Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

Batch effects are notorious technical variations that are common in multiomic data and may result in misleading outcomes. With the era of big data, tackling batch effects in multiomic integration is urgently needed. As part of the Quartet Project for quality control and data integration of multiomic profiling, we comprehensively assess the performances of seven batch-effect correction algorithms (BECAs) for mitigating the negative impact of batch effects in multiomic datasets, including transcriptomics, proteomics, and metabolomics. Performances are evaluated based on accuracy of identifying differentially expressed features, robustness of predictive models, and the ability of accurately clustering cross-batch samples into their biological sample groups. Ratio-based method is more effective and widely applicable than others, especially in cases when batch effects are highly confounded with biological factors of interests. We further provide practical guidelines for the implementation of ratio-based method using universal reference materials profiled with study samples. Our findings show the promise for eliminating batch effects and enhancing data integration in increasingly large-scale, cross-batch multiomic studies.

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

ObjectivesRetroperitoneal fibrosis (RPF) is a rare autoimmune disease with fibrous tissue growth and inflammation in retroperitoneum. Its current treatments involve long-term uptake of glucocorticoids (e.g., prednisone) for controlling inflammation; however, side effects are common. We strived for an improved therapy for fibrosis remission while reducing side effects.MethodsWe surveyed gene-disease-drug databases and discovered that mammalian target of rapamycin (mTOR) was a key signalling protein in RPF and the mTOR inhibitor compound sirolimus affected many RPF pathways. We designed a therapy combining a gradual reduction of prednisone with a long-term, stable dosage of sirolimus. We then implemented a single-arm clinical trial and assessed the effects in eight RPF patients at 0, 12 and 48 weeks of treatment by measuring fibrous tissue mass by CT, markers of inflammation and kidney functions by lab tests, immune cell profiles by flow cytometry and plasma inflammatory proteins by Olink proteomics.ResultsWith the combined therapy, fibrous tissue shrunk about by half, markers of acute inflammation reduced by 70% and most patients with abnormal kidney functions had them restored to normal range. Molecularly, fibrosis-related T cell subsets, including TH2, TH17 and circulating TFHcells, were reduced and tumour necrosis factor and related cytokines restored to healthy levels. No severe long-term side effects were observed.ConclusionsOur combined therapy resulted in significant fibrosis remission and an overall regression of the immune system towards healthy states, while achieving good tolerance. We concluded that this new therapy had the potential to replace the steroid monotherapy for treating RPF.

Retroperitoneal fibrosis (RPF) is a rare autoimmune disease with fibrous tissue growth and inflammation in retroperitoneum, whose development could encase surrounding organs and lead to severe conditions. Its current treatments involve long-term uptake of glucocorticoids (e.g., prednisone) for controlling inflammation; however, side effects are common, triggering search for replacement therapies. Here, we surveyed gene-disease databases and discovered that mTOR displayed significant changes in RPF, which we confirmed by immunohistological staining. Next, we inferred from drug-gene databases that mTOR inhibitor compound sirolimus could affect most biological pathways in RPF. We then designed a combined therapy in which a gradual reduction of prednisone was prescribed with a long-term, stable dosage of sirolimus. We implemented a single-arm clinical trial in RPF patients and assessed the treatment effects at three timepoints (0, 12 weeks and 48 weeks of treatment). By assessing fibrous tissue mass by computed tomography, inflammation markers and kidney functions by lab tests, immune cell types and abundances by flow cytometry, and plasma inflammation-related proteins by Olink proteomics, we revealed that our combined therapy resulted in significant fibrosis remission and an overall regression of the immune system towards healthy states. In addition, no obvious side effects were observed. We concluded that this new therapy had the potential to replace long-term steroid monotherapy for treating RPF.