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Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package. Label-free quantitative proteomics by data dependent acquisition offers high protein identification rates but is often limited by missing values. Here, the authors develop a quantification workflow that substantially reduces missing values while maintaining high identification rates and quantification accuracy.

High‐throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh‐frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single‐pot solid‐phase‐enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96‐well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directly analyzed by LCMS, thereby significantly reducing hands‐on time, reducing variability in protein quantification, and improving longitudinal reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low‐input samples, reproducibly quantifying 500–1,000 proteins from 100 to 1,000 cells. Furthermore, we applied this approach to a cohort of clinical FFPE pulmonary adenocarcinoma (ADC) samples and recapitulated their separation into known histological growth patterns. Finally, we integrated autoSP3 with AFA ultrasonication for the automated end‐to‐end sample preparation and LCMS analysis of 96 intact tissue samples. Collectively, this constitutes a generic, scalable, and cost‐effective workflow with minimal manual intervention, enabling reproducible tissue proteomics in a broad range of clinical and non‐clinical applications. Synopsis The study presents an automated sample preparation pipeline for low‐input proteomics based on the SP3 method. The seamless integration of tissue lysis with autoSP3 in a 96‐well format features low variability, high sensitivity and longitudinal reproducibility for clinical studies. An automated, scalable, and cost‐effective workflow (autoSP3) allows reproducible tissue proteomics in a broad range of clinical and non‐clinical applications. Automated tissue lysis is integrated with autoSP3 for an end‐to‐end workflow with minimal manual interference. The workflow allows reduced variability in protein quantification and increased longitudinal reproducibility. Minimal sample losses facilitate low‐input applications in a standardized workflow. Graphical Abstract The study presents an automated sample preparation pipeline for low‐input proteomics based on the SP3 method. The seamless integration of tissue lysis with autoSP3 in a 96‐well format features low variability, high sensitivity and longitudinal reproducibility for clinical studies.

The analysis of proteins that are newly synthesized upon a cellular perturbation can provide detailed insight into the proteomic response that is elicited by specific cues. This can be investigated by pulse-labeling of cells with clickable and stable-isotope-coded amino acids for the enrichment and mass spectrometric characterization of newly synthesized proteins (NSPs), however convoluted protocols prohibit their routine application. Here we report the optimization of multiple steps in sample preparation, mass spectrometry and data analysis, and we integrate them into a semi-automated workflow for the quantitative analysis of the newly synthesized proteome (QuaNPA). Reduced input requirements and data-independent acquisition (DIA) enable the analysis of triple-SILAC-labeled NSP samples, with enhanced throughput while featuring high quantitative accuracy. We apply QuaNPA to investigate the time-resolved cellular response to interferon-gamma (IFNg), observing rapid induction of targets 2 h after IFNg treatment. QuaNPA provides a powerful approach for large-scale investigation of NSPs to gain insight into complex cellular processes. Analysis of newly synthesized proteins upon perturbation can provide detailed insights into immediate proteome remodeling, which drives cellular responses. Here, the authors report an optimized semi-automated workflow for the quantitative analysis of the newly synthesized proteome.

In order to obtain a systems‐level understanding of a complex biological system, detailed proteome information is essential. Despite great progress in proteomics technologies, thorough interrogation of the proteome from quantity‐limited biological samples is hampered by inefficiencies during processing. To address these challenges, here we introduce a novel protocol using paramagnetic beads, termed Single‐Pot Solid‐Phase‐enhanced Sample Preparation (SP3). SP3 provides a rapid and unbiased means of proteomic sample preparation in a single tube that facilitates ultrasensitive analysis by outperforming existing protocols in terms of efficiency, scalability, speed, throughput, and flexibility. To illustrate these benefits, characterization of 1,000 HeLa cells and single Drosophila embryos is used to establish that SP3 provides an enhanced platform for profiling proteomes derived from sub‐microgram amounts of material. These data present a first view of developmental stage‐specific proteome dynamics in Drosophila at a single‐embryo resolution, permitting characterization of inter‐individual expression variation. Together, the findings of this work position SP3 as a superior protocol that facilitates exciting new directions in multiple areas of proteomics ranging from developmental biology to clinical applications. Synopsis A new proteomic sample preparation protocol allows fast, efficient and ultra‐sensitive analyses. The method is illustrated by profiling proteomes from sub‐microgram amounts of material, including the first proteome screen of Drosophila development at a single‐embryo resolution. A novel protocol using paramagnetic beads, termed Single‐Pot Solid‐Phase‐enhanced Sample Preparation (SP3) is presented. SP3 enables protein and peptide enrichment, cleanup, digestion, chemical isotope labeling and fractionation in a single tube, without limitations arising from reagent compatibility. SP3 allows unmatched ultra‐sensitive proteome profiling from sub‐microgram amounts of material, as low as 1,000 HeLa cells or a single fly embryo. The first quantitative analysis of early Drosophila development at a single‐embryo resolution reveals dynamic trends in the developmental proteome. Graphical Abstract A new proteomic sample preparation protocol allows fast, efficient and ultra‐sensitive analyses. The method is illustrated by profiling proteomes from sub‐microgram amounts of material, including the first proteome screen of Drosophila development at a single‐embryo resolution.

Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here formalize and comprehensively evaluate a DIA data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We assess the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfer are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-γ) in single cells, and recapitulate the time-resolved induction of IFN-γ response proteins as observed in bulk material. Moreover, we uncover co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to-implement strategy for single-cell proteomics. Single-cell proteomics needs improved workflows for reliable proteome characterization in minute samples. Here, the authors demonstrate that matching data to a higher-input sample faithfully improves proteome coverage to examine proteome response and heterogeneity in single-cell populations.

Cancer cells rely on dysregulated gene expression. This establishes specific transcriptional addictions that may be therapeutically exploited. Yet, the mechanisms that are ultimately responsible for these addictions are poorly understood. Here, we investigated the transcriptional dependencies of transformed cells to the transcription factors YAP and TAZ. YAP/TAZ physically engage the general coactivator bromodomain-containing protein 4 (BRD4), dictating the genome-wide association of BRD4 to chromatin. YAP/TAZ flag a large set of enhancers with super-enhancer-like functional properties. YAP/TAZ-bound enhancers mediate the recruitment of BRD4 and RNA polymerase II at YAP/TAZ-regulated promoters, boosting the expression of a host of growth-regulating genes. Treatment with small-molecule inhibitors of BRD4 blunts YAP/TAZ pro-tumorigenic activity in several cell or tissue contexts, causes the regression of pre-established, YAP/TAZ-addicted neoplastic lesions and reverts drug resistance. This work sheds light on essential mediators, mechanisms and genome-wide regulatory elements that are responsible for transcriptional addiction in cancer and lays the groundwork for a rational use of BET inhibitors according to YAP/TAZ biology. Interdependence between YAP/TAZ and BRD4 drives transcriptional addiction in cancer cells and determines sensitivity to BET inhibition.

Owing to their preeminent biological functions, the repertoire of expressed RNA-binding proteins (RBPs) and their activity states are highly informative about cellular systems. We have developed a novel and unbiased technique, called interactome capture, for identifying the active RBPs of cultured cells. By making use of in vivo UV cross-linking of RBPs to polyadenylated RNAs, covalently bound proteins are captured with oligo(dT) magnetic beads. After stringent washes, the mRNA interactome is determined by quantitative mass spectrometry (MS). The protocol takes 3 working days for analysis of single proteins by western blotting, and about 2 weeks for the determination of complete cellular mRNA interactomes by MS. The most important advantage of interactome capture over other in vitro and in silico approaches is that only RBPs bound to RNA in a physiological environment are identified. When applied to HeLa cells, interactome capture revealed hundreds of novel RBPs. Interactome capture can also be broadly used to compare different biological states, including metabolic stress, cell cycle, differentiation, development or the response to drugs.

The distal pathways for differentiation into monocytes and monocyte-derived macrophages are not fully elucidated. Feuerer and colleagues describe a clonogenic, monocyte- and macrophage-restricted progenitor derived from the macrophage–dendritic cell progenitor. Monocytes, macrophages and dendritic cells (DCs) are developmentally related regulators of the immune system that share the monocyte-macrophage DC progenitor (MDP) as a common precursor. Unlike differentiation into DCs, the distal pathways for differentiation into monocytes and monocyte-derived macrophages are not fully elucidated. We have now demonstrated the existence of a clonogenic, monocyte- and macrophage-restricted progenitor cell derived from the MDP. This progenitor was a Ly6C + proliferating cell present in the bone marrow and spleen that generated the major monocyte subsets and macrophages, but not DCs or neutrophils. By in-depth quantitative proteomics, we characterized changes in the proteome during monocyte differentiation, which provided insight into the molecular principles of developing monocytes, such as their functional maturation. Thus, we found that monocytes and macrophages were renewed independently of DCs from a committed progenitor.

Transcription factors (TFs) control cell fates by precisely orchestrating gene expression. However, how individual TFs promote transcriptional diversity remains unclear. Here, we use the Hox TF Ultrabithorax (Ubx) as a model to explore how a single TF specifies multiple cell types. Using proximity-dependent Biotin IDentification in Drosophila , we identify Ubx interactomes in three embryonic tissues. We find that Ubx interacts with largely non-overlapping sets of proteins with few having tissue-specific RNA expression. Instead most interactors are active in many cell types, controlling gene expression from chromatin regulation to the initiation of translation. Genetic interaction assays in vivo confirm that they act strictly lineage- and process-specific. Thus, functional specificity of Ubx seems to play out at several regulatory levels and to result from the controlled restriction of the interaction potential by the cellular environment. Thereby, it challenges long-standing assumptions such as differential RNA expression as determinant for protein complexes. Many transcription factors regulate gene expression in a lineage- and process-specific manner, despite being expressed in several cell types. Here, the authors show that the Hox transcription factor Ubx has lineage-specific interactomes, which contribute to its cell context-dependent functions.

Immunotherapies targeting cancer-specific neoantigens have revolutionized the treatment of cancer patients. Recent evidence suggests that epigenetic therapies synergize with immunotherapies, mediated by the de-repression of endogenous retroviral element (ERV)-encoded promoters, and the initiation of transcription. Here, we use deep RNA sequencing from cancer cell lines treated with DNA methyltransferase inhibitor (DNMTi) and/or Histone deacetylase inhibitor (HDACi), to assemble a de novo transcriptome and identify several thousand ERV-derived, treatment-induced novel polyadenylated transcripts (TINPATs). Using immunopeptidomics, we demonstrate the human leukocyte antigen (HLA) presentation of 45 spectra-validated treatment-induced neopeptides (t-neopeptides) arising from TINPATs. We illustrate the potential of the identified t-neopeptides to elicit a T-cell response to effectively target cancer cells. We further verify the presence of t-neopeptides in AML patient samples after in vivo treatment with the DNMT inhibitor Decitabine. Our findings highlight the potential of ERV-derived neoantigens in epigenetic and immune therapies. Epigenetic therapies are known to synergize with immunotherapies through the de-repression of endogenous retroviral element (ERV)-encoded promoters. Here the authors identify treatment-induced neoantigens and validate their ability to induce T cell response and anti-tumor effects in vitro and in patient samples.