Because of low throughput and limited robustness, nano-scale liquid chromatography has been a bottleneck for advancing proteomics in biomedical research. Here, we developed and evaluated two new LC concepts—“pre-formed gradients” and “offset gradients for peptide re-focusing”—that are both implemented in the Evosep One instrument. We evaluated robustness with more than 2000 HeLa runs, demonstrated absence of cross-contamination with crude plasma samples, high proteome coverage by fractionated HeLa and routinely measuring more than 5000 proteins/sample in just 21 minutes.

Neurodegenerative diseases are a growing burden, and there is an urgent need for better biomarkers for diagnosis, prognosis, and treatment efficacy. Structural and functional brain alterations are reflected in the protein composition of cerebrospinal fluid (CSF). Alzheimer's disease (AD) patients have higher CSF levels of tau, but we lack knowledge of systems-wide changes of CSF protein levels that accompany AD. Here, we present a highly reproducible mass spectrometry (MS)based proteomics workflow for the in-depth analysis of CSF from minimal sample amounts. From three independent studies (197 individuals), we characterize differences in proteins by AD status (> 1,000 proteins, CV < 20%). Proteins with previous links to neurodegeneration such as tau, SOD1, and PARK7 differed most strongly by AD status, providing strong positive controls for our approach. CSF proteome changes in Alzheimer's disease prove to be widespread and often correlated with tau concentrations. Our unbiased screen also reveals a consistent glycolytic signature across our cohorts and a recent study. Machine learning suggests clinical utility of this proteomic signature.

Plasma and serum are rich sources of information regarding an individual's health state, and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)‐based proteomics now allows highly specific and quantitative readout of the plasma proteome. Here, we employ Plasma Proteome Profiling to define quality marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma, and whole blood of 20 individuals (> 6,000 proteins), and compare serum and plasma proteomes. Based on spike‐in experiments, we determine sample quality‐associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource ( http://www.plasmaproteomeprofiling.org) to assess overall sample‐related bias in clinical studies and to prevent costly miss‐assignment of biomarker candidates.

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A deeper understanding of COVID‐19 on human molecular pathophysiology is urgently needed as a foundation for the discovery of new biomarkers and therapeutic targets. Here we applied mass spectrometry (MS)‐based proteomics to measure serum proteomes of COVID‐19 patients and symptomatic, but PCR‐negative controls, in a time‐resolved manner. In 262 controls and 458 longitudinal samples of 31 patients, hospitalized for COVID‐19, a remarkable 26% of proteins changed significantly. Bioinformatics analyses revealed co‐regulated groups and shared biological functions. Proteins of the innate immune system such as CRP, SAA1, CD14, LBP, and LGALS3BP decreased early in the time course. Regulators of coagulation (APOH, FN1, HRG, KNG1, PLG) and lipid homeostasis (APOA1, APOC1, APOC2, APOC3, PON1) increased over the course of the disease. A global correlation map provides a system‐wide functional association between proteins, biological processes, and clinical chemistry parameters. Importantly, five SARS‐CoV‐2 immunoassays against antibodies revealed excellent correlations with an extensive range of immunoglobulin regions, which were quantified by MS‐based proteomics. The high‐resolution profile of all immunoglobulin regions showed individual‐specific differences and commonalities of potential pathophysiological relevance.

Pathogenic mutations in the Leucine-rich repeat kinase 2 (LRRK2) are the predominant genetic cause of Parkinson’s disease (PD). They increase its activity, resulting in augmented Rab10-Thr73 phosphorylation and conversely, LRRK2 inhibition decreases pRab10 levels. Currently, there is no assay to quantify pRab10 levels for drug target engagement or patient stratification. To meet this challenge, we developed an high accuracy and sensitivity targeted mass spectrometry (MS)-based assay for determining Rab10-Thr73 phosphorylation stoichiometry in human samples. It uses synthetic stable isotope-labeled (SIL) analogues for both phosphorylated and non-phosphorylated tryptic peptides surrounding Rab10-Thr73 to directly derive the percentage of Rab10 phosphorylation from attomole amounts of the endogenous phosphopeptide. The SIL and the endogenous phosphopeptides are separately admitted into an Orbitrap analyzer with the appropriate injection times. We test the reproducibility of our assay by determining Rab10-Thr73 phosphorylation stoichiometry in neutrophils of LRRK2 mutation carriers before and after LRRK2 inhibition. Compared to healthy controls, the PD predisposing mutation carriers LRRK2 G2019S and VPS35 D620N display 1.9-fold and 3.7-fold increased pRab10 levels, respectively. Our generic MS-based assay further establishes the relevance of pRab10 as a prognostic PD marker and is a powerful tool for determining LRRK2 inhibitor efficacy and for stratifying PD patients for LRRK2 inhibitor treatment.

The goal of clinical proteomics is to identify, quantify, and characterize proteins in body fluids or tissue to assist diagnosis, prognosis, and treatment of patients. In this way, it is similar to more mature omics technologies, such as genomics, that are increasingly applied in biomedicine. We argue that, similar to those fields, proteomics also faces ethical issues related to the kinds of information that is inherently obtained through sample measurement, although their acquisition was not the primary purpose. Specifically, we demonstrate the potential to identify individuals both by their characteristic, individual-specific protein levels and by variant peptides reporting on coding single nucleotide polymorphisms. Furthermore, it is in the nature of blood plasma proteomics profiling that it broadly reports on the health status of an individual—beyond the disease under investigation. Finally, we show that private and potentially sensitive information, such as ethnicity and pregnancy status, can increasingly be derived from proteomics data. Although this is potentially valuable not only to the individual, but also for biomedical research, it raises ethical questions similar to the incidental findings obtained through other omics technologies. We here introduce the necessity of—and argue for the desirability for—ethical and human-rights-related issues to be discussed within the proteomics community. Those thoughts are more fully developed in our accompanying manuscript. Appreciation and discussion of ethical aspects of proteomic research will allow for deeper, better-informed, more diverse, and, most importantly, wiser guidelines for clinical proteomics.

Existing tests for detecting liver fibrosis, inflammation and steatosis, three stages of liver disease that are still reversible are severely hampered by limited accuracy or invasive nature. Here, we present a paired liver-plasma proteomics approach to infer molecular pathophysiology and to identify biomarkers in a cross-sectional alcohol-related liver disease cohort of nearly 600 individuals. Metabolic functions were downregulated whereas fibrosis-associated signaling and novel immune responses were upregulated, but only half of tissue proteome changes were transmitted to the circulation. Machine learning models based on our biomarker panels outperformed existing tests, laying the foundation for a generic proteomic liver health assessment.