What are proteomes and proteome profiles?

The proteome is defined as the complete set of proteins encoded by the genes of a living organism. In humans, the proteome comprises at least 20,000 non-modified (“canonical”) proteins. Under normal physiological conditions, however, only those proteins required for the function of specific organs at a given time are expressed, resulting in organ-specific proteome profiles. Since these profiles reflect the physiological state of an organ at the molecular level, the proteome profile of an organ changes during the development and progression of disease, often before changes in organ function can be detected clinically. Some of these proteins enter the bloodstream, making it possible to identify and monitor disease progression through blood-based analyses.

Proteome analysis

To gain insight into the molecular mechanisms of disease, changes in the proteome must be analyzed as comprehensively as possible. The Proteomics Research Laboratory is equipped with a high-throughput immuno-qPCR-based analysis platform. This technology enables the targeted, highly specific, and highly sensitive simultaneous quantitative measurement of 92 proteins in 88 samples. These 92 proteins are grouped into individual panels. Currently, 14 panels from Olink Proteomics (Uppsala, Sweden) are available, covering a total of 1,288 human proteins. These panels include 7 disease-focused panels (2 cardiovascular, 3 oncology, 2 neurology) and 7 panels focusing on important biological processes (inflammation, immune response, cell regulation, metabolism, cardiometabolism, development, and organ damage). In addition, one panel for the determination of 92 mouse proteins is available. Measurable sample types primarily include blood plasma and serum, but also urine, cerebrospinal fluid, saliva, synovial fluids, and tissue lysates. The Proteomics Research Laboratory has been certified by Olink Proteomics to perform these analyses and is registered as a Shared Expertise (SE175) with the German Centre for Cardiovascular Research (DZHK) (for further information click here).
The measurement of several of these panels has already enabled the identification of protein biomarkers in the fields of venous thromboembolic events, heart failure, and type 2 diabetes mellitus. Protein measurements are performed on Fluidigm chips with integrated fluidic circuits (IFCs). The use of these chips allows protein determination from a sample volume of only one microliter (0.001 milliliters).

The high specificity of the immuno-qPCR-based protein measurements results from the requirement that two different antibodies bind in close proximity to the same target protein molecule – the basis of the Olink Proximity Extension Assay (PEA) technology. Both antibodies are chemically linked to complementary oligonucleotides that hybridize due to their spatial proximity (“proximity”). Only after successful hybridization do protein-specific DNA reporter sequences form through enzymatic extension of the oligonucleotides (“extension”), which are subsequently detected by amplification using qPCR. This amplification of the reporter sequences results in the exceptionally high sensitivity of the assay. The resulting signal is proportional to the concentration of the respective protein in the sample.

Studies with Biobanking

The Department of Preventive Cardiology is committed to translating scientific findings into clinical application through its research activities. In science, this cardiovascular clinical epidemiological research often serves as a link between basic research and medical application. High-quality patient-oriented research can improve the prevention, diagnosis, treatment, therapy, and prognosis of cardiovascular diseases. A central resource of our scientific work consists of existing, well-characterized, and mostly interdisciplinary cohort studies, including biomaterial banks. These are based on comprehensive characterization and documentation of study participants and their health and disease trajectories (including subclinical and clinical disease as well as factors such as personality, environment, and lifestyle), combined with the collection of a wide range of biomaterials (molecular markers, including genetics). This approach aims to decipher and better understand complex, multifactorial mechanisms and processes involved in the development and progression of diseases using both confirmatory and exploratory approaches. The foundation for generating and utilizing these resources consists of highly standardized processes and comprehensive quality management across all our projects. Standardized methods are used for sample processing, while a semi-automated and temperature-monitored biorepository is used for storage. A biomarker laboratory and a genetics laboratory are available for the analysis of large sample volumes. The biobank of the Department of Preventive Cardiology currently comprises approximately 5.3 million biospecimens from a wide variety of biomaterials.

Our goal is to prepare our study data in the best possible and most comparable way for all types of analyses through standardization and quality control. The pillars of our quality management (QM) are:

• Standardized data collection: To ensure comparable and standardized data acquisition, SOPs (Standard Operating Procedures) have been developed that precisely define the procedures for individual examinations and data collection processes. Together with regular staff training, this helps ensure that all collected data are as complete and comparable as possible.
• Standardized biobanking: Standardized sample collection, processing, and storage are prerequisites for performing high-quality biomaterial analyses. SOPs have been established for all processing steps. In addition, samples are processed using pipetting robots and stored using a standardized sample management system (sorting by quality, mirrored storage) in temperature-monitored freezers (biobanking).
• Data quality control: The data management staff ensure the provision and continuous updating of data collection forms (eCRFs = electronic Case Report Forms), review raw data for completeness and plausibility, and maintain the databases. In the QM databases, quality-controlled data are stored within the department’s firewall-secured server infrastructure. These QM databases are used by statisticians as the data source for scientific publications.
• Transparency and documentation: To provide a clear overview of all variables (measurements, questionnaire/interview/eCRF items), variable manuals are used in all studies and at all assessment time points. These manuals support researchers in planning analyses and scientific projects. Adjustments made during quality control are documented through error reports, ensuring that all changes remain traceable at all times.

The Department of Biometry and Statistics has many years of experience in both the analysis of large observational studies and the planning, conduct, and evaluation of clinical studies. We advise researchers within our own projects as well as collaborative projects from clinical and experimental disciplines regarding their research objectives. This includes experimental planning, project design, the selection of statistical methods, and the analysis of collected data. Our core expertise lies in conducting statistical analyses and preparing results through standardized outputs. Data visualization also plays a key role in making abstract and complex relationships more understandable. In addition, we support researchers in interpreting the results of clinical and epidemiological studies and conduct critical reviews of scientific publications.

Our main areas of expertise:

• Study design and sample size calculation
• Imputation of missing values
• Statistical modeling
• Regression analyses
• Survival analysis
• Non-parametric methods
• Machine learning methods
• Data visualization
• Statistical research, including internal training on statistical topics and software (R, SPSS, SAS)

The Bioinformatics team is dedicated to addressing the continuously growing challenges involved in the analysis and interpretation of high-dimensional and complex biomedical data.
The department’s central task is the analysis and integration of diverse data layers, with a particular focus on molecular ‘omics’ data of the human genome, transcriptome, and proteome generated within cohort studies. To achieve this, we apply state-of-the-art methods from the fields of bioinformatics, machine learning, and systems medicine.
Our analyses provide new insights into the development of diseases and contribute to a better understanding of the underlying mechanisms. Furthermore, the machine learning approaches we employ enable improved individualized risk prediction for disease development.