Philipp E. Geyer

Revisiting biomarker discovery by plasma proteomics

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)‐based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous “triangular strategies” aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a “rectangular” plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision‐making, and we discuss some first steps in this direction.

Ultra-deep and quantitative saliva proteome reveals dynamics of the oral microbiome

BackgroundThe oral cavity is home to one of the most diverse microbial communities of the human body and a major entry portal for pathogens. Its homeostasis is maintained by saliva, which fulfills key functions including lubrication of food, pre-digestion, and bacterial defense. Consequently, disruptions in saliva secretion and changes in the oral microbiome contribute to conditions such as tooth decay and respiratory tract infections. Here we set out to quantitatively map the saliva proteome in great depth with a rapid and in-depth mass spectrometry-based proteomics workflow.MethodsWe used recent improvements in mass spectrometry (MS)-based proteomics to develop a rapid workflow for mapping the saliva proteome quantitatively and at great depth. Standard clinical cotton swabs were used to collect saliva form eight healthy individuals at two different time points, allowing us to study inter-individual differences and interday changes of the saliva proteome. To accurately identify microbial proteins, we developed a method called “split by taxonomy id” that prevents peptides shared by humans and bacteria or between different bacterial phyla to contribute to protein identification.ResultsMicrogram protein amounts retrieved from cotton swabs resulted in more than 3700 quantified human proteins in 100-min gradients or 5500 proteins after simple fractionation. Remarkably, our measurements also quantified more than 2000 microbial proteins from 50 bacterial genera. Co-analysis of the proteomics results with next-generation sequencing data from the Human Microbiome Project as well as a comparison to MALDI-TOF mass spectrometry on microbial cultures revealed strong agreement. The oral microbiome differs between individuals and changes drastically upon eating and tooth brushing.ConclusionRapid shotgun and robust technology can now simultaneously characterize the human and microbiome contributions to the proteome of a body fluid and is therefore a valuable complement to genomic studies. This opens new frontiers for the study of host–pathogen interactions and clinical saliva diagnostics.

Proteomics reveals the effects of sustained weight loss on the human plasma proteome

Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individuals health state remain ill‐defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry‐based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual‐specific protein levels with wide‐ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte‐secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of −16% and +37%, respectively (P < 10−13), and the entire apolipoprotein family showed characteristic differential regulation. Clinical laboratory parameters are reflected in the plasma proteome, and eight plasma proteins correlated better with insulin resistance than the known marker adiponectin. Nearly all study participants benefited from weight loss regarding a ten‐protein inflammation panel defined from the proteomics data. We conclude that plasma proteome profiling broadly evaluates and monitors intervention in metabolic diseases.

Loss-less nano-fractionator for high sensitivity, high coverage proteomics

Recent advances in mass spectrometry (MS)-based proteomics now allow very deep coverage of cellular proteomes. To achieve near-comprehensive identification and quantification, the combination of a first HPLC-based peptide fractionation orthogonal to the on-line LC-MS/MS step has proven to be particularly powerful. This first dimension is typically performed with milliliter/min flow and relatively large column inner diameters, which allow efficient pre-fractionation but typically require peptide amounts in the milligram range. Here, we describe a novel approach termed “spider fractionator” in which the post-column flow of a nanobore chromatography system enters an eight-port flow-selector rotor valve. The valve switches the flow into different flow channels at constant time intervals, such as every 90 s. Each flow channel collects the fractions into autosampler vials of the LC-MS/MS system. Employing a freely configurable collection mechanism, samples are concatenated in a loss-less manner into 2–96 fractions, with efficient peak separation. The combination of eight fractions with 100 min gradients yields very deep coverage at reasonable measurement time, and other parameters can be chosen for even more rapid or for extremely deep measurements. We demonstrate excellent sensitivity by decreasing sample amounts from 100 μg into the sub-microgram range, without losses attributable to the spider fractionator and while quantifying close to 10,000 proteins. Finally, we apply the system to the rapid automated and in-depth characterization of 12 different human cell lines to a median depth of 11,472 different proteins, which revealed differences recapitulating their developmental origin and differentiation status. The fractionation technology described here is flexible, easy to use, and facilitates comprehensive proteome characterization with minimal sample requirements.

Region and cell-type resolved quantitative proteomic map of the human heart.

The heart is a central human organ and its diseases are the leading cause of death worldwide, but an in-depth knowledge of the identity and quantity of its constituent proteins is still lacking. Here, we determine the healthy human heart proteome by measuring 16 anatomical regions and three major cardiac cell types by high-resolution mass spectrometry-based proteomics. From low microgram sample amounts, we quantify over 10,700 proteins in this high dynamic range tissue. We combine copy numbers per cell with protein organellar assignments to build a model of the heart proteome at the subcellular level. Analysis of cardiac fibroblasts identifies cellular receptors as potential cell surface markers. Application of our heart map to atrial fibrillation reveals individually distinct mitochondrial dysfunctions. The heart map is available at maxqb.biochem.mpg.de as a resource for future analyses of normal heart function and disease.The human heart is composed of distinct regions and cell types, but relatively little is known about their specific protein composition. Here, the authors present a region- and cell type-specific proteomic map of the healthy human heart, revealing functional differences and potential cell type markers.

BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes

Great advances have been made in sensitivity and acquisition speed on the Orbitrap mass analyzer, enabling increasingly deep proteome coverage. However, these advances have been mainly limited to the MS2 level, whereas ion beam sampling for the MS1 scans remains extremely inefficient. Here we report a data-acquisition method, termed BoxCar, in which filling multiple narrow mass-to-charge segments increases the mean ion injection time more than tenfold as compared to that of a standard full scan. In 1-h analyses, the method provided MS1-level evidence for more than 90% of the proteome of a human cancer cell line that had previously been identified in 24 fractions, and it quantified more than 6,200 proteins in ten of ten replicates. In mouse brain tissue, we detected more than 10,000 proteins in only 100 min, and sensitivity extended into the low-attomolar range.BoxCar, a mass spectrometry data acquisition method, greatly increases sensitivity and the detection of low-abundance peptides with a minimal amount of instrument time.

HCD Fragmentation of Glycated Peptides

Protein glycation is a concentration-dependent nonenzymatic reaction of reducing sugars with amine groups of proteins to form early as well as advanced glycation (end-) products (AGEs). Glycation is a highly disease-relevant modification but is typically only studied on a few blood proteins. To complement our blood proteomics studies in diabetics, we here investigate protein glycation by higher energy collisional dissociation (HCD) fragmentation on Orbitrap mass spectrometers. We established parameters to most efficiently fragment and identify early glycation products on in vitro glycated model proteins. Retaining standard collision energies does not degrade performance if the most dominant neutral loss of H6O3 is included into the database search strategy. Glycation analysis of the entire HeLa proteome revealed an unexpected intracellular preponderance for arginine over lysine modification in early and advanced glycation (end-) products. Single-run analysis from 1 μL of undepleted and unenriched blood plasma identified 101 early glycation sites as well as numerous AGE sites on diverse plasma proteins. We conclude that HCD fragmentation is well-suited for analyzing glycated peptides and that the diabetic status of patients can be directly diagnosed from single-run plasma proteomics measurements.

Gastric Adenocarcinomas Express the Glycosphingolipid Gb3/CD77: Targeting of Gastric Cancer Cells with Shiga Toxin B-Subunit

The B-subunit of the bacterial Shiga toxin (STxB), which is nontoxic and has low immunogenicity, can be used for tumor targeting of breast, colon, and pancreatic cancer. Here, we tested whether human gastric cancers, which are among the most aggressive tumor entities, express the cellular receptor of Shiga toxin, the glycosphingolipid globotriaosylceramide (Gb3/CD77). The majority of cases showed an extensive staining for Gb3 (36/50 cases, 72%), as evidenced on tissue sections of surgically resected specimen. Gb3 expression was detected independent of type (diffuse/intestinal), and was negatively correlated to increasing tumor–node–metastasis stages (P = 0.0385), as well as with markers for senescence. Gb3 expression in nondiseased gastric mucosa was restricted to chief and parietal cells at the bottom of the gastric glands, and was not elevated in endoscopic samples of gastritis (n = 10). Gb3 expression in established cell lines of gastric carcinoma was heterogeneous, with 6 of 10 lines being positive, evidenced by flow cytometry. STxB was taken up rapidly by live Gb3-positive gastric cancer cells, following the intracellular retrograde transport route, avoiding lysosomes and rapidly reaching the Golgi apparatus and the endoplasmic reticulum. Treatment of the Gb3-expressing gastric carcinoma cell line St3051 with STxB coupled to SN38, the active metabolite of the topoisomerase type I inhibitor irinotecan, resulted in >100-fold increased cytotoxicity, as compared with irinotecan alone. No cytotoxicity was observed on gastric cancer cell lines lacking Gb3 expression, demonstrating receptor specificity of the STxB–SN38 compound. Thus, STxB is a highly specific transport vehicle for cytotoxic agents in gastric carcinoma. Mol Cancer Ther; 15(5); 1008–17. ©2016 AACR.

Single fiber proteomics of respiratory chain defects in mitochondrial disorders

Mitochondrial DNA mutations progressively compromise the respiratory chain of skeletal muscle, resulting in a mosaic of metabolically healthy and defective fibers. The single fiber investigation of this important diagnostic feature has been beyond the capability of large-scale technologies so far. We used laser capture microdissection (LCM) to excise thin sections of individual muscle fibers from frozen biopsies of patients suffering from chronic progressive external ophthalmoplegia. We then applied a highly sensitive mass spectrometry (MS)-based proteomics workflow to analyze healthy and defective muscle fibers within the same biopsy. We quantified more than 4000 proteins in each patient, covering 75% of all respiratory chain subunits, and compared their expression in metabolically healthy and defective muscle fibers. Our findings show that mitochondrial disease causes extensive proteomic rearrangements, affecting the OPA1-dependent cristae remodeling pathway and mitochondrial translation. We provide fiber type-specific information showing that increased expression of fatty acid oxidation enzymes occurs in defective slow but not fast muscle fibers. Our findings shed light on compensatory mechanisms in muscle fibers that struggle with energy shortage and metabolic stress.

Proteomics for blood biomarker exploration of severe mental illness: pitfalls of the past and potential for the future

Recent improvements in high-throughput proteomic approaches are likely to constitute an essential advance in biomarker discovery, holding promise for improved personalized care and drug development. These methodologies have been applied to study multivariate protein patterns and provide valuable data of peripheral tissues. To highlight findings of the last decade for three of the most common psychiatric disorders, namely schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD), we queried PubMed. Here we delve into the findings from thirty studies, which used proteomics and multiplex immunoassay approaches for peripheral blood biomarker exploration. In an explorative approach, we ran enrichment analyses in peripheral blood according to these results and ascertained the overlap between proteomic findings and genetic loci identified in genome-wide association studies (GWAS). The studies we appraised demonstrate that proteomics for psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results constraining the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges for the implementation of proteomic signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of proteomics in mental disease diagnostics, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.