Tumor and paired cell line whole transcriptome rna standard substance and preparation method and application

Abstract

The application discloses a tumor and paired cell line whole transcriptome RNA standard substance and a preparation method and application, wherein the tumor and paired cell line is a lung cancer and paired paracancer tissue immortalized cell line, is preserved in the China General Microbiological Culture Collection Center, and the preservation date is October 11, 2025; the lung cancer cell line biological preservation number is CGMCC No. 46703; and the paired paracancer tissue cell line biological preservation number is CGMCC No. 46704. The RNA standard substance includes two samples, is whole transcriptome RNA extracts of the tumor and paracancer tissue immortalized cell line of the same individual source in the Chinese genetic background, and the quantity value is the copy number concentration and the ratio of high confidence genes in the two samples. The whole transcriptome RNA standard substance can be used for performance evaluation of absolute quantification or relative quantification of multi-gene expression of detection data of different transcriptome sequencing technologies and sequencing platforms.

Description

Technical Field

[0001] This invention relates to the field of metrological standard material technology, and in particular to a tumor and paired cell line whole transcriptome RNA standard material, its preparation method and application. Background Technology

[0002] RNA sequencing (RNA-Seq) technology has become a core tool for studying gene expression, discovering new transcripts, identifying fusion genes and alternative splicing events, playing an irreplaceable role in molecular subtyping of tumors, biomarker discovery, drug guidance, and prognostic assessment. However, the entire RNA-Seq process is complex, involving multiple steps such as sample preparation, RNA extraction, library construction, sequencing, and bioinformatics analysis. Each step can introduce errors and batch effects, making it difficult to reproducible and compare experimental results across different laboratories, platforms, and batches, severely impacting data reliability and the credibility of clinical applications.

[0003] The use of reference materials (RMs) is crucial for quality control, workflow optimization, and result calibration throughout the RNA-Seq process. Currently, reference materials successfully applied to RNA-seq quality assessment include natural samples in whole transcriptome form used as independent samples and artificially synthesized RNA fragments used as internal controls. Whole transcriptome reference materials include MAQC reference materials and Quartet reference materials, while artificially synthesized reference materials include ERCC RNA spike-in and RNA sequins. However, current reference materials and related technical solutions in this field have the following significant limitations:

[0004] (1) Lack of a "tumor-paired cell line" design: Current tumor RNA sequencing quality assessment mainly relies on the MAQC reference material, which was developed based on 10 cancer cell lines (MAQC Sample A) and 23 donor brain tissues (MAQC Sample B). Sample A contains a mixture of different types of human tissue RNA, providing a multi-tissue, multifunctional background representing a broad gene expression profile. Sample B is derived from the adult brain, providing a specific tissue-specific background with specific brain-related gene expression characteristics. However, the MAQC reference material lacks a paired control that is completely consistent with the genetic background of tumor tissues. This deficiency makes it impossible to provide stable, real differential expression signals between samples in data analysis, making it difficult to distinguish between true tumor-specific expression changes and background noise introduced by the technology. In particular, the accuracy and sensitivity are severely insufficient when identifying low-abundance expressed genes, chimeric RNAs, or small allele-specific expression.

[0005] (2) Apart from the Chinese Family Lineage 1, existing standard materials are only from the United States and Australia. Transcriptomic standard materials based on the genetic background of East Asians or even Chinese people are still lacking. When analyzing Chinese transcriptomic samples by referring to standard materials derived from Western gene sequences, the bias introduced by sequence-specific expression profiles cannot be accurately identified. This leads to a lack of accuracy and scientific validity of biomarkers discovered through RNA-seq, which cannot truly guide clinical applications. Although the Chinese Family Lineage 1 whole transcriptomic RNA standard material is based on the genetic background of Chinese people, it still cannot represent the gene expression profile information of tumor samples.

[0006] (3) Lack of absolute quantitative standard materials: Most existing standard materials only provide relative quantitative standards, i.e., used to compare the expression ratios of different genes or samples. The Chinese Family No. 1 Project obtained whole transcriptome RNA standard materials by constructing immortalized B lymphocyte lines from four members of a family of identical twins, and established a ratio-based gene expression reference dataset based on multi-laboratory RNA-seq data generated by 21 batches of different measurement procedures to evaluate the reliability of RNA-seq technology in terms of batch proficiency and cross-batch reproducibility (CN2022110012391). However, they lack absolute molecular copy number information for each RNA transcript in the standard materials and cannot provide the expression profile of all genes in the sample. Without absolute quantitative standards, it is impossible to achieve true direct comparison and normalization of data between different laboratories, and it is also impossible to accurately evaluate the sensitivity, linear range, and limit of detection of the detection method, which greatly limits the application of RNA-Seq in precision medicine (such as companion diagnostics based on gene expression levels).

[0007] In summary, there is an urgent need in this field for a new type of tumor and paired cell line whole transcriptome RNA standard material and a matching reference dataset. It should: (1) represent the gene expression characteristics of tumor or specific tumor transcriptome; (2) provide paired controls with completely consistent genetic backgrounds to accurately distinguish technical noise from biological signals; (3) provide a standard dataset of absolute molecular copy number for each transcript to achieve true absolute quantification; and (4) ensure a high degree of uniformity and traceability between the standard material and the standard dataset to guarantee the wide applicability and consistency of the application. Summary of the Invention

[0008] To address the aforementioned problems in the prior art, the purpose of this invention is to provide a tumor and paired cell line whole transcriptome RNA standard substance, its preparation method, and its application. This standard substance has a clear source, accurate values, and is stable and reliable. It is a whole transcriptome RNA standard substance with a Chinese genetic background, paired with tumor and non-tumor samples from the same individual, and can be used to assess the absolute quantification of gene expression profiles in tumor and normal samples. It also includes an absolute quantification reference dataset and its construction method.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A whole transcriptome RNA standard for tumor and paired cell lines, wherein the tumor and paired cell lines are immortalized cell lines of lung cancer and paired adjacent normal tissue, deposited at the China General Microbiological Culture Collection Center (CGMCC) on October 11, 2025. The biological accession number of the lung cancer cell line is CGMCC No. 46703, and the biological accession number of the paired adjacent normal tissue cell line is CGMCC No. 46704.

[0011] The RNA standard material comprises two samples, which are whole transcriptome RNA extracts from immortalized cell lines of tumors and adjacent normal tissues of Chinese genetic background and from the same individual. The values ​​are the copy number concentration and ratio of high-confidence genes in the two samples.

[0012] The RNA standard material consists of two tubes containing a large number of RNA aliquots (e.g., >200 units / sample) from multiple independent culture and amplification batches of paired tumor (LCA) and non-tumor (LCN) samples from the same source. Each standard material unit contains enough RNA (e.g., ≥ 2 μg / unit) for one batch of RNA-seq library construction with three technical replicates. The quality of the RNA standard material meets the following requirements: OD260 / 280 approximately 2.0, OD260 / 230 > 2.0, RIN value > 9.0, 28S / 18S ribosomal RNA band ratio greater than 1.8; and free from DNA contamination and / or RNA degradation.

[0013] The method for preparing tumor and paired cell line whole transcriptome RNA standard materials according to the present invention includes the following steps:

[0014] (1) The tumor cell lines were obtained from the tumor tissue of patients with lung adenocarcinoma. Primary cells were obtained by tissue block seeding and cultured. Adjacent tissue cells were obtained from the distal tumor tissue of the same individual and immortalized cell lines were obtained by transfection with EBV virus. The cell lines were lung cancer and paired adjacent tissue immortalized cell lines with Chinese genetic background. They were LCA and LCN cells cultured to a certain concentration, such as 10 7 cells / mL;

[0015] (2) Both lung cancer cells and paired cells were cultured to passage 10 or higher, preferably to passage 12-25, to ensure cell stability and reproducibility. Commercially available high-purity RNA extraction kits (such as Qiagen's RNeasy® Mini Kit) were used for RNA extraction and purification to obtain high-purity whole transcriptome RNA standard materials;

[0016] (3) The RNA concentration and purity, DNA contamination and RNA integrity of the extracted and purified whole transcriptome RNA standard materials were detected;

[0017] (4) Construct a set of high-confidence gene copy number concentrations (e.g., copies / μL) and their expression ratios (Tumor / Normal or Normal / Tumor) in two samples for the whole transcriptome RNA standard material, i.e., the whole transcriptome RNA absolute quantification reference dataset, which serves as the quantitative value of the whole transcriptome RNA standard material. The construction of the whole transcriptome RNA absolute quantification reference dataset adopts a multi-center, multi-platform validation strategy to ensure the accuracy of the absolute quantification dataset.

[0018] In step (3), RNA concentration and purity are detected by using a UV spectrophotometer to detect the mass concentration (ng / μL) of the standard substance. The OD260 / 280 ratio is required to be approximately 2.0 and the OD260 / 230 ratio is greater than 2.0.

[0019] RNA integrity testing: The RNA integrity index (RIN value) was tested using instruments such as the Agilent Bioanalyzer (model 2100 or 4200). The RIN value was required to be > 9.0, and the 28S / 18S ribosomal RNA band ratio was greater than 1.8, with no RNA degradation.

[0020] DNA contaminant detection: Ensure the absence of genomic DNA contamination. Using the sample as a template, add reverse transcriptase to synthesize cDNA. Select primers for housekeeping genes (such as GAPDH, β-actin) for qPCR amplification, and confirm the absence of amplification signal.

[0021] This process also includes the aliquoting of standard substances: Prepared RNA standard substances are mixed in one tube, thoroughly shaken in an ice-water bath to ensure homogeneity, and the required measurement concentration is determined (e.g., RNA ≥ 2 μg, concentration ≥ 2000 ng / μL). The aliquots are then dispensed into 0.5 mL RNase-free cryovials, with at least 10 μL per tube, and more than 200 tubes per sample. The dispensed RNA is then placed in plastic boxes containing 100 tubes each and stored at -80°C.

[0022] The method for determining high-confidence genes of whole transcriptome RNA standard material in step (4) includes the following steps:

[0023] (1) Transcriptome sequencing was performed using a high-throughput sequencing platform for tumor and paired cell line whole transcriptome RNA standard materials to obtain multiple batches of raw data.

[0024] (2) Gene expression was quantified using a unified bioinformatics process to obtain the TPM value of each gene; where TPM refers to the number of transcripts of a certain gene per million transcripts.

[0025] (3) If a gene is detected in all datasets, in all batches, in datasets of more than two technical replicates of a specific batch, and the TPM of the gene is > 1, then the gene is identified as a high-confidence gene. This set ensures the stability and reproducibility of gene expression.

[0026] The bioinformatics process for gene expression quantification involves using a unified process (such as HISAT2 / STAR for sequence alignment and featureCounts / StringTie for gene expression quantification) to analyze all batches of data and obtain the TPM (number of transcripts per million) value for each gene.

[0027] The method for identifying high-confidence genes in whole transcriptome RNA standard materials involves at least six batches of raw sequencing data, and includes sequencing platforms and library construction methods based on different principles. The sequencing platforms include Illumina NovaSeq Xplus and MGI DNBSEQ-T7 platforms, and the library construction methods include PolyA mRNA enrichment and RiboZero rRNA removal library construction.

[0028] The method for constructing the absolute quantitative reference dataset of whole transcriptome RNA is as follows: mixing whole transcriptome RNA standard substances with internal reference standard substances with known values, obtaining multiple batches of transcriptome sequencing data in multiple laboratories based on different principles and technologies, screening correction factors by internal reference standard substance quality values, and then converting the transcriptome sequencing data to obtain the dataset.

[0029] Furthermore, the method for constructing the absolute quantification reference dataset for whole transcriptome RNA includes the following steps:

[0030] (1) In any transcriptome sequencing of any sample, add internal reference standard material for co-construction of library and co-sequencing. Based on the standard value (copy number concentration) of the internal reference standard material, screen correction factors within a certain error range (e.g. ±5%), and establish a linear relationship standard curve between the original expression level (e.g. TPM, FPKM, etc.) of the correction factor obtained by a certain standardization method and its copy number concentration standard value.

[0031] (2) The copy number concentration of the gene to be tested is calculated using the standard curve described in step (1) based on the original expression level of the gene to be tested in the sample (such as TPM, FPKM, etc.).

[0032] (3) Using the methods described in steps (1) and (2), calculate the copy number concentration of high-confidence genes in any transcriptome sequencing of the selected batches of the two samples;

[0033] (4) Using the method described in step (3), for each high-confidence gene in two samples (tumor and normal) of whole transcriptome RNA standard material, calculate the high-confidence gene copy number concentration for each sample in each batch and each technical replicate (the same sample is measured 3 times, which is 3 technical replicates). Use the average value of each batch as the gene copy number concentration of the corresponding sample. Screen for common high-confidence genes in the two samples and calculate the copy number concentration ratio of each gene in the tumor sample and the non-tumor sample.

[0034] (5) The gene copy number concentration of each high confidence gene in the two samples and the ratio of the copy number concentration of the common high confidence gene in the tumor and non-tumor samples are used as the standard values ​​of the whole transcriptome RNA standard material of the tumor and paired cell lines, that is, the absolute quantitative reference dataset of whole transcriptome RNA.

[0035] The internal reference standard materials have the following characteristics: they have traceable standard values ​​(such as isotope dilution mass spectrometry standard values ​​that meet the general requirements of metrology); the original expression level of each internal reference is calculated as the relative difference from the standard value after the logarithmic level is mean-normalized with the standard value; the number of correction factors used to establish the linear relationship between the original expression level (such as TPM, FPKM, etc.) and its copy number concentration standard value is not less than 20, and the linear fitting correlation coefficient is not less than 0.95;

[0036] Furthermore, the internal reference standard material includes a first composition and a second composition, both of which contain multiple RNA calibrators, wherein each RNA calibrator has a certified absolute copy number concentration and its uncertainty determined by a primary reference measurement procedure and traceable to the International System of Units (SI); wherein at least one RNA calibrator has a different concentration in the first composition and the second composition; the number of RNA calibrators is at least 20, selected from sequences shown as shown in SEQ ID NO: 1-100, and includes at least one of sequences shown as shown in SEQ ID NO: 51 and SEQ ID NO: 53-100;

[0037] Furthermore, the absolute copy number concentration of the RNA calibrator in the first and second compositions can be selected to span a concentration gradient of at least three orders of magnitude, such as a gradient of 10. 4 -10 6 The ratio of the copy number concentrations of the RNA calibrator in the two compositions, taken as log2, can be chosen with a range of at least log2. -2 ~log22 That is, at least 16 times.

[0038] Furthermore, the method for constructing the absolute quantitative reference dataset of whole transcriptome RNA also includes evaluating the uncertainty of the value of each high-confidence gene in the two samples, and evaluating the uncertainty of the value of the high-confidence gene in the two samples.

[0039] For each sample, the uncertainty of the gene copy number concentration is the relative standard deviation (RSD) of the corresponding gene copy number concentration for the selected batch and technical replicates divided by the square root of the number of measurements n, i.e. , where n is the number of selected batches; the uncertainty of the ratio of the copy number concentration of each gene in the two samples is the square root of the sum of the squares of the uncertainties of the copy number concentration of the corresponding gene in the two samples.

[0040] Standard reference dataset: The absolute values ​​and uncertainties of high-confidence genes in each paired sample, and the copy number concentration ratio and uncertainty of high-confidence genes shared by the paired samples are included in the absolute quantitative reference dataset of whole transcriptome RNA of tumor and paired cell lines in this invention.

[0041] The tumor and paired cell line whole transcriptome RNA standard materials described in this invention can be used for performance evaluation of absolute or relative quantification of multi-gene expression data detected by different transcriptome sequencing technologies and sequencing platforms.

[0042] Compared with the prior art, the outstanding effect of the present invention is as follows:

[0043] (1) The tumor and paired cell line whole transcriptome RNA standard material of the present invention has a tumor and pairing design, that is, the whole transcriptome RNA standard material is derived from tumor tissue and paired control (adjacent tissue) that is completely consistent with its genetic background. The standard material derived from the same individual helps to distinguish true tumor-specific gene expression changes and sequencing technology errors.

[0044] (2) The tumor and paired cell line whole transcriptome RNA standard material of the present invention has the absolute molecular number of gene expression, and achieves absolute quantification for the first time in whole transcriptome RNA standard material. It can directly evaluate and compare the accuracy of gene expression quantification results of sequencing data from different laboratories, different sequencing platforms and different batches.

[0045] The following description, in conjunction with the accompanying drawings and specific embodiments, further illustrates the tumor and paired cell line whole transcriptome RNA standard materials, their preparation methods, and applications described in this invention.

[0046] Biological Preservation Information

[0047] An immortalized lung cancer cell line, named NIM_LCA, with accession number CGMCC No. 46703, is deposited at the China General Microbiological Culture Collection Center (CGMCC), located at No. 3, Courtyard 1, Beichen West Road, Chaoyang District, Beijing, on October 11, 2025.

[0048] A paired immortalized cell line adjacent to normal tissue was named NIM_LCN, with accession number CGMCC No. 46704. It is deposited at the China General Microbiological Culture Collection Center (CGMCC), located at No. 3, Courtyard 1, Beichen West Road, Chaoyang District, Beijing, on October 11, 2025. Attached Figure Description

[0049] Figure 1 It is a transcript type.

[0050] Figure 2 The distribution of transcripts is shown in (a) for length distribution and (b) for length proportion.

[0051] Figure 3 This refers to the GC content.

[0052] Figure 4 The theoretical concentration range and ratio distribution range of 100 transcripts were designed for two sets of calibrator compositions.

[0053] Figure 5 The certified absolute quantities, ratios, and measurement uncertainty distributions of 100 transcripts in the calibrator compositions are shown below (top shows the certified absolute quantities and measurement uncertainty distributions of composition 1, middle shows the certified absolute quantities and measurement uncertainty distributions of composition 2, and bottom shows the ratios and uncertainty distributions of each transcript in the two compositions).

[0054] Figure 6 Orthogonal validation of the certification values ​​was performed using RT-dPCR.

[0055] Figure 7 The result is the orthogonality verification of the sequences.

[0056] Figure 8 The absolute measurement deviation (a for composition 1, b for composition 2) and the ratio (c) measurement deviation are given.

[0057] Figure 9 The steps and requirements for constructing library-specific calibration.

[0058] Figure 10 Based on 8 sequencing batches Figure 9 The remaining transcripts that meet the requirements are selected based on the screening criteria.

[0059] Figures 11-12 Used in 8 sequencing batches Figure 10 The results of linear fitting of the transcripts that meet the requirements after screening.

[0060] Figure 13 For use Figures 11-12 The calibration curve is the ratio of the absolute number of 100 transcripts in composition 1 to the theoretical value after calibrating other transcriptomes that are uncertain (the closer it is to 1, the more accurate the result after calibration).

[0061] Figure 14 For use Figures 11-12 The calibration curve is the ratio of the absolute number of 100 transcripts in composition 2 to the theoretical value after calibrating other transcriptomes that are uncertain (the closer it is to 1, the more accurate the result after calibration).

[0062] Figure 15 This is a graph showing the coefficient of variation (CV) distribution of the calibration method of the present invention for the measurement results of 100 transcripts in each of the two compositions (the median CV was >85% before calibration across laboratories, platforms, and library construction protocols, and significantly decreased to <15% after calibration). Figure 15 d).

[0063] Figure 16 This is a schematic diagram illustrating the preparation and determination process of whole transcriptome RNA standard materials for tumors and paired cell lines in this invention.

[0064] Figure 17 Electrophoresis images of RNA standard materials from tumor cells and paired cell lines analyzed using Agilent 4200. EL represents the molecular weight marker, and the band sizes from bottom to top are 25 (green), 200, 500, 1000, 2000, 4000, and 6000 nt. The labels above the samples indicate the sample number. The RNA standard material samples were analyzed three times each for LCA and LCN. The labels below indicate the RNA integrity (RIN) value of the corresponding standard material sample.

[0065] Figure 18 This diagram illustrates the multi-platform, multi-laboratory sequencing data of whole transcriptome RNA standard materials for tumors and paired cell lines in this invention. Two different library construction methods and four mainstream RNA sequencing platforms were used, producing 13 batches of data from 8 laboratories. Each batch included three technical replicates of each of two samples of the RNA standard material, meaning each batch contained 6 RNA sequencing libraries.

[0066] Figure 19 This is a flowchart illustrating the sequencing data analysis of whole transcriptome RNA standard materials from tumor cells and paired cell lines in this invention.

[0067] Figure 20This is a schematic diagram of the high-confidence gene screening and absolute quantification reference dataset of whole transcriptome RNA standard materials for tumors and paired cell lines in this invention. Detailed Implementation

[0068] Examples 1-4 illustrate the preparation, calibration method construction, and verification of the internal reference standard substances used in this invention.

[0069] Examples 5-6 describe the construction of standard RNA materials for tumor and paired cell lines and their corresponding absolute quantitative reference datasets for whole transcriptome RNA.

[0070] In the following examples, the internal reference standard substances are all based on 100 RNA calibrators. In other beneficial embodiments, 20 or more can be selected to achieve the calibration purpose.

[0071] Example 1: Preparation and Characterization of SI Traceable Calibrator Composition (TranScale) Figure 1-7 )

[0072] 1. Purpose

[0073] This embodiment aims to illustrate the preparation process and metrological characterization of the core physical entity of the present invention—the TranScale calibrator composition (internal reference standard material)—to demonstrate its qualification and reliability as an "absolute benchmark".

[0074] 2. Preparation method

[0075] A library containing 100 unique RNA transcripts was designed and synthesized, with sequences shown in SEQ ID NO: 1-100. These transcripts were designed following principles to faithfully simulate the complexity of the real transcriptome:

[0076] Complexity simulations include sequences representing complex biological events, such as alternative splicing isoforms, gene fusions (e.g., BCR-ABL1), single nucleotide variants (SNVs), and families with high sequence homology. Figure 1 ).

[0077] Biomimicry: Sequence length (200-5000 nt), GC content (40-60%), and Poly(A) tail length all cover the typical distribution of the human transcriptome. Figure 2-3 ).

[0078] Orthogonality: All sequences showed no significant homology with known human, rat, mouse, zebrafish, and common microbial genome sequences, ensuring no cross-interference during detection in biological samples.

[0079] The above 100 RNA molecules were prepared on a large scale by in vitro transcription (IVT), followed by purification and precise quantification.

[0080] 3. Quantitative Characterization

[0081] The copy number concentrations of 100 purified RNA molecules were absolutely quantified using isotope dilution mass spectrometry (IDMS), the highest-precision nucleic acid quantification method recognized in the field. The quantification results were rigorously assessed for measurement uncertainty and traceable to the International System of Units (SI). The quantification results and measurement uncertainties are shown in Table 1. The mass concentration of each RNA transcript was independently calculated from four NMPs (AMP, GMP, CPM, UMP) using IDMS, and the average value was converted to copy number. IDMS quantified each transcript twice; the two independent measurements showed good reproducibility, and the average of the two experiments was taken as the copy number of each transcript, with an RSD of less than 10%. The concentrations of all 100 RNA transcripts were determined using the IDMS quantification method. Each RNA transcript was measured 12 times.

[0082] Table 1

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] 4. Preparation by gravimetric method

[0089] One hundred purified and valued transcripts were separated by gravimetric method according to... Figure 4 The gradient mixing formulation consists of two compositions (Mix1 and Mix2). Figure 4 The horizontal axis represents the concentration range of 100 transcripts in the two compositions, and the vertical axis represents the range of the ratio of each transcript in the two compositions (at the log2 level). Ultimately, these 100 SI-assigned RNA molecules were compared across a concentration gradient spanning six orders of magnitude, with a ratio spanning 378-fold (log2). -3.84 ~log2 4.73 The components were precisely mixed to form the TranScale calibrator composition. The final assigned copy number concentration relative expanded uncertainty (k=2) was better than 20%, and the ratio uncertainty (k=2) was better than 27%. See details... Figure 5 .

[0090] 5. Orthogonal verification

[0091] Orthogonal validation was performed using a one-step reverse transcription digital PCR method with 20 sequence-specific sequences, and the correlation between the two methods reached over 0.98. Figure 6 As shown, this demonstrates the accuracy of the IDMS setting results.

[0092] 6. Sequence Orthogonality Verification

[0093] Two prepared mixed calibrators were used for RNA sequencing, employing two different platforms (IlluminaNovaSeq, MGI DNBSEQ-T7) and two different library preparation strategies (poly(A) selection, rRNA depletion). The results showed that over 99.1% of reads aligned to the calibrator's own sequence, while <0.0015% aligned to the human reference genome. This indicates that there is no possibility of sequence contamination when co-sequencing the calibrators and the human genome sample. Figure 7 As shown.

[0094] 7. Conclusion

[0095] This embodiment successfully prepared an RNA calibrator composition with well-defined physical properties and concentration values ​​accurately determined through SI traceability. This serves as the foundation for all subsequent diagnostic, calibration, and quantification methods.

[0096] Example 2: The use of a dual-index evaluation framework to reveal hidden measurement deficiencies ( Figure 8 )

[0097] 1. Purpose

[0098] This embodiment aims to illustrate the unique dual-index evaluation method of the present invention and to demonstrate that the method can reveal measurement defects that cannot be detected by existing technologies and that pose a fatal threat to data reliability.

[0099] 2. Evaluation Methods

[0100] TranScale calibrators were added to the RNA samples to be tested, and standard RNA-seq library preparation and sequencing were performed. After obtaining the sequencing data, two core evaluation metrics were calculated:

[0101] Absolute measurement error (ME): assesses the degree of dispersion of sequencing signals (such as FPKM) from different calibrators from their theoretical expected values, reflecting the severity of sequence dependence bias.

[0102] Ratio Measurement Error (Ratio ME): Evaluates the consistency between the measurement ratio between any two calibrators and the ratio calculated from the SI traceability true value, reflecting the accuracy of relative quantification.

[0103] 3. Experimental Results (Discovery of the Double Paradox)

[0104] When evaluating sequencing data from multiple different batches and platforms, two "hidden killers" were discovered that were completely undetectable by existing quality control (QC) methods:

[0105] Paradox 1 (L2_DNB_P batch): This batch exhibits extremely low Ratio ME (relative quantification accuracy), but its Absolute ME is extremely high and chaotic (absolute quantification inaccuracy). This demonstrates that "relative accuracy" can mask severe sequence dependence bias.

[0106] Paradox 2 (L2_DNB_R batch): This batch exhibits extremely low Absolute ME (small sequence dependence bias), but its Ratio ME deviates significantly (relative quantification inaccuracy). This proves that "good absolute signal mode" can also mask global proportional distortion.

[0107] 4. Conclusion

[0108] This embodiment demonstrates that traditional QC methods based on single, relative relationships have significant blind spots. The dual-index evaluation framework of this invention is necessary because it is the first to comprehensively evaluate the reliability of sequencing procedures from two orthogonal dimensions of "structure" and "proportion," discovering and defining an unprecedented "double paradox" measurement failure mode.

[0109] Example 3: Construction of library-specific calibration curves ( Figure 9 )

[0110] The construction of library-specific calibration curves must ensure both rigor (ensuring only high-fidelity data is used for model building) and robustness (retaining a sufficient number of calibrators to establish a statistically reliable model), and must meet the following conditions:

[0111] 1. Measurement error (ME) threshold (±5%)

[0112] Choosing an ME threshold of ±5% is central to the functionality of this framework. A more lenient threshold (such as ±10% or ±20%) might introduce calibrators that exhibit significant nonlinear behavior or are heavily influenced by sequence-specific bias. This would “contaminate” the linear regression model, resulting in inaccurate slopes and intercepts that fail to reflect the true systematic bias of the workflow. Conversely, an overly stringent threshold (such as ±1% or ±2%) is too punitive and may discard a large number of valid calibrators due to the inherent random noise in the sequencing process. This could lead to insufficient data points for robust regression or even the incorrect rejection of an otherwise acceptable library. Therefore, this invention establishes an ME threshold of ±5% as an optimal setpoint that effectively isolates the main linear response trends of the workflow while tolerating a reasonable range of measurement noise.

[0113] 2. FPKM threshold (>0.1)

[0114] This threshold was empirically determined based on the performance of the lowest abundance extrinsic calibrator. The inventors' research team observed that transcripts with FPKM values ​​below this level exhibited poor reproducibility across technical replicates, and crucially, their corrected values ​​after calibration deviated significantly from their SI-traceable certified values. Therefore, a threshold of FPKM > 0.1 was established to exclude these unreliable data points, ensuring that the calibration model is constructed only from calibrators that have been robustly and accurately quantified.

[0115] 3. Dynamic range threshold (>2) 10 times)

[0116] This standard ensures that the calibration model is not built on a narrow concentration range. A wide dynamic range is crucial for the model to gain sufficient "leverage" to accurately determine the slope, thereby ensuring its effectiveness when applied to endogenous transcripts covering the entire expression profile.

[0117] 4. Coefficient of determination (R²) 2 Threshold (>0.95)

[0118] R 2 >0.95 is a widely accepted standard for demonstrating a strong linear relationship. Setting this as the final quality control serves as final confirmation that the fundamental assumption of linearity holds for the specific library being processed, thus providing a high degree of confidence for subsequent calibration.

[0119] 5. Minimum number of calibrators used for robust model fitting

[0120] A key and implicit quality control principle within this invention framework is the number of calibrators successfully screened by 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 multiple batch analyses, even lower-quality libraries that can ultimately be "saved" through calibration consistently provide a large number of calibrators (typically >30). Therefore, this invention establishes a practical guideline: a library should generate at least 20-30 high-confidence calibrators before proceeding to high-confidence absolute quantification.

[0121] Example 4: Establishment of a calibration model and absolute quantitative correction based on TranScale

[0122] 1. Purpose

[0123] This embodiment aims to illustrate the present invention's calibration method for converting sequencing reads into SI traceable absolute copy number, and to demonstrate that the method can effectively correct the serious measurement defects found in Example 2.

[0124] 2. Calibration Method

[0125] First, transcripts with ME < 5% are screened using the ME index established above. Figure 10 Then, using the measurement signals (reads or FPKM) of TranScale calibrators from the same sequencing data and their SI-traceable copy number truth values, a unique calibration function (y = ax + b, where y is the sequencing signal and x is the absolute copy number) is established for each sequencing library (i.e., each sample) through least squares linear regression. The goodness of fit of this model (R²) is then measured. 2 ) is generally better than 0.97 ( Figures 11-12 ).

[0126] 3. Correction effect

[0127] The established calibration model was applied to the entire dataset of its source library (including the calibrators themselves and endogenous genes).

[0128] Universal validity: For all test batches, the ratio (log2 scale) of the calibrated calibrator measurement to its true value converges tightly to 1 (i.e., the ratio is 1), demonstrating the universal accuracy of the calibration.

[0129] Strong error correction capability: Crucially, this calibration method successfully corrected both "contradictory" failure modes found in Example 2 simultaneously—whether it was the relatively accurate but absolutely chaotic L2_DNB_P batch, or the absolutely good but relatively distorted L2_DNB_R batch, the corrected results were restored to the gold standard level. Figures 13-14 ).

[0130] Significantly reduced coefficient of variation (CV): After calibrating previously uncertain calibrators, the median CV of measurements from 100 calibrators across laboratories, platforms, and library construction methods was significantly reduced from 85% to 25%. Figure 15 a- Figure 15 d).

[0131] 4. Conclusion

[0132] This embodiment demonstrates that the calibration method of the present invention can establish an empirical "converter" for each sample, mapping relative signals to absolute scales. This method is not only universal but also powerful, capable of correcting complex biases of drastically different natures generated in upstream sequencing processes, achieving true absolute quantification.

[0133] Example 5: Preparation of standard RNA materials for lung cancer and paired cell transcriptomes

[0134] This embodiment details the preparation process of transcriptome RNA standard materials based on lung cancer cell lines and paired adjacent normal tissue cell lines. Figures 16-17 ).

[0135] (1) Cell line culture and collection: The basic culture medium for tumor cells and paired cells was DMEM / F12 (Gibco). Other components added were: 5% FBS, 1% penicillin-streptomycin (Gibco), 1% insulin-transferrin-selenium (ITS-G, 100X, Gibco), 500 μL of mycoplasma removal reagent (TransGold, FM421), and 10 ng / mL of human epidermal growth factor (EGF). 10 μM ROCK inhibitor Y-27632 (AbMole, not added for tumor cells). The cell medium was changed every 2 days. Cells cultured to passage 21 (4 trays of T225 cells) were divided into two 50 mL centrifuge tubes, digested, centrifuged at 1000 rpm at room temperature for 5 min, resuspended in 10 mL PBS, and pipetted until no white cell clumps were found. The cells were diluted 20-fold (190 μL PBS + 10 μL cell suspension). The total number of cells in each tube was counted, labeled, and recorded. Discard the supernatant, resuspend the cells in 4 mL of PBS, centrifuge at 1000 rpm at room temperature for 5 min, discard the supernatant, and quickly freeze the cell pellet in liquid nitrogen.

[0136] (2) Transcriptome RNA extraction: Whole transcriptome RNA from lung cancer and paired cells was extracted and purified using Qiagen's RNeasy® Mini Kit (#74104) to obtain candidate standard materials. All operations were performed on ice using wide-mouth pipette tips to minimize mechanical shearing of RNA. Specific steps are as follows:

[0137] 1) Cell density reaches 10 7 The cells / mL count was transferred to a centrifuge tube and centrifuged at 300 g for 5 min. All supernatant was then carefully aspirated.

[0138] 2) Add 600 μL Buffer RLT to lyse the cells, then vortex or pipette to mix thoroughly.

[0139] 3) Add an equal amount of 70% ethanol to the cell lysis buffer and mix thoroughly.

[0140] 4) Add 700 μL of cell lysis buffer to the RNeasy spin column, centrifuge at ≥8000g (≥10000 rpm) for 15 s, and discard the waste liquid.

[0141] 5) Repeat the previous step with the remaining solution.

[0142] 6) Add 700 μL of Buffer RW1 to the RNeasy spin column, centrifuge at ≥8000 g (≥10000 rpm) for 15 s, and discard the waste liquid.

[0143] 7) Add 500 μL of Buffer RPE to the RNeasy spin column. Gently cap the column and centrifuge at ≥8000g (≥10000 rpm) for 2 min. Discard the waste liquid.

[0144] 8) Place the RNeasy spin column into a new 2 mL collection tube and discard the old collection tube along with the waste liquid. Gently cap the tube and centrifuge at full speed for 1 min.

[0145] 9) Place the RNeasy spin column into a new 1.5 mL collection tube and add 30-50 μL of enzyme-free water to the spin column. Gently cap the column and centrifuge at ≥8000g (≥10000 rpm) for 1 min.

[0146] 10) To increase RNA yield, repeat the elution with the elution buffer from step 9 to obtain a higher concentration of RNA. Collect the high-concentration RNA solution and store the RNA at -80°C.

[0147] (3) RNA standard material quality control: RNA concentration and purity were detected using a Nanodrop 2000 micro spectrophotometer (OD260 / 280 approximately 2.0, OD260 / 230 > 2.0). RNA integrity was detected using an Agilent 4200 bioanalyzer and an RNA Nano6000 microarray electrophoresis kit. The DIN value of all samples was greater than 9.9, and the 28S / 18S ribosomal RNA band ratio was greater than 1.8, indicating good RNA integrity and meeting the requirements of high-throughput sequencing. Figure 17 ).

[0148] (4) Aliquoting and storage of RNA standards: The purified RNA was adjusted to a certain concentration (e.g., 250 ng / μL) using RNA storage solution (Thermo #AM7001), and thoroughly mixed at 4°C and 140 rpm on a horizontal shaker before aliquoting. Each aliquot was 10 μL (containing 2.5 μg RNA / unit). The aliquoted standards were stored in an ultra-low temperature freezer at -80°C for long-term storage.

[0149] Example 6: Construction of a reference dataset for absolute quantification of transcriptome RNA in lung cancer and paired cells

[0150] This embodiment illustrates how to construct an absolute quantitative standard reference dataset and evaluate its uncertainty using the internal reference standard material prepared in Example 1 and the RNA standard material prepared in Example 5.

[0151] (1) Sequencing sample preparation: Mix the aliquoted RNA standard material (LCA and LCN, two samples in total) with the internal reference standard material in a certain proportion, wherein the amount of internal reference standard material added is 1 / 1000 of LCA or LCN (mass concentration).

[0152] The internal reference standard prepared in Example 1 includes two mixtures of 100 transcripts in different gradient ratios and quantities, namely Composition 1 and Composition 2. The standard value of 100 transcripts in either composition is the copy number concentration, which has been traced by isotope dilution mass spectrometry. It has been fully verified and confirmed and will not contaminate the detection of the target gene.

[0153] (2) Multi-platform sequencing and data output: The mixed RNA standard materials were distributed to 8 laboratories. Three laboratories used the Illumina Novaseq Xplus sequencing platform, four used the MGI DNBSEQ-T7 platform, and one used the MGI DNBSEQ-T10×4RS and DNBSEQ-T20×2 platforms, employing PolyA enrichment and RiboZero rRNA removal methods for library construction, respectively. The minimum data output for PolyA library construction samples was required to be at least 10G, and for RiboZero library construction samples, at least 20G. A total of 13 batches of sequencing data were generated, producing 78 valid transcriptome sequencing datasets (2 standard materials × 3 technical replicates × 13 batches). Figure 18 ).

[0154] (3) Quantitative gene expression ( Figure 19 )

[0155] 1) Data quality control and filtering

[0156] Fastp was used to perform quality control filtering on the raw sequencing data, removing low-quality bases, adapter sequences, and reads that were too short. FastQC was then used to generate a quality control report to ensure that the data quality met the requirements for subsequent analysis.

[0157] 2) Comparison and annotation

[0158] The filtered data were aligned to a human reference genome (e.g., Ensembl GRCh38) using STAR software. FastQScreen was used to verify the sample origin and rule out contamination. The alignment results were statistically evaluated using Qualimap. Simultaneously, the same alignment software was used to align the data to the reference sequence of the internal control standard, obtaining the corresponding BAM files for each batch of technical replicates for each sample. Alternatively, an integrated genome and annotation file of the internal control standard sequence and the selected human reference genome can be constructed here, allowing for simultaneous alignment results of each gene in the test sample with the internal control gene in a single alignment.

[0159] 3) Quantitative gene expression

[0160] The original counts matrix was obtained by counting reads that uniquely aligned to gene regions using featureCounts, HTSeq, and StringTie, respectively. Simultaneously, the counts of all genes detected in each sample for each batch of library construction were standardized to obtain the TPM value.

[0161] (4) Correction factor screening Figure 19 )

[0162] In any technically replicated dataset of any batch of a specific sample, the TPM value of the log-transformed internal reference gene is compared with the copy number concentration of its input for library construction. The TPM value is standardized based on the standard value of the corresponding gene to obtain a standardized value. The relative error between the detected internal reference gene and the standard value is calculated and denoted as the measurement error (ME). Based on the known copy number of the internal reference gene, internal reference genes with a measurement error within ±5% are selected as correction factors. A linear relationship between the TPM of the selected internal reference genes and the input copy number is established, ensuring that the correlation coefficient of the linear fit is not less than 0.95. A linear fit standard curve is established, and datasets that do not meet the above requirements are removed, retaining only the selected datasets as valid batches.

[0163] In this embodiment, by comparing data from different quantitative methods with the standard values ​​of the internal reference standard, the quantitative method with the highest consistency with the standard value was used for data analysis of all batches. After comparison and screening, this embodiment selected StringTie as the quantitative method for obtaining the gene counts in the mixed sample of RNA standard and internal reference standard.

[0164] (5) High-confidence gene screening and absolute quantification ( Figure 20 )

[0165] Based on the established linear relationship model between TPM and absolute copy number, the absolute copy number of each gene was calculated according to the TPM of each gene in each batch and technical replicate sample obtained from the quantitative RNA standard material. High-confidence genes (detected in all batches, detected in more than two technical replicates in any batch, and with TPM > 1) were selected for subsequent reference dataset construction.

[0166] In this embodiment, based on sequencing data from 3 technical replicates of two samples in 13 batches, a total of 18,253 and 17,768 high-confidence genes were obtained from the LCA and LCN samples, respectively. For each high-confidence gene in each sample (tumor and normal), the arithmetic mean of its copy number concentration calculated across all valid batches and technical replicates was taken as the final standard copy number concentration value of that gene in that sample. Furthermore, high-confidence genes common to both samples were identified, and the ratio of their copy number concentrations in the tumor sample to the normal sample was calculated as the standard ratio of the gene expression difference.

[0167] (6) Evaluation of uncertainty of the set value ( Figure 20 )

[0168] By performing statistical analysis on the results of multiple library construction, sequencing, and quantification, the measurement error and uncertainty of the absolute quantification value of each gene are evaluated to ensure the reliability and reproducibility of the reference data. Uncertainty evaluation is performed on each quantified value. The uncertainty of the gene copy number concentration (Uc) is the relative standard deviation (RSD) of the measured values of each batch divided by the square root of the number of measurements n ( ). The uncertainty of the gene expression ratio is the square root of the sum of the squares of the uncertainties of the gene copy number concentrations in the two samples.

[0169] (7) Construction of the absolute quantification reference data set

[0170] Integrate the absolute quantification data and relative uncertainties of all high-confidence genes in the valid batches of paired samples, as well as the copy number concentration ratios and uncertainties of the high-confidence genes common between paired samples, and assemble them into an RNA absolute quantification reference data set for the whole transcriptome RNA reference material of tumors and paired samples. In this embodiment, genes with a relative quantification uncertainty not greater than 40% are included in the transcriptome RNA absolute quantification standard reference data set of the present invention. Among them, the absolute quantification reference data set of the LCA sample covers 18,147 genes, accounting for 99.4% of its high-confidence genes; the absolute quantification reference data set of the LCN sample covers 17,630 genes, accounting for 99.2% of its high-confidence genes; the copy number concentration ratio reference data set of the LCA and LCN samples covers 14,228 genes, accounting for 89.9% of its high-confidence genes.

[0171] The above-described embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A standard RNA material for the whole transcriptome of tumor cells and paired cell lines, characterized in that: The tumor and paired cell lines are immortalized cell lines of lung cancer and paired adjacent normal tissue, deposited at the China General Microbiological Culture Collection Center (CGMCC) on October 11, 2025. The biological accession number of the lung cancer cell line is CGMCC No. 46703, and the biological accession number of the paired adjacent normal tissue cell line is CGMCC No. 46704. The RNA standard substance is a whole transcriptome RNA extract from the immortalized cell lines of the lung cancer and paired adjacent normal tissues. The amount of the RNA standard substance is the copy number concentration and ratio of high-confidence genes in the two samples. The method for identifying high-confidence genes in whole-transcriptome RNA standard materials includes the following steps: (1) Transcriptome sequencing was performed using a high-throughput sequencing platform for tumor and paired cell line whole transcriptome RNA standard materials to obtain multiple batches of raw data. (2) Gene expression was quantified using a unified bioinformatics process to obtain the TPM value of each gene; where TPM refers to the number of transcripts of a certain gene per million transcripts. (3) If a gene is detected in all datasets, in all batches, in a specific batch of more than two technical replicates, and the TPM of the gene is > 1, then the gene is identified as a high-confidence gene.

2. The application of the tumor and paired cell line whole transcriptome RNA standard material as described in claim 1 in the performance evaluation of absolute or relative quantification of multi-gene expression data detected by different transcriptome sequencing technologies and sequencing platforms.

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