Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics

SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

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.

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling

BACKGROUND

Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets.

RESULTS

As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols.

CONCLUSIONS

Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

M-type pyruvate kinase 2 (PKM2) tetramerization alleviates the progression of right ventricle failure by regulating oxidative stress and mitochondrial dynamics

BACKGROUND

Right ventricle failure (RVF) is a progressive heart disease that has yet to be fully understood at the molecular level. Elevated M-type pyruvate kinase 2 (PKM2) tetramerization alleviates heart failure, but detailed molecular mechanisms remain unclear.

OBJECTIVE

We observed changes in PKM2 tetramerization levels during the progression of right heart failure and in vitro cardiomyocyte hypertrophy and explored the causal relationship between altered PKM2 tetramerization and the imbalance of redox homeostasis in cardiomyocytes, as well as its underlying mechanisms. Ultimately, our goal was to propose rational intervention strategies for the treatment of RVF.

METHOD

We established RVF in Sprague Dawley (SD) rats by intraperitoneal injection of monocrotaline (MCT). The pulmonary artery pressure and right heart function of rats were assessed using transthoracic echocardiography combined with right heart catheterization. TEPP-46 was used both in vivo and in vitro to promote PKM2 tetramerization.

RESULTS

We observed that oxidative stress and mitochondrial disorganization were associated with increased apoptosis in the right ventricular tissue of RVF rats. Quantitative proteomics revealed that PKM2 was upregulated during RVF and negatively correlated with the cardiac function. Facilitating PKM2 tetramerization promoted mitochondrial network formation and alleviated oxidative stress and apoptosis during cardiomyocyte hypertrophy. Moreover, enhancing PKM2 tetramer formation improved cardiac mitochondrial morphology, mitigated oxidative stress and alleviated heart failure.

CONCLUSION

Disruption of PKM2 tetramerization contributed to RVF by inducing mitochondrial fragmentation, accumulating ROS, and finally promoted the progression of cardiomyocyte apoptosis. Facilitating PKM2 tetramerization holds potential as a promising therapeutic approach for RVF.

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.

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

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

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.