Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.

Mass spectrometry (MS)-based proteomics typically employs multistep sample-preparation workflows that are subject to sample contamination and loss. We report an in-StageTip method for performing sample processing, from cell lysis through elution of purified peptides, in a single, enclosed volume. This robust and scalable method largely eliminates contamination or loss. Peptides can be eluted in several fractions or in one step for single-run proteome analysis. In one day, we obtained the largest proteome coverage to date for budding and fission yeast, and found that protein copy numbers in these cells were highly correlated (R(2) = 0.78). Applying the in-StageTip method to quadruplicate measurements of a human cell line, we obtained copy-number estimates for 9,667 human proteins and observed excellent quantitative reproducibility between replicates (R(2) = 0.97). The in-StageTip method is straightforward and generally applicable in biological or clinical applications.

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The sudden global emergence of SARS-CoV-2 urgently requires an in-depth understanding of molecular functions of viral proteins and their interactions with the host proteome. Several omics studies have extended our knowledge of COVID-19 pathophysiology, including some focused on proteomic aspects 1-3 . To understand how SARS-CoV-2 and related coronaviruses manipulate the host we here characterized interactome, proteome and signaling processes in a systems-wide manner. This identified connections between the corresponding cellular events, revealed functional effects of the individual viral proteins and put these findings into the context of host signaling pathways. We investigated the closely related SARS-CoV-2 and SARS-CoV viruses as well as the influence of SARS-CoV-2 on transcriptome, proteome, ubiquitinome and phosphoproteome of a lung-derived human cell line. Projecting these data onto the global network of cellular interactions revealed relationships between the perturbations taking place upon SARS-CoV-2 infection at different layers and identified unique and common molecular mechanisms of SARS coronaviruses. The results highlight the functionality of individual proteins as well as vulnerability hotspots of SARS-CoV-2, which we targeted with clinically approved drugs. We exemplify this by identification of kinase inhibitors as well as MMPase inhibitors with significant antiviral effects against SARS-CoV-2. Main text:To identify interactions of SARS-CoV-2 and SARS-CoV with cellular proteins, we transduced A549 lung carcinoma cells with lentiviruses expressing individual HA-tagged viral proteins (Figure 1a;Extended data Fig. 1a; Supplementary Table 1). Affinity purification followed by mass spectrometry analysis (AP-MS) and statistical modelling of the MS1-level quantitative data allowed identification of 1484 interactions between 1086 cellular proteins and 24 SARS-CoV-2 and 27 SARS-CoV bait proteins (Figure 1b; Extended data Fig. 1b; Supplementary Table 2). The resulting virus-host interaction network revealed a wide range of cellular activities intercepted by SARS-CoV-2 and SARS-CoV (Figure 1b; Extended data Table 1; Supplementary Table 2). In particular, we discovered Extended data Figure 1 | Expression of viral proteins in transduced A549 cells induces changes to the host proteome. (a) Expression of HA-tagged viral proteins, in stably transduced A549 cells, used in AP-MS and proteome expression measurements. (b) The extended version of the virus-host protein-protein interaction network with 24 SARS-CoV-2 and 27 SARS-CoV proteins, as well as ORF3 of HCoV-NL63 and ORF4 and 4a of HCoV-229E, used as baits. Host targets regulated upon viral protein overexpression or SARS-CoV-2 infection (based on the analysis of all data of this study) are highlighted (see the in-plot legend). (c-f) Co-precipitation experiments in HEK 293T cells showing a specific enrichment of (c) endogenous MAVS co-precipitated with c-term HA-tagged ORF7b of SARS-CoV-2 and SARS-CoV (negative controls: SARS-CoV-2 ORF6-HA, ORF7a-HA), (d) ORF7b-H...

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.