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

Comprehensive characterization of changes in the proteome, both locally in individual tissues or body fluids and systemically, provides insights into the molecular pathomechanisms underlying disease.

The Proteomics Analysis Laboratory is equipped with high-throughput mid-plex (up to approximately 100 proteins) and high-plex (hundreds to thousands of proteins) platforms for highly specific and sensitive targeted quantification using protein-specific antibodies.

Depending on the assay kit used, proteins can be quantified either relatively or absolutely. Relative quantification measures changes or differences in protein abundance between samples, whereas absolute quantification determines the exact concentration or copy number of a protein in a sample.

Sample types suitable for analysis primarily include blood plasma and serum, as well as urine, cerebrospinal fluid, saliva, synovial fluid, and cell and tissue lysates. Sample volumes of only 1 to 10 µL (0.001 to 0.01 mL) are required for analysis.

The laboratory is certified by Olink Proteomics (Uppsala, Sweden) to perform analyses using Olink assay kits and is registered as an Olink service laboratory. The laboratory's measurement service is also registered as Shared Expertise (SE175) with the German Centre for Cardiovascular Research (DZHK).

The mid-plex platform currently offers Olink assay kits for both relative and absolute protein quantification.

Target 96 kits enable the simultaneous relative quantification of 92 proteins in 88 samples per run. The 92 proteins are grouped into predefined panels. Ten panels are currently available. These include panels focused on specific disease areas, including cardiology, oncology, and neurology, as well as panels targeting key biological processes such as inflammation, immune response, metabolism, and cardiometabolism. In addition, a panel for the measurement of 92 mouse proteins is available.

Target 48 kits enable the simultaneous absolute quantification of 41, 44, or 45 proteins in 40 samples. Three panels are currently available: Cytokine (45 proteins), Immune Surveillance (44 proteins), and Neurodegeneration (41 proteins).

Flex kits enable the simultaneous absolute quantification of 5 to 30 proteins in 40 samples. Proteins can be individually selected from a pool of 197 available proteins.

The high-plex platform currently supports relative protein quantification using next-generation sequencing (NGS).

Reveal kits enable the simultaneous relative quantification of 1,034 proteins, particularly proteins involved in inflammation and immune responses, in 86 samples.

Explore HT kits enable the simultaneous relative quantification of 5,401 proteins in 172 samples.

The high specificity of protein measurements is achieved through the requirement for two different antibodies to bind in close proximity to the same target protein molecule to generate a measurable signal. This principle is used, for example, in Olink's Proximity Extension Assay (PEA) technology and largely prevents nonspecific signals that may occur with detection methods based on a single antibody. Both antibodies are chemically conjugated to partially complementary oligonucleotides. When the antibodies bind to the same target protein, the resulting spatial proximity allows the oligonucleotides to hybridize. Only after successful hybridization does enzymatic extension of the oligonucleotides generate protein-specific DNA reporter sequences, which are subsequently amplified and detected using quantitative polymerase chain reaction (qPCR) or next-generation sequencing (NGS). Amplification of the reporter sequences provides the exceptionally high sensitivity of the assay. The resulting signal is proportional to the abundance 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.