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.

No abstract

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.

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 read-out of the plasma proteome. Here we employ Plasma Proteome Profiling to define contamination 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 (>6000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments we determine contamination-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 (www.plasmaproteomeprofiling.org) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates.

Background‘Chlorochromatium aggregatum’ is a phototrophic consortium, a symbiosis that may represent the highest degree of mutual interdependence between two unrelated bacteria not associated with a eukaryotic host. ‘Chlorochromatium aggregatum’ is a motile, barrel-shaped aggregate formed from a single cell of ‘Candidatus Symbiobacter mobilis”, a polarly flagellated, non-pigmented, heterotrophic bacterium, which is surrounded by approximately 15 epibiont cells of Chlorobium chlorochromatii, a non-motile photolithoautotrophic green sulfur bacterium.ResultsWe analyzed the complete genome sequences of both organisms to understand the basis for this symbiosis. Chl. chlorochromatii has acquired relatively few symbiosis-specific genes; most acquired genes are predicted to modify the cell wall or function in cell-cell adhesion. In striking contrast, ‘Ca. S. mobilis’ appears to have undergone massive gene loss, is probably no longer capable of independent growth, and thus may only reproduce when consortia divide. A detailed model for the energetic and metabolic bases of the dependency of ‘Ca. S. mobilis’ on Chl. chlorochromatii is described.ConclusionsGenomic analyses suggest that three types of interactions lead to a highly sophisticated relationship between these two organisms. Firstly, extensive metabolic exchange, involving carbon, nitrogen, and sulfur sources as well as vitamins, occurs from the epibiont to the central bacterium. Secondly, ‘Ca. S. mobilis’ can sense and move towards light and sulfide, resources that only directly benefit the epibiont. Thirdly, electron cycling mechanisms, particularly those mediated by quinones and potentially involving shared protonmotive force, could provide an important basis for energy exchange in this and other symbiotic relationships.

Green sulfur bacteria are obligate photolithoautotrophs that require highly reducing conditions for growth and can utilize only a very limited number of carbon substrates. These bacteria thus inhabit a very narrow ecologic niche. However, several green sulfur bacteria have overcome the limits of immobility by entering into a symbiosis with motile Betaproteobacteria in a type of multicellular association termed phototrophic consortia. One of these consortia, “Chlorochromatium aggregatum,” has recently been established as the first culturable model system to elucidate the molecular basis of this symbiotic interaction. It consists of 12–20 green sulfur bacteria epibionts surrounding a central, chemoheterotrophic betaproteobacterium in a highly ordered fashion. Recent genomic, transcriptomic, and proteomic studies of “C. aggregatum” and its epibiont provide insights into the molecular basis and the origin of the stable association between the two very distantly related bacteria. While numerous genes of central metabolic pathways are upregulated during the specific symbiosis and hence involved in the interaction, only a limited number of unique putative symbiosis genes have been detected in the epibiont. Green sulfur bacteria therefore are preadapted to a symbiotic lifestyle. The metabolic coupling between the bacterial partners appears to involve amino acids and highly specific ultrastructures at the contact sites between the cells. Similarly, the interaction in the equally well studied archaeal consortia consisting of Nanoarchaeum equitans and its host Ignicoccus hospitalis is based on the transfer of amino acids while lacking the highly specialized contact sites observed in phototrophic consortia.