Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10-fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable-core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease.

The posttranslational modification of proteins by arginine methylation is functionally important, yet the breadth of this modification is not well characterized. Using high-resolution mass spectrometry, we identified 8030 arginine methylation sites within 3300 human proteins in human embryonic kidney 293 cells, indicating that the occurrence of this modification is comparable to phosphorylation and ubiquitylation. A site-level conservation analysis revealed that arginine methylation sites are less evolutionarily conserved compared to arginines that were not identified as modified by methylation. Through quantitative proteomics and RNA interference to examine arginine methylation stoichiometry, we unexpectedly found that the protein arginine methyltransferase (PRMT) family of arginine methyltransferases catalyzed methylation independently of arginine sequence context. In contrast to the frequency of somatic mutations at arginine methylation sites throughout the proteome, we observed that somatic mutations were common at arginine methylation sites in proteins involved in mRNA splicing. Furthermore, in HeLa and U2OS cells, we found that distinct arginine methyltransferases differentially regulated the functions of the pre-mRNA splicing factor SRSF2 (serine/arginine-rich splicing factor 2) and the RNA transport ribonucleoprotein HNRNPUL1 (heterogeneous nuclear ribonucleoprotein U-like 1). Knocking down PRMT5 impaired the RNA binding function of SRSF2, whereas knocking down PRMT4 [also known as coactivator-associated arginine methyltransferase 1 (CARM1)] or PRMT1 increased the RNA binding function of HNRNPUL1. High-content single-cell imaging additionally revealed that knocking down CARM1 promoted the nuclear accumulation of SRSF2, independent of cell cycle phase. Collectively, the presented human arginine methylome provides a missing piece in the global and integrative view of cellular physiology and protein regulation.

Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.

SUMMARY Most high-grade serous ovarian cancer (HGSOC) patients develop resistance to platinum-based chemotherapy and recur, but 15% remain disease free over a decade. To discover drivers of long-term survival, we quantitatively analyzed the proteomes of platinum-resistant and -sensitive HGSOC patients from minute amounts of formalin-fixed, paraffin-embedded tumors. This revealed cancer/testis antigen 45 (CT45) as an independent prognostic factor associated with a doubling of disease-free survival in advanced-stage HGSOC. Phospho- and interaction proteomics tied CT45 to DNA damage pathways through direct interaction with the PP4 phosphatase complex. In vitro, CT45 regulated PP4 activity, and its high expression led to increased DNA damage and platinum sensitivity. CT45-derived HLA class I peptides, identified by immunopeptidomics, activate patient-derived cytotoxic T cells and promote tumor cell killing. This study highlights the power of clinical cancer proteomics to identify targets for chemo- and immunotherapy and illuminate their biological roles.

Dominant missense mutations in the human serine protease FAM111A underlie perinatally lethal gracile bone dysplasia and Kenny-Caffey syndrome, yet how FAM111A mutations lead to disease is not known. We show that FAM111A proteolytic activity suppresses DNA replication and transcription by displacing key effectors of these processes from chromatin, triggering rapid programmed cell death by Caspase-dependent apoptosis to potently undermine cell viability. Patient-associated point mutations in FAM111A exacerbate these phenotypes by hyperactivating its intrinsic protease activity. Moreover, FAM111A forms a complex with the uncharacterized homologous serine protease FAM111B, point mutations in which cause a hereditary fibrosing poikiloderma syndrome, and we demonstrate that disease-associated FAM111B mutants display amplified proteolytic activity and phenocopy the cellular impact of deregulated FAM111A catalytic activity. Thus, patient-associated FAM111A and FAM111B mutations may drive multisystem disorders via a common gain-of-function mechanism that relieves inhibitory constraints on their protease activities to powerfully undermine cellular fitness.

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis, and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Herein we describe a MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from histopathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including low-input cancer tissue isolated by laser microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent acquisition (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total workup took less than a day. We observed a moderate trend towards lower protein identification in long-term stored samples (>15 years), but clustering into distinct proteomic subtypes was independent of archival time. Our results underscore the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and they prove the feasibility of analyzing large tissue cohorts in a robust, timely, and streamlined manner.

The CHD1 gene, encoding the chromo-domain helicase DNAbinding protein-1, is one of the most frequently deleted genes in prostate cancer. Here, we examined the role of CHD1 in DNA double-strand break (DSB) repair in prostate cancer cells. We show that CHD1 is required for the recruitment of CtIP to chromatin and subsequent end resection during DNA DSB repair. Our data support a role for CHD1 in opening the chromatin around the DSB to facilitate the recruitment of homologous recombination (HR) proteins. Consequently, depletion of CHD1 specifically affects HR-mediated DNA repair but not non-homologous end joining. Together, we provide evidence for a previously unknown role of CHD1 in DNA DSB repair via HR and show that CHD1 depletion sensitizes cells to PARP inhibitors, which has potential therapeutic relevance. Our findings suggest that CHD1 deletion, like BRCA1/2 mutation in ovarian cancer, may serve as a marker for prostate cancer patient stratification and the utilization of targeted therapies such as PARP inhibitors, which specifically target tumors with HR defects.

Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary. Chemical labeling-based single-cell approaches introduce hundreds of cells into the MS, but direct analysis of single cells has not yet reached the necessary sensitivity, robustness and quantitative accuracy to answer biological questions. Here, we develop a robust workflow combining miniaturized sample preparation, very-low flow-rate chromatography and a novel trapped ion mobility mass spectrometer, resulting in a more than ten-fold improved sensitivity. We accurately and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators such as CDK2NA, E2 ubiquitin ligases such as UBE2S and highlights potential novel ones. Comparing the variability in more than 420 single-cell proteomes to transcriptome data revealed a stable core proteome despite perturbation. Our technology can readily be applied to ultra-high sensitivity analysis of tissue material, including post-translational modifications and to small molecule studies.