Considerations for powering a clinical proteomics study: Normal variability in the human plasma proteome

Mass spectrometric immunoassays for discovery, screening and quantification of clinically relevant proteoforms

Human proteins can exist as multiple proteoforms with potential diagnostic or prognostic significance. MS top-down approaches are ideally suited for proteoforms identification because there is no prerequisite for a priori knowledge of the specific proteoform. One such top-down approach, termed mass spectrometric immunoassay utilizes antibody-derivatized microcolumns for rapid and contained proteoforms isolation and detection via MALDI-TOF MS. The mass spectrometric immunoassay can also provide quantitative measurement of the proteoforms through inclusion of an internal reference standard into the analytical sample, serving as normalizer for all sample processing and data acquisition steps. Reviewed here are recent developments and results from the application of mass spectrometric immunoassays for discovery of clinical correlations of specific proteoforms for the protein biomarkers RANTES, retinol binding protein, serum amyloid A and apolipoprotein C-III.

Delineation of concentration ranges and longitudinal changes of human plasma protein variants

Human protein diversity arises as a result of alternative splicing, single nucleotide polymorphisms (SNPs) and posttranslational modifications. Because of these processes, each protein can exists as multiple variants in vivo. Tailored strategies are needed to study these protein variants and understand their role in health and disease. In this work we utilized quantitative mass spectrometric immunoassays to determine the protein variants concentration of beta-2-microglobulin, cystatin C, retinol binding protein, and transthyretin, in a population of 500 healthy individuals. Additionally, we determined the longitudinal concentration changes for the protein variants from four individuals over a 6 month period. Along with the native forms of the four proteins, 13 posttranslationally modified variants and 7 SNP-derived variants were detected and their concentration determined. Correlations of the variants concentration with geographical origin, gender, and age of the individuals were also examined. This work represents an important step toward building a catalog of protein variants concentrations and examining their longitudinal changes.

An introduction to statistical process control in research proteomics

BACKGROUND

Statistical process control is a well-established and respected method which provides a general purpose, and consistent framework for monitoring and improving the quality of a process. It is routinely used in many industries where the quality of final products is critical and is often required in clinical diagnostic laboratories [1,2]. To date, the methodology has been little utilised in research proteomics. It has been shown to be capable of delivering quantitative QC procedures for qualitative clinical assays [3] making it an ideal methodology to apply to this area of biological research.

OBJECTIVE

To introduce statistical process control as an objective strategy for quality control and show how it could be used to benefit proteomics researchers and enhance the quality of the results they generate.

RESULTS

We demonstrate that rules which provide basic quality control are easy to derive and implement and could have a major impact on data quality for many studies.

CONCLUSIONS

Statistical process control is a powerful tool for investigating and improving proteomics research work-flows. The process of characterising measurement systems and defining control rules forces the exploration of key questions that can lead to significant improvements in performance.

BIOLOGICAL SIGNIFICANCE

This work asserts that QC is essential to proteomics discovery experiments. Every experimenter must know the current capabilities of their measurement system and have an objective means for tracking and ensuring that performance. Proteomic analysis work-flows are complicated and multi-variate. QC is critical for clinical chemistry measurements and huge strides have been made in ensuring the quality and validity of results in clinical biochemistry labs. This work introduces some of these QC concepts and works to bridge their use from single analyte QC to applications in multi-analyte systems. This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics.

Chromosome-centric approach to overcoming bottlenecks in the Human Proteome Project

The international Human Proteome Project (HPP), a logical continuation of the Human Genome Project, was launched on 23 September 2010 in Sydney, Australia. In accordance with the gene-centric approach, the goals of the HPP are to prepare an inventory of all human proteins and decipher the network of cellular protein interactions. The greater complexity of the proteome in comparison to the genome gives rise to three bottlenecks in the implementation of the HPP. The main bottleneck is the insufficient sensitivity of proteomic technologies, hampering the detection of proteins with low- and ultra-low copy numbers. The second bottleneck is related to poor reproducibility of proteomic methods and the lack of a so-called 'gold' standard. The last bottleneck is the dynamic nature of the proteome: its instability over time. The authors here discuss approaches to overcome these bottlenecks in order to improve the success of the HPP.

Quality assessment for clinical proteomics

Proteomics has emerged from the labs of technologists to enter widespread application in clinical contexts. This transition, however, has been hindered by overstated early claims of accuracy, concerns about reproducibility, and the challenges of handling batch effects properly. New efforts have produced sets of performance metrics and measurements of variability that establish sound expectations for experiments in clinical proteomics. As researchers begin incorporating these metrics in a quality by design paradigm, the variability of individual steps in experimental pipelines will be reduced, regularizing overall outcomes. This review discusses the evolution of quality assessment in 2D gel electrophoresis, mass spectrometry-based proteomic profiling, tandem mass spectrometry-based protein inventories, and proteomic quantitation. Taken together, the advances in each of these technologies are establishing databases that will be increasingly useful for decision-making in clinical experimentation.

Addressing the challenge of defining valid proteomic biomarkers and classifiers

Background

The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.

Results

We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential.

Conclusions

Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.

Genomics and proteomics: Applications in autoimmune diseases

Tremendous progress has been made over the past decade in the development and refinement of genomic and proteomic technologies for the identification of novel drug targets and molecular signatures associated with clinically important disease states, disease subsets, or differential responses to therapies. The rapid progress in high-throughput technologies has been preceded and paralleled by the elucidation of cytokine networks, followed by the stepwise clinical development of pathway-specific biological therapies that revolutionized the treatment of autoimmune diseases. Together, these advances provide opportunities for a long-anticipated personalized medicine approach to the treatment of autoimmune disease. The ever-increasing numbers of novel, innovative therapies will need to be harnessed wisely to achieve optimal long-term outcomes in as many patients as possible while complying with the demands of health authorities and health care providers for evidence-based, economically sound prescription of these expensive drugs. Genomic and proteomic profiling of patients with autoimmune diseases holds great promise in two major clinical areas: (1) rapid identification of new targets for the development of innovative therapies and (2) identification of patients who will experience optimal benefit and minimal risk from a specific (targeted) therapy. In this review, we attempt to capture important recent developments in the application of genomic and proteomic technologies to translational research by discussing informative examples covering a diversity of autoimmune diseases.