Understanding the biology that underlies health and disease is fundamental to drive sorely needed improvements in drug development and future healthcare. To meet these challenges, scientists are employing more comprehensive analyses of disease phenotypes that combine multiple molecular and cellular assays with clinical/phenotypic data. The development and combination of large-scale “omics” methodologies is critical in these efforts. Genomics, transcriptomics, epigenomics, metabolomics, lipidomics, mass cytometry etc are increasingly combined in large population health or systems biology studies, and proteomics is now set to take center stage in multiomics research.
Here we will briefly look at some examples of how different combinations of omics methods can be combined to answer key biological questions.
Population health & wellness studies
Groups such as those headed by Mathias Uhlén at Stockholm’s Royal Institute of Technology (KTH) and Leroy Hood at the Institute for Systems Biology (ISB) in Seattle have been spearheading the use of large-scale, multiomic studies of healthy populations as a tool to enable knowledge-driven advances in precision medicine. One landmark study from the KTH group demonstrated that individuals generally maintain a remarkably stable molecular profile over time, but that these profiles vary significantly among different people.
By following these “wellness” cohorts longitudinally as diseases develop later in some individuals, invaluable predictive biomarkers can be uncovered. A wellness study from the ISB group, for example, identified a predictive marker for metastatic cancer that could be measured up to 2 years prior to clinical diagnosis.
Proteogenomics and the unique power of pQTLs
The combination of genetic and protein expression data (“proteogenomics”) has long been recognized as a highly relevant and powerful combination of molecular analysis. In its simplest form, this approach can be used to calculate the percentage of variation in protein expression among individuals that is due to genetics. This is important information when analyzing the results of population cohorts etc. A good example of this was reported by Ulf Gyllensten’s group from Uppsala University, who described strong effects of genetic and lifestyle factors on protein biomarker variation in a population cohort from northern Sweden.
When cis-pQTL data is combined with phenotypic data in a Mendelian Randomization (MR) analysis, it provides extremely strong evidence that the protein plays a causal role in the disease or biological process being studied.
More recently, proteogenomics is increasingly used to identify specific genetic loci that affect the levels of individual proteins. These associations are known as protein Quantative Trait Loci (pQTLs), and when located very close to the genetic locus encoding the affected protein (“cis-pQTL”), these are powerful tools for gaining actionable insights into disease biology and to drive new drug development. When cis-pQTL data is combined with phenotypic data in a Mendelian Randomization (MR) analysis, it provides extremely strong evidence that the protein plays a causal role in the disease or biological process being studied. This is particularly important for drug target identification, where causality is a prerequisite to take the protein into a drug discovery and development program but cannot be reliably determined from either genomics or proteomics alone.
This powerful approach is being rapidly adopted by the scientific community and has also resulted in a major international consortium dedicated to large-scale collaboration and pQTL data sharing. SCALLOP is an independent collaborative framework for the discovery and follow-up of genetic associations with proteins for researchers generating data using the Olink platform. The aim of the SCALLOP consortium is to identify novel molecular connections and protein biomarkers that are causal in diseases, and to date, 35 PIs from 28 research institutions have joined the effort, which now comprises summary level data for almost 70,000 patients and controls from 45 cohort studies.
Genetic Regulation of the Human Plasma Proteome in the UK Biobank
In a landmark study, 13 pharmaceutical companies in conjunction with researchers from the UK Biobank (UKB) present the largest open-access resource of proteomic data analyzing 1,463 proteins across 54,306 UK Biobank participants. Recently published early findings reported over 10,000 pQTLs, 85% of which are novel findings. Read more about this and other collaborative initiatives aimed towards future drug development in our blog post.
Population scale proteomics accelerates the search for effective new drug targets
Dr. Chris Whelan (Chair and Principal Investigator of the UK Biobank – Pharma Proteomics Project) discusses how data from the UK Biobank – Pharma Proteomics Project has revealed new insights into associations between gene variants and protein concentrations enabling new causal biomarkers for diseases to be identified as well as new drug targets with higher probabilities of success.
Proteomics helps provide a much needed layer of resolution to genetic data. Genomics is the beautiful, reliable but slightly ageing car – proteomics might serve as the new engine
Dr. Chris Whelan
Empowering genomics with proteomics
A new white paper describes how the integration of genomics and proteomics data heralds a new era of discovery. Data from the landmark UK Biobank – Pharma Proteomics Project, as well as the SCALLOP consortium have identified many new associations between genetic variants and circulating protein levels. The article discusses how such population-scale proteogenomic projects are delivering a wealth of actionable data to drive future drug development.
Therapeutic Targets for Heart Failure Identified With Plasma Proteome and Genome Analysis
This study may not only inform new heart failure treatments, wrote the authors of the analysis, but their methods can provide a roadmap for discovering drug targets in other diseases using proteomic and genomic data.
As well as providing a powerful tool to determine protein causality, cis-pQTLs also provide strong orthogonal validation that the proteomics technology being used has good specificity and is really detecting the protein it was designed to detect!
Combining plasma proteomics with tissue-specific transcriptomics
The transcription of genes as mRNA plays an essential intermediate role in converting genetic information to phenotype. Quantitative RNA measurements in the form of transcriptomics have long been used as a relevant proxy for biological output from the genome. Powerful techniques such as RNAseq have enabled large-scale studies of gene expression that have contributed to many systems biology studies. Since many factors other than the rate of gene transcription are involved in protein abundance and function, however, proteomics solution can now provide the key “missing link” between genetic information and real-time biological activity.
An increasing number of studies are combining transcriptomics and Olink proteomics to gain a more complete picture, leveraging on the complementary potential of these approaches, for example to study localised changes at the tissue/cellular level by RNA analysis in relation to systemic changes at the proteomics level.
A study in collaboration with Massachusetts General Hospital Cancer Center, performed deep proteomic profiling together with single-cell RNAseq in a metastatic melanoma cohort during treatment with checkpoint inhibitor immunotherapy. Around 1500 proteins were measured, resulting in plasma protein profiles associated with both predicted treatment response and overall survival. Single-cell RNAseq data from individual immune cells within the tumors of melanoma patients showed that differentially expressed genes between responders and non-responders were primarily derived from myeloid cells, macrophages and dendritic cells, with an excellent correlation to the plasma proteomics data.
Proteomic plasma biomarkers for predicting response and resistance with immune checkpoint inhibitors in cancer patients
This study demonstrates how circulatory proteins may provide a road-map to inform biological insights about the localized tumor response to therapy. Performed by Olink in collaboration with Massachusetts General Hospital Cancer Center.
Approaching complex immune system questions with proteomics and mass cytometry
Biological systems typically involve an intricate interplay between different cell types, signaling molecules and other proteins. Approaches that combine cellular and molecular analyses can provide unique insights into complex biological questions, and nowhere is this more important than in studies involving the immune system. Advanced, high throughput technologies such as mass cytometry (e.g. CyTOF) enable detailed analysis of immune cell populations and how they change during health and disease. More recently, mass cytometry has been used in combination with Olink analysis to gain unique insights into immunological questions through this systems-level approach.
Olink and mass cytometry studies
In a landmark study from Dr. Petter Brodin’s group at Karolinska Institute, Stockholm, they used Olink and CyTOF to show for the first time that the immune systems of newborn children evolve in a stereotypic manner that is similar in diverse children, not predictable from cord blood measurements and driven by environmental factors such as the colonizing microbiome.
Such studies have provided vital insights into severe versus mild disease, recovery from severe disease, and in better understanding variants of the disease
This combination of omics also found great utility during the huge research effort into the COVID-19 pandemic, where the complex immune system response to viral infection is a critical factor in the development and severity of the disease. Such studies have provided vital insights into severe versus mild disease, recovery from severe disease, and in better understanding variants of the disease such as Multisystem Inflammatory Syndrome in Children.
“Leveraging plasma and single-cell proteomics integration in translational studies”