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Multi‐omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi‐Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network‐level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi‐omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi‐omics studies. SYNOPSIS A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes. A causal multi‐omics network is built by integrating multiple ressources spanning signaling, metabolism and allosteric regulations. Transcriptomics, phosphoproteomics and metabolomics data are integrated in a set of coherent mechanistic hypotheses using CARNIVAL, a tool contextualizing causal networks. This set of coherent mechanistic hypotheses can be mined to identify disease mechanisms and therapeutic targets. A network built for a cohort of kidney cancer patients shows coherence with other studies and known therapeutic targets. Graphical Abstract A new approach integrates multi‐omics datasets with a prior knowledge network spanning signaling, metabolism and allosteric regulations. Application to a kidney cancer patient cohort captures relevant cross‐talks among deregulated processes.

Quantitative phosphoproteomics has transformed investigations of cell signaling, but it remains challenging to scale the technology for high-throughput analyses. Here we report a rapid and reproducible approach to analyze hundreds of phosphoproteomes using data-independent acquisition (DIA) with an accurate site localization score incorporated into Spectronaut. DIA-based phosphoproteomics achieves an order of magnitude broader dynamic range, higher reproducibility of identification, and improved sensitivity and accuracy of quantification compared to state-of-the-art data-dependent acquisition (DDA)-based phosphoproteomics. Notably, direct DIA without the need of spectral libraries performs close to analyses using project-specific libraries, quantifying > 20,000 phosphopeptides in 15 min single-shot LC-MS analysis per condition. Adaptation of a 3D multiple regression model-based algorithm enables global determination of phosphorylation site stoichiometry in DIA. Scalability of the DIA approach is demonstrated by systematically analyzing the effects of thirty kinase inhibitors in context of epidermal growth factor (EGF) signaling showing that specific protein kinases mediate EGF-dependent phospho-regulation. Localizing phosphorylation sites by data-independent acquisition (DIA)-based proteomics is still challenging. Here, the authors develop algorithms for phosphosite localization and stoichiometry determination, and incorporate them into single-shot DIA-phosphoproteomics workflows.

Comprehensive mass spectrometry (MS)-based proteomics is now feasible, but reproducible quantification remains challenging, especially for post-translational modifications such as phosphorylation. Here, we compare the most popular quantification techniques for global phosphoproteomics: label-free quantification (LFQ), stable isotope labeling by amino acids in cell culture (SILAC) and MS 2 - and MS 3 -measured tandem mass tags (TMT). In a mixed species comparison with fixed phosphopeptide ratios, we find LFQ and SILAC to be the most accurate techniques. MS 2 -based TMT yields the highest precision but lowest accuracy due to ratio compression, which MS 3 -based TMT can partly rescue. However, MS 2 -based TMT outperforms MS 3 -based TMT when analyzing phosphoproteome changes in the DNA damage response, since its higher precision and larger identification numbers allow detection of a greater number of significantly regulated phosphopeptides. Finally, we utilize the TMT multiplexing capabilities to develop an algorithm for determining phosphorylation site stoichiometry, showing that such applications benefit from the high accuracy of MS 3 -based TMT. Quantitative phosphoproteomics has become a standard method in molecular and cell biology. Here, the authors compare performance and parameters of phosphoproteome quantification by LFQ, SILAC, and MS 2 -/MS 3 -based TMT and introduce a TMT-adapted algorithm for calculating phosphorylation site stoichiometry.

Ubiquitination is a post-translational modification (PTM) that is essential for balancing numerous physiological processes. To enable delineation of protein ubiquitination at a site-specific level, we generated an antibody, denoted UbiSite, recognizing the C-terminal 13 amino acids of ubiquitin, which remain attached to modified peptides after proteolytic digestion with the endoproteinase LysC. Notably, UbiSite is specific to ubiquitin. Furthermore, besides ubiquitination on lysine residues, protein N-terminal ubiquitination is readily detected as well. By combining UbiSite enrichment with sequential LysC and trypsin digestion and high-accuracy MS, we identified over 63,000 unique ubiquitination sites on 9,200 proteins in two human cell lines. In addition to uncovering widespread involvement of this PTM in all cellular aspects, the analyses reveal an inverse association between protein N-terminal ubiquitination and acetylation, as well as a complete lack of correlation between changes in protein abundance and alterations in ubiquitination sites upon proteasome inhibition.

Mass spectrometry (MS)-based proteomics workflows typically involve complex, multi-step processes, presenting challenges with sample losses, reproducibility, requiring substantial time and financial investments, and specialized skills. Here we introduce One-Tip, a proteomics methodology that seamlessly integrates efficient, one-pot sample preparation with precise, narrow-window data-independent acquisition (nDIA) analysis. One-Tip substantially simplifies sample processing, enabling the reproducible identification of >9000 proteins from ~1000 HeLa cells. The versatility of One-Tip is highlighted by nDIA identification of ~6000 proteins in single cells from early mouse embryos. Additionally, the study incorporates the Uno Single Cell Dispenser™, demonstrating the capability of One-Tip in single-cell proteomics with >3000 proteins identified per HeLa cell. We also extend One-Tip workflow to analysis of extracellular vesicles (EVs) extracted from blood plasma, demonstrating its high sensitivity by identifying >3000 proteins from 16 ng EV preparation. One-Tip expands capabilities of proteomics, offering greater depth and throughput across a range of sample types. Traditional proteomics methods are complex and resource-intensive. Here, the authors develop One-Tip, a highly simplified approach that enables efficient, sensitive, and comprehensive analysis across various sample types, from blood plasma to single cells.

Dynamic change in subcellular localization of signaling proteins is a general concept that eukaryotic cells evolved for eliciting a coordinated response to stimuli. Mass spectrometry-based proteomics in combination with subcellular fractionation can provide comprehensive maps of spatio-temporal regulation of protein networks in cells, but involves laborious workflows that does not cover the phospho-proteome level. Here we present a high-throughput workflow based on sequential cell fractionation to profile the global proteome and phospho-proteome dynamics across six distinct subcellular fractions. We benchmark the workflow by studying spatio-temporal EGFR phospho-signaling dynamics in vitro in HeLa cells and in vivo in mouse tissues. Finally, we investigate the spatio-temporal stress signaling, revealing cellular relocation of ribosomal proteins in response to hypertonicity and muscle contraction. Proteomics data generated in this study can be explored through https://SpatialProteoDynamics.github.io . Protein activity regulated by phosphorylation can result in subcellular relocation. Here, the authors present a high throughput spatial phosphoproteomics approach to profile six subcellular compartments, providing insights into EGFR and stress signalling dynamics.

Colorectal cancer molecular signatures derived from omics data can be employed to stratify CRC patients and aid decisions about therapies or evaluate prognostic outcome. However, molecular biomarkers for identification of patients at increased risk of disease relapse are currently lacking. Here, we present a comprehensive multi-omics analysis of a Danish colorectal cancer tumor cohort composed of 412 biopsies from tumors of 371 patients diagnosed at TNM stage II or III. From mass spectrometry-based patient proteome profiles, we classified the tumors into four molecular subtypes, including a mesenchymal-like subtype. As the mesenchymal-rich tumors are known to represent the most invasive and metastatic phenotype, we focused on the protein signature defining this subtype to evaluate their potential as relapse risk markers. Among signature-specific proteins, we followed-up Caveolae-Associated Protein-1 (CAVIN1) and demonstrated its role in tumor progression in a 3D in vitro model of colorectal cancer. Compared to previous omics analyses of CRC, our multi-omics classification provided deeper insights into EMT in cancer cells with stronger correlations with risk of relapse. Synopsis Proteomics profiling of a pre-metastatic colorectal cancer cohort identifies four proteomics-based subtypes and highlights the role of CAVIN1 in relapse in EMT-like tumors. Mass-spectrometry-based analysis of the proteomes of 361 colorectal cancer tumor biopsies identifies four distinct molecular subtypes. Key differences between proteome and transcriptome-based classifications are highlighted. CAVIN1 levels in epithelial-to-mesenchymal transition-like tumors correlate with a higher risk of relapse. Hybrid-DIA-based analysis of the phosphoproteome of a smaller validation cohort identified MTOR as a potential kinase differentially regulated in relapse. Proteomics profiling of a pre-metastatic colorectal cancer cohort identifies four proteomics-based subtypes and highlights the role of CAVIN1 in relapse in EMT-like tumors.

p38 and c-Jun N-terninal kinase (JNK) are activated in response to acute stress and inflammatory signals. Through modification of a plethora of substrates, these kinases profoundly re-shape cellular physiology for the optimal response to a harmful environment and/or an inflammatory state. Here, we utilized phospho-proteomics to identify several hundred substrates for both kinases. Our results indicate that the scale of signaling from p38 and JNK are of a similar magnitude. Among the many new targets, we highlight the regulation of the transcriptional regulators grb10-interacting GYF protein 1 and 2 (GIGYF1/2) by p38-dependent MAP kinase-activated protein kinase 2 (MK2) phosphorylation and 14–3–3 binding. We also show that the Golgi apparatus contains numerous substrates, and is a major target for regulation by p38 and JNK. When activated, these kinases mediate structural rearrangement of the Golgi apparatus, which positively affects protein flux through the secretory system. Our work expands on our knowledge about p38 and JNK signaling with important biological ramifications.

Protein identification and quantification is an important tool for biomarker discovery. With the increased sensitivity and speed of modern mass spectrometers, sample-preparation remains a bottleneck for studying large cohorts. To address this issue, we prepared and evaluated a simple and efficient workflow on the Opentrons OT-2 (OT-2) robot that combines sample digestion, cleanup and Evotip loading in a fully automated manner, allowing the processing of up to 192 samples in 6 hours. Our results demonstrate a highly sensitive workflow yielding both reproducibility and stability even at low sample inputs. The workflow is optimized for minimal sample starting amount to reduce the costs for reagents needed for sample preparation, which is critical when analyzing large biological cohorts. Building on the digesting workflow, we incorporated an automated phosphopeptide enrichment step using magnetic Ti-IMAC beads. This allows for a fully automated proteome and phosphoproteome sample preparation in a single step with high sensitivity. Using the integrated workflow, we evaluated the effects of cancer immune therapy on the plasma proteome in metastatic melanoma patients.Competing Interest StatementDorte B. Bekker-Jensen, Joel Mario Vej-Nielsen, Magnus Huusfeldt, and Nicolai Bache are employees of Evosep Biosystems, manufacturer of instrumentation used in this work. Other authors declare no competing interests.