The dia-PASEF technology exploits ion mobility separation for high-sensitivity analysis of complex proteomes. Here, we demonstrate neural network-based processing of the ion mobility data, which we implement in the DIA-NN software suite. Using spectral libraries generated with the MSFragger-based FragPipe computational platform, the DIA-NN analysis of dia-PASEF raw data increases the proteomic depth by up to 69% compared to the originally published dia-PASEF workflow. For example, we quantify over 5200 proteins from 10ng of HeLa peptides separated with a 95-minute nanoflow gradient, and over 5000 proteins from 200ng using a 4.8-minute separation with an Evosep One system. In complex samples, featuring a mix of human and yeast lysates, the workflow detects over 11700 proteins in single runs acquired with a 100-minute nanoflow gradient, while demonstrating quantitative precision. Hence, the combination of FragPipe and DIA-NN provides a simple-to-use software platform for dia-PASEF data analysis, yielding significant gains in high-sensitivity proteomics.

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SummaryFunctional genomic strategies help to address the genotype phenotype problem by annotating gene function and regulatory networks. Here, we demonstrate that combining functional genomics with proteomics uncovers general principles of protein expression, and provides new avenues to annotate protein function. We recorded precise proteomes for all non-essential gene knock-outs in Saccharomyces cerevisiae. We find that protein abundance is driven by a complex interplay of i) general biological properties, including translation rate, turnover, and copy number variations, and ii) their genetic, metabolic and physical interactions, including membership in protein complexes. We further show that combining genetic perturbation with proteomics provides complementary dimensions of functional annotation: proteomic profiling, reverse proteomic profiling, profile similarity and protein covariation analysis. Thus, our study generates a resource in which nine million protein quantities are linked to 79% of the yeast coding genome, and shows that functional proteomics reveals principles that govern protein expression.Highlights-Nine million protein quantities recorded in ~4,600 non-essential gene deletions in S. cerevisiae reveal principles of how the proteome responds to genetic perturbation-Genome-scale protein expression is determined by both functional relationships between proteins, as well as common biological responses-Broad protein expression profiles in slow-growing strains can be explained by chromosomal aneuploidies-Protein half-life and ribosome occupancy are predictable from protein abundance changes across knock-outs-Functional proteomics annotates missing gene function in four complementary dimensions

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. There is an urgent need for predictive markers that can guide clinical decision-making, inform about the effect of experimental therapies, and point to novel therapeutic targets. Here, we characterize the time-dependent progression of COVID-19 through different stages of the disease, by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalization. We report that the time-resolved patient molecular phenotypes reflect an initial spike in the systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution and immunomodulation. Further, we show that the early host response is predictive for the disease trajectory and gives rise to proteomic and diagnostic marker signatures that classify the need for supplemental oxygen therapy and mechanical ventilation, and that predict the time to recovery of mildly ill patients. In severely ill patients, the molecular phenotype of the early host response predicts survival, in two independent cohorts and weeks before outcome. We also identify age-specific molecular response to COVID-19, which involves increased inflammation and lipoprotein dysregulation in older patients. Our study provides a deep and time resolved molecular characterization of COVID-19 disease progression, and reports biomarkers for risk-adapted treatment strategies and molecular disease monitoring. Our study demonstrates accurate prognosis of COVID-19 outcome from proteomic signatures recorded weeks earlier.

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