Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling

OmicsCam Enables Trimodal Profiling of Mitochondrial Genome Editing

Mitochondrial DNA (mtDNA) editing can generate cellular and animal models of mitochondrial genetic disorders and holds promise for future ex vivo and in vivo therapeutic applications. However, due to the quantitative nature of mitochondrion genetics, as more base-editing tools evolve, it is crucial to evaluate not only their efficiency and specificity on the sequence level but also the resulting molecular phenotypes. Here, we devised a novel Omics Carrier microcapsule, abbreviated as OmicsCam, that achieves homogeneous reactions within a heterogeneous carrier membrane, enabling highly efficient multistep biochemistry workflows. Incorporating magnetic beads into the carrier enables high-throughput automation. We demonstrated simultaneous trimodal assessment of mtDNA editing efficiency, postediting cellular transcriptome, and chromatin accessibility in minute cell samples containing as few as 25,000 cells. Applying OmicsCam to two TALE-DdCBE-edited human cell lines revealed that ND4 gene knockdown led to the downregulation of the mitochondrial oxidative phosphorylation pathway and changes in NF-Y transcription factor-associated histone modification pathways in the cell nucleus. Our study provides the most comprehensive analysis of mitochondrial gene editing efficiency and molecular phenotypes to date, which not only facilitates the establishment of mitochondrial genotype-molecular phenotype relationships but also helps assess the global safety of mitochondrial genome nucleases prior to clinical use.

Construction of RNA reference materials for improving the quantification of transcriptomic data

RNA reference materials and their corresponding reference datasets act as the 'ground truth' for the normalization of experimental values and are indispensable tools for reliably measuring intrinsically small differences in RNA-sequencing data, such as those between molecular subtypes of diseases in clinical samples. However, the variability in 'absolute' expression profiles measured across different batches, methods or platforms limits the use of conventional RNA reference datasets. We recently proposed a ratio-based method for constructing reference datasets. The ratio for a gene is defined as the normalized expression levels between two sample groups and produces more reliable values than the 'absolute' values obtained across diverse transcriptomic technologies and batches. Our gene ratios have been used for the successful generation of omics-wide reference datasets. Here, we describe a step-by-step process for establishing RNA reference materials and reference datasets, covering three stages: (1) reference materials, including material preparation, homogeneity testing and stability testing; (2) ratio-based reference datasets, including characterization, uncertainty estimation and orthogonal validation; and (3) applications, including definition of performance metrics, performing proficiency tests and diagnosing and correcting batch effects. This approach established the Quartet RNA reference materials and reference datasets (chinese-quartet.org) that have been approved as the first suite of nationally certified RNA reference materials by China's State Administration for Market Regulation. The protocol can be utilized to establish and apply reference materials to improve RNA-sequencing data quality in diverse clinical settings. The procedure can be completed in 2 d and requires expertise in molecular biology and bioinformatics.

Establishment of genomic RNA reference materials for BCR-ABL1 P210 measurement

Quantitation of BCR-ABL1 with the quantitative reverse transcriptase polymerase chain reaction (RT-PCR) is very important in monitoring chronic myeloid leukemia (CML), which relies on an RNA reference material. A genomic RNA reference material (RM) containing the BCR-ABL1 P210 fusion mutation was developed, and an absolute quantitative method based on one-step reverse transcription digital PCR (RT-dPCR) was established for characterizing the RM. The proposed dPCR method demonstrates high accuracy and excellent analytical sensitivity, as shown by the linear relationship (0.94 < slope < 1.04, R2≧0.99) between the measured and nominal values of b2a2, b3a2, and ABL1-ref within the dynamic range (104-101 copies/reaction). Homogeneity and stability assessment based on dPCR indicated that the RM was homogeneous and stable for 24 months at -80 °C. The RM was used to evaluate inter-laboratory reproducibility in eight different laboratories, demonstrating that participating laboratories could consistently produce copy concentrations of b3a2 and ABL1-ref, as well as the BCR-ABL1/ABL1 ratio (CV < 2.0%). This work suggests that the RM can be employed in establishing metrological traceability for detecting mutations in the BCR-ABL1 fusion gene, as well as in quality control for testing laboratories.

Establishment of potential reference measurement procedure and reference materials for EML4-ALK fusion variants measurement

Echinoderm microtubule-associated protein 4 (EML4) - anaplastic lymphoma kinase (ALK) fusion gene detection is of great significance in personalized tumor treatment. With the development of EML4-ALK fusion variants detection, it is necessary to establish traceability to ensure the consistency and comparability of its detection results in clinical practice. The establishment of traceability relies on SI traceable reference materials (RMs) and potential reference measurement procedures (RMPs). In this study, a potential RMP for the quantitative detection of V1 and V3 fusion mutations and the reference type (ALK-ref, including wild type, V1 and V3 mutant type) based on reverse transcription dPCR (RT-dPCR) and EML4-ALK fusion gene RMs were established. The proposed potential RMP has high specificity, good inter-laboratory reproducibility (CV < 7.3%) and good linear relationship (0.92 < slope < 1.06, R2 ≧ 0.99). The limit of detection (LoD) of V1, V3, and ALK-ref are 2 copies/reaction, 2 copies/reaction, and 1 copy/reaction, respectively. Interlaboratory studies using the EML4-ALK RMs and potential RMP showed that participating laboratories can produce consistent copy concentrations of fusion variant and ALK-ref as well as the ratio of EML4-ALK/ALK-ref. The established potential RMP with high specificity and accuracy can be used to characterize the EML4-ALK RMs, and the potential RMP and RM are useful to establish the traceability of EML4-ALK fusion measurement to improve the comparability and consistency in clinical tests.

Reference Materials for Improving Reliability of Multiomics Profiling

High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications, offering a more comprehensive understanding of biological processes and diseases. Omics reference materials play a pivotal role in ensuring the accuracy, reliability, and comparability of laboratory measurements and analyses. However, the current application of omics reference materials has revealed several issues, including inappropriate selection and underutilization, leading to inconsistencies across laboratories. This review aims to address these concerns by emphasizing the importance of well-characterized reference materials at each level of omics, encompassing (epi-)genomics, transcriptomics, proteomics, and metabolomics. By summarizing their characteristics, advantages, and limitations along with appropriate performance metrics pertinent to study purposes, we provide an overview of how omics reference materials can enhance data quality and data integration, thus fostering robust scientific investigations with omics technologies.

Reliable biological and multi-omics research through biometrology

Metrology is the science of measurement and its applications, whereas biometrology is the science of biological measurement and its applications. Biometrology aims to achieve accuracy and consistency of biological measurements by focusing on the development of metrological traceability, biological reference measurement procedures, and reference materials. Irreproducibility of biological and multi-omics research results from different laboratories, platforms, and analysis methods is hampering the translation of research into clinical uses and can often be attributed to the lack of biologists' attention to the general principles of metrology. In this paper, the progresses of biometrology including metrology on nucleic acid, protein, and cell measurements and its impacts on the improvement of reliability and comparability in biological research are reviewed. Challenges in obtaining more reliable biological and multi-omics measurements due to the lack of primary reference measurement procedures and new standards for biological reference materials faced by biometrology are discussed. In the future, in addition to establishing reliable reference measurement procedures, developing reference materials from single or multiple parameters to multi-omics scale should be emphasized. Thinking in way of biometrology is warranted for facilitating the translation of high-throughput omics research into clinical practices.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Plasma-Free Blood as a Potential Alternative to Whole Blood for Transcriptomic Analysis

RNA sequencing (RNAseq) technology has become increasingly important in precision medicine and clinical diagnostics, and emerged as a powerful tool for identifying protein-coding genes, performing differential gene analysis, and inferring immune cell composition. Human peripheral blood samples are widely used for RNAseq, providing valuable insights into individual biomolecular information. Blood samples can be classified as whole blood (WB), plasma, serum, and remaining sediment samples, including plasma-free blood (PFB) and serum-free blood (SFB) samples that are generally considered less useful byproducts during the processes of plasma and serum separation, respectively. However, the feasibility of using PFB and SFB samples for transcriptome analysis remains unclear. In this study, we aimed to assess the suitability of employing PFB or SFB samples as an alternative RNA source in transcriptomic analysis. We performed a comparative analysis of WB, PFB, and SFB samples for different applications. Our results revealed that PFB samples exhibit greater similarity to WB samples than SFB samples in terms of protein-coding gene expression patterns, detection of differentially expressed genes, and immunological characterizations, suggesting that PFB can serve as a viable alternative to WB for transcriptomic analysis. Our study contributes to the optimization of blood sample utilization and the advancement of precision medicine research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00121-1.

Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance

BACKGROUND

Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome.

RESULTS

We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data.

CONCLUSIONS

The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method

BACKGROUND

Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios.

RESULTS

As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies.

CONCLUSIONS

Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.