 mL/min maintained at 30 °C. Signals of the well-separated NMPs were detected by SCIEX QTRAP® 6500+ LC-MS/MS in positive ion and multiple reaction monitoring (MRM) mode. Other instrumental and mass spectrometer data acquisition parameters, including Q1, Q3, DP, EP, CE and CXP, were optimized for the NMPs and LNMPs detection (Supplementary Table 3). A known amount of a corresponding stable isotope-labeled internal standard (LNMPs) for each ribonucleoside (NMPs). Mixtures with mass ratios of NMPs to LNMPs of 0.4, 0.8, 1.2, 1.6, and 2.0 were performed, respectively. NMP concentrations (μg/g) in transcripts were determined according to the standard curves of mass ratio and IDMS peak area ratio of NMPs to LNMPs (Supplementary Fig. 2). The raw mass spectrometry data were processed for absolute quantification using Analyst software (version 1.6, AB Sciex).

Hydrolysis of RNA transcripts. An aliquot of the purified RNA transcript was subjected to complete enzymatic hydrolysis to break it down into its constituent ribonucleosides. According to the protocol of our previous studies, briefly, each of the generated transcripts was diluted to a concentration of 1 ng/μL, and then 50 μL RNA sample was mixed with Phosphodiesterase I from Crotalus adamanteus venom (SVP, Sigma-Aldrich, USA) at the final concentration of 0.002 U/μL, 5 μL of LNMPs mixture was added. All reactions were formulated by the gravimetric method. After 25 min of incubation at 25 °C, the reaction was terminated by incubation at 80 °C for 15 min48.

Quantification of target transcripts. The molar concentration of each of the four NMPs was determined with high precision and accuracy by measuring the abundance ratio of the natural analyte to its corresponding LNMPs. According to the quantification method of NMPs, each RNA sample was measured twice independently, and each digestion was analyzed in triplicate. All the above measurements were independently conducted by two operators. The final mass fraction of each NMP in the digested RNA sample was expressed as Eq. (1):

W RNA = W N × M RNA / M NMP × R N (1)
where W RNA is the mass fraction of the RNA sample in micrograms per gram, W N is the mass fraction of the selected NMP in the RNA sample, M RNA is the molecular mass of the RNA molecule, M NMP is the molecular mass of the selected NMP, and N is the number of the selected NMP in the RNA sample. The copy number of RNA (n, in copies per microgram) was expressed as Eq. (2) according to the Avogadro constant and RNA molecular weight48. Where NA is Avogadro’s constant. A density of 1.00 g/mL was taken into account in the calculation. The copy numbers (copies/μL) of transcripts were calculated.

n = W RNA × NA / M RNA × 10^9 (2)

Consistency check of IDMS. Concentration of a specific RNA transcript was calculated independently from the concentration of each of the four nucleosides, based on the known sequence of that transcript (e.g., [RNA Transcript] = [Adenosine] / number of ‘A’s in sequence). The final certified value for each transcript was assigned as the average of the four independent calculations, provided they showed high consistency (e.g., relative standard deviation <1%). This cross-validation ensures the certified value is robust against potential biases in hydrolysis or measurement for any single nucleoside.

Characterization, Homogeneity and Stability Assessment of RMs
Preparation of TranScale RNA RM
Based on the certified copy numbers of individual transcript stocks determined by IDMS, the two master mixes, named Mix1 and Mix2, each containing all the components of 100 transcripts, were prepared via precision gravimetry. The two mixtures were presented at defined copy number ratios, described by several sub-mixtures, and each of them contains at least 10 spike-ins with the balanced transcript size distribution and GC content. The two mixtures were prepared by diluting all 100 in-vitro transcribed RNAs with RNA storage solution gravimetrically (Thermo Fisher, USA). During the mixing process, the mass of each component was recorded using a balance for accurate preparation. Mix1 and Mix2 were formulated, spanning approximately 10^5- and 10^6-fold concentration range of transcripts, respectively.

RT-dPCR assays. To verify the concentrations of transcripts of each gradient of TranScale and the ratio between Mix1 and Mix2, the consortium established one-step reverse transcription digital PCR (RT-dPCR) assays on a Sniper dPCR platform (DQ24, Sniper, China). The 22 μL reaction mixture included 11 μL of 2× dPCR probe master mix, 5 μL of transcripts, primers, and probe with the optimized concentrations, and RNase-free water. The RNA template was gravimetrically diluted before it was added to the reaction. Specifically, RT-dPCR assays for two transcripts were established in each concentration gradient of the consortium (20 assays in total), all primers and probes were listed in Supplementary Data 4.

The RT-dPCR thermocycling conditions were as follows: 45 °C for 15 min of reverse transcription, 95 °C for 5 min of enzyme activation, 40 cycles of 95 °C for 15s of denaturation and 30s of annealing/extension at the optimized temperature, following 60 °C for 1 min of fluorescence signal acquisition. For each optimized RT-dPCR assay, annealing temperatures of 54, 56, 58, and 60 °C were tested, and following the performance, 50 °C was used for all RT-dPCR assays. The RT-dPCR method was validated with a mixture of 99 transcripts that did not contain the target template.

Specificity of RT-dPCR assays. When performing gene-specific PCR amplification using a multiplexed system containing 100 reference genes as template, rigorous confirmation of PCR specificity must be achieved. To ensure specificity of RT-dPCR assays, we used a mixture containing 99 transcripts except the target spike-in as the PCR template; the observed negative droplets that was same as the no template control will explain no cross-reaction with the RT-dPCR assays.

Dynamic range of RT-dPCR assays. The dynamic ranges of RT-dPCR assays were investigated by a serial dilution. Each of the 20 RNA transcripts was diluted in gradients according to the gravimetric method and subsequently subjected to RT-dPCR amplification reactions, with 3-6 replicates per gradient. A good linearized correlation was determined for the RT-dPCR assays in the concentration range of the RNA transcripts.

Reverse transcription efficiency. The in vitro transcribed RNA mass fraction obtained by IDMS was converted into copy number concentration, which was used as the absolute copy number concentration of the RNA molecule. The copy number concentration of the cDNA molecule was reversed as determined by the established RT-dPCR. The efficiency of reverse transcription of the RNA template into cDNA was determined and expressed as the ratio of the RNA copy number concentration of RT-dPCR and IDMS.

Assessment of homogeneity. With reference to JJF 1343-202249 (equivalent to ISO 33405), homogeneity testing was performed through random sampling of sub-packaged samples by RT-dPCR. Eleven vials of TranScale RMs Mix1 and Mix2 were randomly selected, and the established RT-dPCR assays were performed. Each vial was measured with three replicates. F test was used to assess the homogeneity. M between and M within were calculated according to Eqs. (3) and (4). If F < F ɑ, it indicates that the RM is well homogenized. Where a is the total number of RM units. For the ith unit, n replicates measurements are performed, x ij represents the jth observed value of unit i. M between and M within are between-unit and within-unit mean squares.

M between = P a i=1 n i (x̄ i − x̄) 2, Freedom v = a − 1 (3)
M within = P a i=1 P n i j=1 (x ij − x̄ i) 2, Freedom v = P a i=1 n i − a (4)

Assessment of stability. Stability of TranScale RM was evaluated by 20 established RT-dPCR assays. For short-term stability, the RMs were stored at 4 °C and 20 °C for 0, 3, 7 and 14 days. Students’ t-test was performed on the assay results to assess the short-term stability. For long-term stability, the RMs were placed at −80 °C and measured at 1, 2, 3, 6, and 12 months. The long-term stability was evaluated according to Eq. (5) and (6). A linear regression model is employed, where β₁ is the slope coefficient, xᵢ is the observation time, yᵢ corresponds to the observed value at time xᵢ, x̄ signifies the mean value of all observation time points, s is the standard deviation of the residuals at each data point along the fitted regression line.

|β 1| ≤ t 0.95, n−2 × s(β 1) (5)
β 1 = P n i=1 (x i − x̄) (y i − ȳ) / P n i=1 (x i − x̄) 2, s(β 1) = s / qffi P n i=1 (x i − x̄) 2 (6)

Certified values and measurement uncertainty. IDMS was used for value assignment of all 100 mother solutions of RNA transcripts; the certified value for each transcript is established by taking the arithmetic mean of 12 independent measurements. These values are derived through gravimetric determination, where absolute quantities in the RM are calculated based on precisely weighed masses of high-purity RNA transcripts.

The uncertainty of our TranScale RM (U c-Mix1 and U c-Mix1) can be classified into characterization uncertainties (u char, combining uncertainties from both IDMS and gravimetric dilution), sample inhomogeneities (u hom) and instabilities (u s), and then combined to form the combined uncertainties (u c) by using Eq. (7). The expanded uncertainties (U Mix1 and U Mix1) are calculated from the u C multiplied by the coverage factor (k=2) at a confidence level of 95%. Mix1/Mix2 represented the ratio of copy number concentration of reference gene in Mix1 and Mix2, and its uncertainty is the arithmetic square root of the sum of the squared uncertainties of Mix1 and Mix2, as Eq. (8).

u C = qffi u 2 char + u 2 hom + u 2 lts (7)
u Mix1=Mix2 = qffi u 2 Mix1 + u 2 Mix2 (8)

Sequencing orthogonality validation for RNA-seq. Considering the potential for spike-in reads to cross-align to the reference human genome, we initially sequenced a neat mixture of 100 spike-ins of the RM without a natural RNA sample, at equimolar concentrations to ensure sufficient coverage for validation of spike-in design with experimental reads. For the neat RM sample, four batches of RNA-seq were generated in two laboratories (L1 and L2) using PolyA and Ribo-Zero library construction protocols, and two platforms Illumina NovaSeq 6000 and DNBSEQ-T7 with 150 bp paired-end reads. 1 µg of RNA was used in each library preparation. The reference genome and gene model annotation files were prepared, the FASTA file of genome index was combined with hg38 (https://ftp.ensembl.org/pub/release-94/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.toplevel.fa.gz) and all the 100 artificial DNA template sequences of RNA spike-ins, as well as the combined GTF file comprising both of the annotated human genes (http://ftp.ensembl.org/pub/release-94/gtf/homo_sapiens/Homo_sapiens.GRCh38.93.gtf.gz) and the artificial genes. The clean reads were aligned to a combined genome index and hg38, respectively. Hisat2 v2.0.5 was used as the mapping tool, and the respective mapping rate was calculated.

TranScale as spike-ins for RNA-seq
Strand-specific library. RNA integrity was assessed using the Bioanalyzer 2100 system, RNA library for strand-specific RNA-seq was prepared. Chinese Quartet RMs D5 and D6 (from NCRM39) were used as target samples in this study. We precisely spiked 1 ng of the RM into 1 μg total RNA sample; this was executed gravimetrically. We used TranScale Mix1 mixed to D5 and Mix2 mixed to D6. mRNA was purified from total RNA using polyT capture or rRNA deletion and then fragmented into 300~350 bp fragments. The first strand cDNA was reverse-transcribed using fragmented RNA and dNTPs (dATP, dTTP, dCTP and dGTP), and the second strand cDNA was synthesized using DNA polymerase I and dNTPs (dATP, dUTP, dCTP and dGTP). The remaining steps of library construction were performed according to the standard protocol. After conversion of the 3’ ends of DNA fragments, sequencing adaptors were ligated to the cDNA, and the library fragments were purified. The template without U was enriched by PCR, and the PCR product was purified to obtain the final library with a concentration of no less than 1.5 nM. In this cross-batch transcriptomic sequencing study, PCR cycle numbers were first optimized to ensure linear-range amplification. Ultimately, 13 cycles were selected for the subsequent eight experimental batches (Supplementary Fig. 16). After library construction, the concentration of the library was measured by the qubit fluorometer. The accurate concentration of the cDNA library was again examined using qPCR.

Data quality control. Raw reads of FASTQ format were first processed by the fastp software. In this step, clean data were obtained by removing reads with adapters, reads with more than 3 N, and reads with more than 20% nucleotides with Qphred≤5. At the same time, Q20, Q30, and GC content of the clean data were calculated. Then, we map the clean reads to the SILVA database to remove the rRNA. All the downstream analyses were based on the clean data without rRNA.

Reads mapping to the reference genome. The same reference genome and gene model annotation files were used with the neat TranScale RM. An index of the reference genome that contains TranScale reference sequences and the human reference genome GRCh38 was built using Hisat2 v2.0.5, and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. For the mixed sample of spike-ins and target D5 or D6 samples, the combined genome index was used for read mapping. Counts obtained for different dosages of total input spike-ins through simulated data comparison.

Quantification of gene expression. FeatureCounts v1.5.0-p3 was used to count the number of reads mapped to each gene. And then FPKM (number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene was calculated based on the length of the gene and reads count mapped to this gene.

Performance metrics for absolute quantification of RNA-seq
ME. ME introduced in this study as a TranScale RM-based metric, serves as the most intuitive indicator for assessing RNA-seq absolute quantification performance. It directly quantifies deviations between observed values and certified reference values. ME of all three replicates of 100 transcripts among TranScale in each sample were respectively calculated according to Eq. (9), which represents the percentage difference between FPKM with mean normalization (Normalized FPKM) and its reference copy numbers (Certified). All data is in a log₂ scale. Additionally, a Ratio ME was calculated from the ratio of the measurements for each transcript pair in Mix1 and Mix2 against the ratio of their certified values. This metric is designed to be insensitive to global systematic shifts, thus isolating sequence-dependent biases.

ME = (Normalized FPKM − Certified) / Certified × 100% (9)

RMSE. RMSE50 was calculated using observed values of spike-ins and the corresponding certified values, representing the distances of absolute expression profiles. All three replicates of 100 transcripts among TranScale in each sample were calculated after mean centering analysis of FPKM and certified copy numbers. All data was transformed using log₂ scaling. The calculation first determined squared residuals for all 100 spike-ins, averaged these squared differences, then took the square root to express error in original concentration units as Eq. (10), where n is the number of measured genes, y i is the i-th observed value, and x i is the i-th reference value.

RMSE = qffi 1/n P n i=1 (y i − x i) 2 (10)

RC. RC51 was calculated based on the Spearman correlation coefficient between the observed absolute expressions of observed spike-in values and the corresponding certified values of Mix1 and Mix2 of the RMs. It is referred to as the ‘correlation with reference values’ metric, representing the numerical consistency of the absolute expression profiles. To improve reliability, all three replicates of each sample group were calculated after mean centering analysis of FPKM and certified copy numbers. All data was transformed using log₂ scaling.

SNR13. The absolute expressions-based SNR is defined as the average distance between the two sample groups of D5 and D6 (or LCA and LCN) from the average distance among technical replicates of the same groups. Based on PCA, distances of two samples in the space defined by the first two PCs were used to represent distances between the two samples. SNR was calculated as Eq. (11) described previously. Where m is the number of donors, and n is the number of replicates in each donor. W p represent the p-th principal component of variances. PCp,i,x, PCp,j,x and PCp,j,y represent the p-th component values of replicate i and replicate j in donor x or donor y, respectively.

SNR = 10 × log 10 [ (m × n) / (m × n) × P m x=1 P m y=1 P n i=1 P n j=1 P 2 p=1 (PC p,i,x − PC p,j,x) 2 / P m x=1 P n i=1 P n j=1 P 2 p=1 (PC p,i,x − PC p,j,x) 2 ] (11)

Justification for the QC-gated calibration parameters
The parameters for our QC-gated calibration framework were selected to strike a critical balance between stringency (ensuring only high-fidelity data informs the model) and robustness (retaining a sufficient number of calibrators to build a statistically sound model).

Measurement Error (ME) threshold (±5%). The choice of a ±5% ME threshold is central to the framework’s function. A more lenient threshold (e.g., ±10% or ±20%) would risk including calibrators that exhibit significant non-linear behavior or are subject to strong sequence-specific biases. This would “pollute” the linear regression, leading to an inaccurate slope and intercept that do not reflect the true systemic bias of the workflow. Conversely, an overly stringent threshold (e.g., ±1% or ±2%) would be overly punitive, potentially discarding a large number of valid calibrators due to inherent stochastic noise in the sequencing process. This could lead to an insufficient number of data points for a robust regression, or even the false rejection of an otherwise acceptable library. The ±5% ME threshold was therefore established as an optimal setpoint that effectively isolates the dominant linear response trend of the workflow while tolerating a reasonable degree of measurement noise.

FPKM threshold (>0.1). This threshold was empirically determined based on the performance of the lowest-abundance spike-in calibrators. We observed that transcripts with FPKM values below this level exhibited poor reproducibility across technical replicates and, critically, their corrected values after calibration showed significant deviation from their certified SI-traceable values. Therefore, the FPKM > 0.1 threshold was established to exclude these unreliable data points, ensuring that the calibration model is constructed only from robust and accurately quantified calibrators.

Dynamic Range threshold (>2¹⁰-fold). This criterion ensures that the calibration model is not built on a narrow concentration range. A wide dynamic range is essential for the model to have sufficient leverage to accurately map the clean reads to the SILVA database to remove the rRNA. All the downstream analyses were based on the clean data without rRNA.

Coefficient of determination (R²) threshold (>0.95). An R² > 0.95 is a widely accepted standard for demonstrating a strong linear relationship. Setting this as the final gate serves as the ultimate confirmation that the foundational assumption of linearity holds true for the specific library being processed, providing high confidence in the subsequent calibration.

Minimum number of calibrators for robust model fitting. A critical, implicit QC gate in our framework is the number of calibrators that successfully pass the ME, FPKM, and dynamic range criteria. While a linear model can be generated from a few points, its reliability and predictive power for the entire transcriptome depend on a statistically robust foundation. Based on our multi-batch analysis, even lower-quality libraries that were ultimately salvageable for calibration consistently yielded a substantial number of calibrators (typically >30, as shown in Fig. 4a). Therefore, we establish a practical guideline: a library should yield a minimum of 20–30 high-confidence calibrators to proceed with high-confidence absolute quantification.

This recommendation is based on the following rationale: (1) Statistical Stability: A sufficient number of calibrators ensures that the linear model is not overly sensitive to individual outliers, providing a stable and reliable calibration for the entire library. (2) Diagnostic Power: The number of calibrators serves as a powerful meta-indicator of overall library quality. A failure to meet this minimum threshold strongly suggests underlying issues (e.g., significant non-linear biases, poor library preparation) that make the data unsuitable for absolute quantification, even if a model with a high R² can be formally generated. Libraries falling below this guideline should be flagged, and their uses should be limited to relative analyses.

Construction and metrological characterization of endogenous gene referencedatasets
To establish robust referencedatasets for endogenous gene expression (designated D5 and D6), we processed data from eight independent measurement batches through a stringent filtering and characterization workflow. Beginning with the complete Ensembl annotation (n=58,735), genes were retained only if detected in at least six of eight batches, present in all three technical replicates per batch, and exhibited an FPKM > 0.1 across all replicates. For this filtered set, FPKM values were converted to absolute copy numbers using the library-specific linear models derived from the co-processed TranScale calibrators. The final certified reference value for each gene was assigned as the arithmetic mean of its corrected absolute copy numbers from all qualifying batches. For the purpose of this study, the measurement uncertainty was evaluated based on the dominant experimental component. This characterization uncertainty (u_char), a Type A evaluation, was calculated as the relative standard deviation (RSD) of the final calibrated copy numbers for each gene across all contributing measurement batches. This approach is based on the principle that for a complex, multi-stage workflow like RNA-seq, the experimentally observed reproducibility (u_char) is the largest and most practically relevant contributor to the combined uncertainty, significantly outweighing the Type B uncertainties propagated from the calibration standards. Therefore, u_char provides a direct and robust measure of the quality and consistency of the final reference values. Accordingly, the final reference gene sets were filtered based on this primary uncertainty metric, retaining only those with a relative u_char of ≤20%. For the D5/D6 ratio dataset, values and their combined uncertainties were calculated by propagating the respective u_char values using the standard formula for division according to Eq. (12).

u D5=D6 = qffi u 2 D5 + u 2 D6 (12)

Validation of referencedatasets using RT-dPCR
The absolute gene expressions in the referencedatasets were further validated by dPCR; primers of target genes in D5 and D6 were listed in Supplementary Data 12. First, initial cDNA synthesis was performed by incubating 2 μL RNA with 4 μL of 5× PrimeScript IV cDNA Synthesis Mix (Takara #6215A) containing PrimeScript IV RTase, RNase Inhibitor, OligodT Primer, and dNTPs supplemented with 1 μL random hexamers. Nuclease-free water was added to achieve a 20-μL reaction volume. This reaction mixture was incubated at 30 °C for 10 min and then for 15 min at 42 °C and finally for 5 min at 95 °C for termination. Second, dPCR reactions were employed by the Bio-Rad QX200 Droplet Digital PCR System with 20-μL mixtures containing: 10 μL EvaGreen Supermix, 2 μL primer pair, 2 μL cDNA template, and 6 μL RNase-free ddH₂O. Following droplet generation with 70 μL oil, 40-μL droplets were transferred to a 96-well plate. Amplification conditions comprised: 5 min at 95 °C; 40 cycles of 95 °C for 30s and 60 °C for 1 min; followed by signal stabilization (4 °C for 5 min and 90 °C for 5 min). Signals were acquired using the Droplet Reader, with all reactions performed in duplicate. Absolute quantities were verified by comparing measured absolute copy numbers of selected genes in D5 and D6 against absolute copy numbers in the reference dataset, and also assessing D5/D6 copy number ratios versus established reference ratios for Ratio consistency.

Diagnosticrobustness simulation
To simulate a clinical diagnostic scenario, we utilized ERBB2 expression data from 12 sequencing libraries. A diagnostic cutoff was empirically defined as the midpoint separating the dPCR-quantified absolute copy numbers of the tumor model (LCA) and normal controls (Quartet D5 and D6). For the uncalibrated analysis, raw FPKM values were log₂ transformed. For the calibrated analysis, TranScale-derived absolute copy numbers were used. We calculated the consistency of diagnostic classification (Tumor vs. Normal) across all batches relative to the dPCR-defined ground truth.

Statistics & Reproducibility
Study design and Sample size. No statistical method was used to predetermine sample size. The sample sizes were 12 technical replicates for initial measurement of each transcript of TranScale, and 3 independent technical replicates for values validation to ensure sufficient precision for measuring absolute copy number concentrations and to rigorously evaluate batch-effect correction as per metrological standards. We sequenced 3 replicates of each of the four RNA samples (D5, D6, LCA, and LCN) using 2 commercially available short-read sequencing protocols: PolyA and RiboZero, generating 12 data batches. Each batch included 6 libraries, resulting in a total of 72 libraries. These sample sizes are sufficient to provide within-batch technical replication, cross-protocol comparisons, and cross-batch/laboratory reproducibility assessment. Details are illustrated explicitly in Fig. 1.

Data exclusion. All data from planned experiments have been included. All attempts at replication were successful.

Randomization. The experiments were not randomized, as the study did not involve group comparisons requiring random allocation. Samples were allocated by a pre-specified, balanced design. Each batch contained 6 libraries; there were 3 technical replicates for 2 paired samples (samples D5 and D6, or samples LCA and LCN).

Blinding. The investigators were not blinded to allocation during experiments and outcome assessment, as the study relied on objective bioinformatic pipelines and predefined calibration standards.

Data analysis. All attempts at replication were successful. Data processing and statistical analyses were performed using R (version 4.5.0) and associated packages, including ggplot2 (version 3.5.2) for visualization. Graphs were generated using GraphPad Prism (version 10.1.2), Origin (version 2021), and Adobe Illustrator (version 2025). Specific statistical tests used for homogeneity and stability assessments are detailed in the relevant results and methods sections above.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study introduces TranScale, a set of spike-ins with biomimetic design and SI-traceable concentrations for RNA-seq. The authors demonstrated that TranScale can be used to remove confounding batch effects and convert data into absolute quantities for inter-gene comparisons. Notably, they claimed that calibration with TranScale shows higher performance than widely-used tool ComBat on measurement correction and enables absolute comparisons of SI-traceable quantities between any two genes for the first time. While these advancements are promising, several critical concerns warrant consideration.\n(1) Although TranScale incorporates SI-traceable standards, the biological insights derived from its application remain limited. This raises questions about the method’s broader impact, and whether similar results could be achieved using existing spike-in approaches.\n(2) The benchmarking against existing approaches is insufficient. A more comprehensive side-by-side comparison is necessary to conclusively establish the advantages or potential trade-offs of TranScale in terms of overall performance compared to the published spike-in methods.\n(3) UMIs (unique molecular identifiers) have been widely used to count absolute numbers of molecules in next-generation sequencing methods [PMID: 22101854]. The authors should discuss the advantages and limitations of TranScale compared to UMI-based approaches.\n(4) The authors spiked 1 ng of the reference material (RM) into 1 μg total RNA sample. However, the percentage of reads assigned to RM in libraries co-processed with TranScale remains unclear. Furthermore, it is uncertain whether this RM input ratio represents the optimal configuration after thorough optimization.\n(5) PCR amplification bias can compromise the accuracy, particularly with varying cycle numbers. It is crucial to define the range of PCR cycles that maintain a linear relationship during library preparation, especially when TranScale is applied to samples with different starting material quantities.\n(6) The selection process for the 100 biomimetic RNA spike-in transcripts used in this study lacks transparency. It is unclear how many transcripts were initially tested or what criteria guided their final selection.", "reviewer_2": "The manuscript “A metrological foundation for absolute transcriptomics using SI-anchored calibrators” introduces TranScale, a panel of ~100 synthetic biomimetic RNA spike-ins with assigned SI-traceable concentrations via isotope dilution mass spectrometry (IDMS). These controls emulate key features of human transcripts (multi-exon genes, alternative splicing, fusions) and enable absolute quantification of accompanying RNA-seq libraries.\nTo demonstrate the approach, the authors conduct a multi-laboratory, multi-platform, multi-protocol sequencing study (12 batches across two sites using Illumina and DNBSEQ) and show that TranScale reduces inter-batch variability. Orthogonal validation using droplet digital PCR (ddPCR) shows good concordance.\nOverall, the methodology is detailed, and the experimental execution, including IDMS workflows, spike-in design, and calibration procedures, is technically rigorous. The manuscript is generally well prepared; however, its lengthy, descriptive and overstated text and large multi-panel figures can sometime obscure a clear and concise understanding of the technology.\nMajor Points\n1. Conceptual novelty relative to prior controls. The overall framework using synthetic RNA spike-ins to assess and correct sequencing and quantification biases remains similar to prior approaches (ERCC, Sequins, MAQC/SEQC controls). While the use of IDMS to generate SI-traceable absolute concentrations is a notable advance, its practical significance is difficult to assess without clear demonstration that SI-anchored calibration enables new analytical capabilities or biological insights.\n2. The manuscript would be strengthened by practical examples showing how IDMS-based calibration improves performance relative to existing RNA controls in applied or clinical contexts. For example, to substantiate the benefits of absolute scaling, analyses such as differential expression with and without calibration, biomarker detection, clinical diagnostic cases where absolute scaling reveals insights not achievable with standard normalization would be valuable. The authors highlight examples such as the MET/GRB2 ratio and ERBB2 amplification, however these remain unvalidated and constitute observational hypotheses. The advantages of TranScale are not demonstrated for any specific diagnostic application or biological analysis, and it is difficult to .\n3. The benchmarking focuses on computational batch-correction methods (SVA, ComBat), and a fairer, more informative comparison would include other biomimetic RNA controls (ERCC, Sequins) to compare corrected versus uncorrected workflows.\n4. The IDMS workflows and statistical analyses are complex, requiring specialized equipment and expertise, and use statistical approaches that are complex, which would be difficult for adoption. To support broader adoption, a simplified description of the statistics and analytical workflow would be helpful to support adoption.\nMinor Comments\n5. The manuscript text can often read overstated based on the data and advance. For examples statements such as “we establish this missing metrological foundation,” “provides the essential tools to resolve the fundamental limitations,” “transitioning RNA-seq from a comparative to a truly quantitative discipline” These statements do not advance the study, and can obscure an understanding of the methods significance.\n6. The text can often read with lengthy repeated of methodological descriptions. For example, concepts such as biomimetic RNA intron-exon structure are described multiple times, and could be consolidated improve clarity and flow.\n7. The figures include many panels ( 8–20 panels) that can result in crowded layouts with font sizes that are difficult to read and interpret. Numerous related panels (such as the many PCA plots) could be consolidated or moved to Supplementary Figures.\nWhile the study is technically rigorous and well executed, the practical benefits of assigning SI-traceable concentrations to RNA controls via IDMS are not demonstrated through applications or biological examples. Lengthy and overstated text, dense statistical analyses, and complex figure panels do not help to clarify the significance of the method. Accordingly, in its current form, I cannot endorse the manuscript for publication in Nature Communications."}, "author_response": "Point-by-point response to the reviewers’ comments\nReviewer #1 (Remarks to the Author):\nThis study introduces TranScale, a set of spike-ins with biomimetic design and\nSI-traceable concentrations for RNA-seq. The authors demonstrated that\nTranScale can be used to remove confounding batch effects and convert data\ninto absolute quantities for inter-gene comparisons. Notably, they claimed that\ncalibration with TranScale shows higher performance than widely-used tool\nComBat on measurement correction and enables absolute comparisons of SI-\ntraceable quantities between any two genes for the first time. While these\nadvancements are promising, several critical concerns warrant consideration.\n(1) Although TranScale incorporates SI-traceable standards, the\nbiological insights derived from its application remain limited. This raises\nquestions about the method’s broader impact, and whether similar results\ncould be achieved using existing spike-in approaches.\nResponse: We thank the reviewer for this crucial point. We agree that the\nbiological insights presented in the original manuscript were primarily\nobservational and required further validation to demonstrate the broader impact\nof TranScale. We performed further analysis and elaboration as follows.\n1. Orthogonal Validation of Biological Insights:\nTo address this, we have performed droplet digital PCR (dPCR) experiments\nto independently and absolutely quantify the transcripts of key genes (MET,\nGRB2, ERBB2) in our Quartet samples. It should be clarified at the outset that\nin the original manuscript, we inferred biological insights for these four genes\n(MET, GRB2, ERBB2, GRB7) by estimating their absolute quantities using\nTransCale calibration, with data derived from a single sequencing library but\nnot reference dataset of the Quartet D5 sample, which led to our erroneous\nhypothesis regarding the co-amplification of the ERBB2 and GRB7 genes in\nnormal samples. In the revised manuscript, to further strengthen the scientific\nvalidity of these inferences, we evaluated and corrected the reference datasets\nbased on TransCale while accounting for inter-batch reproducibility. This\nresulted in an updated reference dataset for Quartet D5 and D6 with reduced\nuncertainties. During this update, we found that GRB7 was not consistently\ndetectable in the normal sample Quartet D5, consequently, it was excluded\nfrom the reference dataset and was not included in the absolute quantity\nvalidation for the Quartet D5 and D6 samples.\nTherefore, in the revised manuscript we have performed dPCR experiments to\nindependently and absolutely quantify the three genes MET, GRB2, and\nERBB2 in our Quartet samples. The results are now presented in a new\nSupplementary Figure 13b. We found an excellent concordance (y=x, r2 = 0.99)\nbetween the absolute copy numbers determined by our TranScale-calibrated\nRNA-seq and those measured by dPCR, also supporting our hypothesis\nregarding the MET/GRB2 ratio.\nSupplementary Fig. 13b Orthogonal validation of reference datasets D5 and\nD6 with absolute gene expression profiles.\nScatter plots compare log absolute copy numbers of constructed reference datasets\n2\nby TranScale calibration with those from RT-dPCR assays. Key genes MET, GRB2\nand ERBB2 were measured and showed (N=3).\n2. From Observation to Validated Discovery:\nAs mentioned above, due to the low basal expression of the GRB7 gene in\nnormal samples, which prevented its consistent detection across different\nsequencing batches for Quartet samples, we excluded this gene from both the\nconstruction of the reference dataset and the subsequent validation process in\nthe revised manuscript. In contrast, our analysis of the tumor sample LCA\nrevealed that GRB7 was stably detectable across batches. This provided\nsupporting evidence for our biological hypothesis regarding the co-amplification\nand expression of ERBB2 and GRB7 in tumor samples. Consequently, we\nperformed analysis and dPCR validation of GRB7 and ERBB2 based on the\nTransScale-calibrated absolute quantities for the LCA tumor sample.\nThis orthogonal validation is significant because it elevates our original\nobservations into validated, quantitative biological findings. In the revised\nmanuscript, we initially verified that the basal expression level of GRB7 is\nextremely low in normal sample (11.78 log copies per μg of total RNA for D5),\n2\nbut is elevated by over 400-fold (20.42 log copies per μg of total RNA) in the\n2\ntumor sample LCA (Supplementary Fig. 13a). Furthermore, the expression\nlevels of GRB7 and ERBB2 are approximately equal, as an excellent\nconcordance (20.1 vs. 20.4 log copies per μg of total RNA) between\n(Supplementary Fig. 13b, see above), this co-overexpression is now a\nrigorously confirmed stoichiometric relationship, supports biological insight into\nthe enhancement of oncogenic signaling. This provides the direct evidence\nfrom a generalizable transcriptomic method of how a genomic co-amplification\nevent translates into a precise, equimolar transcript output.\nSupplementary Fig. 13a Gene expression copy numbers of Quartet and\ntumor samples.\nThe plot showed gene expression copy numbers per μg total RNA (in log scale) from\n2\nnormal samples (Quartet RNA D5 and D6) and tumor sample LCA (RNA reference\nmaterial of lung cancer).\n3. Broader Impact and Future Directions:\nThe ability to reliably determine such stoichiometric relationships has a broad\npotential impact. It allows researchers to move beyond simple differential\nexpression to a more systems-level understanding of biological pathways. For\nexample, understanding the absolute quantitative balance between a receptor\n(ERBB2) and its signaling partners could provide new insights into pathway\nactivation, oncogenic dependency, and mechanisms of drug resistance in\ncancer. We have expanded our Discussion section to better articulate these\npotential applications.\nExpanded Discussion section in the revised manuscript:\nCorrecting these biases enables mapping an absolute molecular landscape.\nThis method facilitates direct and quantitative inter-gene comparison on an\nabsolute scale. This allows analyzing quantitative relationships with high clarity,\nproviding promising biological insights, which signals a transition toward\nconstructing quantitative, systems-level models of cellular networks.\n4. Infeasibility with Existing Spike-in Approaches:\nCrucially, our new comparative analysis against ERCC spike-ins\n(Supplementary Fig. 14e) demonstrates that similar results could not be\nachieved with this widely-used existing approach. Our data shows that ERCC-\nbased calibration yields absolute copy numbers that have poor concordance\nwith dPCR measurements. This directly answers the reviewer's question and\nhighlights that the unique combination of biomimicry and SI-traceability in\nTranScale is what enables these novel and accurate biological insights.\nSupplementary Fig. 14e. Orthogonal validation of ERCC corrected\ndataset.\nScatter plot compares log absolute copy numbers from RNA-seq with those from RT-\n2\ndPCR for sample D6 (N=3).\nIn summary, through new experimental validation and comparative analysis,\nwe have now demonstrated that TranScale not only provides a metrologically\nsound measurement but also empowers the discovery and validation of\nquantitative biological principles that were previously inaccessible with existing\ntranscriptomic methods.\n(2) The benchmarking against existing approaches is insufficient. A more\ncomprehensive side-by-side comparison is necessary to conclusively\nestablish the advantages or potential trade-offs of TranScale in terms of\noverall performance compared to the published spike-in methods.\nResponse: We thank the reviewer for this critical feedback. We agree that a\ncomprehensive, side-by-side comparison is necessary to establish the\nadvantages of TranScale. Following the reviewer's suggestion, we have\nperformed a new, in-depth benchmark analysis against the widely-used ERCC\nspike-in controls (Supplementary Fig. 14) and have also clarified the conceptual\ndifferences with Sequins.\n1. Rationale for selecting ERCC for Head-to-Head comparison\nWhile various reference materials exist, we distinguished between external\nreferences (such as MAQC/SEQC), which are typically used for parallel cross-\nplatform testing, and internal spike-ins (such as ERCC and Sequins). A key\nrequirement for TranScale is the co-processing of controls and endogenous\ntranscripts to ensure consistent behavior during library preparation. Since\nERCC remains the most prevalent internal control in the field, it was chosen for\na head-to-head comparison. Additionally, although biomimetic standards like\nSequins have been developed, they share the same fundamental limitation as\nERCC: the absence of SI-traceable value assignment, which precludes their\nuse as true absolute calibrators. Consequently, we ultimately selected ERCC\nas the most representative benchmark to highlight the paradigm shift enabled\nby TranScale.\n2. Head-to-Head Benchmark against ERCC using a \"Real-World\" Dataset:\nTo ensure a robust and unbiased comparison, we leveraged data from a large-\nscale, multi-center study recently published in Nature Communications (Wang,\nD., et al., 2024). From this study, which involved 45 laboratories, we analyzed\ndata from four representative batches using ERCC-spiked samples.\nOur analysis of this well-defined dataset, presented in a new Supplementary\nFigure 14, yielded informative results.\nWe found that using ERCCs for absolute abundance calibration in this real-\nworld setting led to unexpected outcomes, including a decrease in biological\nsignal (SNR) (Supplementary Fig. 14b-c), an increase in data variability (CV)\n(Supplementary Fig. 14d), and the derived absolute copy numbers showed\npoor concordance (r=0.32) with orthogonal dPCR measurements\n(Supplementary Fig. 14e, see above). This provides strong empirical evidence\nthat a biomimetic design is a critical attribute for a standard intended for\nabsolute calibration.\nSupplementary Fig. 14b-c Principal Component Analysis (PCA) of\ntranscriptomic data from Quartet M8 and D6.\nIn all plots, samples are colored by biological group (M8 or D6) and distinguished by\nshape according to laboratories. The Signal-to-Noise Ratio (SNR) for the separation\nof biological groups is shown. Panels show absolute FPKM before (b) and copy\nnumbers after (c) ERCC correction.\nSupplementary Fig. 14d Distribution of Coefficient of Variation (CV) for\nendogenous gene in Quartet samples.\nData from these four laboratories span different library preparation protocols and\nplatforms, Violin plot showed CV of absolute quantification data across the four\nlaboratories before and after applying the potential calibration method.\nFurthermore, after performing potential absolute quantification calibration via\nERCC, we calculated the inter-sample gene expression ratios (M8/D6) and\nused the published Quartet reference dataset13 as the \"gold standard\" for\nexternal validation. As shown in the Supplementary Table 21, among the genes\ndetected across the four laboratories, 7,300, 8,040, 6,775, and 9,152 genes\noverlapped with those covered by the M8/D6 reference dataset. However, less\nthan 6% of these genes matched the reference dataset after the potential\ncalibration by ERCC. These results consistently demonstrate that ERCC spike-\nins fail to achieve absolute quantification calibration for transcriptomic gene\nexpression in the same manner as TranScale.\nSupplementary Table 21. Validation of copy number ratios obtained by the potential\nERCC correction method\nReference Discordant Corrected Correction\nBatches\nratios genes genes rate\nL1_ILL_PolyA 10,728 7300 340 4.66%\nL2_ILL_RiboZero 10,728 8040 240 2.99%\nL3_DNB_PolyA 10,728 6775 246 3.62%\nL4_DNB_RiboZero 10,728 9152 514 5.62%\nThe quantification matrix used for this analysis were obtained from a published multi-\ncenter study5. The tested samples were Quartet RNA M8 and D6 that mixed with\nERCC Mix1 and Mix2 respectively, and the reference ratios were derived from the\nQuartet reference dataset of M8/D613. Corrected genes are defined as follows: after\nperforming the potential absolute quantification calibration via ERCC, the calculated\ncopy number ratios of M8 to D6 in the corresponding batch must fall within the\nuncertainty range of the reference dataset. ILL_PolyA and ILL_RiboZero represent\nRNA-seq data with Illumina Novaseq 6000 platform with PolyA and RiboZero protocols,\nrespectively. DNB_PolyA and DNB_RiboZero represent RNA-seq data with DNBSEQ-\nT7 platform with PolyA and RiboZero protocols, respectively.\n3. Conceptual Comparison with Sequins:\nWe have also expanded our Discussion to place TranScale in the context of\nother biomimetic standards like Sequins. We clarify that while Sequins share\nthe biomimetic design principle, they are designed with known relative ratios for\nvalidating fold-change measurements and lack the SI-traceable absolute\nconcentration values necessary for true absolute quantification.\nConclusion of the Comparison of TranScale and ERCC:\nThis comprehensive comparison now establishes two key advantages of\nTranScale:\nIts biomimetic design leads to more effective and reliable data harmonization\ncompared to non-biomimetic standards like ERCC.\nIts SI-traceable absolute values provide a unique capability for true absolute\nquantification that other standards, including Sequins, do not offer.\nWe are grateful for the reviewer's push for a more comprehensive comparison,\nas it has allowed us to not only demonstrate the advantages of TranScale but\nalso to highlight fundamental principles for the design of future transcriptomic\nstandards. We believe this new analysis conclusively addresses the reviewer's\nconcern.\nNew paragraph about ERCC to the Discussion section in the revised\nmanuscript:\nOur method’s efficacy stems from integrating two key principles: biomimicry\nin design and metrological rigor in value assignment. This integrated approach\nis crucial for advancing beyond the limitations of previous standards. For\ninstance, while non-biomimetic controls like ERCC have been valuable for\nassessing platform performance4, prior studies have suggested their use in\nnormalization can introduce systemic biases due to their distinct\nphysicochemical properties28, 29. Our analysis of data from a recent, large-scale\ninter-laboratory study5 provides new quantitative evidence that supports this\nconcern in the context of absolute calibration. In this \"real-world\" dataset, we\nobserved that attempting to use ERCCs for absolute calibration led to a\ndecrease in the signal-to-noise ratio (SNR) (Supplementary Fig. 14b-c), an\nincrease in inter-batch variability (CV) (Supplementary Fig. 14d) and the\nderived absolute copy numbers showed poor concordance (r=0.32) with\northogonal dPCR measurements (Supplementary Fig. 14e). Similarly, at the\nrelative level, the copy number ratios obtained using the potential ERCC\ncorrection method also deviated significantly from those of the Quarte reference\ndataset M8/D613 (Supplementary Table 21). We hypothesize this unexpected\noutcome is a direct consequence of ERCCs’ non-biomimetic nature, as their\nbehavior may not be representative of endogenous transcripts during the\ncomplex sequencing workflow. This finding underscores that a standard’s\nability to mimic endogenous molecules is a critical consideration for effective\nabsolute calibration.\n(3) UMIs (unique molecular identifiers) have been widely used to count\nabsolute numbers of molecules in next-generation sequencing methods\n[PMID: 22101854]. The authors should discuss the advantages and\nlimitations of TranScale compared to UMI-based approaches.\nResponse: This is an excellent suggestion. We have added a new paragraph\nto the Discussion section to explicitly distinguish the metrological utility of\nTranScale from that of UMI-based methods. In the revised text, we clarify that:\nUMI's Role (Downstream): We acknowledge that UMIs are an established\nmethod for mitigating stochastic amplification noise and counting distinct cDNA\nmolecules.\nTranScale's Role (Upstream): However, UMIs do not typically account for\nbiases occurring prior to cDNA synthesis, such as variable reverse transcription\nefficiency or adapter ligation. TranScale, being co-processed from the start,\ncaptures these upstream physical losses which are not captured by UMI\ncounting.\nComplementarity: We conclude that combining UMI-based counting (for PCR\nprecision) with TranScale calibration (for pre-PCR recovery correction)\nrepresents a comprehensive strategy for absolute transcriptomics.\nNew paragraph to the Discussion section in the revised manuscript:\nWe also distinguish the metrological utility of TranScale from that of Unique\nMolecular Identifiers (UMIs)32, 33. While UMIs are effective at mitigating PCR\nduplication artifacts by collapsing identical barcodes, they track cDNA\nmolecules and do not typically account for losses occurring upstream of UMI\nassignment. Such losses, arising from variations in reverse transcription\nefficiency, fragmentation, or adapter ligation, may contribute to discrepancies\nbetween the original RNA abundance and the amplifiable cDNA pool. In\ncontrast, TranScale calibrators are introduced at the beginning of the workflow,\nthereby capturing these cumulative pre-PCR inefficiencies. Consequently, we\npropose that the two approaches are complementary: UMIs provide digital\ncounting for downstream amplification, while TranScale provides a reference\nfor correcting biases associated with the initial sample processing.\n(4) The authors spiked 1 ng of the reference material (RM) into 1 μg total\nRNA sample. However, the percentage of reads assigned to RM in\nlibraries co-processed with TranScale remains unclear. Furthermore, it is\nuncertain whether this RM input ratio represents the optimal\nconfiguration after thorough optimization.\nResponse: We thank the reviewer for this important question regarding the\noptimization of the spike-in amount. We have addressed this in two ways:\nClarifying Read Percentage: We have analyzed our sequencing data and now\nreport in the Methods section that with our standard protocol (1 ng TranScale\nper 1 μg total RNA), the reads assigned to TranScale calibrators typically\nconstitute 1.40%-4.57% of the total library reads (Supplementary Table 19).\nThis demonstrates that our recommended input ratio provides sufficient\ncoverage for robust calibration without being wasteful of sequencing resources.\nDefining the Optimal Range via In Silico Downsampling: To determine the\noptimal input ratio, we performed a systematic in silico downsampling analysis.\nThe detailed results are presented in Supplementary Figure 10. In the main text,\nwe have now included a paragraph that clarifies our findings: To ensure the\nrobustness of our calibration framework, we first established an optimized input\nratio for the TranScale calibrators. Our standard protocol uses 1 ng of\nTranScale per 1 µg of total RNA, which resulted in TranScale-derived reads\nconstituting approximately 1.40%-4.57% of the total library (Supplementary\nTable 19). Through an in silico downsampling analysis (Supplementary Fig.\n10a), we confirmed that this input amount provides a safe margin above the\nminimum requirement for robust model fitting across different platforms and\nlibrary preparation methods. Guided by this validated and stringent QC process\n(Supplementary Fig. 10b), we then applied the calibration framework to resolve\ninter-laboratory batch effects for endogenous genes.\nThis analysis confirms that our recommended 1 ng input is well within the\noptimal range, providing a robust calibration while maintaining cost-\neffectiveness. In consideration of Reviewer #2's feedback on figure complexity,\nwe have kept this detailed optimization analysis in the supplementary materials\nto maintain the focus of the main figures on the primary results of the study.\n(5) PCR amplification bias can compromise the accuracy, particularly with\nvarying cycle numbers. It is crucial to define the range of PCR cycles that\nmaintain a linear relationship during library preparation, especially when\nTranScale is applied to samples with different starting material quantities.\nResponse: We thank the reviewer for this crucial technical point. To explicitly\ndemonstrate that our library amplification was performed within the linear range,\nwe have conducted an additional experiment. We prepared a library from a\nrepresentative sample and performed PCR amplification with a gradient of cycle\nnumbers (from 9 to 19 cycles). As shown in the new Supplementary Figure 15,\nthe resulting library yield, quantified by Qubit, results showed that the library\nconcentration exhibited excellent linearity (log scale) across the selected range\n2\nof cycle number gradients, indicating that the PCR amplification was in the\nexponential phase. Our standard protocol uses initial input total RNA dosage of\n400 ng with 13 PCR cycles, which is clearly positioned within the robust\nexponential phase of the amplification. This ensures that the quantitative\nrelationships between transcripts are faithfully maintained and not distorted by\namplification saturation. Furthermore, we confirmed that this chosen cycle\nnumber remains within the linear range even for samples with 10-fold lower\nstarting material, demonstrating the robustness of our protocol.\nManuscript Revision: We have added a sentence in the Methods section\nexplicitly stating that PCR cycle numbers were optimized to ensure linear-range\namplification and have referenced this new supplementary figure 15.\nNew sentence to the Methods section in the revised manuscript:\nIn this cross-batch transcriptomic sequencing study, PCR cycle numbers\nwere first optimized to ensure linear-range amplification. Ultimately, 13 cycles\nwere selected for the subsequent eight experimental batches (Supplementary\nFig. 15).\nSupplementary Fig.15 Effect of PCR cycle numbers on RNA library\nconcentration\nOur standard protocol uses 1 ng of TranScale per µg of total RNA (Quartet D5), library\npreparation was optimized in a single batch by varying PCR cycle numbers using\nstarting inputs of 1,000 ng and 100 ng of total RNA. For each total RNA input amount,\n5 different PCR cycle gradients were evaluated, with each group performed in\nduplicate (two independent library preparations). Following the manufacturer’s\nstandard protocol (VAHTS Universal V8 RNA-seq Library Prep Kit), library\nconcentrations were measured using Qubit fluorometric quantification. To ensure\nprecision, three technical replicates were performed for each library, resulting in a total\nof N=6 measurements per experimental condition.\n(6) The selection process for the 100 biomimetic RNA spike-in transcripts\nused in this study lacks transparency. It is unclear how many transcripts\nwere initially tested or what criteria guided their final selection.\nResponse: We thank the reviewer for highlighting the need for greater\ntransparency in our calibrator selection process. We completely agree and have\nsubstantially revised the Methods section to provide a clear, step-by-step\naccount of the development of the TranScale panel.\nTo improve clarity, we have restructured this section using a numbered, three-\nstage framework: 1. Biomimetic Design and Sequence Selection, 2. Production,\nOptimization, and Quality Control, and 3. Final Panel Composition and\nSufficiency. The details of TranScale are as follows:\nOur initial design criteria, which included creating biomimetic inverted\nsequences, incorporating functional diversity (fusions, isoforms), and targeting\na broad range of physicochemical properties (Stage 1).\nThe practical challenges encountered during production, specifically the\ndifficulty in synthesizing very long transcripts (>3,800 nt) with the high purity\nrequired for metrological standards.\nOur quality-driven optimization process, where we pragmatically shortened\nthese problematic transcripts to a maximal length that ensured their analytical\npurity and integrity. This crucial step demonstrates our commitment to\nmetrological quality over adherence to an idealized, but practically unattainable,\ndesign (Stage 2).\nThe validation of the final 100-transcript panel, confirming that it is more than\nsufficient for robust calibration, as supported by our analysis in Supplementary\nFigure 9 (Stage 3).\nWe are confident that this new, highly structured description makes our entire\ndevelopment process fully transparent and addresses the reviewer's concerns\nby clarifying the criteria, the practical optimization steps, and the rationale\nbehind the final composition of the TranScale panel.\nDetailed amendments to the Methods section in the revised manuscript:\nThe TranScale panel was meticulously developed through a three-stage\nprocess to create a high-fidelity biomimetic standard for RNA sequencing.\n1. Biomimetic design and sequence selection\nOur design process targeted a panel of approximately 100 transcripts that\ncollectively mirror the human transcriptome. The selection criteria were\nmultifaceted:\n(i) Biomimetic sequence strategy: To preserve structural characteristics while\neliminating sequence homology, each transcript was designed by inverting a\nselected human coding sequence (CDS). This novel strategy maintains native\nsequence complexity, such as a GC content similar to the hg38 genome, while\nensuring that TranScale reads do not misalign during bioinformatic analysis.\n(ii) Functional diversity: The selected sequences represented a range of\nbiological features, including wild-type genes, clinically relevant fusion genes,\nand alternative splicing isoforms.\n(iii) Physicochemical range: The initial design aimed for a broad length\ndistribution from ~500 nt to over 4,000 nt to cover the majority of the human\nmRNA length spectrum.\n2. Production, optimization, and quality control\nThis stage focused on translating the computational design into physical RNA\nmolecules of the highest metrological quality.\n(i) Template generation: The full DNA sequence for each calibrator—\ncomprising a 5' BamHI site, a T7 promoter, the inverted CDS, a 3' 28 bp poly(A)\ntail, a T7 terminator, and a final EcoRI site—was synthesized and cloned into a\npMV vector (BGI Genomics). Following sequence verification (Supplementary\nTable 1), recombinant vectors were transformed into E. coli DH5α. Template\nDNA for in vitro transcription (IVT) was obtained by BamHI/EcoRI digestion and\npurified with the Universal DNA Purification Kit (Tiangen).\n(ii) In vitro transcription and pragmatic optimization: IVT was performed using\nthe T7 High Yield RNA Synthesis Kit (Yeasen). A 20 µL reaction containing 1\nµg of template DNA, 10 mM of each NTP, 1× reaction buffer, and 2 µL T7 RNA\nPolymerase Mix was incubated at 37°C for 4 hours, followed by DNase I\ntreatment. At this critical stage, we observed that some of the longer target\ntranscripts (>3,800 nt) yielded impure products due to premature termination.\nPrioritizing analytical validity, we systematically shortened these sequences to\na maximal length that permitted consistent, high-purity synthesis.\n(iii) Purification and verification: The resulting full-length RNA was purified using\nthe MEGAclear Kit (Thermo Fisher), and its concentration was estimated with\na Nanodrop. The integrity and purity of each of the 100 final transcripts were\nrigorously verified as a single, sharp peak on an Agilent 2100 Bioanalyzer (RNA\n3. Final panel composition and sufficiency\nThe final TranScale panel consists of 100 well-defined, high-purity RNA\ntranscripts. Despite the necessary length adjustments for quality control, the\npanel spans a significant range of lengths (500-3,800 nt). Importantly, the utility\nof the panel is not contingent on the detection of all 100 calibrators. Our\noperational analyses show that robust calibration models (R² > 0.95) can be\nreliably constructed using as few as 20-30 well-distributed calibrators,\nconfirming the sufficiency of the final panel for its intended application\n(Supplementary Fig. 10).\nReviewer #2 (Remarks to the Author):\nThe manuscript “A metrological foundation for absolute transcriptomics using\nSI-anchored calibrators” introduces TranScale, a panel of ~100 synthetic\nbiomimetic RNA spike-ins with assigned SI-traceable concentrations via\nisotope dilution mass spectrometry (IDMS). These controls emulate key\nfeatures of human transcripts (multi-exon genes, alternative splicing, fusions)\nand enable absolute quantification of accompanying RNA-seq libraries.\nTo demonstrate the approach, the authors conduct a multi-laboratory, multi-\nplatform, multi-protocol sequencing study (12 batches across two sites using\nIllumina and DNBSEQ) and show that TranScale reduces inter-batch variability.\nOrthogonal validation using droplet digital PCR (ddPCR) shows good\nconcordance.\nOverall, the methodology is detailed, and the experimental execution, including\nIDMS workflows, spike-in design, and calibration procedures, is technically\nrigorous. The manuscript is generally well prepared; however, its lengthy,\ndescriptive and overstated text and large multi-panel figures can sometime\nobscure a clear and concise understanding of the technology.\nMajor Points\n1. Conceptual novelty relative to prior controls. The overall framework\nusing synthetic RNA spike-ins to assess and correct sequencing and\nquantification biases remains similar to prior approaches (ERCC, Sequins,\nMAQC/SEQC controls). While the use of IDMS to generate SI-traceable\nabsolute concentrations is a notable advance, its practical significance is\ndifficult to assess without clear demonstration that SI-anchored\ncalibration enables new analytical capabilities or biological insights.\n2. The manuscript would be strengthened by practical examples showing\nhow IDMS-based calibration improves performance relative to existing\nRNA controls in applied or clinical contexts. For example, to substantiate\nthe benefits of absolute scaling, analyses such as differential expression\nwith and without calibration, biomarker detection, clinical diagnostic\ncases where absolute scaling reveals insights not achievable with\nstandard normalization would be valuable. The authors highlight\nexamples such as the MET/GRB2 ratio and ERBB2 amplification, however\nthese remain unvalidated and constitute observational hypotheses. The\nadvantages of TranScale are not demonstrated for any specific diagnostic\napplication or biological analysis, and it is difficult to.\nResponse to Major Points 1 & 2: Conceptual Novelty and Practical\nSignificance\nWe thank the reviewer for these insightful and closely related points, which\naddress the core of our study's contribution: the practical significance of SI-\nanchored absolute quantification. We agree that the conceptual advance of SI-\ntraceability must be demonstrated through new analytical capabilities and\nvalidated biological insights. We have performed substantial new experiments\nto address this directly.\n1). Enabling New Analytical Capabilities:\nA key conceptual advance of TranScale is its ability to enable quantification on\nan absolute scale and inter-gene comparison, moving beyond the relative\ncomparisons afforded by prior controls. This new analytical capability allows us\nto ask not just \"is gene A upregulated?\", but \"what is the absolute molecular\nratio (stoichiometry) of gene A to gene B?\".\n2). Demonstrating Practical Significance with a Validated Finding:\nAs the reviewer requested, we provide a concrete example by focusing on our\nobservation such as the MET/GRB2 ratio and ERBB2 amplification. We\nperformed droplet digital PCR (dPCR) to orthogonally validate this finding.\nIt should be clarified that in the original manuscript, we inferred biological\ninsights for these four genes (MET, GRB2, ERBB2, GRB7) by estimating their\nabsolute quantities using TransCale calibration, with data derived from a single\nsequencing library of the Quartet D5 sample but not reference dataset, which\nled to our erroneous hypothesis regarding the co-amplification of the ERBB2\nand GRB7 genes in normal samples. To further strengthen the scientific validity\nof these inferences, we evaluated and corrected the reference datasets based\non TransCale while accounting for inter-batch reproducibility. This resulted in\nan updated reference dataset for Quartet D5 and D6 with reduced uncertainties\nthan before. During this update analysis, we found the low basal expression of\nthe GRB7 gene in normal samples, and was not consistently detectable in the\nnormal sample Quartet D5, consequently, it was excluded from the reference\ndataset and was not included in the absolute quantity validation for the Quartet\nD5 and D6 samples. We have performed dPCR experiments to independently\nand absolutely quantify the genes MET, GRB2, and ERBB2 in our Quartet\nsamples in the revised manuscript. As validation of Quartet reference datasets\nD5 and D6, the new data shows an excellent concordance (y=x, r2=0.99)\nbetween our TranScale-calibrated RNA-seq and dPCR absolute\nmeasurements (Supplementary Fig. 13b, as follows), supporting our hypothesis\nregarding the MET/GRB2 ratio.\nIn contrast, our analysis of the tumor sample LCA revealed that GRB7 was\nstably detectable across batches. This provided supporting evidence for our\nbiological hypothesis regarding the co-amplification and expression of ERBB2\nand GRB7 in tumor samples. Consequently, we performed analysis and dPCR\nvalidation of GRB7 and ERBB2 based on the TransScale-calibrated absolute\nquantities for the LCA tumor sample as the supplement. As results, this\nvalidation elevated our original observation to a quantitative finding. By\ncomparing the expression of ERBB2 and GRB7 in normal (Quartet D5 and D6)\nversus tumor sample (LCA), we observed significantly elevated overexpression\nlevels in the latter (Supplementary Fig. 13a, as follows). Furthermore, dPCR\nresults showed that the expression levels of ERBB2 and GRB7 in LCA were\nnearly equivalent (Supplementary Fig. 13a, as follows), the hypothesis of co-\namplification for these key genes was successfully validated at the\ntranscriptional level in the tumor sample LCA. This direct, quantitative evidence\nis no longer an \"observational hypothesis,\" but a quantitative result supported\nby two independent methods.\nSupplementary Fig. 13. Absolute expression profiles validation by RT-dPCR\na, Gene expression copy numbers of Quartet and tumor samples. The plot showed\ngene expression copy numbers per μg total RNA (in log scale) from normal samples\n2\n(Quartet RNA D5 and D6) and tumor sample LCA (RNA reference material of lung\ncancer). b, Orthogonal validation of reference datasets D5 and D6 with absolute gene\nexpression profiles. Scatter plots compare log absolute copy numbers of constructed\n2\nreference datasets with those from RT-dPCR assays. Key genes MET, GRB2 and\nERBB2 were measured and showed (N=3).\n3). Comparison with Prior Controls:\nFurthermore, our new comparative analysis against ERCCs (Supplementary\nFig. 14, as follows) indicates that this level of accuracy was not achieved with\nthis non-biomimetic approach. The ERCC-calibrated absolute values showed\npoor concordance with dPCR (Supplementary Fig. 14e, as follows), which\nsuggests that combining a biomimetic design with SI-traceability is an important\nfactor for this new analytical capability.\nIn summary, by providing a validated example of determining absolute\nmolecular stoichiometry—a task not reliably achieved in our comparison with a\nprevious standard—we have now demonstrated the practical significance and\nconceptual novelty of our SI-anchored approach. We have also expanded the\nDiscussion to better highlight potential applications, such as understanding\npathway stoichiometry in cancer.\n3. The benchmarking focuses on computational batch-correction\nmethods (SVA, ComBat), and a fairer, more informative comparison\nwould include other biomimetic RNA controls (ERCC, Sequins) to\ncompare corrected versus uncorrected workflows.\nResponse: We sincerely thank the reviewer for this exceptionally insightful and\nconstructive point. We completely agree that a comparison with other physical\nstandards is essential for a fairer and more informative assessment. Your\nsuggestion prompted us to conduct a new analysis that has, in our view,\nsignificantly deepened the conclusions of our study.\nSince ERCC remains the most prevalent internal control in the field, it was\nchosen for a head-to-head comparison. Additionally, although biomimetic\nstandards like Sequins have been developed, they share the same fundamental\nlimitation as ERCC: the absence of SI-traceable value assignment, which\nprecludes their use as true absolute calibrators. Consequently, we ultimately\nselected ERCC as the most representative benchmark to highlight the\nparadigm shift enabled by TranScale.\nTo perform this benchmark, we leveraged data from a large-scale, \"real-world\"\nmulti-center study recently published in Nature Communications (Wang, D., et\nal., 2024). This landmark study assessed RNA-seq performance across 45\nlaboratories. From this comprehensive dataset, we selected four representative\nlaboratory batches that used Quartet samples spiked with ERCC controls to\nconduct our analysis. Using a well-defined subset from such a peer-reviewed\npublic dataset provides a robust and unbiased foundation for our evaluation.\nThe complete analysis of this data is now presented in a new Supplementary\nFigure 14. The results were highly informative. When we attempted to use the\nERCC data from this real-world benchmark for absolute abundance calibration,\nwe observed several unexpected outcomes, including a decrease in biological\nsignal (SNR) (Supplementary Fig. 14b-c, as follows), an increase in inter-batch\ndata variability (CV) (Supplementary Fig. 14d, as follows), and the derived\nabsolute copy numbers showed poor concordance with orthogonal dPCR\nmeasurements (Supplementary Fig. 14e, as follows).\nFurthermore, after performing potential absolute quantification calibration via\nERCC, we calculated the inter-sample gene expression ratios (M8/D6) and\nused the published Quartet reference dataset13 as the \"gold standard\" for\nexternal validation. As shown in the Supplementary Table 21, among the genes\ndetected across the four laboratories, 7,300, 8,040, 6,775, and 9,152 genes\noverlapped with those covered by the M8/D6 reference dataset. However, less\nthan 6% of these genes matched the reference dataset after the potential\ncalibration by ERCC. These results consistently demonstrate that ERCC spike-\nins fail to achieve absolute quantification calibration for transcriptomic gene\nexpression in the same manner as TranScale.\nWe have now incorporated a concise discussion of these findings in our\nmanuscript. We hypothesize that these results, observed in a large-scale, real-\nworld setting, stem from the non-biomimetic nature of ERCCs. This provides\nstrong empirical evidence that a calibrator's design is a critical consideration for\nits intended application.\nWe are truly grateful for your guidance. By pushing us to perform this\ncomparison, you have enabled us to anchor our findings in a major, community-\nwide benchmark study, which we believe makes our conclusions about the\nimportance of biomimicry much more powerful and generalizable.\nSupplementary Fig. 14b-e Calibration for real world RNA-seq data by ERCC\nb-c, Principal Component Analysis (PCA) of transcriptomic data from Quartet M8 and\nD6. In all plots, samples are colored by biological group (M8 or D6) and distinguished\nby shape according to laboratories. The Signal-to-Noise Ratio (SNR) for the separation\nof biological groups is shown. Panels show absolute FPKM before (b) and copy\nnumbers after (c) ERCC correction. d, Distribution of Coefficient of Variation (CV) for\nendogenous gene in Quartet samples. Data from these four laboratories span different\nlibrary preparation protocols and platforms, Violin plot showed CV of absolute\nquantification data across the four laboratories before and after applying the potential\ncalibration method. e, Orthogonal validation of ERCC corrected dataset. Scatter plot\ncompares log absolute copy numbers from RNA-seq with those from RT-dPCR for\n2\nsample D6 (N=3).\nSupplementary Table 21. Validation of copy number ratios obtained by the potential\nERCC correction method\nReference Discordant Corrected Correction\nBatches\nratios genes genes rate\nL1_ILL_PolyA 10,728 7300 340 4.66%\nL2_ILL_RiboZero 10,728 8040 240 2.99%\nL3_DNB_PolyA 10,728 6775 246 3.62%\nL4_DNB_RiboZero 10,728 9152 514 5.62%\nThe quantification matrix used for this analysis were obtained from a published multi-\ncenter study5. The tested samples were Quartet RNA M8 and D6 that mixed with\nERCC Mix1 and Mix2 respectively, and the reference ratios were derived from the\nQuartet reference dataset of M8/D613. Corrected genes are defined as follows: after\nperforming the potential absolute quantification calibration via ERCC, the calculated\ncopy number ratios of M8 to D6 in the corresponding batch must fall within the\nuncertainty range of the reference dataset. ILL_PolyA and ILL_RiboZero represent\nRNA-seq data with Illumina Novaseq 6000 platform with PolyA and RiboZero protocols,\nrespectively. DNB_PolyA and DNB_RiboZero represent RNA-seq data with DNBSEQ-\nT7 platform with PolyA and RiboZero protocols, respectively.\nAmendments to the Discussions section in the revised manuscript:\nThis principle of biomimicry is shared by other standards like Sequins12\nwhich were designed with exon-intron structures. However, effective absolute\nquantification requires a second, equally important attribute: SI-traceable\ncertified values. Sequins are provided with defined relative ratios, making them\nexcellent tools for validating a workflow’s ability to measure fold-changes, but\nthey are not designed to provide the absolute concentration anchor needed to\nbuild a true calibration curve from sequencing signal to molecular count30, 31.\nTranScale is thus developed to address both requirements. It combines a\nbiomimetic design for representative behavior—a feature empirically shown to\nbe critical through our ERCC comparison—with the metrological foundation for\ntrue absolute scaling. While no synthetic standard can perfectly replicate all\nendogenous transcripts, our results indicate this dual-principled approach\nprovides a practical and robust solution for absolute transcriptomics.\n4. The IDMS workflows and statistical analyses are complex, requiring\nspecialized equipment and expertise, and use statistical approaches that\nare complex, which would be difficult for adoption. To support broader\nadoption, a simplified description of the statistics and analytical workflow\nwould be helpful to support adoption.\nResponse: We thank the reviewer for this insightful comment regarding the\ncomplexity and accessibility of our method. We agree that this is a crucial point\nfor broader adoption. To address this, we have substantially revised our\nDiscussion to present a clearer framework for understanding the application of\nTranScale.\nIn the revised paragraph, we now frame our method as a tiered approach,\ndistinguishing the roles of the producer, validator, and end-user:\nMetrological Characterization (Producer's Role): We clarify that the complex\nIDMS and statistical workflows are confined to the foundational, one-time\ncharacterization of the TranScale reference material, a task performed by a\nnational metrology institute or a certified producer.\nRoutine Application (End-User's Role): We emphasize that for the end-user,\nthe process is designed to be straightforward. We now directly reference our\noperational guide in Supplementary Figure 9b (upper panel) to illustrate that the\nuser-facing workflow involves simple spike-in and a standard linear calibration.\nWorkflow Validation (Validator's Role): We integrate this with our discussion of\nthe dual-metric framework, presenting it as a tool for an initial, rigorous\nvalidation of a lab's entire workflow (Supplementary Figure 9b, lower panel),\nwhich is distinct from routine, single-mix use.\nBy structuring the discussion around this tiered framework, we believe we have\neffectively addressed the reviewer's concern about the barrier to adoption. The\nrevised text now clearly demonstrates that while TranScale is built upon a\ncomplex metrological foundation, its application in a typical research laboratory\nis practical, scalable, and accessible.\nMinor Comments\n5. The manuscript text can often read overstated based on the data and\nadvance. For examples statements such as “we establish this missing\nmetrological foundation,” “provides the essential tools to resolve the\nfundamental limitations,” “transitioning RNA-seq from a comparative to a\ntruly quantitative discipline” These statements do not advance the study,\nand can obscure an understanding of the methods significance.\nResponse: We sincerely thank the reviewer for this candid and valuable\nfeedback. Upon review, we agree that some of our statements were overstated\nand could obscure the significance of our work with unnecessarily strong\nlanguage.\nFollowing the reviewer's excellent advice, we have performed a thorough\nrevision of the entire manuscript—particularly the Abstract, Introduction, and\nDiscussion sections—to tone down these statements. We have systematically\nreplaced declarative and potentially hyperbolic phrases (such as \"fundamental\nlimitations,\" \"truly quantitative\", “essential tool”) with more measured and\nobjective language (e.g., \"significant challenges,\" \"a more quantitative\", “a set\nof tools”).\nOur goal with this revision is to allow the data and the methodological advances\nto speak for themselves. We are grateful for the opportunity to improve the\nmanuscript's tone and believe it is now more professional, objective, and in line\nwith the standards of Nature Communications.\n6. The text can often read with lengthy repeated of methodological\ndescriptions. For example, concepts such as biomimetic RNA intron-exon\nstructure are described multiple times, and could be consolidated\nimprove clarity and flow.\nResponse: We thank the reviewer for this helpful advice on improving the\nmanuscript's clarity and flow. We agree that some methodological concepts\nwere described repetitively.\nTo address this, we have performed a thorough editorial revision of the entire\nmanuscript. We followed a \"define once, then cite\" principle for key concepts\nsuch as our \"biomimetic design\" and \"intron-exon structure\". We have\nconsolidated the detailed descriptions into their first appearance (primarily in\nthe Introduction and the beginning of the Results section) and subsequently\nused simplified terms (e.g., \"the TranScale\" or \"these biomimetic standards\") in\nthe rest of the text.\nThis consolidation has allowed us to remove redundant phrases and\nparagraphs, resulting in a more concise and reader-friendly manuscript. We\nbelieve these changes have significantly improved the overall clarity and\nnarrative flow, as the reviewer suggested.\n7. The figures include many panels (8–20 panels) that can result in\ncrowded layouts with font sizes that are difficult to read and interpret.\nNumerous related panels (such as the many PCA plots) could be\nconsolidated or moved to Supplementary Figures.\nResponse: We thank the reviewer for this valuable suggestion on improving\nthe readability of our figures. We agree that some figures were overly crowded.\nFollowing the reviewer's advice, we have significantly consolidated our main\nfigures. Specifically, for Figure 5, which the reviewer highlighted as an example\nwith many PCA plots, we have moved five panels (the original Fig. 5a, b, c, i, j,\nand m) to the Supplementary Information (now Supplementary Fig. 11a-f).\nThis major revision has allowed us to streamline Figure 5, focusing it on the\nmost critical comparisons and significantly improving its layout and font\nreadability. We have also reviewed the other figures to ensure clarity. We\nbelieve these changes have effectively addressed the reviewer's concern and\nhave made the manuscript's key visual evidence much easier to interpret.\nWe are confident that these substantial revisions have addressed the\nreviewers' and editor's concerns and have significantly improved the\nmanuscript. We thank the reviewers again for the thorough and\nconstructive evaluation."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Most of my concerns have been addressed with additional experiments and analyses in the revised manuscript. However, I suggest that the comparisons of TranScale with other methods should be more detailed, such as supplying a supplemental table to show its advantages and limitations in comparison with existing methods. Furthermore, as an RNA-seq method, demonstrating its advantages in biological insights at the transcriptomic level will strengthen its applications.", "reviewer_2": "The authors have substantially addressed my concerns, and the revised manuscript is improved, with claims now appropriately aligned with the data. The expanded benchmarking against ERCC and the inclusion of orthogonal validation (including ddPCR concordance) strengthen confidence in the methodology. While the ERBB2 and GRB7 analyses raise interesting observations for further investigation, the method could be further strengthened by identifying specific examples where the use of the TransScale controls might directly impact diagnostic interpretation or clinical decision-making. Nevertheless, with these revisions and the additional validation data, my major concerns have been satisfactorily addressed, and I support acceptance of the manuscript in its current form."}, "author_response": "Point-by-point response to the reviewers’ comments\nReviewer #1 (Remarks to the Author):\nMost of my concerns have been addressed with additional experiments and\nanalyses in the revised manuscript. However, I suggest that the comparisons\nof TranScale with other methods should be more detailed, such as supplying\na supplemental table to show its advantages and limitations in comparison\nwith existing methods. Furthermore, as an RNA-seq method, demonstrating\nits advantages in biological insights at the transcriptomic level will strengthen\nits applications.\nResponse to Reviewer 1:\nAs requested, we have included a comprehensive comparison of TranScale\nwith other transcriptomic control strategies (ERCC, Sequins, SIRV, UMI) in\nSupplementary Table 22. The table highlights TranScale's unique advantages\nin biomimicry and SI-traceability. We have expanded our Discussion section to\nbetter articulate these advantages.\nExpanded Discussion section in the revised manuscript:\nUnlike synthetic controls that may lack traceability or fail to mimic endogenous\nmRNA behavior, TranScale combines biomimicry with SI-traceability. This\nunique feature allows for the correction of sequence-dependent biases that\ncompromise traditional relative quantification (Supplementary Table 22).\nFurthermore, our ERBB2 analysis highlights a critical advantage of this\nmetrological approach: the ability to define universal diagnostic cutoffs.\nCurrently, clinical transcriptomics is often limited by the need for batch-specific\ncontrols or relative comparisons. Our data suggest that TranScale-calibrated\nabsolute quantification allows for the establishment of fixed, transferrable\ndecision thresholds—similar to those used in clinical chemistry (e.g., blood\nglucose levels)—thereby bridging the gap between research-grade RNA-seq\nand robust clinical diagnostics.\nSupplementary Table 22. Comparative analysis of TranScale and other transcriptomic\ncontrol strategies\nFeature TranScale SIRV52 Sequins12 ERCC10 UMI33\nFull-process Non- Random\nDesign Isoform/Splicing Biomimetic\nBiomimetic biomimetic Sequence\nPrinciple Complexity (Mirror)\n(Exon/Antisense) Synthetic Tag\nSI-traceable\nMetrological Relative Ratio Relative Undefined No Physical\nAbsolute\nStatus Sets Ratios Concentration Anchor\nConcentration\nCross-batch Isoform\nFold- Platform PCR\nPrimary Alignment & Quantification &\nchange Performance Duplicate\nUtility Absolute Splicing\nValidation Benchmarking Correction\nQuantification Validation\nComprehensive\nWorkflow Partial (Focus Sequencing\n(Extraction to High Minimal\nCoverage on Informatics) Only\nSequencing)\nHighest\nDiagnostic\n(Provides Moderate Moderate Low Low\nRobustness\nPhysical Scale)\nSIRV: Spike-In RNA Variants. 52 Paul, L. et al. SIRVs: Spike-In RNA Variants as External\nIsoform Controls in RNA-Sequencing. Preprint at https://doi.org/10.1101/080747 (2016).\nFurthermore, to address the request for demonstrating biological insights, we\nutilized the HER2 (ERBB2) oncogene and performed a Diagnostic Robustness\nSimulation as a case study. A key challenge in biological interpretation is\ndistinguishing true biological variation (e.g., tumor overexpression) from\ntechnical artifacts. We used this diagnostic to illustrate how TranScale prevents\ndiagnostic errors caused by batch effects. A diagnostic cutoff was empirically\ndefined as the midpoint separating the dPCR-quantified absolute copy numbers\nof the tumor model (LCA) and normal controls (Quartet D5 and D6, see\nSupplementary Fig. 13a).\nIn our analysis of 12 independent sequencing libraries, we found that standard\nrelative quantification (FPKM) frequently suffered from \"trend reversal\", where\ntechnical noise completely masked the biological distinction between the tumor\nstate and the normal state (Supplementary Figure 14). The results showed the\ntumor sample's signal drifted below the equivalent relative threshold in 6 out of\n12 libraries, leading to False Negative interpretations. This demonstrates that\ntraditional RNA-seq cannot support a fixed, universal diagnostic cutoff across\ndifferent laboratories, which might lead to erroneous biological conclusions (i.e.,\nfailing to identify oncogene overexpression).\nIn contrast, by applying library-specific TranScale calibration, effectively\nneutralized the variation in biochemical efficiency. We were able to accurately\nrecover the true biological abundance of HER2 (consistent with dPCR\nvalidation), thereby revealing the distinct expression phenotype of the tumor\nsamples that was otherwise lost in the raw data. This demonstrates that\nTranScale provides the necessary accuracy to derive reliable biological insights\nfrom multi-batch transcriptomic data.\nWe have expanded our Results section to better articulate the advantages in\nbiological insights at the transcriptomic level.\nExpanded Results section in the revised manuscript:\nDemonstration of robust diagnostic classification\nFinally, to illustrate the potential of TranScale in identifying biological\ninsights and supporting clinical decision-making, we conducted a diagnostic\nsimulation targeting the ERBB2 (HER2) oncogene across 12 independent\nsequencing libraries. We established a \"ground truth\" diagnostic cutoff (18.5 of\nlog copies/µg total RNA) based on reference dPCR data, which clearly\n2\ndistinguished the tumor model (LCA) from normal controls (Quartet D5/D6). We\nobserved that technical batch effects in uncalibrated relative quantification\n(FPKM) frequently masked the overexpression phenotype of the tumor samples,\nleading to inconsistent classifications. In contrast, TranScale calibration\neffectively neutralized these variations, recovering the true absolute abundance\nof ERBB2 and ensuring 100% concordance with the dPCR-defined diagnostic\nclassification across all platforms and laboratories (Supplementary Fig. 14).\nThese results highlight the capability of SI-anchored absolute quantification to\nreveal biological signals that may be obscured by technical noise in multi-center\nstudies.\nSupplementary Figure 14. Diagnostic simulation targeting the ERBB2 (HER2)\noncogene\nFor uncalibrated values, a global conversion coefficient was derived via least-squares\nlinear regression (log -transformed FPKM vs. absolute molecular abundance) across 12\n2\nindependent libraries. This model represents a 'universal' conversion logic that attempts to\ninfer absolute quantities from relative signals without accounting for library-specific\nbiochemical biases. TranScale-calibrated values were applied library-specific calibrations.\n12 independent sequencing libraries involving different platforms and laboratories\n(L1_ILL_P, L2_ILL_P, L2_ILL_R, L3_DNB_R) with three replicates. The diagnostic cutoff\nwas empirically defined as the midpoint separating the dPCR-quantified absolute copy\nnumbers of the tumor model (LCA) and normal controls (Quartet D5 and D6).\nReviewer #2 (Remarks to the Author):\nThe authors have substantially addressed my concerns, and the revised\nmanuscript is improved, with claims now appropriately aligned with the data.\nThe expanded benchmarking against ERCC and the inclusion of orthogonal\nvalidation (including ddPCR concordance) strengthen confidence in the\nmethodology. While the ERBB2 and GRB7 analyses raise interesting\nobservations for further investigation, the method could be further strengthened\nby identifying specific examples where the use of the TransScale controls might\ndirectly impact diagnostic interpretation or clinical decision-making.\nNevertheless, with these revisions and the additional validation data, my major\nconcerns have been satisfactorily addressed, and I support acceptance of the\nmanuscript in its current form.\nResponse to Reviewer 2:\nWe appreciate the reviewer’s positive assessment and the insightful suggestion\nto demonstrate the practical clinical utility of our method. We agree that moving\nbeyond technical metrics to specific diagnostic examples strengthens the\nmanuscript significantly.\nTo address this, we performed a Diagnostic Robustness Simulation using\nHER2 (ERBB2), a critical biomarker in oncology, to illustrate how TranScale\nprevents diagnostic errors caused by batch effects.\n1) Establishing a Metrological Anchor (The Cutoff):\nFirst, we utilized our dPCR validation data (which showed high concordance,\nR2=0.99) as the \"ground truth.\" Based on the absolute quantification results, we\nobserved a clear separation between the tumor model (LCA) and normal\ncontrols (Quartet D5/D6). We defined a diagnostic cutoff at 18.5 (log copies/µg\n2\ntotal RNA), which represents the empirically determined discriminatory\nthreshold that perfectly distinguishes tumor from normal samples in the\nreference dPCR data (midpoint of tumor and normal cells, see Supplementary\nFig. 13a).\n2) Simulation of Clinical Decision-Making:\nWe then evaluated whether this diagnostic distinction could be maintained\nacross 12 independent sequencing libraries generated from different platforms\nand laboratories (L1_ILL_P, L2_ILL_P, L2_ILL_R, L3_DNB_R) with three\nreplicates. We compared two strategies:\nScenario A: Universal Cutoff on Relative Quantification (FPKM/TPM)\nWhen we attempted to apply a \"universal diagnostic rule\" to the uncalibrated\nFPKM data, the results were highly inconsistent. Due to severe batch effects\nand compositional biases, the relative expression levels of the same tumor\nsample fluctuated wildly across batches. As shown in the new Supplementary\nFigure 14, the technical noise completely masked the biological signal. In\napproximately 50% of the sequencing libraries (6 out of 12), the tumor sample's\nsignal drifted below the equivalent relative threshold, leading to False Negative\ninterpretations. This demonstrates that traditional RNA-seq cannot support a\nfixed, universal diagnostic cutoff across different laboratories.\nScenario B: Universal Cutoff on TranScale Absolute Quantification\nIn stark contrast, TranScale calibration acted as a metrological anchor. By\napplying library-specific calibration, TranScale effectively neutralized the\nvariation in biochemical efficiency. Regardless of the platform or laboratory, the\nTranScale-calibrated absolute quantities of the tumor samples remained\nconsistently above the diagnostic threshold (18.5 of log copies/µg total RNA),\n2\nachieving 100% diagnostic consistency with the dPCR reference.\nThis specific example demonstrates that TranScale transforms RNA-seq from\na qualitative discovery tool into a robust diagnostic instrument. It allows for the\nestablishment of universal diagnostic benchmarks (absolute copy numbers)\nthat are transferrable across laboratories, preventing false negatives caused by\ntechnical variability.\nWe have expanded our Results section to better articulate the advantages in\nbiological insights at the transcriptomic level.\nExpanded Results section in the revised manuscript:\nDemonstration of robust diagnostic classification\nFinally, to illustrate the potential of TranScale in identifying biological\ninsights and supporting clinical decision-making, we conducted a diagnostic\nsimulation targeting the ERBB2 (HER2) oncogene across 12 independent\nsequencing libraries. We established a \"ground truth\" diagnostic cutoff (18.5 of\nlog copies/µg total RNA) based on reference dPCR data, which clearly\n2\ndistinguished the tumor model (LCA) from normal controls (Quartet D5/D6). We\nobserved that technical batch effects in uncalibrated relative quantification\n(FPKM) frequently masked the overexpression phenotype of the tumor samples,\nleading to inconsistent classifications. In contrast, TranScale calibration\neffectively neutralized these variations, recovering the true absolute abundance\nof ERBB2 and ensuring 100% concordance with the dPCR-defined diagnostic\nclassification across all platforms and laboratories (Supplementary Fig. 14).\nThese results highlight the capability of SI-anchored absolute quantification to\nreveal biological signals that may be obscured by technical noise in multi-center\nstudies.\nSupplementary Figure 14. Diagnostic simulation targeting the ERBB2 (HER2)\noncogene\nFor uncalibrated values, a global conversion coefficient was derived via least-squares\nlinear regression (log -transformed FPKM vs. absolute molecular abundance) across 12\n2\nindependent libraries. This model represents a 'universal' conversion logic that attempts to\ninfer absolute quantities from relative signals without accounting for library-specific\nbiochemical biases. TranScale-calibrated values were applied library-specific calibrations.\n12 independent sequencing libraries involving different platforms and laboratories\n(L1_ILL_P, L2_ILL_P, L2_ILL_R, L3_DNB_R) with three replicates. The diagnostic cutoff\nwas empirically defined as the midpoint separating the dPCR-quantified absolute copy\nnumbers of the tumor model (LCA) and normal controls (Quartet D5 and D6)."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The manuscript has been improved by the authors during the revisions. I agree with the publication."}, "author_response": "Response to Reviewer 1:\nWe are glad we have satisfactorily answered all of Reviewer #1 comments."}]

Decision letter