Single-cell proteomics (SCP) promises to revolutionize biomedicine by providing an unparalleled view of the proteome in individual cells. Here, we present a high-sensitivity SCP workflow named Chip-Tip, identifying >5,000 proteins in individual HeLa cells. It also facilitated direct detection of post-translational modifications in single cells, making the need for specific post-translational modification-enrichment unnecessary. Our study demonstrates the feasibility of processing up to 120 label-free SCP samples per day. An optimized tissue dissociation buffer enabled effective single-cell disaggregation of drug-treated cancer cell spheroids, refining overall SCP analysis. Analyzing nondirected human-induced pluripotent stem cell differentiation, we consistently quantified stem cell markers OCT4 and SOX2 in human-induced pluripotent stem cells and lineage markers such as GATA4 (endoderm), HAND1 (mesoderm) and MAP2 (ectoderm) in different embryoid body cells. Our workflow sets a benchmark in SCP for sensitivity and throughput, with broad applications in basic biology and biomedicine for identification of cell type-specific markers and therapeutic targets.

Achieving sufficient coverage of regulatory phosphorylation sites by mass spectrometry (MS)-based phosphoproteomics for signaling pathway reconstitution is challenging, especially when analyzing tiny sample amounts. To address this, we present a hybrid data-independent acquisition (DIA) strategy (hybrid-DIA) that combines targeted and discovery proteomics through an Application Programming Interface (API) to dynamically intercalate DIA scans with accurate triggering of multiplexed tandem mass spectrometry (MSx) scans of predefined (phospho)peptide targets. By spiking-in heavy stable isotope labeled phosphopeptide standards covering seven major signaling pathways, we benchmark hybrid-DIA against state-of-the-art targeted MS methods (i.e., SureQuant) using EGF-stimulated HeLa cells and find the quantitative accuracy and sensitivity to be comparable while hybrid-DIA also profiles the global phosphoproteome. To demonstrate the robustness, sensitivity, and biomedical potential of hybrid-DIA, we profile chemotherapeutic agents in single colon carcinoma multicellular spheroids and evaluate the phospho-signaling difference of cancer cells in 2D vs 3D culture.

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.

This study presents a comparative analysis of three LysC homologues from Achromobacter lyticus, Pseudomonas aeruginosa, and Lysobacter enzymogenes for mass spectrometry-based proteomics. Utilizing a protein aggregation capture (PAC) workflow with HeLa cell lysate, we assessed the enzymes’ cleavage specificity, digestion efficiency, and performance across various experimental conditions. Results showed that while all three homologues exhbihited high cleavage specificity at lysine residues, A. lyticus LysC outperformed the two others with its superior peptide identification, digestion efficiency, and protein coverage, especially at short digestion times. Combination of A. lyticus LysC and trypsin demonstrated that importance of LysC for signifancanlty minimizing missed cleavage rates in tryptic digests. This study underscores A. lyticus LysC’s potential as an optimal choice for enhancing mass spectrometry-based proteomics.

Omics-based molecular signatures can be employed to stratify colorectal cancer (CRC) patients and aid therapeutic treatment or evaluate prognostic outcome. However, reliable molecular biomarkers for identification of patients at risk of relapse are currently lacking. Here, we present a comprehensive multi-omics analysis of a Danish colorectal cancer tumor cohort composed of 371 patients diagnosed at TNM stage II or III. Utilizing proteome profiles, we classified the tumors into four molecular subtypes, including a mesenchymal-like subtype. Mesenchymal-rich tumors are known to represent the most invasive and metastatic phenotype. Therefore, we focused on this subtype protein signature to identify potential relapse risk markers. Among mesenchymal-subtype markers, 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 CRC studies, our proteomics analysis contributes unique insights into epithelial-to-mesenchymal transition in cancer cells with stronger correlations with risk of relapse.

The human spermatozoon is among the most specialised cells in the body, yet where its proteins reside, where they come from and how they vary between and within men remain largely unresolved. Here we present the deepest proteomic atlas of human spermatozoa to date, identifying 10,828 proteins across six donors. Abundances correlate well between individuals and are anchored by a conserved core of mitochondrial energy-metabolism proteins, while a smaller fraction varies between donations from the same man. By mechanically separating heads from tails, we assigned thousands of previously unlocalised proteins to a compartment, and by comparing seminal fluid from vasectomised and non-vasectomised men we distinguished sperm-and epididymis-derived proteins from accessory-gland secretions. Using a single-cell analysis workflow we further show that the proteomes of individual spermatozoa can be measured, recovering up to ∼1,000 protein groups per cell. In a cohort with total fertilisation failure, we find recurrent mitochondrial rather than sperm–egg fusion dysregulation. Together or data provides novel insights into the dynamics of the sperm proteome and male infertility