Four Areas of Engagement Requiring Strengthening in Modern Proteomics Today

The power of proteomics to monitor senescence-associated secretory phenotypes and beyond: toward clinical applications

INTRODUCTION

Cellular senescence is a rapidly growing field with potential relevance for the treatment of multiple human diseases. In the last decade, cellular senescence and the senescence-associated secretory phenotype (SASP) have emerged as central drivers of aging and many chronic diseases, including cancer, neurodegeneration, heart disease and osteoarthritis. Major efforts are underway to develop drugs that selectively eliminate senescent cells (senolytics) or alter the SASP (senomorphics) to treat age-related diseases in humans. The translation of senescence-targeting therapies into humans is still in early stages. Nonetheless, it is clear that proteomic approaches will facilitate the discovery of important SASP proteins, development of senescence- and SASP-derived biomarkers, and identification of therapeutic targets for senolytic and senomorphic drugs.

AREAS COVERED

We review recent proteomic studies of cellular senescence and their translational relevance and, particularly, characterization of the secretory phenotype and preclinical development of biomarkers (from 2008-2020, PubMed). We focus on emerging areas, such as the heterogeneity of senescent cells and the SASP, extracellular vesicles released by senescent cells, and validating biomarkers of aging in vivo.

EXPERT OPINION

Proteomic and multi-omic approaches will be important for the development of senescence-based biomarkers to facilitate and monitor future therapeutic interventions that target senescent cells.

Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies

Background

Data from biological samples and medical evaluations plays an essential part in clinical decision making. This data is equally important in clinical studies and it is critical to have an infrastructure that ensures that its quality is preserved throughout its entire lifetime. We are running a 5-year longitudinal clinical study, KOL-Örestad, with the objective to identify new COPD (Chronic Obstructive Pulmonary Disease) biomarkers in blood. In the study, clinical data and blood samples are collected from both private and public health-care institutions and stored at our research center in databases and biobanks, respectively. The blood is analyzed by Mass Spectrometry and the results from this analysis then linked to the clinical data.

Method

We built an infrastructure that allows us to efficiently collect and analyze the data. We chose to use REDCap as the EDC (Electronic Data Capture) tool for the study due to its short setup-time, ease of use, and flexibility. REDCap allows users to easily design data collection modules based on existing templates. In addition, it provides two functions that allow users to import batches of data; through a web API (Application Programming Interface) as well as by uploading CSV-files (Comma Separated Values).

Results

We created a software, DART (Data Rapid Translation), that translates our biomarker data into a format that fits REDCap's CSV-templates. In addition, DART is configurable to work with many other data formats as well. We use DART to import our clinical chemistry data to the REDCap database.

Conclusion

We have shown that a powerful and internationally adopted EDC tool such as REDCap can be extended so that it can be used efficiently in proteomic studies. In our study, we accomplish this by using DART to translate our clinical chemistry data to a format that fits the templates of REDCap.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

Cancer heterogeneity determined by functional proteomics

Current manuscript gives a synopsis of tumor heterogeneity related to patient samples analyzed by proteomics, protein expression analysis and imaging mass spectrometry. First, we discuss the pathophysiologocal background of cancer biology as a multifactorial and challenging diseases. Disease pathology forms the basis for protein target selection. Therefore, histopathological diagnostics and grading of tumors is highlighted. Pathology is the cornerstone of state-of-the-art diagnostics of tumors today both by establishing dignity and - when needed - describing molecular properties of the cancers. Drug development by the pharmaceutical industry utilizes proteomics studies to pinpoint the most relevant targets. Molecular studies profiling affinity-interactions of the protein(s) with targeted small drug molecules to reach efficacy and optimal patient safety are today requested by the FDA and other agencies for new drug development. An understading of basic mechanisms, controlling drug action and drug binding is central, as a new era of personalized medicine becomes an important milestone solution for the healthcare sector as well as the Pharma and Biotech industry. Development of further diagnostic, prognostic and predictive tests will aid current and future treatment of cancer patients. In the paper we present current status of Proteomics that we believe requires attention in order to collectively advance forward in the fight against cancer, addressing the burning opportunities and challenges.

Biomarkers of early chronic obstructive pulmonary disease (COPD) in smokers and former smokers. Protocol of a longitudinal study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is an irreversible disease, diagnosed predominantly in smokers. COPD is currently the third leading cause of death worldwide. Far more than 15 % of smokers get COPD: in fact, most develop some amount of pulmonary impairment. Smoking-related COPD is associated with both acute exacerbations and is closely correlated to comorbidities, such as cardiovascular disease and lung cancer. The objective of our study (KOL-Örestad) is to identify biomarkers in smokers and ex-smokers, with early signs of COPD, and compare these biomarkers with those of non-smokers and healthy smokers/ex-smokers. The participants in the study are recruited from Örestadskliniken, a primary health care clinic in Malmö, Sweden.

METHODS

Two hundred smokers and ex-smokers diagnosed with COPD with airflow restriction according to GOLD stages 1-4 will be included and compared with 50 healthy never-smokers, and 50 healthy smokers/ex-smokers without airflow restriction (total n = 300). The age distribution is 35-80 years. The participants undergo a health examination including medical history, smoking history, lung function measurements, and respond to a "Quality of Life" questionnaire. Blood samples are drawn every 6 months during a period of 5 years. Additional blood sample collection is performed if participants are experiencing an exacerbation. The blood fractions will be analyzed by standard clinical chemistry assays and by proteomics utilizing mass spectrometry platforms. Optimal sample integrity is ensured by rapid handling with robotic biobank processing followed by storage at -80 °C. The study has been approved by the Regional Ethical Review Board in Lund ( http://epn.se/en ), (Approval number: DNR 2013/480), and registered at the NIH clinical trial registry ( http://clinicaltrials.gov ).

RESULTS AND DISCUSSION

Currently, 220 subjects are enrolled in the study.

CONCLUSIONS AND FUTURE DIRECTIONS

The study design will enable discovery of new biomarkers by using novel mass spectrometric techniques that define early changes of COPD. Such panels of novel biomarkers may be able to distinguish COPD from closely related diseases, co-morbidities, and contribute to an increased understanding of these diseases. Graphical abstract KOL-Örestad Study.

Semi-automated biobank sample processing with a 384 high density sample tube robot used in cancer and cardiovascular studies

Background

In the postgenomic era, it has become evident that analysis of genetic and protein expression changes alone is not sufficient to understand most disease processes in e.g. cardiovascular and cancer disease. Biobanking has been identified as an important area for development and discovery of better diagnostic tools and new treatment modalities. Biobanks are developed in order to integrate the collection of clinical samples from both healthy individuals and patients and provide valuable information that will make possible improved patient care. Modern healthcare developments are intimately linked to information based on studies of patient samples from biobank archives in large scale studies. Today biobanks form important national, as well as international, networks that share and combine global resources.

Methods

We have developed and validated a novel biobanking workflow process that utilizes 384-tube systems with a high speed sample array robot with unique processing principles.

Results

The 384-tube format and robotic processing is incorporated into a cancer and cardiovascular diagnostic/prognostic research program with therapeutic interventions. Our biobank practice has gained acceptance within many hospitals and research units and is based on high-density sample storage with small aliquot sample volumes. The previous standard of 5–10 mL sample volume tubes is being replaced by smaller volumes of 50–70 μL blood fractions that typically result in hundreds of thousands of aliquot fractions in 384-tube systems.

Conclusions

Our novel biobanking workflow process is robust and well suited for clinical studies.